Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy 3030644766, 9783030644765

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Zaheer-Ud-Din Babar Editor-in-Chief

Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy

Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy

Zaheer-Ud-Din Babar Editor-in-Chief

Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy With 119 Figures and 170 Tables

Editor-in-Chief Zaheer-Ud-Din Babar Medicines and Healthcare Department of Pharmacy University of Huddersfield Huddersfield, UK

ISBN 978-3-030-64476-5 ISBN 978-3-030-64477-2 (eBook) https://doi.org/10.1007/978-3-030-64477-2 © Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

To Danyal Zaheer

Preface

The world is changing at a very fast pace; life expectancy is increasing, and consumers are living in a very informed world. Medicines are the most effective interventions, and together with pharmacy services, they are the key to improving healthcare and effective systems. In this context, it is becoming increasingly vital to invest in health research which is related to medicines use and pharmacy services. The research in pharmacy has increased manifold; however, the evidence globally in Western developed countries and in low-income economies is scarce. This is on what research is effective, how the evidence is used, and what impact it has created. In this context, the present encyclopedia fulfills an important gap. The encyclopedia is a comprehensive single resource document that portrays evidence in a large and interesting set of topics ranging from pharmacy services, medicines use and access, as well as methods used in research. It covers the full stream of pharmaceutical public health research and health services research in pharmacy and the evidence around it. The encyclopedia has several sections ranging from global changes, evidence, and impact; health services research; pharmacoepidemiology and pharmacovigilance research, as well as pharmacy research in low-middle and high-income countries. The contemporary topics include continuing education for pharmacists, digital health and pharmacy, research evidence in mental health, and the impact of smoking cessation services. There are lessons to learn from these examples, and they could be used in a variety of healthcare settings and a multinational context. The issues and topics are diverse. It also includes chapters on the impact of early detection program for cancer, the impact of pharmacist immunization, interventions related to medication wastage, pharmacy-related interventions to decrease polypharmacy, and interventions to improve medication adherence. The encyclopedia has a complete section on pharmacoepidemiology and pharmacovigilance research. These chapters have documented evidence related to drug safety in children, medicines safety in pregnancy, drug safety in dementia, involvement of patients in pharmacovigilance, and research on pharmacoepidemiology and big data. An interesting component of the encyclopedia is to discuss research methods used in pharmacy practice research. These are key as they determine the quality of research produced and subsequent evidence and impact. vii

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Preface

The chapters include consensus methodologies, producing the evidence, developing, implementing, and evaluating complex interventions, evidence, and the use of theory, as well as experimental approaches generating the evidence. The encyclopedia covers some topics where, normally, the evidence is very scarce. This includes evidence on assuring the quality of medicines, ensuring cultural safety and pharmacy in the time of the pandemic, behavioral science in pharmacy, and the use of non-prescription medicines. It is challenging to compile information on pharmacy impact. In this context, we thank all the contributors and hope that this work will be useful for academics, practitioners, and policymakers. Huddersfield, UK October 2023

Zaheer-Ud-Din Babar

List of Topics

Evidence and Impact of “Medicines Access and Use Research” Section Editors: Andy Gray, Professor Fatima Suleman and Professor Zaheer-Ud-Din Babar Equity in Access to and Quality Use of Medicines in Low- and Middle-Income Countries Evidence and Research on Cancer Medicine Prices Evidence for the Selection of Essential Medicines Evidence Generation on Access to Medicines by Patients

Evidence and Impact of Health Services Research and Pharmaceutical Public Health Section Editor: Assistant Professor Filipa Alves da Costa Evidence of Mental Health Support and the Pharmacist Evidence of the Impact of Early Detection Programs for Cancer Evidence of the Impact of Harm Minimization Programs Evidence of the Impact of Interventions Related to Medication Wastage Evidence of the Impact of Interventions to Decrease Healthcare-Associated Infections Evidence of the Impact of Interventions to Decrease Polypharmacy

Evidence of the Impact of Interventions to Improve Medication Adherence Evidence of the Impact of Interventions to Prevent Obesity Evidence of the Impact of Pharmacists Through Immunization Interventions Evidence of the Impact of Programs to Prevent and Manage Heart Disease and Stroke Evidence of the Impact of Smoking Cessation Evidence of the Role of Pharmacy-Based Interventions in Sexually Transmitted Infections Evidence-Based Public Health Interventions

Evidence and Impact of Health Services Research in Pharmacy and Pharmaceutical Public Health in High-Income Countries Section Editor: Professor Zubin Austin Deprescribing Ensuring Cultural Safety for Indigenous Peoples Accessing Medicines Optimizing Medication Safety for Patients at Transitions of Care Patient Safety from a Pharmacy Perspective Pharmacy Practice for Marginalized Communities Pharmacy Services in the Time of Pandemic Prescribing by Pharmacists Women’s Health from a Pharmacy Perspective

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Evidence and Impact of Health Services Research in Pharmacy and Pharmaceutical Public Health in LMICs Section Editor: Professor Mohamed Izham Mohamed Ibrahim Community Health Outreach Services: Focus on Pharmacy-Based Outreach Programs in Lowto Middle-Income Countries Disaster Management and Emergency Preparedness in Low- and Middle-Income Countries Disease Surveillance in Low- and Middle-Income Countries Geriatric Health Services: Evidence and Impact in Pharmacy and Pharmaceutical Public Health in Low-to-Middle-Income Countries Health Education, Promotion, and Prevention in LMICs Healthcare Education and Training of Health Personnel Home Care Pharmacy in Low-Middle Income Countries Immunization Practice in Low- and MiddleIncome Countries Impact of Digital Healthcare Technology and Services on LMICs Introduction to Pharmaceutical Health Services Research in LMICs Mental Health Services in Low- and MiddleIncome Countries Nonprescription Medicines to Care for Common Ailments Public Health Advocacy Remote Healthcare Services

Evidence and Impact of Interventions, Medicines and Medical Technologies in COVID-19 and Other Pandemics Section Editors: Dr Rabia Hussain and Professor Zaheer-Ud-Din Babar COVID-19 and Medicines Access Pharmacy Practice and Emergency Preparedness, Resilience, and Response Self-Medication Among Elderly: Evidence Synthesis and a Systematic Review of the Literature

List of Topics

Evidence and Impact of Pharmacoeconomics Research Section Editors: Dr Dalia Dawoud and Professor Zaheer-Ud-Din Babar Economic Evaluation Methods and Approaches Economic Evidence for Pharmacist Prescribing in Community Pharmacy Economic Evidence for Pharmacist-Led Medicines Use Review and Medicines Reconciliation Economic Evidence for Pharmacist-Led Vaccination Services Health Economics Pharmacoeconomic Analysis Methods

Evidence and Impact of Pharmacoepidemiology and Pharmacovigilance Research Section Editor: Dr Prasad Nishtala Causal Inference in Pharmacoepidemiology Drug Safety in Children: Research Studies and Evidence Synthesis Drug Safety in Pregnancy: Data, Methods, and Challenges Pharmacoepidemiology Research Delivering Evidence About Drug Safety in Dementia Pharmacoepidemiology Research Delivering Evidence About Drug Safety in Older Adults Pharmacovigilance to Inform Drug Safety: Challenges and Opportunities

Evidence and Impact of Research Methodologies Used in Health Services Research in Pharmacy and Pharmaceutical Public Health Section Editors: Professor Derek Charles Stewart and Professor Zaheer-Ud-Din Babar Consensus Methodologies and Producing the Evidence Developing, Implementing and Evaluating Complex Services/Interventions, and Generating the Evidence Evidence and the Use of Theory in Health Services Research in Pharmacy

List of Topics

Evidence in Evaluation Research Evidence Produced While Using Qualitative Methodologies Including Research Trustworthiness Experimental Approaches and Generating the Evidence Generating Evidence by the Use of Action Research and Participatory Action Research Approaches Measuring Structures, Processes, and Outcomes and Generating the Evidence Methodological Approaches to Literature Review Public Health Research Ethics Surveys in Health Services Research in Pharmacy Using Administrative Data from Public Health and Drug Programs

Evidence and Impact of Research Related to Pharmacogenomics and Precision Medicine Section Editors: Christine Y. Lu, Dr Jason Hsu and Dr Rachele Hendricks-Sturrup Evidence on Real-World Data and Real-World Evidence as a Driver for Precision Medicine Implementation in Pharmacy Practice Evidence on the Impact of Direct-to-Consumer Pharmacogenetic Testing Evidence on the Impact of Pharmacogenetics to Treat and Manage Asthma Evidence on the Impact of Pharmacogenetics to Treat and Manage Cardiovascular Diseases Evidence on the Utility and Limitations of Artificial Intelligence for Predicting Personalized Disease Prognosis and Treatment Decisions Evidence on the Utility and Limitations to Using AI for Personalized Drug Safety Prediction Pharmacogenomics and Cancer Treatment

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Global Changes, Development, Evidence and Impact in General Section Editor: Professor Zaheer-Ud-Din Babar Continuous Education for Pharmacists: Documenting Research Evidence Digital Health and Pharmacy: Evidence Synthesis and Applications Pharmaceutical Health Services Administration, Planning, Management, and Leadership: Lessons Learned for LMICs Research Evidence in Improving Vaccine Practices in Low- and Middle-Income Countries: Examples of Community Engagement, Barriers, and Implementation Strategies Trends in Prescribing Antibiotics Between 2012 and 2022: High-Income Versus Low-MiddleIncome Countries

Topical Issues in Evidence and Impact of Pharmaceutical Public Health Research and Health Services Research in Pharmacy Section Editor: Professor Zaheer-Ud-Din Babar Behavioral Medicine/Behavioral Science in Pharmacy Global Evidence on Assuring Quality of Medicines Intercultural Challenges to Consider When Designing Pharmaceutical and Behavioral Interventions in Health Services Research Interventions and Public Health Activities Performed by Community Pharmacists Involvement of Patients in Pharmacovigilance Pharmaceutical Public Health in Africa: The Contributions of Pharmacy Professionals to Public Health Pharmacoepidemiology and Big Data Research

About the Editor-in-Chief

Zaheer-Ud-Din Babar is a Professor of Medicines and Healthcare and the Director of the Centre of Pharmaceutical Policy and Practice Research at the University of Huddersfield, United Kingdom. Prof. Babar comes with a global experience of working as an academic in universities in Malaysia, New Zealand, and the UK, and he is globally known for his work in pharmaceutical policy and practice. Prof. Babar has over 300 research outputs including in some leading high-impact journals, such as PLoS Medicine and The Lancet, and has acted as a consultant for the WHO, Royal Pharmaceutical Society, Health Action International, Management Sciences for Health, International Union Against Tuberculosis and Lung Disease, World Bank, European Union, International Pharmaceutical Federation (FIP), and the Pharmaceutical Management Agency of New Zealand. His edited work includes Economic Evaluation of Pharmacy Services, Pharmaceutical Prices in the 21st Century, Pharmaceutical Policies in Countries with Developing Healthcare Systems, Global Pharmaceutical Policy, Access to HighCost Medicines, Pharmacy Practice Research Methods, Pharmacy Practice Research Case Studies, Encyclopedia of Pharmacy Practice and Clinical Pharmacy, and Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy. Prof. Babar is the Editor-in-Chief of the BMC Journal of Pharma ceutical Policy and Practice and can be contacted at [email protected]

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Section Editors

Filipa Alves da Costa Faculty of Pharmacy Research Institute for Medicines (iMED.ULisboa) University of Lisbon Lisbon, Portugal

Zubin Austin Leslie Dan Faculty of Pharmacy and the Institute for Health Policy Management, and Evaluation – Temerty Faculty of Medicine University of Toronto Toronto, Canada

Zaheer-Ud-Din Babar Medicines and Healthcare Department of Pharmacy University of Huddersfield Huddersfield, UK

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Section Editors

Dalia Dawoud Faculty of Pharmacy Cairo University Cairo, Egypt National Institute for Health and Care Excellence (NICE) England, UK

Andy Gray Division of Pharmacology Discipline of Pharmaceutical Sciences University of KwaZulu-Natal Durban, South Africa

Rachele Hendricks-Sturrup Duke-Margolis Center for Health Policy Washington, DC, USA

Section Editors

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Jason C. Hsu College of Management Taipei Medical University Taipei, Taiwan Clinical Data Center Office of Data Science Taipei Medical University Taipei, Taiwan Research Center of Health Care Industry Data Science College of Management Taipei Medical University Taipei, Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Medical University Taipei, Taiwan

Rabia Hussain Discipline of Social and Administrative Pharmacy School of Pharmaceutical Sciences Universiti Sains Malaysia Penang, Malaysia

Christine Y. Lu Department of Population Medicine Harvard Medical School and Harvard Pilgrim Health Care Institute Boston, MA, USA Kolling Institute, Faculty of Medicine and Health The University of Sydney and the Northern Sydney Local Health District Sydney, NSW, Australia School of Pharmacy, Faculty of Medicine and Health The University of Sydney Sydney, NSW, Australia

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Section Editors

Mohamed Izham B. Mohamed Ibrahim College of Pharmacy QU Health Qatar University Doha, Qatar

Prasad Nishtala Department of Life Sciences Centre for Therapeutic Innovation University of Bath Bath, UK University of Otago Medical School Christchurch, New Zealand

Derek Stewart College of Pharmacy QU Health Qatar University Doha, Qatar

Fatima Suleman Pharmaceutical Sciences University of KwaZulu-Natal Durban, South Africa

Contributors

Kiran Abbas Community Health Sciences, Aga Khan University Hospital, Karachi, Pakistan Sameen Abbas Department of Pharmacy, Quaid-i-Azam University, Islamabad, Pakistan Ali Ahmed School of Pharmacy, Monash University, Subang Jaya, Selangor, Malaysia Moiz Ahmed Department of Cardiology, National Institute of Cardiovascular Diseases (NICVD), Karachi, Pakistan Nagham J. Ailabouni The Pharmacy Australia Centre of Excellence, The School of Pharmacy, Health and Behavioural Sciences Faculty, University of Queensland, Brisbane, Australia UniSA: Clinical and Health Sciences, Quality Use of Medications and Pharmacy Research Centre, University of South Australia, Adelaide, Australia Amal Akour Department of Pharmacology and Therapeutics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy, School of Pharmacy, The University of Jordan, Amman, Jordan Yazid N Al Hamarneh University of Alberta, Edmonton, AB, Canada Muaed Alomar Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates Raja’a A. Al-Qudah Faculty of Pharmacy, Applied Science Private University, Amman, Jordan Hamzeh M. Alrawashdeh Ibn Al Haytham Hospital, Amman, Jordan Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan Fahad Alshahrani Security Forces Hospital, Riyadh, Saudi Arabia Mohammed Alshakka Section of Clinical Pharmacy, Department of Pharmaceutics, Faculty of Pharmacy, University of Aden, Aden, Yemen

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Ala’a B. Al-Tammemi Migration Health Division, International Organization For Migration (IOM), Amman, Jordan Department of Epidemiology and Global Health, Faculty of Medicine, Umeå University, Umeå, Sweden Department of Family and Occupational Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary Filipa Alves da Costa Research Institute for Medicines (iMED.ULisboa), Faculty of Pharmacy, University of Lisbon, Lisbon, Portugal Faten Amer Doctoral School of Health Sciences, Faculty of Health Sciences, University of Pécs, Pécs, Hungary School of Pharmacy, Faculty of Medicine and Health Sciences, An Najah National University, Nablus, Palestine Vibhuti Arya Amirfar College of Pharmacy and Health Sciences, St. John’s University, New York City, NY, USA Safiur Rahman Ansari D Code Technology, Kathmandu, Nepal Rajender R. Aparasu Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX, USA Diane Ashiru-Oredope School of Pharmacy, University College London, London, UK School of Pharmacy, University of Nottingham, Nottingham, UK Fahmida Aslam International Food and Drug Policy and Law Research Centre, School of Business Administration, Shenyang Pharmaceutical University, Shenyang, China Ahmed Awaisu Department of Clinical Pharmacy and Practice, College of Pharmacy, QU Health, Qatar University, Doha, Qatar Zaheer-Ud-Din Babar Medicines and Healthcare, Department of Pharmacy, University of Huddersfield, Huddersfield, UK Peter Ahabwe Babigumira Infectious Diseases Institute, Makerere University College of Health Sciences, Kampala, Uganda Public Health Emergency Operations Centre, Ministry of Health, Kampala, Uganda Wafa F. S. Badulla Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Aden, Aden, Yemen Michelle Baker Department of Pharmacy Services, University Health Network, Toronto, ON, Canada Shamala Balan Pharmacy Department, Hospital Tengku Ampuan Rahimah, Klang, Malaysia Muna M. Barakat Faculty of Pharmacy, Applied Science Private University, Amman, Jordan

Contributors

Contributors

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Yazan S. Batarseh Faculty of Pharmacy and Medical Sciences, University of Petra, Amman, Jordan J. J. Beckeringh Westwijk Pharmacy, Amstelveen, The Netherlands Marion Bennie University of Strathclyde, Glasgow, UK Shalom I. Benrimoj Pharmaceutical Care Research Group, Faculty of Pharmacy, University of Granada, Granada, Spain Jérôme Berger Center for Primary Care and Public Health (Unisanté), Department of ambulatory care, University of Lausanne, Lausanne, Switzerland Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, Geneva, Lausanne, Switzerland Kebede Beyene Department of Pharmaceutical and Administrative Sciences, University of Health Sciences and Pharmacy, St. Louis, MO, USA School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand Kaustuv Bhattacharya Center for Pharmaceutical Marketing and Management, University of Mississippi School of Pharmacy, University, MS, USA Department of Pharmacy Administration, University of Mississippi School of Pharmacy, University, MS, USA Asima Bibi Department of Pharmacy, Quaid-i-Azam University, Islamabad, Pakistan S. N. Blake Department of Clinical Pharmacology and Pharmacy, Amsterdam University Medical Centers, Location VUMC, Amsterdam, The Netherlands Fundashon Prevenshon, Willemstad, Curaçao Hege Salvesen Blix Section of Clinical Pharmacy, Department of Pharmacy, University of Oslo, Oslo, Norway Department of Drug statistics/WHO Collaborating Centre for Drug Statistics Methodology/NIPH Research Centre on Antimicrobial Resistance, Norwegian Institute of Public health (NIPH), Oslo, Norway Kwame Peprah Boaitey Institute of Evidence-Based Medicine, Bond University, Robina, QLD, Australia Anna Bryndís Blöndal University of Iceland, Faculty of Pharmaceutical Sciences, Reykjavik, Iceland

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Cathal Cadogan School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland Catia Caneiras Institute of Preventive Medicine and Public Health (IMP&SP), Faculty of Medicine, Universidade de Lisboa, Lisboa, Portugal Microbiology Research Laboratory of Environmental Health (EnviHealthMicro Lab), Institute of Environmental Health (ISAMB), Faculty of Medicine, Universidade de Lisboa, Lisboa, Portugal Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Instituto Universitàrio Egas Moniz (IUEM), Caparica, Portugal Maria Cary Centre for Health Evaluation and Research (CEFAR), Lisbon, Portugal Cesar Casas Business Administration and Management. Institute of Social and Political Sciences, University of Lisbon, Lisbon, Portugal P. Cavaco-Silva Egas Moniz-School of Health and Science, Egas Moniz Interdisciplinary Center (CiiEM), Almada, Portugal Amy Hai Yan Chan Commonwealth Pharmacists’ Association, London, UK School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand Research Department of Practice and Policy, UCL School of Pharmacy, London, UK Rachel A. Charlton Department of Life Sciences, University of Bath, Bath, UK Satabdi Chatterjee Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA Alexandra Clavarino School of Public Health, University of Queensland, Brisbane, QLD, Australia Phoebe Corke Notre Dame University, Fremantle, WA, Australia Dinesh Dharel Department of Pediatrics, University of Alberta, Edmonton, AB, Canada Gereltuya Dorj Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia Imbi Drame College of Pharmacy, Howard University, Washington, DC, USA Alla El-Awaisi Clinical Pharmacy and Practice Department, College of Pharmacy, QU Health, Qatar University, Doha, Qatar Faris El-Dahiyat College of Pharmacy, Al Ain University, Al Ain, UAE Hager ElGeed Department of Clinical Pharmacy and Practice, College of Pharmacy, QU Health, Qatar University, Doha, Qatar

Contributors

Contributors

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Myriam ElJaam Clinical Pharmacy and Practice Department, College of Pharmacy, QU Health, Qatar University, Doha, Qatar Marthe Everard Independent Consultant in Essential Medicines, Vienna, Austria Rita Faria University of York, York, UK Barbara Farrell Bruyère Research Institute, Department of Family Medicine, University of Ottawa, Ottawa, ON, Canada School of Pharmacy, University of Waterloo, Ottawa, ON, Canada Olavo A. Fernandes Department of Pharmacy Services, University Health Network, Toronto, ON, Canada Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada Fernando Fernandez-Llimos CINTESIS – Center for Health Technology and Services Research, Laboratory of Pharmacology, Department of Drug Sciences, Faculty of Pharmacy, University of Porto, Porto, Portugal Débora Figueira Centre for Health Evaluation and Research (CEFAR), Lisbon, Portugal Holly Foot Faculty of Medical and Health Sciences, School of Pharmacy, University of Auckland, Auckland, New Zealand Dixil Francis PwC, Atlanta, GA, USA Victoria Garcia-Cardenas Pharmaceutical Care Research Group, Faculty of Pharmacy, University of Granada, Granada, Spain Sara Garfield UCL School of Pharmacy, London, UK Imperial College Healthcare NHS Trust, London, UK Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK Begashaw Melaku Gebresillassie School of Pharmacy, University of Gondar, Gondar, Ethiopia School of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia Johnson George Centre for Medicine Use and Safety, Monash Institute of Pharmaceutical Sciences, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia I. Gomes Bravio Curacao Medical Center, Willemstad, Curaçao Sajni Gudka School of Global and Population Health, University of Western Australia, Fremantle, WA, Australia Urban Impact Project, Fremantle, WA, Australia Maya Harris College of Pharmacy, Howard University, Washington, DC, USA

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Rachele Hendricks-Sturrup Duke-Margolis Center for Health Policy, Duke University, Washington, DC, USA Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA Martin C. Henman School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland Susan Heydon School of Pharmacy, University of Otago, Dunedin, New Zealand Certina Ho Department of Psychiatry, Temerty Faculty of Medicine, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada Hans V. Hogerzeil University Medical Centre Groningen, Groningen, The Netherlands Kathleen Holloway Institute of Development Studies, University of Sussex, Brighton, UK Sherilyn K. D. Houle School of Pharmacy, University of Waterloo, Waterloo, ON, Canada Jason C. Hsu International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan Carmel M. Hughes School of Pharmacy, Queen’s University Belfast, Belfast, UK Christine A. Hughes Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada J. G. Hugtenburg Department of Clinical Pharmacology and Pharmacy, Amsterdam University Medical Centers, Location VUMC, Amsterdam, The Netherlands Fundashon Prevenshon, Willemstad, Curaçao Rabia Hussain Discipline of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia Sushmitha Inguva Health Economics and Outcomes Research, Amgen Inc., Thousand Oaks, CA, USA Naveed Jafri Immunization and Vaccine Preventable Disease Unit WHO, East Mediterranean Region (EMRO), Cairo, Egypt Yogini Jani UCL School of Pharmacy, London, UK Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK Dina Jankovic University of York, York, UK Farideh Javid Department of Pharmacy, University of Huddersfield, Huddersfield, UK

Contributors

Contributors

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Julia Jones Urban Impact Project, Fremantle, WA, Australia Bhuvan K. C. School of Pharmacy, Monash University Malaysia, Subang Jaya, Selangor, Malaysia Faculty of Pharmacy and Pharmaceutical Sciences, Monash University Parkville Campus, Melbourne, Victoria, Australia Ameer Kakaje University Hospital Geelong, Barwon Health, VIC, Australia Faculty of Medicine, Damascus University, Damascus, Syria Lisa M. Kalisch Ellett UniSA: Clinical and Health Sciences, Quality Use of Medications and Pharmacy Research Centre, University of South Australia, Adelaide, Australia Gizat M. Kassie UniSA: Clinical and Health Sciences, Quality Use of Medications and Pharmacy Research Centre, University of South Australia, Adelaide, Australia Fiona S. Kelly School of Pharmacy and Medical Sciences, Griffith University, Gold Coast, Queensland, Australia Anna Kemp-Casey Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Services, University of South Australia, Adelaide, SA, Australia Amjad Khan Department Islamabad, Pakistan

of

Pharmacy,

Quaid-i-Azam

University,

Gul Majid Khan Department of Pharmacy, Quaid-i-Azam University, Islamabad, Pakistan Islamia College University, Peshawar, Pakistan Saval Khanal Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK Asmita Priyadarshini Khatiwada Department of Pharmaceutical and Health Service Research, Nepal Health Research and Innovation Foundation, Lalitpur, Nepal N. Kheir College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand Aisha Khokhar Lahore College for Women University, Lahore, Pakistan Rabia Khokhar Institute of Pharmaceutical Sciences, University of Veterinary and Animal Sciences, Lahore, Pakistan Amir Khorram-Manesh Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden Jeeyun A. Kim Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

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Michelle A. King School of Pharmacy and Medical Sciences, Griffith University, Gold Coast, Queensland, Australia Amanj Kurdi University of Strathclyde, Glasgow, UK Amy S. M. Lam Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong Bradley J. Langford Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada Vivian W. Y. Lee Centre for Learning Enhancement and Research, The Chinese University of Hong Kong, Shatin, Hong Kong Leticia P. Leonart Pharmaceutical Sciences Postgraduate Program, Federal University of Paraná, Curitiba, Brazil Joyce T. S. Li Centre for Learning Enhancement and Research, The Chinese University of Hong Kong, Shatin, Hong Kong Renly Lim Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia R. Ljumanovic Antillean Adventist Hospital, Willemstad, Curaçao Zhe Chi Loh Discipline of Social and Administrative pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia Christine Y. Lu Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA Cherie Lucas Graduate School of Health, (Discipline of Pharmacy), University of Technology Sydney, Sydney, Australia Alpana Mair Effective Prescribing and Therapeutics Division, Scottish Government, Edinburgh Napier University, Edinburgh, UK Kenneth K. C. Man Research Department of Practice and Policy, UCL School of Pharmacy, London, UK Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, People’s Republic of China Smriti Maskey Department of Epidemiology and Population Health, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA Lisa M. McCarthy Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada Institute for Better Health and Pharmacy Department, Mississauga, ON, Canada Elizabeth M. McCourt Disaster Pharmacy Solutions, Brisbane, QLD, Australia Redland Hospital, Metro South, Queensland Health, Cleveland, QLD, Australia

Contributors

Contributors

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Princess Alexandra Southside Clinical Unit, Faculty of Medicine, University of Queensland, Brisbane, Australia Royal Brisbane and Women’s Hospital, Brisbane, QLD, Australia Anita McGrogan Department of Life Sciences, University of Bath, Bath, UK Sara S. McMillan School of Pharmacy and Medical Sciences, Griffith University, Gold Coast, Queensland, Australia Fouad Moghrabi Department of Chemistry, Faculty of Science, Bethlehem University, Bethlehem, Palestine Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Butler University, Indianapolis, IN, USA School of Pharmacy, Faculty of Pharmacy, Al-Quds University, Jerusalem, Palestine Mohamed Izham Mohamed Ibrahim Department of Clinical Pharmacy and Practice, College of Pharmacy, QU Health, Qatar University, Doha, Qatar Dzul Azri Mohamed Noor Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia Tanja Mueller University of Strathclyde, Glasgow, UK Saima Mushtaq Department of Healthcare Biotechnology, Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan Ranjana Nagi Department of Health Policy, London School of Economics and Political Science, London, UK Global Strategy Lab, Dahdaleh Institute for Global Health Research, Faculty of Health, and Osgoode Hall Law School, York University, Toronto, ON, Canada Shubhdeep Nagi School of Global Health, York University, Toronto, ON, Canada Phyllis Muffuh Navti Continuing Professional Development, Weill Cornell Medicine-Qatar, Doha, Qatar Hamde Nazar School of Pharmacy, Newcastle University, Newcastle upon Tyne, UK Zachariah Jamal Nazar Clinical Pharmacy and Practice Department, College of Pharmacy, QU Health, Qatar University, Doha, Qatar Danielle Newby University of Oxford, Oxford, UK Xiaoyan Nie Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing, China

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Anne Niquille Center for Primary Care and Public Health (Unisanté), Department of ambulatory care, University of Lausanne, Lausanne, Switzerland Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, Geneva, Lausanne, Switzerland Nkem P. Nonyel College of Pharmacy, Howard University, Washington, DC, USA Daneh Obaid College of Pharmacy, Al Ain University, Al Ain, UAE Siew Chin Ong Discipline of Social and Administrative pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia Yaw Owusu Department of Clinical Pharmacy and Practice, College of Pharmacy, QU Health, Qatar University, Doha, Qatar Subish Palaian Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, UAE Vibhu Paudyal School of Pharmacy, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK Clémence Perraudin Center for Primary Care and Public Health (Unisanté), Department of ambulatory care, University of Lausanne, Lausanne, Switzerland Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, Geneva, Lausanne, Switzerland H. M. Pinedo Fundashon Prevenshon, Willemstad, Curaçao Sofia Pintado Egas Moniz-School of Health and Science, Instituto Universitário Egas Moniz, Almada, Portugal Tamara Power Susan Wakil School of Nursing and Midwifery, Faculty of Health and Medicine, University of Sydney, Sydney, Australia Daniel Rainkie Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada

Contributors

Contributors

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Lalitha Raman-Wilms College of Pharmacy, Rady Faculty of Health Sciences and Centre on Aging, University of Manitoba, Winnipeg, MB, Canada Huma Rasheed Institute of Pharmaceutical Sciences, University of Veterinary and Animal Sciences, Lahore, Pakistan Raffaella Ravinetto Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium School of Public Health, University of the Western Cape, Cape Town, South Africa Ghaleb El Refae College of Business, Al Ain University, Al Ain, UAE Sofia Ribeiro Instituto de Saúde Ambiental, Faculdade de Medicina de Lisboa, Lisbon, Portugal Marie Rocchi Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada António Teixeira Rodrigues Centre for Health Evaluation and Research (CEFAR), Lisbon, Portugal School of Medicine, Universidade do Minho, Braga, Portugal Elizabeth E. Roughead Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Services, University of South Australia, Adelaide, SA, Australia Binaya Sapkota Faculty of Health Sciences, Department of Pharmaceutical Sciences, Nobel College, Sinamangal, Kathmandu, Nepal S. Scahill School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand Theresa J. Schindel Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada Sadia Shakeel Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Dow College of Pharmacy, Dow University of Health Sciences, Karachi, Pakistan P. Ravi Shankar IMU Centre for Education, International Medical University, Kuala Lumpur, Malaysia Rajeev Shrestha Department of Pharmacy, District Hospital Lamjung, Besisahar, Lamjung, Nepal Sunil Shrestha School of Pharmacy, Monash University Malaysia, Subang Jaya, Selangor, Malaysia Jean M. Spinks Centre for the Business and Economics of Health, University of Queensland, St Lucia, Brisbane, QLD, Australia Lotte Stig Nørgaard University of Copenhagen, Faculty of Health and Medical Sciences, Department of Pharmacy, Copenhagen, Denmark

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Martina Teichert Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands Royal Dutch Pharmacist Association for the Advancement of Pharmacy, Pharmaceutical care and Innovation, The Hague, The Netherlands Dennis Thomas Centre of Excellence in Treatable Traits, College of Health, Medicine and Wellbeing, University of Newcastle, Hunter Medical Research Institute Asthma and Breathing Programme, Newcastle, NSW, Australia Wade Thompson Department of Anesthesiology, Pharmacology, and Therapeutics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada Research Unit of General Practice, University of Southern Denmark, Odense, Denmark Fernanda S. Tonin Pharmaceutical Sciences Postgraduate Programme, Federal University of Paraná, Curitiba, Brazil H&TRC- Health and Technology Research Center, ESTeSL- Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Lisbon, Portugal Chloe Tuck School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK Commonwealth Pharmacists’ Association, London, UK Sherry Y. Wang Department of Pharmaceutical Economics and Policy, School of Pharmacy, Chapman University, Irvine, CA, USA Kaitlyn E. Watson EPICORE Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada Disaster Pharmacy Solutions, Brisbane, QLD, Australia Alice Watt Institute for Safe Medication Practices Canada, Toronto, ON, Canada Oak Valley Health, Markham, ON, Canada Amanda J. Wheeler Menzies Health Institute, Griffith University, Nathan, QLD, Australia School of Pharmacy, University of Auckland, Auckland, New Zealand Karen Whitfield School of Pharmacy, The University of Queensland, Brisbane, QLD, Australia Kyle John Wilby College of Pharmacy, Faculty of Health, Dalhousie University, Halifax, Canada Yang Yue International Food and Drug Policy and Law Research Centre, School of Business Administration, Shenyang Pharmaceutical University, Shenyang, China School of Pharmaceutical Sciences, Tsinghua University, Beijing, China Institute of Pharmaceutical Regulatory Sciences, Tsinghua University, Beijing, China

Contributors

Contributors

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Key Laboratory of Innovative Drug Research and Evaluation, National Medical Products Administration, Beijing, China Nese Yuksel Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada Hadzliana Zainal Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia Elida Zairina Department of Pharmacy Practice, Faculty of Pharmacy, Universitas Airlangga, Surabaya, Indonesia Peter Chengming Zhang Leslie Dan Faculty of Pharmacy, Rotman School of Management, University of Toronto, Toronto, ON, Canada Xinyi Zhang Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing, China Yuxuan Zhao Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing, China

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Behavioral Medicine/ Behavioral Science in Pharmacy Amy Hai Yan Chan1, Kebede Beyene2,3 and Holly Foot1 1 School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand 2 Department of Pharmaceutical and Administrative Sciences, University of Health Sciences and Pharmacy, St. Louis, MO, USA 3 School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand

Abstract

Behavioral science – the study of the science of human behavior – seeks to explain what behavior is, how behavior can be described, modelled and changed, and how this can be applied to improve health service delivery and pharmaceutical research. The field encompasses social and health psychology, and economics, and has grown rapidly in the last decade, with governments taking an interest in the use of behavioral economics to inform healthcare policy and improve health outcomes more effectively. As pharmacists continue to move away from traditional medicines supply

to provision of patient-centered pharmaceutical care, the role of behavioral science in the design and delivery of healthcare will be increasingly pivotal to their practice and research. For healthcare interventions to be effective, these need to be underpinned by health psychology theories and behavior change principles. This chapter describes the role of behavior change in health service and pharmaceutical research, and compares and contrasts common theories, models and frameworks that have been applied to pharmacy interventions. The chapter ends with a discussion of the challenges and recommendations to consider when conducting behavioral research, including how to select the right tool to measure behavior, considerations when developing and validating a new tool, and tips for intervention design. Keywords

Behavior change · Health psychology · Behavioral science

Introduction to Behavioral Science and Behavioral Medicine Behavioral science refers to the study of the science of human behavior – defined as an interdisciplinary approach to the study of human behavior

© Springer Nature Switzerland AG 2023 Z.-U.-D. Babar (ed.), Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy, https://doi.org/10.1007/978-3-030-64477-2

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disciplines including psychology, sociology, anthropology, and economics (Glass and McAtee 2006). Behavioral science seeks to explain what behavior is, how behavior can be described, modelled and changed, and how this science and knowledge can be applied to improve health service delivery and intervention design (Rubinstein 2018). The science helps explain why people do what they do, and consequently understand what factors can be changed influence the behavior. The discipline of behavioral science is informed by social and health psychology, and evolved to challenge traditional economic theory where scientists attempted to use mathematics and economics to explain human behavior – leading to formation of behavioral economics – how psychological, cognitive, emotional, cultural, and social factors impact on the decisions of individuals and organizations, and how these decisions may differ from how classical economic theory may explain the behavior. The application of behavioral science to health and medicines is referred to as “behavioural medicine” – an “interdisciplinary field concerned with the development and integration of behavioural and biomedical science knowledge and techniques relevant to health and illness and the application of this knowledge and these techniques to prevention, diagnosis, treatment, and rehabilitation” (Grigoryan et al. 2006). The field is underpinned by health psychology and focuses on several aspects: understanding how people react to, cope with and recover from illness, personalizing treatments and interventions, and improving health care systems and health policy. Behavioral medicine seeks to understand and explain, and develop theories to test the role of psychological factors in the promotion and maintenance of health and quality of life, and causes of illness (Chater and Cook 2014); and how to prevent illness – looking at how the way people think, feel, and learn influences the use of medicines or other healthcare intervention, and how this affects health outcomes. It considers the “mind and body” approach to healthcare, rather than only focusing on the biological aspects of health service delivery and is informed by the biopsychosocial model of health.

Behavioral Medicine/Behavioral Science in Pharmacy

Some examples of the types of questions behavioral science and behavioral medicine answers include: “Can our thoughts influence our health behaviours?” “What influences people’s decisions around healthcare?”; “Why do different people have different perceptions of their long-term health condition and their treatments?”; “Why don’t people take their medication?”

Why Is Behavioral Science Important When Considering Health Research and Why Conduct Behavioral Research? All of health is underpinned by human behavior, regardless of whether we are referring to management of long-term health conditions or shorterterm acute health issues. Effective management of any health condition, and its associated intervention or treatment, relies on uptake by the end user. While there are key opportunities to optimize health with new health innovations, treatments, and medical devices, how effective these new advances are, and to what extent they bring patients benefit, will depend on engagement from the target end user, and ultimately on human behavior. For example, whether patients engage with a treatment or technology or not, can limit their efficacy. To optimize end user engagement, one must consider the role of behavioral sciences in explaining engagement (or non-engagement) with the health intervention, and what factors can be changed to improve engagement or reduce uptake barriers and ultimately optimize health outcomes. This is true at both an individual and population health level (Glanz and Bishop 2010), as part of evidencebased decision making. To improve population health effectively, there needs to be an evidence-based approach to intervention design that is informed by the behavioral sciences and by health psychology theory. The UK Medical Research Council’s framework for the development and evaluation of complex interventions recommends that interventions should be underpinned by theory (Craig et al. 2008). Indeed, this is in line with evidence from key systematic

Behavioral Medicine/Behavioral Science in Pharmacy

reviews that show that health behavior change interventions are more effective when informed by health psychology and behavior change theory (Cole-Lewis and Kershaw 2010; Webb et al. 2010). Being able to draw on evidence about what works and what does not work to change target behaviors is key to ensure cost effectiveness, so resources are not spent on interventions that do not work, and there is an ability to be able to identify when interventions do not go as planned, why the intervention did not work. Healthcare interventions which aim to change health behaviors must target the motivations and abilities which drive the behaviors of the target audience (Horne et al. 2019), and should follow the EAST framework for successful implementation into practice – to be Easy, Attractive, Social and Timely (Team BI 2014). This ensures that interventions successfully align with the interests of the population and addresses the modifiable behavioral factors which limit health and treatment outcomes, in a way that is easy to follow, attractive, supports social norms and aligns with what others are doing, and timely in a way that responds to identified needs at the time. Examples of these behavioral factors that can influence engagement with an intervention include people’s attitudes, perceptions and beliefs, as well as practical barriers to behaviors such as limitations in resourcing. These barriers can be addressed by introducing an intervention using simple messaging at the time the target behavior occurs, that is attractive and allows comparison with the individual’s peer group – for example, providing persuasive messages to support hand washing after people have just used toilet, and explaining that most people wash their hands after using the toilet.(Judah et al. 2009) Other examples that draw on behavior change theory include the use of tailored text message reminders to promote medication adherence, that is personalized to an individual’s barriers to adherence and their treatment beliefs (Petrie et al. 2012). For any research or intervention to be effective, there need to be considerations that take into account behavioral science – the science and research related to understanding, analyzing and assessing human behaviors to identify enablers and barriers

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influencing these behaviors as part of an iterative cycle of change and refinement. The importance of considering behavioral science is reflected in both global health and national guidelines (Curtis et al. 2018). The World Health Organization (WHO) recommend supporting population-health programs with behavioral science evidence as does the UK National Institute for Health and Care Excellence (National Institute for Health and Care Excellence (Great Britain) 2014), which focuses on behavior change approaches. Many governments around the world have now implemented a behavioral insights team to inform government and public policy-making (Delaney 2018), though the success of this and sustainability of this approach is yet to be determined (Sanders et al. 2018).

Behavior Change Approaches As pharmacists continue to make the transition from a focus on medication dispensing to patient-centered services, their role in developing and delivering interventions aimed at complex public health issues (e.g., substance misuse, smoking cessation, vaccine hesitancy, medication non-adherence) is increasing. However, most previous interventions in pharmacy practice were not developed using a systematic approach. They were mostly developed based on a pragmatic approach or the ISLAGIATT principle – that is, “It seemed like a good idea at the time.” Little attention has been paid to their development, content, and mode of delivery. As recommended by the UK Medical Research Council (MRC), intervention development should be guided by appropriate theory and the best available evidence (Craig et al. 2008). Theories and models are useful to generate testable hypotheses and explore potential causal mechanisms underlying the intervention’s effect (Michie and Prestwich 2010). Thus, they can help to overcome the inherent limitations of pharmacist-led interventions (Improved Clinical Effectiveness through Behavioural Research Group (ICEBeRG) 2006). A complete enumeration of the theories and models in pharmacy practice is beyond the scope of this chapter. Instead,

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we describe some examples of commonly used theories, models, and frameworks that have been applied to pharmacy interventions.

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Cognitive Theory (SCT) (Zimmerman and Vernberg 1994). The HBM can be more effective if it is integrated with other behavior change models that consider environmental factors and provide strategies for change.

Health Belief Model (HBM) The HBM proposes that individuals’ likelihood of engaging in preventative health behavior is a function of their beliefs about susceptibility to the health problem, the severity of the health problem, the benefits and barriers related to the health behavior, their ability to perform a specific behavior (i.e., self-efficacy), as well as whether they experience a cue to action (Rosenstock 1974). These six components of the HBM together provide a useful framework for predicting behavior and designing behavioral interventions (Orji et al. 2012). An illustration of the application of HBM in pharmacy practice comes from Alatawi et al.’s study of adherence to diabetes medications in 220 patients in Saudi Arabia (Alatawi et al. 2016). In the study, the authors reported that patients’ self-efficacy affected the likelihood of them being adherent. Furthermore, patients’ perceptions of the benefits of their medications, and perceptions about the possible adverse effects also affected their likelihood of being adherent. Although the HBM is one of the most popular health behavior models, it has several weaknesses which limit its utility in pharmacy practice. For example, it does not explain the influence of habit on behavior (Michie et al. 2014; Janz and Becker 1984). It does not also consider the environmental, social, or economic factors that may hinder or promote a particular behavior (Yarbrough and Braden 2001). Furthermore, the way in which the constructs of the HBM interact with one another has not been clearly specified (Conner and Norman 2015; Orji et al. 2012). Overall, there is some evidence for the ability of the individual constructs of the HBM in predicting different health behaviors (Jones et al. 2014; Carpenter 2010; Harrison et al. 1992), but the predictive ability of the model as a whole is limited compared with other social cognitive models of health behavior, such as the Theory of Planned Behavior (TPB) and Social

The Necessity-Concerns Framework (NCF) The NCF is developed by Horne et al. based on the HBM (Horne and Weinman 1999). The NCF is a useful model to understand patients’ perspectives on their prescribed medications. This model posits that patients’ necessity beliefs and concerns about taking medications determine whether they adhere to their prescribed medications (Horne and Weinman 1999). According to this model, the more patients’ beliefs in the necessity of the medication compared to their concerns about medications, the greater their adherence will be (Horne et al. 2013). Horne et al. also developed the Beliefs about Medicines Questionnaire (BMQ) to measure patients’ perceptions about the necessity and concerns regarding taking medications (Horne et al. 1999). This questionnaire is useful to quantify NCF constructs. In a meta-analysis of 94 studies involving different medical conditions, beliefs in the necessity and concerns of medications and the necessity-concerns differential predicted medication adherence across several medical conditions including asthma, cancer, mental health, and cardiovascular disorders, but the effect sizes are generally small (Foot et al. 2016). However, it should be noted that beliefs about medications is not the only factor influencing medication adherence, and the NCF does not consider several other potential predictors of adherence, which limits its usability in practice.

Theory of Planned Behavior (Theory of Reasoned Action) The TPB is another theory that is widely used to explain and predict health-related behaviors. The TPB started as the theory of reasoned action (TRA) in 1980 to predict an individual’s intention

Behavioral Medicine/Behavioral Science in Pharmacy

to engage in a behavior (Ajzen 1991). TPB hypothesizes that intention is a precursor (proxy) for behavior where intention is a function of the person’s attitude about the consequence of performing the behavior (behavioral beliefs), the person’s perception of others’ approval or disapproval of him/her engaging in the behavior (normative beliefs), and the extent to which the person feels she or he can control factors impeding or facilitating the behavior (control beliefs) (Ajzen 1991). The TPB has been used successfully to predict and explain a wide range of health behaviors and intentions relevant to pharmacy practice, including non-adherence to medication, (Bane et al. 2006) smoking (Godin et al. 1992), drinking (Norman et al. 1998), and illicit drug use (Judson and Langdon 2009). However, TPB does not consider the person’s past experience, and environmental and economic factors that may influence a person’s intention to perform the target behavior (Munro et al. 2007). It does not also account for other variables that factor into behavioral intention and motivation, such as fear, threat or mood (Ajzen 2011). In a meta-analysis of 206 prospective studies, Charlotte et al. examined the efficacy of the TPB in predicting intention and actual behavior across different types of health behaviors. Overall, the TPB explained 44.3% and 19.3% of the variance in intention and behavior across studies, respectively. Particularly, physical activity and diet behaviors were better predicted, while abstinence from drug use and safer sex behaviors were poorly predicted. The prediction is also superior for selfreported than observed behaviors (McEachan et al. 2011).

Social Cognitive Theory (SCT) The SCT hypothesizes that someone’s behavior is the result of a dynamic and reciprocal interaction of the individual, environment, and behavior (Bandura 2002). Someone’s past experience with respect to the behavior may also influence their decision to engage in certain behavior (Bandura 2002). Moreover, the influence of role models and anticipated outcomes of performing the behavior

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may determine the person’s engagement in the behavior (Bandura 2000). The goal of SCT is to explain how people regulate their behavior through control and reinforcement to achieve goal-directed behavior that can be maintained over time. The SCT has been used to inform interventions for improving medication adherence among patients with hypertension (Nili et al. 2020), HIV/AIDS (Garofalo et al. 2016), stroke (Kamal et al. 2015), and those with a liver transplant (Dobbels et al. 2017). However, due to its wide-ranging focus and the difficulty of operationalizing its constructs, SCT has limited ability to guide the development of specific interventions (Munro et al. 2007). Additionally, it does not consider nonvoluntary factors (e.g., habit) that can influence behavior and psychological or behavioral skills needed to perform the behavior (Michie et al. 2011). SCT also focuses only on the target behavior, and it does not provide sufficient explanation about any competing behaviors (Michie et al. 2014).

Transtheoretical Model (TTM) The TTM (also called the Stages of Change Model) developed in the late 1970s (Prochaska and Velicer 1997). It focuses on the decision-making of the individual and is a model of intentional change. The TTM operates on the assumption that people do not change behaviors quickly and decisively. Rather, changes in behavior, especially habitual behavior, occurs continuously through a cyclical process. The TTM is not a theory but a model; different behavioral theories and constructs can be applied to various stages of the model where they may be most effective (Prochaska et al. 2008). The TTM postulates that individuals move through six stages of change: precontemplation, contemplation, preparation, action, maintenance, and termination (Prochaska and Velicer 1997). For each stage of change, different intervention strategies are most effective at moving the person to the next stage of change and subsequently through the model to maintenance, the ideal stage of behavior. This model has some weaknesses. For example, it ignores the social

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context, such as socioeconomic status and income, and it is not clear how long a person can remain in each stage. In a systematic review of 37 randomized controlled trials (RCTs), there was limited evidence for the effectiveness of interventions based on TTM across many health behaviors (Bridle et al. 2005).

Motivational Interviewing (MI) MI is a behavior change method that helps patients to change ingrained risky behaviors (Miller 1983). The main goals of MI are to enhance patients’ motivation and to encourage them to make the commitment to change (Rollnick and Miller 1995). MI is particularly useful when patients are reluctant or ambivalent about change (e.g., not wanting to stop drinking or smoking); if they do not engage in self-care behaviors (e.g., not adherent to medications); if they have an entrenched belief (e.g., vaccine hesitancy); or if they are unaware of the health risks (e.g., not using condoms during sex) (Bundy 2004; Emmons and Rollnick 2001). MI is a patient-centered approach and guided by three main core principles that emphasize a collaborative relationship with the patient, acceptance of the patient’s autonomy, and evocative counselling technique (i.e., enhancing a patient’s own motivations for change, rather than telling them what to do) (Smedslund et al. 2011; Rollnick et al. 2010). In addition, motivational interviewers often act as facilitators rather than experts, and they avoid confrontational and paternalistic approaches of counselling.(Miller 1983) Open questioning techniques, affirmations, reflective listening, and summarizing are key skills for motivational interviewers (Rollnick and Miller 1995). In recent meta-analyses, MI was found to be effective in reducing binge drinking, increasing physical activity, reducing substance abuse in people with dependency or substance use disorders, and improving adherence to long-term medications (Frost et al. 2018; Morton et al. 2015; Palacio et al. 2016; Zomahoun et al. 2017; DehghanNayeri et al. 2019; Lundahl et al. 2010). While MI has proven to be successful across various

Behavioral Medicine/Behavioral Science in Pharmacy

settings and for a range of conditions (Britt et al. 2003), it has some weaknesses. MI only works when patients fully participate in the behavior change process. It could be difficult for some patients to maintain their commitment to making behavior change, even if they are motivated at the beginning (Jacobsen et al. 2005; Jansink et al. 2010). Individuals who do not have a clear understanding of the advantages and disadvantages of their behavior are also unlikely to benefit from MI, for example, those with lower education or those living with an intellectual disability (Jansink et al. 2010). Furthermore, MI only works for individuals who are initially resistant to changing their behavior, and therefore MI may be inappropriate for those who are already ready to make the change (Ahluwalia et al. 2006). Pharmacists have played a strong role in implementing behavior change strategies to improve a range of health behaviors in their patients. There are a number of studies that have demonstrated pharmacists using motivation interviewing strategies improves health outcomes in patients. Some examples include community pharmacists being upskilled in motivational interviewing techniques to improve adherence to medicines in people with chronic conditions (Skelton and Binaso 2012; Spears et al. 2020) and practice- based pharmacists using motivational interviewing to encourage and maintain smoking cessation (Cheng et al. 2019; Deeks et al. 2019). These studies demonstrate the importance of pharmacists integrating behavior change strategies into their patient counselling. These discussions are likely to be particularly effective due to the trust patients generally have in their community pharmacists as well as the tailored knowledge the pharmacist can provide about the patient’s situation.

Behavior Change Wheel (BCW) The BCW framework has three main components: the Capability, Opportunity, Motivation – Behavior (COM-B) model, intervention functions, and policy categories (Michie et al. 2011). The starting point when using the BCW is identifying what

Behavioral Medicine/Behavioral Science in Pharmacy

needs to be changed by using the COM-B model in order to identify the target behavior. According to the COM-B, for any behavior to occur the person has to be psychologically or physically capable of performing the behavior, there must be a favorable physical or social environment (opportunity) for the behavior to occur, and the person should be adequately motivated to engage in the behavior (Michie et al. 2011). The COM-B further assumes that there is a dynamic interaction between the capability, opportunity and motivation components. After examining the target behavior using COM-B, the next step is choosing interventions to enhance the desired behavior (Michie et al. 2011). Intervention functions, the second component of the BCW, encompass nine broader classes of intervention strategies that can help to maximize capability/opportunity or to motivate the person to engage in the desired behavior (Michie et al. 2011). BCW has detailed guidelines to link each of the intervention functions with the COM-B components. The third stage of designing the interventions using the BCW involves identifying policy categories that would support the delivery of intervention functions to change the target behavior (Michie et al. 2011). The BCW framework incorporates seven policy categories that are likely to be effective in supporting each of the nine intervention functions. The elements of BCW framework have been used to examine different health behaviors. For example, Jackson et al. used the COM-B model to systematically categories factors associated with medication adherence (Jackson et al. 2014). The authors noted that COM-B can provide a better understanding of factors influencing medication adherence behavior compared to other existing theories or models of adherence (Jackson et al. 2014). The COM-B model has also been used to identify factors influencing the uptake of vaccine and antiretroviral medications by the general public during a pandemic flu outbreak scenario (Rubinstein et al. 2015). Using a structural equation modelling technique, Willmott et al. examined the capacity of the COM-B model in explaining eating and physical activity behaviors in young adults (aged 18–35 years) (Willmott et al. 2021). The authors

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conducted an online survey among 455 and 582 young adults to empirically test the COM-B model in the context of eating and physical activity behaviors, respectively. The findings support the COM-B model’s explanatory potential. Overall, the COM-B model explained 31% of the variance in physical activity behavior and 23% of the variance in eating behavior. Further, the model helped to systematically identify the barriers and enablers of young adults’ physical activity and eating behaviors that could be targeted by future interventions.

Challenges and Recommendations for Conducting Behavioral Research There are many opportunities for researchers to undertake behavioral research alongside clinical practice, which can help inform the research, ensure it is relevant and acceptable to those it aims to research. However, behavioral research involves the study of people and therefore is naturally complex, time-intensive and often, expensive. Health behavior is also dynamic in nature and can change over time. These factors lead to a number of challenges across all aspects of behavioral research including, but not limited to, selecting the right tool to measure a behavior, scale development and validation (when no appropriate tool is available) and developing a well-designed and theory based intervention, while ensuring intervention fidelity. The following section describes these challenges and provides some recommendations for researchers new to the area of behavioral medicine research.

Selecting the Right Tool to Measure the Behavior There are many tools available to researchers to measure health behaviors. These tools are often categorized as objective or subjective, based on how they measure the given behavior. It is important to recognize that there are benefits and limitations of each available tool.

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An example of the challenges of selecting the right measurement tool is in the area of medication-taking behavior, where there is a plethora of tools available to measure medication adherence (Lam and Fresco 2015). Objective measures of medication-taking include biochemical measures, directly observed therapy (DOT), pill counts of medication remaining in packaging, using pharmacy prescription refill records to measure the amount of time between supplies and electronic monitoring of medication administration (e.g., the Medication Event Monitoring System, MEMS) to monitor the date and time a patient removes the medication from its packaging. Objective measures of medication-taking are often said to be the most accurate measure of a patient’s medication-taking as they are unaffected by patient recall bias and social conformity (Lam and Fresco 2015). Prescription refill data assumes perfect medication-taking and different dosage amounts and/or timing cannot be examined (Steiner and Prochazka 1997). MEMS can measure the date and time doses removed from packaging, but intentional dose dumping may occur. MEMS are also expensive and not available for all medication packaging. Also, these measures cannot provide reasons for non-adherence, such as whether the patient is intentionally, or unintentionally non-adherent nor can they differentiate between primary and secondary nonadherence and therefore may not answer the research question the researcher is interested in. Objective measures of other health behaviors include measuring hair nicotine levels and returned cigarette butts as measure of smoking behavior, liver function tests as a measure of alcohol use and, urine toxicology as a measure of illicit drug use. Although these may be the most accurate measures of the given behavior, they are generally intrusive, expensive and difficult to perform to in the research environment. Subjective measures of health behavior include self-report scales, where a person is asked via interview or questionnaire about a specific health behavior (e.g., smoking, alcohol use, medicationtaking). These methods are more commonly used across all health behaviors as they are generally inexpensive and easier to conduct compared to

Behavioral Medicine/Behavioral Science in Pharmacy

objective measures. For researchers, it can be challenging deciding which scale to select. A systematic review of published self-report adherence tools identified over 43 validated adherence scales (Nguyen et al. 2014). Similarly, there are a wide range of self-report tools to measure other behaviors such as alcohol use (Allen et al. 1995; Jones 2011), and quality of life (Garratt et al. 2002). When choosing a subjective measurement tool, it is critical for researchers to identify the tool that will provide them with the outcome they interested in, as not all scales focus on all aspects of the behavior. Some scales can quantify the behavior, while others only focus on identifying barriers and beliefs associated with the behavior. A common limitation of self-report tools is that they can lead a patient to answer in a socially desirable way, whether it be intentional or unintentional (Lam and Fresco 2015). For this reason, self-completed questionnaires, posited in a non-judgmental manner, are preferred to faceto-face interviews. Overall, it is important to note the limitations of each technique and to interpret any results with this in mind. The choice of measure used in research or practice is often based upon cost, patient acceptability and time taken to use. If possible, using multiple (objective and subjective) measures is recommended.

Scale Development and Validation When an Appropriate Tool Does Not Exist The literature is full of published tools to measure different aspects of health behavior and it is important that researchers examine the appropriateness of the scale and its validity and reliability in the population and setting they wish to use it in. Scale development and validation is complex and should be avoided if an appropriate tool already exists. In situations where there is no validated tool available, researchers may choose to embark on scale development and validation. This section provides a brief overview into the development and validation of measures used to elicit behavior and belief constructs and

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highlights some of the challenges that researchers may face along the way. Behavioral medicine scales are composed of latent constructs that are intended to represent the behavior or attitudes that we are unable to measure directly (Boateng et al. 2018). The constructs and items within these scales are commonly developed from literature reviews, focus groups and/or expert opinions. When a health behavior model or theory is guiding the development of the scale, this should also be used to guide the construct development. For example, the Beliefs about Medicines Questionnaire (BMQ) (Horne et al. 1999), is a widely used tool to examine patient’s beliefs about their medicines to long term conditions and medicines in general. It stems from the Health Belief model and was developed by identifying commonly held beliefs about medicines in the literature and from interviews with individuals taking medication for a chronic condition. Once the item pool of questions or statements have been generated, factor analysis is often used to assess whether there are similar patterns of response to some of the items, to determine whether they should be separated into multiple subscales or domains. Once the measure is initially developed, it is essential that it is evaluated. Content validity examines whether the tool is assessing the relevant parts of a larger construct. This is often done through using subject experts and the target population (known as face validity) to determine whether the items are suitable and interpretable. A particularly important aspect of evaluation of behavioral measures is to assess whether the scale is actually measuring the underlying theory or model of what is meant to be measured (Raykov and Marcoulides 2011). This can be assessed through construct validation where the new scale is compared to similar validated measures (convergent validity) and different measures (discriminant validity) to determine how the scale relates to other measures of other constructs. Criterion validity assesses whether the questionnaire is related to a similar measure or outcome at the same time (concurrent validity) and/or whether the questionnaire is related to a similar measure

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or outcome at later time point (predictive validity). The reliability of a measure refers to how well the scores are consistent from the same individuals over time (test-retest reliability). This can be difficult to conduct in health settings, where it can be unethical to deny information to the patient (e.g., discussion on the responses to survey) for the purposes of scale validation. It can also be difficult to determine the appropriate time to re-test the scale to determine test-retest reliability of the behavior being measured. Finally, internal consistency, often measured using Cronbach alpha, assesses how consistent an individual scores within the measure. Internal consistency however is sample dependent and can vary each time the measure is used. In summary, this is a brief overview of the steps taken in scale development and evaluation. Researchers considering developing a new tool to measure health behavior should ensure they have a thorough understanding of this area and consult with experts in the field. This will ensure a well-developed tool that can be used appropriately in the behavioral medicine field.

Designing an Intervention and Ensuring Fidelity Designing a behavioral medicine intervention is complex and requires a comprehensive understanding of the theory underpinning its design. Collaborating with more senior researchers and involving interdisciplinary collaborations will provide a strong grounding for developing a study that is theoretically and practically designed. Researchers should consider their patient population, context, study design and intervention carefully. Pilot and feasibility studies are useful for testing the intervention and its procedures, estimating sample size and recruitment retention and obtaining feedback from end-users (e.g., participants, clinicians, study providers) on the study design and intervention. It is important to note that pilot and feasibility studies should not

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be used primarily for hypothesis testing (Arain et al. 2010). A challenge of designing behavioral medicine interventions is describing the complex nature of the intervention in a clear but concise way. The research community have several useful guides to direct researchers in the design and reporting of behavioral research interventions. The Consolidated Standards of Reporting Trials for social and psychological interventions (CONSORT-SPI 2018) is an official extension of the CONSORT 2010 Statement for reporting RCTs of social and psychological interventions (Grant et al. 2018) and would be a great starting point for new researchers to begin considering the necessary components of a research study. Similarly, the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network has made large headways in aiming to address these issues to ensure interventions are being described in the scientific literature in enough detail to allow replication. The template for intervention description and replication (TIDieR) checklist and guide is for reporting of intervention in scientific publications (Hoffmann et al. 2014). Currently, most research journals do not require submission of TIDieR checklists, but this would be a useful submission item, similarly to trial reporting (i.e., CONSORT) checklists. The TIDieR checklist is also useful for behavioral researchers in the development of interventions to ensure they are considering all aspects of the intervention design and delivery. Some particularly pertinent components in behavioral medicine intervention to consider include describing the rationale and theory behind the components of the intervention and assessing intervention fidelity. Measuring intervention fidelity refers to the strategies used to monitor and ensure the reliability and validity of interventions and is always important when interventions involve people (Bellg et al. 2004). The National Institutes of Health (NIH) Behaviour Change consortium describes a number of best practices and recommendations for ensuring and assessing intervention fidelity (Bellg et al. 2004). Without a known reliable and valid intervention, researchers are

Behavioral Medicine/Behavioral Science in Pharmacy

unable to draw accurate conclusions about their study. If the study shows to be successful, without assessing intervention fidelity, it is not clear whether the study results were due to addition or removal of a component of the intervention. Whereas if the results are not favorable, this could be due to divergence of the intervention. By assessing intervention fidelity, researchers can be more confident in their results and funders are more likely to invest in practice implementation.

Conclusion Pharmacists are tasked on a daily basis with advising patients on medications and health products, promoting good health, and ensuring adherence to treatment. Although it is critical for pharmacists to have a reasonable degree of clinical and pharmaceutical sciences knowledge to perform their daily duties, it is no longer sufficient to depend solely on this knowledge to be an effective pharmacist. The involvement of pharmacist in a wide variety of health promotion roles now necessitates an in-depth understanding and adoption of behavioral medicine. This is particularly important for contemporary pharmacy practice, as many of the health problems pharmacists are dealing with are primarily behavioral in nature as opposed to medical, for example, non-adherence to medication, smoking, or substance use disorders. Behavioral medicine uses the biopsychosocial model of health and disease, rather than the medical model. The biopsychosocial model views health and illness behaviors as products of behavioral, psychological, sociocultural, and socioeconomic factors, as well as biological characteristics. Pharmacists can adopt the principles of behavioral medicine and associated health behavior models/ theories to help patients deal with the behavioral aspects of medication use and illness. This may include developing interventions or guidelines to improve adherence to medications, substance use disorders, smoking cessation, weight loss programs, and so forth. As mentioned above, there is adequate evidence that the application of behavioral theories can help pharmacists influence patients’ beliefs about their illness and treatment.

Behavioral Medicine/Behavioral Science in Pharmacy

There are many behavioral models and theories, and each comes with their own pros and cons. Behavioral theories that focus on individual’s cognitive processes and decisions (e.g., HBM, NCF, and TPB) have been criticized and questioned for neglecting the social and environmental determinants of behaviors. However, these theories should not be rejected because of this. Individual decisions are often central to adoption of a health-related behavior; thus, understanding the cognitive mechanisms underlying behaviors is critical to improve behavior. Individualistically focused theories are also more intuitive and explicit than complex health behavior theories. Nevertheless, social and environmental contexts clearly do play a role in determining behavior, and models that ignore these factors may not provide a complete understanding of behaviors. Therefore, behavior change intervention strategies should take into account both personal and environmental determinants of behaviors. In general, developing, piloting, evaluating, and implementing interventions can be a lengthy process. All these stages are important and neglecting any stage will results in a less effective intervention. Interventions should also be developed based on existing evidence, not just assumptions. It is therefore important to devote sufficient time and resource to evidence gathering. Understanding the whole process of complex behavior change interventions can be challenging for pharmacy professionals. This is often due to their limited training on behavioral sciences and lack of prior experience of intervention development. Incorporating behavioral medicine principles into pharmacy education and re-training qualified pharmacists about health behaviors is therefore useful and may ultimately lead to better health outcomes for their patients. Finally, the importance of behavioral measurement cannot be overstated. Health behavior constructs are often multi-dimensional and imaginary concepts. Hence, a multiple-item tool or questionnaire is often required to precisely specify and measure a health behavior. Furthermore, the tool should be valid and reliable. Several tools are available to assess certain behaviors, such as medication adherence. These tools are different in

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their constructs, response scale formats, recall period, and modes of administration. Some measures are also only validated in specific settings or medical conditions. Apart from psychometric properties, it is important to consider the cost, ease of administration, and burden to patients when choosing among a plethora of tools for measuring health or medicines use behaviors.

Cross-References ▶ Intercultural Challenges to Consider When Designing Pharmaceutical and Behavioral Interventions in Health Services Research

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Behavioral Medicine/Behavioral Science in Pharmacy informed implementation interventions. Implement Sci. 2006;1:1–8. Jackson C, Eliasson L, Barber N, et al. Applying COM-B to medication adherence. Eur Health Psychol. 2014;16(1):7–17. Jacobsen ET, Rasmussen SR, Christensen M, et al. Perspectives on lifestyle intervention: the views of general practitioners who have taken part in a health promotion study. Scand J Public Health. 2005;33(1): 4–10. Jansink R, Braspenning J, van der Weijden T, et al. Primary care nurses struggle with lifestyle counseling in diabetes care: a qualitative analysis. BMC Fam Pract. 2010;11(1):1–7. Janz NK, Becker MH. The health belief model: a decade later. Health Educ Q. 1984;11(1):1–47. Jones LA. Systematic review of alcohol screening tools for use in the emergency department. Emerg Med J. 2011;28(3):182–91. https://doi.org/10.1136/emj. 2009.085324. Jones CJ, Smith H, Llewellyn C. Evaluating the effectiveness of health belief model interventions in improving adherence: a systematic review. Health Psychol Rev. 2014;8(3):253–69. Judah G, Aunger R, Schmidt W, et al. Experimental pretesting of hand-washing interventions in a natural setting. Am J Public Health. 2009;99(S2):S405–11. Judson R, Langdon SW. Illicit use of prescription stimulants among college students: prescription status, motives, theory of planned behaviour, knowledge and self-diagnostic tendencies. Psychol Health Med. 2009;14(1):97–104. Kamal AK, Shaikh Q, Pasha O, et al. A randomized controlled behavioral intervention trial to improve medication adherence in adult stroke patients with prescription tailored Short Messaging Service (SMS)-SMS4Stroke study. BMC Neurol. 2015;15(1):1–11. Lam WY, Fresco P. Medication adherence measures: an overview. Biomed Res Int. 2015;2015:217047. Lundahl BW, Kunz C, Brownell C, et al. A meta-analysis of motivational interviewing: twenty-five years of empirical studies. Res Soc Work Pract. 2010;20(2): 137–60. McEachan RRC, Conner M, Taylor NJ, Lawton RJJ. Prospective prediction of health-related behaviours with the theory of planned behaviour: a meta-analysis. Health Psychol Rev. 2011;5(2):97–144. Michie S, Prestwich A. Are interventions theory-based? Development of a theory coding scheme. Health Psychol. 2010;29(1):1. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42-5908-6-42. Michie S, Atkins L, West R. The behaviour change wheel: a guide to designing interventions. London: Silverback Publishing; 2014. Miller WR. Motivational interviewing with problem drinkers. Behav Cogn Psychother. 1983;11(2):147–72.

13 Morton K, Beauchamp M, Prothero A, et al. The effectiveness of motivational interviewing for health behaviour change in primary care settings: a systematic review. Health Psychol Rev. 2015;9(2):205–23. Munro S, Lewin S, Swart T, et al. A review of health behaviour theories: how useful are these for developing interventions to promote long-term medication adherence for TB and HIV/AIDS? BMC Public Health. 2007;7:104. https://doi.org/10.1186/1471-2458-7-104. National Institute for Health and Care Excellence (Great Britain). Behaviour change: individual approaches. London, UK: National Institute for Health and Care Excellence (NICE); 2014. Nguyen T, Caze AL, Cottrell N. What are validated selfreport adherence scales really measuring?: a systematic review. Br J Clin Pharmacol. 2014;77(3):427–45. Nili M, Mohamed R, Kelly KM. A systematic review of interventions using health behavioral theories to improve medication adherence among patients with hypertension. Transl Behav Med. 2020;10(5):1177–86. Norman P, Bennett P, Lewis H. Understanding binge drinking among young people: an application of the theory of planned behaviour. Health Educ Res. 1998;13(2): 163–9. Orji R, Vassileva J, Mandryk R. Towards an effective health interventions design: an extension of the health belief model. Online J Public Health Inform. 2012;4(3). https://doi.org/10.5210/ojphi.v4i3.4321. Epub 2012 Dec 19. Palacio A, Garay D, Langer B, et al. Motivational interviewing improves medication adherence: a systematic review and meta-analysis. J Gen Intern Med. 2016;31(8):929–40. Petrie KJ, Perry K, Broadbent E, et al. A text message programme designed to modify patients’ illness and treatment beliefs improves self-reported adherence to asthma preventer medication. Br J Health Psychol. 2012;17(1):74–84. Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997;12(1):38–48. Prochaska JO, Wright JA, Velicer WF. Evaluating theories of health behavior change: a hierarchy of criteria applied to the transtheoretical model. Appl Psychol. 2008;57(4):561–88. Raykov T, Marcoulides GA. Introduction to psychometric theory. New York, NY: Routledge; 2011. Rollnick S, Miller WR. What is motivational interviewing? Behav Cogn Psychother. 1995;23(4):325–34. Rollnick S, Butler CC, Kinnersley P, et al. Motivational interviewing. BMJ. 2010:340. (Online). Rosenstock IM. The health belief model and preventive health behavior. Health Educ Behav. 1974;2(4): 354–86. Rubinstein H. Applying behavioural science to the private sector: decoding what people say and what they do. Cham, Switzerland: Springer; 2018. Rubinstein H, Marcu A, Yardley L, et al. Public preferences for vaccination and antiviral medicines under

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Behavioral Medicine/Behavioral Science in Pharmacy Team BI. EAST: four simple ways to apply behavioural insights. London: Behavioural Insight Team; 2014. Webb T, Joseph J, Yardley L, et al. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. 2010;12(1):e1376. Willmott TJ, Pang B, Rundle-Thiele SJ. Capability, opportunity, and motivation: an across contexts empirical examination of the COM-B model. BMC Public Health. 2021;21(1):1–17. Yarbrough SS, Braden CJ. Utility of health belief model as a guide for explaining or predicting breast cancer screening behaviours. J Adv Nurs. 2001;33(5):677–88. Zimmerman RS, Vernberg D. Models of preventive health behavior: comparison, critique, and meta-analysis. Adv Med Sociol. 1994;4:45–67. Zomahoun HTV, Guenette L, Gregoire J, et al. Effectiveness of motivational interviewing interventions on medication adherence in adults with chronic diseases: a systematic review and meta-analysis. Int J Epidemiol. 2017;46(2):589–602.

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Causal Inference in Pharmacoepidemiology Kaustuv Bhattacharya1,2 and Sushmitha Inguva3 1 Center for Pharmaceutical Marketing and Management, University of Mississippi School of Pharmacy, University, MS, USA 2 Department of Pharmacy Administration, University of Mississippi School of Pharmacy, University, MS, USA 3 Health Economics and Outcomes Research, Amgen Inc., Thousand Oaks, CA, USA

Abstract

Observational data is increasingly being used to examine the comparative effectiveness of medical treatments during the post-marketing surveillance period and for regulatory approvals. This chapter introduces the basics of causal inference using observational data, including assumptions, measures of effect, and various biases affecting causal effect estimation. It provides guidance on the considerations required for constructing causal directed acyclic graphs (DAGs) and includes recommendations for a systematic approach to building DAGs. Additionally, it reviews various methodological approaches for causal modeling, and introduces the readers to the target trial framework, key components of a target trial protocol, and considerations that need to be

made for emulating a target trial. Finally, it presents an overview of various statistical approaches employed to account for timeinvariant and time-varying confounding in causal inference studies. Keywords

Causal inference · Pharmacoepidemiology · Causal DAGs · Target trial

Introduction Pharmacoepidemiology is a branch of science that examines drug use and effects in clearly defined groups using epidemiological and clinical pharmacology concepts. Comparing the effectiveness and safety of medication treatments in populations exposed to a specific drug with those unexposed or exposed to a comparator drug is a key aspect of pharmacoepidemiologic research. These comparisons can be made in experimental and nonexperimental settings (through prospective or retrospective observational studies). Randomized controlled trials (RCTs), conducted in experimental settings where exposure to the investigational drug is randomized, have traditionally been the gold standard for generating this evidence. Data on treatment randomization, outcomes of interest, and variables that may affect the relationship between exposure and these

© Springer Nature Switzerland AG 2023 Z.-U.-D. Babar (ed.), Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy, https://doi.org/10.1007/978-3-030-64477-2

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outcomes are captured in RCTs. Individuals included in an RCT are randomly assigned to either the treatment under investigation or comparator (placebo or current standard of care) and followed for a predefined period to assess outcomes of interest. The process of randomization, in addition to the ability to account for important factors that can confound the exposure–outcome relationship, allows for estimating the efficacy and safety of the investigational drug. However, it has a few limitations. While RCTs have high internal validity owing to the use of strict (and restrictive) inclusion and exclusion criteria, the external validity of the study findings is often compromised (Kostis and Dobrzynski 2020). This is due to several reasons. First, individuals enrolled in RCTs are younger and healthier than those in the real world, as elderly individuals with multimorbidity, frailty, and disability are often excluded from clinical trials (Hutchins et al. 1999). Second, there is a significant underrepresentation of minority populations in RCTs (Nazha et al. 2019). Since different individuals may have different experiences of the same disease, the lack of data on individuals from diverse racial groups who may have different lived experiences severely hampers the external validity of RCTs (Diversity and Inclusion in Clinical Trials 2023). Additionally, given that RCTs usually have small sample sizes and the follow-up period is short, these estimates may be biased by dropouts or nonresponses in the follow-up period, especially if the nonresponse is associated with the treatment assignment or outcome of interest (Glymour et al. 2016). Furthermore, due to ethical concerns, certain research questions cannot be answered through RCTs, for example, cases where there is prior evidence reporting benefits of the intervention over not doing anything (placebo) or where preliminary analysis provides clear evidence of the superiority of the investigational treatment (Bhide et al. 2018). Observational studies, conducted using real-world data, can be used to overcome all the limitations listed above. Compared to RCTs, observational studies are less resource intensive, take less time to complete, and have greater external validity due to the availability of data on

Causal Inference in Pharmacoepidemiology

diverse populations. Moreover, real-world data can be used to answer research questions that cannot be investigated in RCTs due to ethical considerations. Evidence generated from realworld data sources are being increasingly used to evaluate the effectiveness and safety of treatments, not only during the post-marketing surveillance period but also for drug regulatory approvals (Baumfeld Andre et al. 2020; ElZarrad and Corrigan-Curay 2019). However, using realworld data to evaluate drug treatment’s comparative effectiveness and safety requires careful selection of appropriate study designs and analytical approaches. This is because treatment cannot be randomized in observational studies, and studies are often limited by the data available in the data source, compared to RCTs, where all information on important variables that can confound the exposure-outcome relationship are collected. The lack of information on all important variables that may bias the estimated treatment effects in studies using real-world data is largely because these data were originally collected for purposes (billing for administrative claims data or capturing clinical notes for electronic medical records) other than examining the particular research question of interest. Additionally, when estimating treatment effects that vary with time, it is important to account for time-varying variables, which requires selecting estimation approaches beyond standard regression. These issues surrounding the use of real-world data necessitate the use of causal inference approaches in pharmacoepidemiologic research. The causal effect of treatments on outcomes of interest can be measured in observational studies using causal inference techniques. A treatment or exposure is deemed to have a causal effect when it leads to a subsequent health outcome. Causal inference methods allow for estimating causal relationships between variables of interest in a research question. The “counterfactual” or “potential outcomes framework”(Rubin 2005) is a useful approach for estimating such causal relationships and helps improve the rigor and transparency of causal inference methods. This methodology would compare the observed potential outcomes if every individual in the study

Causal Inference in Pharmacoepidemiology

sample had each treatment of interest to evaluate an exposure’s causal effect. This chapter will review the concepts and assumptions of causal inference methods, various biases that can impact causal effect estimates, and causal modeling approaches in pharmacoepidemiologic research.

Causal Inference: Brief Review of Principles, Assumptions, and Measures Before embarking upon the framework used for causal inference and its important assumptions, we will first define what constitutes a causal effect. Let’s consider a dichotomous variable indicating treatment (X) with 1 denoting receipt of treatment and 0 denoting no treatment receipt, and a dichotomous outcome variable (Y) with 1 denoting occurrence of the outcome of interest and 0 denoting no occurrence of the outcome of interest. Consider Yx¼1 as the observed outcome for those receiving treatment (X ¼ 1) and Yx ¼ 0 as the observed outcome for those not receiving the treatment of interest (X ¼ 0). Furthermore, let’s consider two individuals in the study – one who had the outcome of interest when treated (Yx¼1 ¼ 1) but would not have had the outcome if untreated (Yx¼0 ¼ 0), and the other who did not have the outcome of interest under treatment (Yx¼1 ¼ 0) and would also not have had the outcome if untreated (Yx¼0 ¼ 0). At an individual level, the causal effect of treatment X on outcome Y can be identified if Yx¼1 6¼ Yx¼0. The random variables, Yx¼1 and Yx¼0, are commonly termed “potential outcomes” or “counterfactuals” in scientific literature. Potential outcomes or counterfactuals can be used as a framework for causal inference. Under this framework, several assumptions are made to identify causal effects from observational data. The first assumption is that of consistency, which states that an individual’s potential outcome (or counterfactual) is equal to their actual observed outcome (Cole and Frangakis 2009; VanderWeele 2009; Pearl 2010). In mathematical terms, consistency implies that Y (observed outcome) ¼ Yx (counterfactual). This also denotes there is only one version of the treatment and

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control. In cases with multiple versions, they do not have any bearing on the estimated causal effect. This characteristic is also commonly referred to as the treatment variation irrelevance assumption. The second assumption of note is the no interference assumption. This means that an individual’s potential outcome is independent of the treatment assignment of other individuals in the data. The no interference assumption and the treatment variance irrelevance assumption are referred to as the stable-unit treatment value assumption (SUTVA) (Rubin 1986). The no interference assumption does not always hold in real-world settings, depending on the research question under examination. This is commonly seen in studies where an individual’s outcomes have a significant probability of being impacted by interaction with other individuals in the study. Examples include studies on HIV prevention or vaccination where treatment assignment (say, vaccination) of one individual may impact potential outcome (being infected) of another individual (Buchanan et al. 2018; Perez-Heydrich et al. 2014). In such cases, causal effect estimation can be done by relaxing the no interference assumption, with a growing body of literature on approaches to quantify causal effects in the face of interference (Buchanan et al. 2018; Tchetgen and VanderWeele 2012; Benjamin-Chung et al. 2018). Another key assumption for causal inference is exchangeability, which means that the potential outcomes are not dependent on the mechanism for treatment assignment (Rosenbaum and Rubin 1983). Last but not least, reliable estimation of causal effects also requires the assumption of positivity, which implies that each individual in the study has a nonzero conditional probability of receiving all possible treatments under consideration (Rosenbaum and Rubin 1983). Together, the exchangeability and positivity assumptions constitute the strongly ignorable treatment assignment assumption (Rosenbaum and Rubin 1983). The most commonly reported measures of estimate in causal inference are odds ratio (OR), risk difference (RD), and risk ratio (RR). Mathematical representations of each of these measures of estimate are as follows:

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Causal RD ¼ p(Yx¼1 ¼ 1) – p(Yx¼0 ¼ 1) ¼ E(Yx¼1) – E(Yx¼0) Causal RR ¼ p(Yx¼1 ¼ 1)/p(Yx¼0 ¼ 1) Causal OR ¼ [p(Yx¼1 ¼ 1)/p(Yx¼1 ¼ 0)]/ [p(Yx¼0 ¼ 1)/p(Yx¼0 ¼ 0)] As per the null hypothesis of no causal effect of exposure (X) on the outcome (Y), the causal RD has a value of 0, whereas the causal RR and causal OR have a value of 1. Causal effects for these estimate measures differ from their noncausal (associational) forms. In a non-causal framework, these measures of effect are defined as follows:

expected under null, even if the null hypothesis cannot be rejected. In an RCT setting, when selection bias and measurement error are not present, the estimates for the associational measure should be equal to the estimates from the causal measure. For observational studies, this exercise helps assess under what circumstances conditional measures of association are equivalent to causal measures and help determine under which assumptions the potential outcomes can be reliably assessed to draw causal inferences.

Types of Biases and Confounding Associational RD ¼ p(Y ¼ 1|X ¼ 1) – p(Y ¼ 1| X ¼ 0) ¼ E(Y|X ¼ 1) – E(Y|X ¼ 0) Associational RR ¼ p(Y ¼ 1|X ¼ 1)/p(Y ¼ 1 |X ¼ 0) Associational OR ¼ [p(Y ¼ 1|X ¼ 1)/p(Y ¼ 0| X ¼ 1)]/[p(Y ¼ 1|X ¼ 0)/p(Y ¼ 0|X ¼ 0)] Conceptually, the difference between the associational and causal measures of effect is presented in Fig. 1. The associational measures are not adjusted for potential confounders that may affect the relationship between the exposure (X) and outcome (Y). Thus, the measure estimates may differ from that Causal Inference in Pharmacoepidemiology, Fig. 1 Conceptual diagram depicting the difference between association and causation

Unlike randomized clinical trials, observational studies are prone to several threats to their validity, and inappropriately accounting for them can lead to spurious relationships and biased estimates. Below, we will explore some commonly encountered types of biases and confounding that researchers must carefully consider when designing and implementing causal inference methods. Measured Confounding The objective in causal inference is to sufficiently account for confounding. In an ideal case, a variable or a set of variables directly affects both the Study Population

Exposed: X=1

Unexposed: X=0

Association

E(Y|X=1)

Causation

E(Y|X=0)

E(Yx=1)

X = Exposure; Y = Outcome

E(Yx=0)

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exposure and outcome variables. However, in an observational study, collecting or measuring such an exhaustive list of confounding variables is not feasible. Therefore, researchers frequently try to reduce bias by measuring as many potential confounders as feasible at the study design and data collection phase and by adjusting for these variables in the statistical model during analysis (regression adjustment or inverse probability of treatment weighting, for example). It is important to take care when choosing potential confounders to measure because failure to do so could result in insufficient confounding adjustment or even introducing confounding where none previously existed (Roy and Mitra 2021). For instance, if the original confounder is not measurable or has not been measured, researchers should consider alternative measurable variables that could allow sufficient control for confounding. On the other hand, it may be better to avoid statistical adjustment of measured variables available to the researcher if they are not directly or indirectly related to the exposure and outcome variables. These variables would not be able to appropriately account for confounding, and conditioning the statistical model on such variables could potentially induce confounding that previously did not exist. Unmeasured Confounding The assumption that confounding has been appropriately accounted for is always an approximation in observational studies, even though researchers typically make every effort to measure and control for a sufficient collection of confounders. We can never rule out the possibility that there might exist another unmeasured variable that could bias the findings of our study. Quantitative bias analysis should be conducted to assess unmeasured confounding in observational studies. Technical details of the analysis is beyond the scope of the current chapter and described in detail elsewhere (VanderWeele and Arah 2011; Lash et al. 2014; Barberio et al. 2021). Collider Bias Collider bias, also known as collider-stratification bias, occurs when the relationship between the

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exposure (X) and the outcome (Y) variables is conditioned upon a third variable (C), which is affected by both the exposure (X) and the outcome (Y) (Greenland 2003). Due to the direction of the arrowheads that “collide” in the path between the variables, this type of variable is frequently referred to as a collider (C) (X ! C Y). Researchers may encounter situations where the relationship between an exposure and the outcome on one stratum of the collider might differ from the relationship observed in the general population (containing all strata of the collider). In other words, stratification on the collider could induce confounding, resulting in spurious estimates of the relationship between the exposure and the outcome. Selection Bias The issue of selection bias is frequently discussed in pharmacoepidemiology, and it stems from methods used to select study participants or variables influencing study participation (Nazha et al. 2019). This occurs when the association between exposure and outcome is distinct among those who participate in the study and those who are eligible for the study. Depending on the type of study, there may be various reasons why selection bias might occur in a study. For instance, it could occur when the outcome or ancestors of the outcome affect selection into the study or study participation, or when the influence of outcome on selection is different in different levels of exposure, or some cases, because of collider bias (Lash and Rothman 2020). Immortal Time Bias Similar to clinical trials, a researcher may choose to study the association between an exposure (such as treatment use) and an outcome (such as mortality) after enrollment or entry into an observational cohort data source. This requires that subjects have survived for a period of time to become eligible for inclusion in the cohort and receive treatment. This period between cohort entry and exposure is referred to as immortal time, i.e., subjects cannot experience mortality during this period (Suissa 2007; Yadav and Lewis 2021). In this scenario, persons who did

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not survive long enough for cohort entry and treatment exposure are excluded, and improperly accounting for the immortal time in the cohort leads to an overestimation of the mortality rate in the untreated group.

may be exaggerated due to the influence of other healthy behaviors and exposure. Researchers can minimize bias due to healthy adherer effect by adjusting for other factors that may impact the outcome of their analysis.

Protopathic Bias or Reverse Causality Protopathic bias or reverse causality occurs when the subject’s exposure changes in response to early signs or symptoms of a dormant, as-of-yet undetected or target outcome (Horwitz and Feinstein 1980; Suissa and Dell’Aniello 2020). Suissa and Dell’Aniello illustrate this concept through an example assessing the association between long-acting bronchodilator inhaler use and the incidence of lung cancer during followup (Baumfeld Andre et al. 2020). Here, subjects may have received bronchodilators to treat a respiratory symptom, which was a consequence of the underlying lung cancer, undiagnosed at the time of the prescription and mistaken for a respiratory health condition. If this bias is to be ignored, the results would wrongly indicate a higher risk of developing lung cancer among bronchodilator users. Including a time lag in studies is one way to overcome protopathic bias (Tamim et al. 2007; Arfè and Corrao 2016). In this example, investigators could also disregard or reclassify cancer diagnoses that occurred within a certain time frame following treatment initiation to overcome this bias (Suissa and Dell’Aniello 2020).

Prevalent User Bias In studies assessing the association between treatment use and outcomes, the use of a prevalent cohort (i.e., individuals who are prevalent users of the treatment) leads to a type of bias called prevalent user bias (Golozar et al. 2016; Webster-Clark et al. 2021). First, prevalent treatment users survived the early phase of the followup period, constituting a relatively healthy group than those who did not survive to be included in the study. Second, continued treatment use could also influence or change the individual’s status on other risk factors. The best way to deal with this type of bias is to collect data from a cohort of new treatment users (Golozar et al. 2016; WebsterClark et al. 2021).

Healthy Adherer or Healthy User Effect The tendency of study participants who get one preventive therapy also to seek out more preventive therapies or engage in additional healthy behaviors is known as the healthy adherer or healthy user effect (Brookhart et al. 2007; Shrank et al. 2011; Ladova et al. 2014). Individuals who seek preventive health-care services may also be more likely to exhibit other healthy behaviors such as working out regularly, eating a wholesome diet, avoiding smoking, and so on. Let’s consider a study where a researcher may be interested in assessing the impact of a preventive care intervention (exposure) on the occurrence of a disease (outcome) in the follow-up. In this case, the association between exposure and outcome

Confounding by Indication Confounding by indication occurs in observational studies, evaluating the effectiveness of alternative therapies when the clinical indication for choosing a certain course of therapy also influences the outcome (Weiss 2008; Kyriacou and Lewis 2016). Consider a scenario where treatment A is being compared to treatment B, where treatment A is indicated for a more severe form of the disease and treatment B is indicated for the mild or moderate disease. When such treatments are compared head-to-head, the results might indicate that patients receiving treatment A also have poorer outcomes. However, the poorer outcomes might result from the greater severity of the disease; hence, the association between treatment and outcomes is confounded by the treatment indication itself. The impact of confounding by indication can be accounted for by deploying usual methods accounting for regular cases of confounding, such as matching, stratification, and so on. Dependent or Informative Censoring Survival or time-to-event analytic techniques are frequently applied to analyze longitudinal

Causal Inference in Pharmacoepidemiology

outcomes associated with the risk of experiencing death or an event of interest during the follow-up period. In these analyses, data are either leftcensored (actual survival time is less than or equal to observed survival time) or right-censored (survival time is cut-off or censored on the right side of the follow-up for subjects who do not experience an event of interest). Independent or noninformative censoring assumption is of relevance in right-censored data. The assumption states that for a subject in the risk set, the probability of being censored at any time t is independent of the subject’s likelihood to experience death or the event of interest (Kleinbaum and Klein 2005; Haneuse 2021). Occasionally, this assumption is violated when a subject’s likelihood of experiencing the event of interest, withdrawal or loss to follow-up is influenced by factors such as a competing risk (e.g., death due to a cause different from the cause of interest), their health status, adverse effects from the treatment or placebo, and so on. In such cases, researchers should closely examine all covariates related to both censoring and the event of interest or consider the use of specialized techniques such as competing risks model (Kleinbaum and Klein 2005) or inverse probability of censoring weighting method (Robins 1993), among others.

Overview of Causal Directed Acyclic Graphs As noted earlier, identifying the assumptions under which the potential outcomes can be estimated is a prerequisite for drawing causal inferences. However, this process can often pose a challenge. Directed acyclic graphs (DAGs) are useful tools for better understanding causal mechanisms and to represent key causal concepts in a structured manner (Greenland et al. 1999; Pearl 1995). DAGs provide a visual depiction of the causal research question and underlying assumptions for the causal inference analysis, helping develop a better understanding of the various confounders and mechanism for addressing them (Pearl 1995; Krieger and Davey 2016). A DAG denotes causal relationships between study

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variables as arrows between nodes, with nodes indicated for both measured and unmeasured variables of interest. When a causal effect is absent, there is no arrow between nodes (Glymour et al. 2016; Pearl 2009; Morgan and Winship 2015). In DAGs, nodes represent random variables: Y for outcome, X for exposure, and L for a covariate. It is usually structured so that time flows from left to right between these variables to establish temporality between them in a DAG. These graphs are termed “directed” since the arrows imply directionality, as in an arrow from L into X means that L may cause X but the reverse is not true. They are also termed “acyclic” since a variable cannot cause itself. Knowledge of the background literature drives the selection of variables to be included in a DAG (Hernán et al. 2002; Sauer et al. 2013; Robins and Wasserman 1999; Robins 2001; Rubin 2008). Moreover, lack of causal effect (indicated by the absence of an arrow between two nodes) is a stronger claim than including an effect in a DAG (Morgan and Winship 2015). Thus, while building DAGs, one should start from a saturated DAG (i.e., a DAG in which all possible variables are interrelated) and then work backward to remove effects that are not thought to be possible. This is commonly referred to as the “backdoor criterion” for ascertaining the variables that need to be included in a DAG to estimate causal effects (Greenland et al. 1999; Tennant et al. 2017). Additionally, caution should be exercised while employing data-driven approaches such as stepwise selection for informing inclusion of variables in DAGs. While these techniques are useful, the use of these methods may also introduce bias in the estimation of causal effects through incorrect adjustment of mediators or colliders (Shrier and Platt 2008). A structured approach for building DAGs is provided by the “Evidence Synthesis for Constructing Directed Acyclic Graphs” (ESC-DAGs). This novel review approach combines causal thinking, represented in the potential outcomes framework, with the rigor of systematic evidence synthesis approaches, such as Cochrane reviews (Ferguson et al. 2020). As per the ESCDAGs approach, one should first conduct a

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systematic search of the extant literature on the focal relationship of interest (i.e., the exposure and outcome variables under examination). Then the findings from this search should be translated into DAGs before combining all the translated DAGs through the synthesis of an “integrated” DAG for the causal effect under examination. The process outlined by the ESC-DAGs approach consists of three steps: mapping, translation, and integration. Mapping entails a review of the background literature for developing an “implied graph” from all studies identified in the systematic search, with the outcome of interest being the DAG outcome and the exposure of interest considered as the DAG exposure (Ferguson et al. 2020). In the translation step, causal theory is applied to each relationship in the implied graph created in the mapping stage to construct the study DAG. A causal criteria approach (Rothman and Greenland 2005; Kundi 2006), performed in a sequential manner, is recommended for assessment of each relation in the implied graph. For relationships that satisfy the causal criteria, a counterfactual thought experiment (Hernán 2018) should be conducted to confirm each relationship’s inclusion in the study DAG. The three causal criteria, sequentially used to assess each relationship in the implied graph, are temporality, face-validity, and recourse to theory (Krieger and Davey 2016; Hill 1965). The temporality criterion posits that the cause should always precede the effect (Glass et al. 2013). Once temporality has been established, the face validity criterion identifies and eliminates implausible relationships based on background literature knowledge. The recourse to theory criterion is an extension of face validity, where relationships are confirmed based on the availability of theoretical support. In the counterfactual thought experiment step, two or more counterfactual exposures are compared to assess whether they would have different potential outcomes, based on which the assessed relationship is included in the study DAG (Glymour et al. 2016; Morgan and Winship 2015; Hernán 2018). The integration step of the ESC-DAGs approach has two parts – synthesis and recombination. In the synthesis phase, all translated DAGs are combined into a single DAG for the study, also known

Causal Inference in Pharmacoepidemiology

as the integrated DAG. The recombination phase entails recombining similar nodes in the integrated DAG, in order to simplify the DAG or to establish consistency (Ferguson et al. 2020). In a causal DAG, paths are indicated by connected arrows between variables. There are two types of paths in a causal DAG – directed and nondirected (Lipsky and Greenland 2022). A directed path is where the arrow direction proceeds from cause to effect. Examples of directed paths include paths from exposure to the outcome or from exposure to outcome through an intermediate variable (mediator, for example). All other paths from exposure to outcome are deemed to be nondirected. Examples of the nondirected path include paths from a confounder to exposure and outcome or paths from exposure and outcome to a collider variable. Figure 2 provides an example of directed and nondirected paths in a causal DAG. Depending on the source of bias originating from a nondirected path, statistical approaches for accounting for them while drawing causal inference differs. If the potential bias originates due to the presence of a confounder, it can be accounted for through regression adjustment. However, in the presence of colliders, statistical adjustment for them induces bias. In such cases, one should not control for the collider variable to avoid biasing the study estimates (Lipsky and Greenland 2022; Glymour and Greenland 2008; Greenland and Pearl 2014).

Methodological Approaches for Causal Modeling The Target Trial Framework For a real-world pharmacoepidemiologic study to estimate the causal effect of a treatment on the outcome of interest, it is important to be able to emulate RCTs for the causal effects drawn from observational studies to be comparable. This can be achieved using the target trial framework (Hernán and Robins 2016; Hernán et al. 2022), which consists of two steps – framing the causal question per the protocol of the hypothetical trial and emulating the components of the specified protocol in the first step using observational

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Causal Inference in Pharmacoepidemiology, Fig. 2 Graphical representation of directed and nondirected paths in a causal directed acyclic graph

M

X

Y

L

C

Directed Paths

Nondirected Paths

X = Exposure; Y = Outcome; M = Mediator; L = Confounder; C = Collider

data. Seven key components need to be specified in a target trial protocol – criteria for identifying the eligible population, strategies for treatment assignment, adjustment for baseline confounders, defining the outcome of interest, follow-up, causal contrasts of interest, and statistical analysis plan (Hernán and Robins 2016; Richardson et al. 1995). While emulating a target trial, one should specify the target population eligibility criteria, for identification in the baseline period. The treatment strategy component of the target trial should contain explicit information on assignment of the eligible individuals into the treatment groups specified and guidelines for treatment discontinuation or switch (Thorpe et al. 2009; Schwartz and Lellouch 1967). Next, one should adjust for all confounders for emulating the random assignment in the hypothetical target trial to ensure that the treatment groups are comparable. This adjustment can be achieved through regression adjustment (propensity score matching/stratification, inverse probability weighting), doubly robust estimation, or g-estimation methods for ensuring comparable covariate distributions across the treatment groups (Hernan and Robins 2020). Selection of covariates for confounding adjustment should be finalized through construction of

causal DAGs (Hernan and Robins 2020), and design considerations for specific types of confounding (such as confounding by indication) should be appropriately handled (Lipsitch et al. 2010). Additionally, the outcome of interest should be clearly specified in the trial protocol. For analysis done using observational data, the algorithm used to identify the outcome should also be explicitly defined, and the sensitivity and specificity of these algorithms should be reported. The follow-up period for outcomes assessment should begin once the eligibility criteria are fulfilled and the treatment strategies are assigned. It is important to clearly define the start and end of the follow-up period in the trial protocol in order to avoid biases (such as prevalent user bias or immortal time bias) that observational studies may be susceptible to (Hernán et al. 2016). A key component for defining follow-up periods in a target trial is an explicit specification of time zero, i.e., the time point by which the assessment for study eligibility requirement should be completed and after which one should start assessing the study outcome. Similar to the clear definition of the start of follow-up, target trials should also be explicit in their specification of the end of follow-up, which may occur at the outcome of

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interest, death or occurrence of any competing events, or end of the study period (Young et al. 2020). Intention-to-treat (ITT) and per-protocol (PP) are the two most common causal contrasts of interest in a target trial protocol. In the ITT approach, causal effect is estimated assuming that individuals continue with their baseline treatment assignment, regardless of whether they continue with the baseline treatment or switch. In the PP approach, individuals are censored at the time they deviate from their baseline treatment assignment (Hernán et al. 2006; Hernán and HernándezDíaz 2012). Both ITT and PP effects are of importance in target trials, and the statistical analysis plan differs based on the chosen causal contrast. Similar to all other components of a target trial, the statistical analysis plan needs to be clearly specified and in concordance with the trial design and causal contrast chosen. While adjustment of baseline confounding may sometimes be adequate for an ITT analysis, a PP analysis will always require adjustment for confounders both at baseline and in the follow-up period. While standard regression methods may often be adequate for an ITT analysis, PP analysis often requires more flexibility in statistical modeling assumptions, for which g-methods are the most suited (Hernan and Robins 2020; Naimi et al. 2017). Regression Adjustment The biggest challenge in observational studies evaluating treatment effects is the absence of randomization, which raises the issue of confounding. Several regression adjustmentbased methods have been developed and are frequently applied in causal inference literature to address this issue appropriately. The most widely used strategy in practice is the inclusion of measured covariates in the statistical model assessing the outcome. This approach is simple to implement, particularly when the confounders are timeinvariant. However, when there are potentially large numbers of confounders and the treatment or outcome of interest is rare, the model may run into issues with estimation (Franklin et al. 2017; Jackson et al. 2017). Another simple technique involves matching exposed subjects to unexposed subjects on one or more potential confounders.

Causal Inference in Pharmacoepidemiology

Matching methods perform better than regression models when there is insufficient overlap in covariates between the exposed and unexposed groups, and their performance can also be easily evaluated using diagnostics (Stuart 2010). Propensity score methods are a suite of methods that use propensity score (the probability of a subject being assigned to treatment, given a set of covariates L) to account for confounding. Once propensity scores have been generated with the treatment assignment as the outcome and potential confounders as the predictors, they can be used in many ways – inclusion as a covariate in an adjusted regression model, matching, and inverse probability weighting (IPW). The inclusion of propensity scores as a covariate requires the specification of the relationship between the outcome, treatment assignment (the main predictor), and the propensity score (as a covariate), as well as the appropriate modeling of this relationship (Austin 2011; Pazzagli and Li 2021). The other methods are not so restrictive and therefore preferable in most cases. Within matching, there are several different types of algorithms in the literature, such as nearest neighbor, caliper, radius, stratification (also referred to as interval matching, blocking, or subclassification), kernel, and local linear (Austin 2011; Caliendo and Kopeinig 2008). Please refer to Caliendo and Kopeinig (2008), and Austin (2009 and 2011) for an overview of the different methods and trade-offs to consider while choosing the appropriate method for your study. IPW is a different application of propensity scores, where instead of matching, the study sample is weighted based on an inverse function of their estimated propensity scores (Jackson et al. 2017; Austin 2011; Linden 2014). This allows for creating a pseudo sample with balanced covariates between the exposed and unexposed groups and precludes the loss of sample, which happens when a matching algorithm is unable to find an appropriate match for an exposed individual. Another method called doubly robust estimation combines the outcome regression model (controlling for covariates) and propensity score methods to account for confounding. This method works on the premise that at least one of the two models is

Causal Inference in Pharmacoepidemiology

specified correctly and can sufficiently account for confounding when used together rather than individually (Bang and Robins 2005; Funk et al. 2011; Li and Shen 2020). The methods we have discussed so far address the issue of measured confounding. Instrumental variable (IV) analysis is an econometric method that helps address the issue of selection bias arising from unmeasured confounding (Mcclellan et al. 1994; Angrist et al. 1996; Newhouse and McClellan 2003; Stukel et al. 2007). IVs have two properties: they are highly correlated with the exposure (e.g., treatment) and are not independently related to the outcome variable. An appropriate IV would act as a natural randomizer variable and identify balanced sources of treatment variation so that selection bias does not affect the treatment effect estimates. This section provided a brief overview of the different regression adjustment methods used often when conducting causal inference studies in pharmacoepidemiology. We encourage the reader to refer to the following studies, which serve as good practical applications of estimating causal effects using: confounders as covariates in the outcome regression model (Wang et al. 2005), propensity score methods (Wang et al. 2005; Palmsten et al. 2013; Sun et al. 2021), and instrumental variable analysis (Wang et al. 2005). Marginal Structural Models Marginal Structural Models (MSM) are used when time-varying exposures are of interest and IPW is the most commonly used method to fit MSM (Robins et al. 2000; Cole and Hernán 2008). A two-step process is used to fit MSM as follows: step 1 involves derivation of inverse probability of treatment weights (IPTW) and step 2 involves estimation of the exposure–outcome association using the IPTW derived in step 1 (Bodnar et al. 2004). MSM should be considered as an approach when there is little-to-no missingness in the data and the availability of rich information on confounders. Additionally, they are the method of choice for studying non-dynamic or static treatment strategies (i.e., regimens which are fixed by the health-care provider in advance regardless of intermediate

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events) (Hernan and Robins 2020). However, in the real world, dynamic treatment strategies are more common, as health-care providers aim to monitor an individual’s response to treatment (through examination, diagnostic tests, etc.) and use this information to make future treatment decisions. In such cases, structural nested models or G-estimation might be a more appropriate methodology. G Methods Estimating causal effects when the exposure of interest and confounders are time-varying is challenging to conduct using conventional statistical approaches and necessitates “g” methods. Approaches that fall under the broad umbrella of g methods include IPW, g estimation, and g formula (Hernan and Robins 2020; Naimi et al. 2017). To estimate potential outcomes from observational data using g methods, counterfactual consistency, exchangeability, and positivity assumptions need to be satisfied. These statistical approaches (g methods) can be used to identify and estimate generalized treatment effects, regardless of whether these effects are time-varying or time-invariant (Naimi et al. 2017; Mansournia et al. 2017). Unlike conventional methods of confounder adjustment, g methods can account for time-varying confounding due to prior exposure. IPW creates a pseudo-population, within which exposures are independent of time-varying confounders. G estimation uses a two-part model for estimating causal effects. In the first step, the relationship between the exposure and the counterfactual outcome under no exposure in the follow-up period is estimated in a causal model, accounting for time-varying confounders. The second part of g estimation predicts exposure status at each time point, based on prior exposure, covariates, and the counterfactual outcome. The parametric g formula works by modeling the joint density of the observed data to assess potential outcomes. It employs weighted means and standardization to generate the standardized mean outcome for each level of the exposure by taking the average of the confounder-specific mean outcomes across the length of the follow-up period. Due to their advantage over conventional

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methods, g methods are increasingly used in observational research (Tilling et al. 2002; Mansournia et al. 2012; Shakiba et al. 2018).

Conclusion Observational data is increasingly being used to examine the comparative effectiveness of medical treatments during the post-marketing surveillance period and for regulatory approvals. To estimate causal effects using observational data for a specific research question, one should first decide on the type of causal contrast they are interested in (ITT or PP) and the target population for the counterfactual contrast of interest. Upon deciding on the target population, one should decide on the appropriate measure of effect for the research question of interest. Then, the protocol should be developed considering all biases affecting the causal relationship of interest. For this purpose, it is recommended to construct DAGs that help graphically represent the causal model. The threestep ESC-DAGs approach is recommended for this purpose. It is important to utilize the target trial framework for estimating causal effects – and all seven components of the target trial protocol should be specified for this purpose. Appropriate statistical analysis plans should be developed depending on whether adjustments need to be made for time-invariant or time-varying confounders and exposure. Care should be taken to ensure that all causal model assumptions required for the analysis are satisfied. It is recommended to conduct sensitivity analyses using a different estimand or model to ensure the robustness of the estimated causal effects.

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Causal Inference in Pharmacoepidemiology theorem?”. Epidemiology. Published online. 2010: 872–5. https://doi.org/10.1097/ede.0b013e3181f5d3fd Perez-Heydrich C, Hudgens MG, Halloran ME, Clemens JD, Ali M, Emch ME. Assessing effects of cholera vaccination in the presence of interference. Biometrics. 2014;70(3):731–41. Richardson WS, Wilson MC, Nishikawa J, Hayward RS. The well-built clinical question: a key to evidence-based decisions. ACP J Club. 1995;123(3): A12–3. Robins JM. Information recovery and bias adjustment in proportional hazards regression analysis of randomized trials using surrogate markers. Proc Biopharm Sect Am Stat Assoc. Published online. 1993:24–33. https:// cdn1.sph.harvard.edu/wp-content/uploads/sites/343/ 2013/03/biopharm.pdf Robins JM. Data, design, and background knowledge in etiologic inference. Epidemiology. Published online. 2001:313–20. https://doi.org/10.1097/00001648-2001 05000-00011 Robins JM, Hernán MÁ, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11(5):550–60. https://doi.org/10.1097/ 00001648-200009000-00011. Robins JM, Wasserman L. On the impossibility of inferring causation from association without background knowledge. Comput Causation Discov. 1999;1999: 305–21. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55. Rothman KJ, Greenland S. Causation and causal inference in epidemiology. Am J Public Health. 2005;95(S1): S144–50. Roy J, Mitra N. Measured and accounted-for confounding in pharmacoepidemiologic studies: some thoughts for practitioners. Pharmacoepidemiol Drug Saf. 2021;30(3):277–82. https://doi.org/10.1002/ PDS.5189. Rubin DB. Statistics and causal inference: comment: which ifs have causal answers. J Am Stat Assoc. 1986;81(396):961–2. Rubin DB. Causal inference using potential outcomes: design, modeling, decisions. J Am Stat Assoc. 2005;100(469):322–31. Rubin DB. For objective causal inference, design trumps analysis. Published online. 2008. https://doi.org/10. 48550/arXiv.0811.1640 Sauer BC, Brookhart MA, Roy J, VanderWeele T. A review of covariate selection for non-experimental comparative effectiveness research. Pharmacoepidemiol Drug Saf. 2013;22(11):1139–45. Schwartz D, Lellouch J. Explanatory and pragmatic attitudes in therapeutical trials. J Chronic Dis. 1967;20(8): 637–48. Shakiba M, Mansournia MA, Salari A, Soori H, Mansournia N, Kaufman JS. Accounting for timevarying confounding in the relationship between obesity and coronary heart disease: analysis with

Community Health Outreach Services G-estimation: the ARIC study. Am J Epidemiol. 2018;187(6):1319–26. Shrank WH, Patrick AR, Brookhart MA. Healthy user and related biases in observational studies of preventive interventions: a primer for physicians. J Gen Intern Med. 2011;26(5):546–50. https://doi.org/10.1007/ S11606-010-1609-1/METRICS. Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res Methodol. 2008;8:1–15. Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci Rev J Inst Math Stat. 2010;25(1):1–1. https://doi.org/10.1214/09STS313. Stukel TA, Fisher ES, Wennberg DE, Alter DA, Gottlieb DJ, Vermeulen MJ. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. JAMA. 2007;297(3):278–85. https://doi.org/10.1001/ JAMA.297.3.278. Suissa S. Immortal time bias in pharmacoepidemiology. Am J Epidemiol. 2007;167(4) https://doi.org/10.1093/ aje/kwm324. Suissa S, Dell’Aniello S. Time-related biases in pharmacoepidemiology. Pharmacoepidemiol Drug Saf. 2020;29(9):1101–10. https://doi.org/10.1002/ PDS.5083. Sun JW, Hernández-Díaz S, Haneuse S, et al. Association of Selective Serotonin Reuptake Inhibitors with the risk of type 2 diabetes in children and adolescents. JAMA Psychiatry. 2021;78(1):91–1. https://doi.org/10.1001/ jamapsychiatry.2020.2762. Tamim H, Tahami Monfared AA, LeLorier J. Application of lag-time into exposure definitions to control for protopathic bias. Pharmacoepidemiol Drug Saf. 2 0 0 7 ; 1 6 ( 3 ) : 2 5 0 – 8 . h tt p s : / / d o i . o rg / 1 0 . 1 0 0 2 / PDS.1360. Tchetgen EJT, VanderWeele TJ. On causal inference in the presence of interference. Stat Methods Med Res. 2012;21(1):55–75. Tennant P, Textor J, Gilthorpe M, Ellison G. OP87 Dagitty and directed acyclic graphs in observational research: a critical review. Published online 2017. http://dx.doi. org/10.1136/jech-2017-SSMAbstracts.86 Thorpe KE, Zwarenstein M, Oxman AD, et al. A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. J Clin Epidemiol. 2009;62(5):464–75. Tilling K, Sterne JA, Szklo M. Estimating the effect of cardiovascular risk factors on all-cause mortality and incidence of coronary heart disease using G-estimation: the atherosclerosis risk in communities study. Am J Epidemiol. 2002;155(8):710–8. VanderWeele TJ. Concerning the consistency assumption in causal inference. Epidemiology. 2009;20(6): 880–3. VanderWeele TJ, Arah OA. Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology.

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Community Health Outreach Services: Focus on PharmacyBased Outreach Programs in Low- to Middle-Income Countries Hager ElGeed1, Phyllis Muffuh Navti2 and Ahmed Awaisu 1 1 Department of Clinical Pharmacy and Practice, College of Pharmacy, QU Health, Qatar University, Doha, Qatar 2 Continuing Professional Development, Weill Cornell Medicine-Qatar, Doha, Qatar

Abstract

Community outreach service encompasses the provision of healthcare services by health professionals in locations other than where they principally practice. The principal target areas for outreach services include rural and remote communities as well as other vulnerable populations such as immigrants and underserved populations. Therefore, outreach services are typically provided in locations where the need for access to health services is higher. However, increasingly, community outreach services, especially those related to

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Community Health Outreach Services

health promotion and disease prevention, are commonly provided in cities and urban areas and targeting all populations. The primary purpose of these services is to increase access to healthcare that the targeted community or population in question would not normally have. Pharmacy in general, and public health pharmacy in particular, plays an important role in the provision of community outreach services either singly or through a multidisciplinary approach. In low- to middle-income countries (LMICs), such outreach activities are not as widely available as they are in the developed world. This entry is primarily intended to provide a general overview of community outreach programs provided by pharmacy professionals and to document the status of such programs from the context of LMIC. Overall, pharmacists in different care settings can play an important role in proving care through community outreach programs. Public health pharmacy can make a significant impact in improving health outcomes through community outreach services in LMICs. Some of the major barriers in developing and implementing effective pharmacy-based community outreach activities in developing countries include resource constraints, especially lack of facilities and limited human resource for health. Efforts should be made to encourage the development and provision of such services, especially among the underserved and vulnerable populations. Keywords

Community outreach programs · Public health · Public health pharmacy · Pharmacy practice · Low- to middle-income countries · LMICs

Introduction to Community Outreach Services in Healthcare Globally, healthcare systems are faced with significant challenges of meeting the healthcare needs of their populations, especially the underserved communities and vulnerable populations.

Often these vulnerable communities are concentrated in rural and remote areas, but in many cases, they may also reside in urban areas and major cities. The challenges to healthcare systems range from lack of facilities and infrastructure to limited human resources for health (Organization WH 2006). There are shortages, uneven distribution, and brain drain of healthcare workers across all counties, which have profound effect on equitable access to healthcare delivery and services. Therefore, health inequalities remain a challenge for most countries and receive attention worldwide (Barreto 2017; Arcaya et al. 2015; Goetjes et al. 2021). The problems of health inequalities are even more profound in low- to middle-income countries (LMICs) when compared to developed countries. In an effort to ensure equitable distribution and delivery of healthcare services in both developed and developing countries, a wide variety of sustainable strategies and initiatives should be in place, including the provision of basic healthcare facilities and skilled and motivated healthcare workforce (Roodenbeke Ed, Organization WH 2011). Ultimately, the overall goal is geared to increasing access to preventative, diagnostic, and treatment services, especially in underserved communities. Different types of healthcare service delivery models are available and play a significant role in access to healthcare services. In particular, customized, multifaceted, community-based strategies are required to increase access to health services and retention of healthcare workers in underserved areas (Roodenbeke Ed, Organization WH 2011; Shin et al. 2020). Community health outreach services provide health-related services to residents who are vulnerable or at a socioeconomic disadvantage (Goetjes et al. 2021; Shin et al. 2020). Such vulnerable and disadvantaged populations are typically exposed to several health risk factors and are at higher risk of communicable and noncommunicable diseases compared to the general population (Weathers et al. 2011). Studies in both developed and developing countries have reported on the benefits and effectiveness of community-based outreach services in improving access and providing customized interventions. In general, community health outreach

Community Health Outreach Services

entails engaging in social work with vulnerable populations to address several issues, including, but not restricted to, drug abuse, mental disorders, youth problems, obesity, and homelessness (Goetjes et al. 2021; Shin et al. 2020). Although the concept and terminology of community outreach is seemingly easy to describe, there is no universally accepted definition of the term in the biomedical or social literature. Shin et al. conducted a concept analysis in an effort to clarify the general definition of community health outreach and to facilitate its understanding and use (Shin et al. 2020). The investigators proposed that community health outreach is defined “as a temporary, mobile project that involves the collaboration of a community to undertake its purposeful health intervention of reaching a population facing health risks” (Shin et al. 2020). Their definition attempted to provide a general understanding of the community outreach undertaken by health workers and demonstrates the connection between healthcare professionals and community residents. However, other scholars may see it differently, depending on the context and the particulars of the particular program, the community served, and the healthcare professional providing the service. For example, a World Health Organization (WHO) report on outreach services as a means to enhance health workers’ attraction to and retention in underserved areas used the term “outreach service” to describe “any type of health service that mobilizes health workers to provide services to the population or to other health workers, away from the location where they usually work and live” (Roodenbeke Ed, Organization WH 2011). Community outreach services are one of the strategies to improve access to health and healthcare workers in remote and rural populations (Roodenbeke Ed, Organization WH 2011). In this, there is mobilization of urban-based healthcare workforce to serve remote or underserved areas. Typically, the services aim to increase access to healthcare and could include services increasing health screening compliance, promoting lifestyle changes, and increasing awareness and knowledge of diseases and their treatment. The community outreach could be

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delivered using physical or technology-driven approaches. Examples of these alternative health service delivery models include mobile clinics, health caravans, telemedicine, and other telephone-based strategies targeted to support population health and frontline community health workers. A few specific examples of outreach health services in LMICs include specialist outreach in western KwaZulu-Natal, South Africa (Caldwell et al. 2018); screening for hypertension among adults in Cairo (Egypt) (Abd Elaziz et al. 2015); outreach screening service in Nigeria (Adebayo et al. 2011); surgical outreach camps in Uganda (Ozgediz et al. 2008); ear camp for pediatric patients in Namibia (Lehnerdt et al. 2005); glaucoma education and screening in Nepal (Thapa et al. 2008); and delivery of healthcare through telemedicine in Cambodia (Brandling-Bennett et al. 2005). Outreach health services involve a variety of stakeholders including the healthcare facilities, professional bodies, nongovernmental organizations (NGOs), government agencies, development partners, and international donor agencies. In addition, the category of healthcare professionals involved in a community outreach program varies based on the strategy and type of the program. Health professionals such as physicians, surgeons, pharmacists, dentists, community health workers, and nurses have provided different types of community outreach services. The modalities for mobilization could include voluntary services or incentivized services. Furthermore, the services can be designed as permanent basis with health workers recruited to serve in remote places according to a predetermined schedule (Roodenbeke Ed, Organization WH 2011). This entry aims to provide an overview of community health outreach services provided to vulnerable and general populations in different countries and contexts, with a focus on pharmacy-based services in LMICs. It is worthwhile to note that learning from experiences is the best way to understand what works well and what does not. Since vulnerable and underserved populations are found in both developed and developing countries, it is important to start the

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review from broader perspective and tailor it to LMICs. Overall, there is paucity of published information on pharmacy-based outreach services, especially from the context of developing countries. However, we attempted to summarize what is available in the published literature and provide an insight on lessons learned and the ways forward.

Evidence of Benefit of Community Outreach Programs in Healthcare Health outreach programs largely serve priority populations who are disadvantaged, have a higher disease burden, and are less likely to use preventative health services compared to the general population. Therefore, such program plays an important role in improving and extending the reach of healthcare services through several activities such as health education, disease management, health screening, and improving access to healthcare services (Health Outreach Partners n.d.). Health outreach services have direct benefits to the served populations and indirect benefits to the healthcare professionals who provide the service through capacity building. In addition, there are expected benefits in terms of health outcomes. Ultimately, these activities can directly and indirectly improve health outcomes of individuals and communities. Monitoring and evaluation of outreach efforts to demonstrate how health outcomes are improved is part and parcel of a well-designed program. This can be achieved by using key performance indicators and evidence-based measures that are linked to populations’ health status. For example, the indicators could be related to the targeted health conditions such as malaria, tuberculosis, diabetes, hypertension, cancer, and antenatal care. Showing the link between an outreach program and improved health outcomes is necessary to provide the platform for evidence-based policy and practice. Policymakers can no longer invest in services that do not demonstrate evidence of benefits to the population and society. Health outcomes partners have proposed five steps to show that outreach activities are influencing health outcomes: (Organization WH 2006)

Community Health Outreach Services

deciding what outcomes to measure, (Barreto 2017) selecting a method and a tool to measure the outcomes, (Arcaya et al. 2015) collecting the data, (Goetjes et al. 2021) analyzing the data, and (Roodenbeke Ed, Organization WH 2011) disseminating the findings through different outlets (Health Outreach Partners n.d.). Although there is a published evidence on initiatives to develop outreach health services globally, there is paucity of rich evidence on the health outcomes associated with the services. Moreover, economic outcome analyses are difficult to obtain from the literature. Several reports and documents have reported the benefit of outreach health services for the population, encompassing both physical outreach services and virtual outreach activities. The available evidence shows that outreach activities could be facility-based, mobile-based, or virtual including telemedicine. The providers and stakeholders as well as the types of health issue covered also vary by country, based on needs and priority. Roodenbeke et al. in a report on outreach services as a strategy to increase access to health workers in remote and rural areas categorized the benefits of outreach health services as follows: (Organization WH 2006) positive health outputs, (Barreto 2017) increasing access to specialists in remote areas, (Arcaya et al. 2015) increasing quality of care and confidence in the health system, (Goetjes et al. 2021) time and cost-saving for patients in remote areas, (Roodenbeke Ed, Organization WH 2011) continuing professional development for health workers, and (Shin et al. 2020) mitigating health professionals’ feelings of isolation (Roodenbeke Ed, Organization WH 2011). Available reports such as project assessments and published studies have reported the benefits of outreach health services. There are some demonstrable positive health outcomes, especially at individual local programs level, but impact on burden of disease at country level is generally lacking (Roodenbeke Ed, Organization WH 2011). Indicators such as number of individuals treated or medical procedures undertaken have been reported in the literature at the level of individual outreach program. Despite this inadequacy, each activity may have a positive health impact on the communities served by increasing access to care. However, large-scale studies and systematic

Community Health Outreach Services

documentation are needed in order to estimate the real impact of outreach health services on health outcomes at country level. In Delhi, India, a single session brief community outreach intervention delivered by health workers to promote tobacco cessation increased tobacco cessation rate. This could significantly impact public health if scaled up with higher coverage across the country. Bang et al. examined the effects of a 2.5-year community outreach program for maternal health in Tigray, Ethiopia, on women’s knowledge about maternal health and family planning and assessed their participation in antenatal care, postnatal care, institutional childbirth, and contraceptive use (Bang et al. 2018). The investigators found that the intervention group who underwent the outreach program showed significant increases in knowledge and behaviors regarding maternal health and family planning and there was a dramatic increase in the institutional birth rate in the intervention group. Gruen et al. (2006) reported increase in specialist follow-up visits following outreach programs to rural indigenous populations in Australia (Gruen et al. 2002; Gruen et al. 2006). A glaucoma screening outreach activity in Nepal resulted in increased demand for eye screening (Thapa et al. 2008). Furthermore, there is some evidence from both developed and developing countries demonstrating that mHealth technology (mobile telephonebased health services) improves the efficiency of healthcare delivery and that the long-term expectation is that this will have a demonstrable and significant positive impact on clinical outcomes (Van Lerberghe 2008). In this, the beneficiary may use mobile telephone calls and messages to communicate with respective healthcare provider in relation to health education, adherence, follow-up appointments, and other health-related matters.

Role of Pharmacy in Community Outreach Programs in Developing Countries Community outreach programs have become very important aspects of pharmacy practice globally. Community outreach can be either international

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or national. International community outreach aligns the most with the Shin et al. definition of outreach “as a temporary, mobile project that involves the collaboration of a community to undertake its purposeful health intervention of reaching a population facing health risks” (Shin et al. 2020). National outreach on the other hand is more reflective of the WHO report which describes “outreach service” as “any type of health service that mobilizes health workers to provide services to the population or to other health workers, away from the location where they usually work and live” (Roodenbeke Ed, Organization WH 2011). Outreach could also be focused either on clinical care or on education to raise awareness. Irrespective of whether it is internationally, nationally, clinically, or educationally focused, the main aim of outreach is ultimately to directly or indirectly improve health outcomes at either the individual or the community level by increasing access to health services and education to underserved communities who are usually at higher risk of communicable and noncommunicable diseases compared to the general population. Pharmacist’s Role in Global (International) Outreach to LMICs Global outreach, which is described above and includes short-term international provision of usually mobile-based healthcare to geographically remote and inaccessible communities, has been increasing in LMICs in recent years. The global outreach range of services includes direct delivery of either surgical, medical, or educational programs to patients or large public health programs which affect the wider healthcare systems. Due to an existing lack of awareness that pharmacists can enhance the programs with their distinctive clinical and logistical knowledge, most global clinical teams still do not include pharmacists (Nair et al. 2017). Consequently, tasks such as formulary development, planning, and managing medicine optimization workflow and setting up and dismantling the pharmacy are assumed by different members of the outreach team, in addition to their assigned patient care functions. This commonly results in suboptimal use of often limited resources, medication safety issues, and burnout

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of non-pharmacy healthcare providers who try to fulfill these role functions in addition to their professional roles. The literature, however, describes several global programs which have integrated pharmacists and report the positive and supportive role pharmacists already play that is adding value to global outreach programs (Angelo and Maffeo 2011). These include the traditional medication focused roles including formulary development and management, ensuring a supply chain, setting up the pharmacy, and leading/supervising the pharmacy workflow plans and processes. Formulary generation is usually crucial to maximize cost-effectiveness, ensure substitutes where there is lack of refrigeration, and reflect local disease prevalence, local antimicrobial resistance, and formulary and controlled drug regulations. The pharmacists also have to prepare written and if required translated information to support medication counseling and liaise with local colleagues to ensure continuity of care. The roles and responsibilities of pharmacists within these teams are also evolving to include patientcentered functions such as clinical consultation and education of staff and patients alike (Nair et al. 2017). This section will focus on pharmacists’ involvement in surgical and medical teams because these are the most common programs. Pharmacist input as part of outreach surgical teams include comprehensive formulary planning, setting up the pharmacy on arrival, preparation of surgical intravenous and other extemporaneous medication, controlling drug governance, dispensing, counseling, and inventory. In addition, pharmacists can support through the provision of clinical consultations including medication dosing, monitoring for adverse drug reactions and interactions, therapeutic substitutions, and antimicrobial stewardship. The antimicrobial stewardship role is often crucial and involves liaison with local colleague and adherence to the local antimicrobial formulary which would cover local pathogens (Angelo and Maffeo 2011). The generic role functions of medical support pharmacist are similar to those described above.

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The difference in pharmacist input is predominantly in ensuring that the formulary is reflective of the specialty of the medical services for the specific outreach program. Medical services usually include pediatrics, general medicines, ophthalmology, dentistry, or gynecology. Another key consideration and input for pharmacists is to review prevalence data of the destination country/ region and use it to plan the formulary, given the wide scope of medicines. This will also ensure that medications for rare and unfamiliar illnesses including malaria, worms, or genetic conditions are planned for with sufficient lead time as stocks might not be easily accessible. Another pivotal role global outreach pharmacists play is the provision of training support or preceptorship to both pharmacy and other healthcare students. Training support would cover relevant medication-related topics relating to the pharmacist roles described above. They also facilitate acquisition of knowledge for healthcare students in other important and intricate aspects of global outreach such as cultural differences, compassionate care delivery, and critical thinking (Steeb et al. 2016). In conclusion, although there is a paucity of data evaluating the contribution of pharmacists in these programs, some studies have described the valuable contributions of pharmacists within these services and make the recommendation that expanding pharmacist involvement will improve use of limited healthcare resources. Additionally, there will be increased medication safety and holistic clinical training support through direct supervision contributing to the development of future global outreach healthcare practitioners. The pharmacy teams involvement in global outreach will go a long way to optimizing and fostering holistic patient care approach. Pharmacist Role in National Outreach in Developing Countries National outreach programs differ from international/global programs in that although care is delivered away from their usual worksite, the outreach workers work within national borders. The need for this is growing rapidly as vulnerable communities continue to be disproportionately

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affected by long-term conditions and the COVID19 pandemic. Social isolation and economic stressors which have impacted communities with fewer resources and unmet needs are resulting in poorer health outcomes. In addition, the role of pharmacists which has been developing over the past decade since the introduction of pharmaceutical care has undergone even more profound changes over the recent years. Most of the role development has been in high-income countries (HICs), and as the best way to understand what works well and what does not is to learn from experiences, we consider some examples from these countries. In most European countries, the tendency seems to be that pharmacists are moving from being productoriented to service-oriented (Martins et al. 2015; Bush and Johnson 1979; Miller and Goodman 2019). One of the most important aspects of this service-oriented era is the outreach activities initiated and directed by pharmacists (Bush and Johnson 1979). This was in part due to easy accessibility and perceived affordability which put pharmacists at the first point of contact in the healthcare system in many developed and developing countries (George et al. 2010). Recently, several developed countries such as Australia, the United States, and the United Kingdom have recognized the new roles of the community pharmacists in the multidisciplinary provision of healthcare and raising the awareness of public about burdensome diseases. Thus, in these countries, pharmacists in general and pharmacists in primary care including community pharmacies provide a wide range of healthcare interventions and lead multiple public healthcare initiatives that can be categorized under the wide range of community outreach activities (George et al. 2010). Despite these pharmacists’ role development in HICs, the extent of community pharmacy practice still varies considerably across LMIC countries, and in some instances pharmacists’ expertise is undoubtedly underutilized (Jin et al. 2014; Mohiuddin 2020). Nevertheless, in these countries, pharmacists often remain the most accessible healthcare providers and first point of contact for patient seeking access to health in many

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underserved communities with shortage of physicians (Hedima et al. 2021). Studies describe roles played by pharmacists during public health emergencies such as the current COVID-19 pandemic and past Ebola epidemic. Pharmacists can input in roles across the four key phases of prevention, preparedness, response, and recovery. Additionally, this role development of pharmacists is not a priority in LMICs, considering that the pharmacist/population ratio is as low as 1/100,000, compared to a ratio in HICs between approximately 50 and 200/100,000 (Goel et al. 1996). With these low ratios, most of the population in the rural underserved regions rarely come in contact with pharmacists. Publications make suggestions that there is a need to adapt the pharmacist role in LMIC to meet local requirement. This is illustrated in the statement by Matowe and Katerere (2002) describing how “The profession has shied away from developing a practice tailormade for the developing world and tried to follow trends in the industrialised world” (Matowe and Katerere 2002). Developing practice that is tailored includes acknowledging the fact that in most LMICs, the economy is generally weak and sometimes unstable and it is thus essential to prioritize efforts to reduce the burden of diseases that can impact the individuals and society (WHO 2004). A large proportion of illnesses in LMICs are entirely avoidable or treatable with existing medicines or interventions. Most of the disease burden in LMICs finds its roots in the consequences of poverty, such as poor nutrition, indoor air pollution, and lack of access to proper sanitation and health education. However, nearly all of these deaths are either treatable with existing medicines or preventable in the first place of care, and medicines, they pay for; they can also provide incentives to healthcare providers for purchasing, prescribing, and dispensing and to patients for using the most clinically appropriate, safe, and cost-effective medicines (WHO 2004). One of the most important ways to raise public awareness about disease treatment and prevention can be the multidisciplinary outreach activities, through which educational materials and sessions

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will be delivered and provided to patients and society members. Some of these educational/ awareness raising activities in LMICs should target some particular aspects of patient care such as preventions, screening, and cost-effective care available in the country (Bigdeli et al. 2014). In LMICs in general, it is noticed that the research conducted to address the outreach activities held by pharmacist is almost absent (Ludwick et al. 2021; Lasker et al. 2018). Nevertheless, there are some individual studies documenting the role of pharmacists in raising the awareness of some burdensome diseases in these countries. For instance, it is believed that pharmacies hold great potential to contribute meaningfully to tuberculosis (TB) control efforts, given their accessibility and extensive utilization by communities in many high burden countries. Despite this promise, the quality of care provided by pharmacies in these settings for a range of conditions has historically been poor (Miller and Goodman 2019). A number of quality challenges are apparent in relation to antiTB medicine availability, pharmacopeial quality of anti-TB medicines stocked, pharmacy workers’ knowledge, and management of patients both prior to and following diagnosis (Miller and Goodman 2019). Poor management practices include inadequate questioning of symptomatic patients, lack of referral for testing, over-the-counter sale of anti-TB medication as well as unnecessary and harmful medicines (e.g., irrational use of antibiotics and steroids), and insufficient counseling (Miller and Goodman 2019). There were some suggestions for potential interventions in order to optimize pharmacy practice related to TB management that can be considered as part of outreach or health promotion initiatives (Miller and Goodman 2019). The most important role of pharmacists was described as raising the awareness of the public to early detect TB symptoms, vaccination, and adherence to medications (Crilly and Kayyali 2020; Miller and Goodman 2016). These interventions require some training for pharmacists to refer symptomatic patients for medical treatment and to educate patients about the medications and adherence in addition to overcoming or

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managing medication adverse drug reactions (Miller and Goodman 2019). Another example of possible area for outreach activities and health promotion campaign in LMICs is diabetes, which became a burdensome disease in these countries and causes a number of health problems that can affect the life of individuals and societies (Mendenhall et al. 2017). Data to indicate the involvement of pharmacists in raising public awareness about diabetes is also sparse, and we believe that there are a lot of gaps to be filled in this area (Mendenhall et al. 2017). One other health problem that could be controlled by raising public awareness through outreach or health promotion campaigns is tobacco smoking. Smoking in LMICs is not limited only to cigarette smoking. There are several substances that are traditionally used and abused in some of these countries that can cause toxicities ranging from mild allergic reactions to fatal malignancies (Chatham-Stephens et al. 2013). It is doubtless that healthcare workers, especially pharmacists, can play an important role in improving public awareness about drug abuse and the harmful effect of these substances and educate public about the best ways of quitting them. A study that assessed the current smoking cessation services provided by healthcare providers in Namibia found that despite good attitudes by healthcare workers to provide education to public, there were some concerns about the knowledge of practitioners especially in the public sector (Hango et al. 2021). This study did not focus on pharmacists alone, and it was one of the rare studies we were able to find in this topic, which may indicate that more gaps need to be filled in this area of pharmacy practice. Unfortunately, the evidence coming from LMICs regarding community outreach activities in general is scarce, and it is almost absent when we target pharmacy outreach initiatives. This is despite the massive need for such activities in these countries, especially under the effect of COVID-19 pandemic. This is particularly true as in the midst of a public health crisis of the current magnitude, it is crucial to examine roles and activities that pharmacists can undertake to help in

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relieving pressure and providing cushion in other areas of the health service, such as general practice and emergency departments (Hedima et al. 2021).

Developing Ideas and Designing Pharmacy-Based Outreach Programs in Resource Constraint Environments Considering the dearth of published literature on the designs of pharmacy-based outreach programs in resource constraint environments such as LMIC, this section seeks to bridge that gap. Pharmacists can significantly contribute to public health initiatives during global natural disasters and pandemic, emergency global circumstances concerning underserved patients, as well as health promotion and disease prevention initiatives. This is clearly articulated in the 2006 American Public Health Association policy statement, “The Role of the Pharmacist in Public Health: The Unique Expertise Afforded by Pharmacists Can Benefit Many Aspects of Public Health” (The Role of the Pharmacist in Public Health 2006). Roles that pharmacists can fill include interprofessional research initiatives, emergency preparation, developing policy, enhancing access to care, prevention of disease and error, as well as disease management. To ensure the most effective output from the pharmacists, it is crucial that the program development is robust. Furthermore, as mentioned previously, for LMICs, which are typically the countries with resource constraints, it is important that the role of pharmacist in LMIC should reflect local needs, instead of following trends in HICs (Matowe and Katerere 2002). However, in acknowledgment that it is useful to learn from the past, reviewing the focus of lessons learned from the past would ensure that the future is informed. Past community outreach session generally addressed many medical issues including medication adherence, understanding of indications and importance, adverse drug reactions, explanation for mechanism of actions of medications, identifying interactions with other substances, recognizing symptoms of disease progression, and improving

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communication to health providers (Hango et al. 2021). The outreach area of focus should be driven by the individual country needs and characteristics. LMICs share some common features in term of limited resources that can be secured for each outreach event, so there could be a need to study the event well and secure some funding bodies before planning for the event (Organization 2020). In addition, there should be some training and education programs provided to the pharmacists before conducting the outreach event. These training may include the knowledge about the topic, communication skills, and teamwork skills (Bridges et al. 2011). In many developed countries, outreach and health promotion concepts are being taught in the undergraduate years, and pharmacy students participate in some of these activities before they graduate. However, the picture from LMICs is not clear, and there is no clear evidence on how well graduate pharmacists are capable of participating or leading outreach events and campaigns (Frehywot et al. 2013). There was a review for some pharmacy curricula in some African countries that found data, which can help identifying some areas of strength and some areas for improvement. In most Francophone African countries (Algeria, Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Côte d’Ivoire, Guinea, Mali, Mauritania, Morocco, Niger, Senegal, and Tunisia), the degree obtained after two cycles of didactic and experiential training is the PharmD. There is an optional additional 4-year period of residency, which gives room for specialization. Nigeria began a special PharmD conversion program in 2018 tailored to university lecturers and hospital pharmacists with a BPharm qualification to support alignment with the PharmD program that is evolving in the country. The program is a collaborative initiative between the Pharmacists Council of Nigeria, the University of Benin, and facilitators from the Nigerian Association of Pharmacists and Pharmaceutical Scientists in the Americas (Anderson et al. 2009). This report does not provide in-depth information about the content of the curricula covered by these countries, but it provides some

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general aspects of clinical basis of pharmacy education in these LMICs. In fact, as the profession of pharmacy continues to evolve to focus on patient care, a shift is occurring from product-centered to a patientcentered model of care (Anderson et al. 2009). To address these changes, community-based pharmacist practitioners spend time and make connections to the needs of the patients in the communities they serve. In most of these countries, there is a small evidence showing that community pharmacist practitioners are participating in outreach events in the communities, churches/ mosques, workplaces, shopping malls, etc. Community pharmacist practitioners are also conducting home visits from traditional community pharmacies. These considerations should then be factored as part of the recommended planning phases for outreach, and the following key details or steps should be the foundation (Moore 2021): • Goals: To ensure sustainability and effectiveness of an outreach program, the objectives and plans for evaluation should be organized at the onset. For medical outreach, the mission should be underpinned by community input, collaboration including a multidisciplinary team including pharmacists, and most important of all engagement of patients. • Partnerships: To be sustainable and long term, it is crucial that the program include collaboration with relevant partners. Partners should be chosen with the aim of community empowerment, for example, local healthcare providers or community groups. Local community pharmacies and public health pharmacies, for example, could provide advice regarding local antibiotic formularies and medication as well as law and ethics pertaining to medication. • Education: To ensure a long-term legacy by facilitating long-term behavior change development, short-term interventions should aim to provide an element of education and capacity building. For example, student pharmacists get the opportunity to participate in global health service programs as part of their training on the

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American Society of Health-System Pharmacists’ accredited pharmacy residency programs. Pharmacist expertise in global health efforts could be increased in this way by deliberately embedding global health programs into pharmacy residencies (Miller et al. 2016). It is encouraging that several American Society of Health-System Pharmacists-accredited residency programs in the United States already integrate global health rotations as part of the resident learning experience. This sort of seminal experience exposure will help reinforce the role of pharmacy in future global health endeavors. • Sustainability: Instead of providing care for a limited period or improving a particular problem, it is important for outreach projects to have long-term goals to remain sustainable. The aim for this is to pass on the responsibility for public health onto the public itself. Pharmacists on outreach can support this through patient education and empowerment for longterm conditions. • Evaluation: To determine success of any medical outreach program, the key performance indicators should be measurable and include flexibility for changes to increase effectiveness. Providers and institutions track progress or control quality and can carry out evaluations periodically, giving it a better chance of achieving its goals.

Challenges of Pharmacy-Based Outreach Programs in LMICs In LMICs, pharmacies are often patients’ first point of contact with the healthcare system and their preferred channel for purchasing medicines. Unfortunately, pharmacy practice in these settings has been characterized by deficient knowledge and inappropriate treatment (Miller and Goodman 2016; Miller 2018). For example, pharmacy practice in Asia is characterized by insufficient history taking, a lack of appropriate patient referral, poor adherence to treatment guidelines, inappropriate supply of medicines, and insufficient counseling.

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Adequate knowledge alone is not sufficient to ensure appropriate management of patients presenting at the pharmacy (Miller and Goodman 2016; Atif et al. 2020). Profit incentives and the regulatory environment must be taken into consideration when designing interventions to improve pharmacy practice in these settings. Intervention research in this area appears to be lacking, and more research is particularly required on non-pharmacist-run pharmacies and unregistered drug shops (Miller and Goodman 2016). In addition to that, most of developing countries face some major challenges to implementing effective pharmacy practice services in general. These barriers are related to education, policy, regulation, practice, economy, technology, and sociobehavioral factors. These challenges may also include inadequate training of pharmacists to provide pharmaceutical care and lack of reimbursement mechanism for provision of pharmaceutical care (Albanese et al. 2010). Some hospitals are not employing pharmacists with advanced training to provide pharmaceutical care, and this can be in part related to the lack of government policies to enforce minimal provision of patient counseling/education with each medication obtained in the pharmacy and the promotion of product-oriented pharmacy practice by pharmacy owners/employers (Upadhyay and Ooi 2018). One of the major challenges in some LMICs is armed conflicts that destroy many healthcare facilities and impacted negatively on the healthcare provided for the public. It also makes it unsafe for pharmacists and other healthcare providers to reach the community members and to provide education about health or medicine (Organization 1995). In the Ottawa Charter, the WHO determined that the prerequisites for health promotion are seven. These fundamental conditions and resources for health are peace, shelter, education, food, income, a stable ecosystem, sustainable resources, social justice, and equity (Organization 1995). Under the effect of these conflicts, these conditions are all absent, which makes it impossible to provide basic health need in addition to outreach and health promotion

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activities. Unfortunately, the outcome is usually sad and tragic as many people will not receive healthcare, and many lives are being lost due to these wars and armed conflicts (Garry and Checchi 2020).

C Lessons Learned and Future Directions Community outreach activities in most of LMICs are very limited or absent. This is despite that in some of these countries, the pharmacy practice has been slowly starting to move from the productoriented to service- and patient-oriented. We believe that community outreach, if implanted correctly, can be of a real benefit to the societies of these countries. People who live in these countries require a lot of education regarding some serious health issues that can be treated, cured, or prevented if the appropriate intervention was applied earlier. Many steps need to be followed in order to optimize these services. It is essential to work on improving the health services provided for the society, that is beyond drug supplying, in these countries, keeping in mind the many diseases in these countries can be either prevented or lessened by education and awareness. As outreach events require integration and collaboration with other healthcare providers and policymakers, it is essential to establish a strong healthcare system led by different health professions to improve the outcomes. Our recommendations can fall under three main domains. First, pharmacy and other medical-related colleges/schools should emphasize respective training graduates to be knowledgeable and competent in providing health promotion education to public. In addition, the introduction of interprofessional education and communication skills for medical professionals can lead to the success of any outreach event or health promotion initiatives. The second domain of recommendation is to involve the policymakers in the decision and try to implement an environment that enables pharmacists to reach out the society and help to improve the public awareness about burdensome diseases and health conditions, which can lead to positive impact on everyone’s health status. Health promotion campaigns, home

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visits, and even social media utilization usually require some funding and incentives for healthcare workers who participate in such events. We recommend that policymakers in the health sectors should keep this in their mind and allow this area to grow and expand, as this will save more in the future as “prevention is better than cure.” Finally, collaboration with some countries that have some experience in health promotion and outreach activities can help. This is especially true in case some LMICs are planning to incorporate this module or topic to their undergraduate programs. This may require some planning and budgeting, but it will be of a great benefit, as the training after obtaining the pharmacy degree will be minimized if it was introduced earlier in the curriculum. LMICs can benefit from the availability of resources such as trained health professionals to optimize care. However, the challenge here will be the migration of health workers from LMICs to HICs, one of the most controversial aspects of globalization, which has attracted considerable attention in the health policy discourse at both the technical and political levels. In many cases, the migration of health professionals from LMICs is unplanned and represents a “brain drain” for source countries. The migration of health professionals is the result of enormous wage differences and poor working conditions such as lack of support, inadequate infrastructure, and lack of career development opportunities in LMICs (Cometto et al. 2013). There is increased recognition that this migration of health personnel contributes to exacerbating human resources for health shortages in LMICs (Cometto et al. 2013). Investing to limit this brain drain may prevent further waste of HRH, with benefits of reducing inappropriate care provided for the older population and associated overconsumption of healthcare resources due to the provision of inappropriate care.

Conclusion In general, pharmacists can play an important role in proving care through pharmacy-based

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community outreach programs, and public health pharmacy can make a significant impact in improving health outcomes through these programs. In general, there is adequate evidence on outreach health services globally including in LMICs, but there is relatively scarce evidence on pharmacy-based community outreach services. There is also lesser evidence on the health outcomes associated with the pharmacy-based outreach services. This lack of data may be partly explained by the fact that pharmacy-based outreach services are uncommon and that activities may be implemented without proper documentation, monitoring, and evaluation framework. Some pharmacy initiatives may provide outreach services without keeping record of activities and with no assessment of the impact of these activities. Some of the major barriers in developing and implementing effective pharmacy-based community outreach activities in developing countries include resource constraints, especially lack of facilities and limited HRH, lack of political will, lack of sustainability, and technological challenges. Efforts should be made to encourage the development and provision of such services, especially among the underserved and vulnerable populations.

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Consensus Methodologies and Producing the Evidence

gaps and opportunities. J Clin Tuberc Other Mycobact Dis. 2019;18:100135. Miller ML, Karwa R, Schellhase EM, Pastakia SD, Crowe S, Manji I, et al. Meeting the needs of underserved patients in Western Kenya by creating the next generation of Global Health pharmacists. Am J Pharm Educ. 2016;80(2):22. Mohiuddin AK. The role of the pharmacist in patient care: achieving high quality, cost-effective and accessible healthcare through a team-based, patient-centered approach. Universal-Publishers; 2020. Moore C. How to Organize an Effective Medical Outreach Program E-Health 2021. Available from: https:// quenza.com/blog/medical-outreach-program/ Nair V, Haberstroh AJ, Berko M, Rapp W, Fowler B, Redborg K. Strengthening Global Health outreach programs through pharmacy services. Ann Glob Health. 2017;83(3–4):621–4. Organization WH. Health promotion: Ottawa charter. World Health Organization; 1995. Organization WH. Guidance on developing a national deployment and vaccination plan for COVID-19 vaccines: interim guidance, 16 November 2020. World Health Organization; 2020. Organization WH. Working together for health: the World health report 2006: policy briefs. World Health Organization; 2006. Ozgediz D, Galukande M, Mabweijano J, Kijjambu S, Mijumbi C, Dubowitz G, et al. The neglect of the global surgical workforce: experience and evidence from Uganda. World J Surg. 2008;32(6):1208–15. Roodenbeke Ed, Organization WH. Outreach services as a strategy to increase access to health workers in remote and rural areas. World Health Organization; 2011. Shin HY, Kim KY, Kang P. Concept analysis of community health outreach. BMC Health Serv Res. 2020;20(1): 417. Steeb DR, Overman RA, Sleath BL, Joyner PU. Global experiential and didactic education opportunities at US colleges and schools of pharmacy. Am J Pharm Educ. 2016;80(1):7. Thapa SS, Kelley KH, Rens GV, Paudyal I, Chang L. A novel approach to glaucoma screening and education in Nepal. BMC Ophthalmol. 2008;8(1):21. The Role of the Pharmacist in Public Health 2006 [Available from: https://www.apha.org/policies-and-advocacy/ public-health-policy-statements/policy-database/2014/ 07/07/13/05/the-role-of-the-pharmacist-in-public-health. Upadhyay DK, Ooi GS. Enhancing quality of patientcentered care services in developing countries: pharmaceutical care approach. Social and administrative aspects of pharmacy in low-and middle-income countries. Elsevier; 2018. p. 311–28. Van Lerberghe W. The world health report 2008: primary health care: now more than ever. World Health Organization; 2008. Weathers B, Barg FK, Bowman M, Briggs V, Delmoor E, Kumanyika S, et al. Using a mixed-methods approach to identify health concerns in an African American

community. Am J Public Health. 2011;101(11): 2087–92. WHO. Diseases of poverty and the 10/90 gap 2004 [cited 2021 30 Oct].

Consensus Methodologies and Producing the Evidence Michelle A. King, Fiona S. Kelly and Sara S. McMillan School of Pharmacy and Medical Sciences, Griffith University, Gold Coast, Queensland, Australia

Abstract

Consensus methods help bridge the gap between what is known and what information needs to be known by combining the opinions of key stakeholders to create knowledge. The three consensus methods that are used most often in pharmacy research are the Delphi, Nominal Group Technique (NGT), and RAND/UCLA Appropriateness Method (RAM). All three draw together experts’ opinions through a voting or rating step, and exchange of ideas through a moderated meeting or feedback summarizing the comments of others, or both. The Delphi, NGT, and RAM were introduced to address limitations of existing exploratory research methods seeking to generate a wider range of ideas, engage more diverse stakeholders, and obtain a sense of priorities to inform resource allocation. While the opinions of a group may be more valid than the opinions of a single person, they are still opinions and not a gold standard. How the opinions are drawn together has a significant impact on the findings, and as such, minimizing bias and maximizing quality are key concerns. Each of these methods has been modified in a range of ways, and this entry provides an overview of the original method, selected modifications, and factors for researchers to consider in both application and reporting of these methods.

Consensus Methodologies and Producing the Evidence

Keywords

Consensus · Delphi technique · Nominal Group Technique · RAND/UCLA Appropriateness Method

Introduction Consensus methods use groups of “experts” to collectively agree on, or rate, an action or item where there is insufficient evidence or conflicting opinions (Fink et al. 1984). They aim to bridge the gap between empirical evidence and the course of action to be taken (Fink et al. 1984). Experts can represent key stakeholders such as health professions or patient groups. While the evidence of the reliability of these methods is limited, purposively selecting experts should provide justifiable, valid, and credible outcomes (Fink et al. 1984). Therefore, experts should include persons able to represent the relevant group of stakeholders (Bourrée et al. 2008), who are unlikely to have their opinions challenged, or who have the power to implement the findings or combinations of these (Fink et al. 1984). Voting, ranking, or rating of actions or items occurs in one or more rounds, within a structured context to solve problems, identify priorities, or provide guidance. With voting, consensus methods quantitatively measure what are generally qualitative data, i.e., expert opinions (James and Warren-Forward 2015), through a process of identification, clarification, and modification. Consensus methods have been used for various reasons, mostly to set standards (Fink et al. 1984) or criteria for appropriateness or inappropriateness (Fitch et al. 2001); to develop health surveys (Harb et al. 2021), guidelines (Moher et al. 2010), or decision aids (Fitch et al. 2001); and to inform policy (James and Warren-Forward 2015).

A Range of Consensus Methods A range of consensus methods are used in pharmacy research. The Delphi method and the Nominal Group Technique (NGT) are the most

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popular, with the RAND/UCLA Appropriateness Method (RAM) used less frequently. While some may argue that NGT is not a consensus method as it leans toward the generation and prioritization of ideas (Campbell and Cantrill 2001), it has been included as it uses a level of consensus from experts to determine outcomes and is a valuable tool for pharmacy and public health researchers. In contrast, the National Institutes of Health (NIH) consensus development panels/conferences (McGlynn et al. 1990) and Glaser’s approach (Glaser 1980) require resources or expertise that are beyond the reach of most pharmacy researchers, and do not appear in mainstream pharmacy literature, and are therefore beyond the scope of this entry. The Delphi was first published in 1963 by Dalkey and Helmer (1963) from the RAND corporation, while the RAND corporation and University of California developed the RAM in the mid-1980s (Fitch et al. 2001). The NGT was designed by Delbecq and Van de Ven as part of a larger program planning model (Delbecq and Van de Ven 1970; Delbecq and Van de Ven 1971). These methods have several aspects in common: the use of experts including lay end users of services, item generation and modification of items based on feedback, and voting/rating/ranking to reach consensus. The Underlying Ontology and Epistemology Linstone and Turoff (2002) stated that “. . .there is no one best or even unique philosophical basis which underlies any scientific procedure or theory.” This is especially true of consensus methods as they do not have a theoretical underpinning and combine both qualitative and quantitative techniques. It could be argued that consensus methods adopt a mixed methods approach where the qualitative methods (analysis of comments, e.g., feedback) facilitate the quantitative methods (analysis of numerical data, e.g., levels of agreement) (Stewart 2001) and therefore aligns with the epistemological paradigm (nature of knowledge and how knowledge is obtained) of pragmatism (Liamputtong 2012). This is the idea that there is a true (objective) reality (positivism) as well as a perceived (interpreted) reality, supporting

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methodological pluralism (Liamputtong 2012). An alternative view is that consensus methods solely align with interpretivism, also known as constructivism, as the individual opinions of experts are brought together through interaction to create group understanding. The iterative, interpretive process of the Delphi aligns with Gadamerian hermeneutics (Guzys et al. 2015), as it involves rounds with feedback that impact the experts’ understanding of the items or statements under consideration. This is also likely to apply to the RAM due to its use of rounds and discussion. It can be argued that the problem-solving component of the NGT aligns with pragmatism, while the qualitative component accesses the explanatory strength of critical realism (Fletcher 2017) drawn together by the interpretative aspects of hermeneutics. The genesis of consensus methods was to solve problems that existed due to evidence gaps, involve a greater range of stakeholders, and address recognized challenges of traditional qualitative discussion groups, in order to more fully understand research problems (Andrew Van De, Ven and Andre L. Delbecq 1971; Delbecq and Van de Ven 1971; Ven and Delbecq 1972). To increase understanding and facilitate researcher choice between the Delphi, RAM, and the NGT, an overview of each is provided, with key commonalities and differences highlighted. The selected pharmacy references show variations in methods within each technique; it is important to remember that they only represent a small subsection of the available literature.

2019). One of the types of Delphi, the Policy Delphi, is about exploring opposing views (Turoff 1975) or a lack of consensus (De Loë et al. 2016), or identifying as many solutions as possible (de Meyrick 2003), to a policy issue. There are significant variations in their methods and virtually no published pharmacy research using the Policy Delphi. Anonymity and iterative rounds with feedback are fundamental characteristics of Delphi (Bourrée et al. 2008); however, other aspects vary. The numerous variations are generally referred to as a modified Delphi; however, there is no consistent definition of this and no uniform approach undertaken by researchers (Keeney et al. 2011). It has been reported that a modified Delphi does not involve an open qualitative round as per the traditional approach (Hasson and Keeney 2011; Jaam et al. 2021). Drumm et al. (2021) provide a useful flow diagram of their perception of the typical features of a Delphi. A questionnaire is informed by either a literature review, previous research (e.g., interviews), exploring expert opinion (in the first round), or a combination of these (Drumm et al. 2021). Experts then respond to the questionnaire by rating items, usually on a Likert scale (Drumm et al. 2021; De Villiers et al. 2005), which may include an opportunity to justify their rating or suggest modifications, including the addition of new items (De Villiers et al. 2005). Quantitative data are analyzed to determine if any items reach a pre-defined level of consensus, and qualitative data are used to develop feedback for further rounds and modify the questionnaire if required. The process is iterative until the agreed decision of what is consensus is reached or the pre-defined number of rounds has been completed (Humphrey-Murto et al. 2017). There are variations in the criteria for the inclusion and exclusion of items and the stopping point for rounds in the literature. These include a priori numerical determination of consensus (Fink et al. 1984), an assessment of convergence or stability of opinion (Campbell and Cantrill 2001), or both (Belton et al. 2019). There is no universally agreed numerical level of consensus (Hasson et al. 2000); however, Barrios et al. (2021) provided some evidence to support the use of 75% as

Delphi The Delphi was developed to deal with a complex problem (Linstone and Turoff 2002), specifically, defense forecasting related to bombing (Dalkey and Helmer 1963). Its use as a consensus method has since exploded across many disciplines including business, education, and health (Diamond et al. 2014). Within pharmacy, the Delphi has been used to define concepts (Pouliot et al. 2018), develop competency frameworks (Koehler et al. 2019) or taxonomies (Almanasreh et al. 2020), inform practice (Gibbins et al. 2017; Shawahna 2017), and define roles (Watson et al.

Consensus Methodologies and Producing the Evidence

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a consensus threshold, and two systematic reviews reported that of the limited studies defining consensus, this value was the median threshold (Diamond et al. 2014; Foth et al. 2016). The reason for having additional rounds is to converge on consensus. However, there comes a point, when the level of consensus is stable or stops increasing (i.e., convergence), when rounds should cease, as graphically represented by Greatorex and Dexter (2000). Many pharmacy studies (Drumm et al. 2021) report a numerical determination of consensus rather than stability; however, combinations of both have been used (Watson et al. 2019; Mubarak et al. 2019). The outcomes of the previous round, i.e., the need for additional or modified items or whether consensus is achieved, determine what is presented in the next round. The items that are associated with consensus at either end of the Likert scale, and no disagreement or modification requests, are generally removed from the next round. Modified items, items scored in the middle of the rating scale, and items with disagreement (e.g., those with bimodal distributions) are retained, i.e., those items in which consensus was not reached (De Villiers et al. 2005). In the next round, these items are accompanied by feedback summarizing the various perspectives which aims to better inform decision-making. Ideally, feedback should include quantitative and qualitative data (Boulkedid et al. 2011). Experts are provided with their previous score for the item and the average and spread of the responses from the group, often in the form of the median, and a frequency distribution or interquartile range (Hasson et al. 2000). This could be presented as a customized bar graph (Kranjc Horvat et al. 2021) with the expert’s individual response highlighted, providing an opportunity for each expert to understand where their ratings fall within the population and reflect on them. The addition of qualitative feedback allows each expert to re-evaluate their initial decision before voting in the next round. Qualitative feedback may be raw comments from the experts or controlled, i.e., where researchers analyze and summarize the data. The characteristics and the quality of pharmacy practice Delphi studies, excluding those related to

education, published between 2015 and 2020, were reported by Jaam et al. (2021); Drumm et al. (2021) also provide examples of five other pharmacy Delphi studies. Table 1 outlines four examples of Delphi papers not included in Drumm et al (2021). Nominal Group Technique (NGT) The NGT was developed as a “group process model” within a larger program planning model to promote innovation by moving beyond reliance on unilateral planning by managers through engagement with diverse stakeholders including “outsiders,” e.g., consumers (Delbecq and Van de Ven 1971; Delbecq and Van de Ven 1970). Informed by social-psychological studies of small group theory and decision-making, the NGT was introduced to facilitate greater creativity and exploration of critical dimensions of strategic problems or solutions than traditional interactive groups where dominant voices can narrow focus (Delbecq and Van de Ven 1970; Ven and Delbecq 1972). A combination of independent activity and group interaction maximizes generation of quality ideas from both subjective and objective perspectives (Ven and Delbecq 1972), to produce quantitative data, i.e., priorities, and rich qualitative description of critical problem dimensions or innovative solutions or both (Delbecq and Van de Ven 1971). Participants report satisfaction with the process and a sense of achievement with the outcome of an agreed set of priorities (Van De Ven and Delbecq 1974). Initially applied to areas such as education, urban planning, and citizen participation in social work program development (Delbecq and Van de Ven 1970; Delbecq and Van de Ven 1971; Van De Ven and Delbecq 1974), there was early recognition of the potential value of the NGT for health service planning (Ven and Delbecq 1972). The NGT was recommended as pilot exploratory research to investigate the subjective and complex nature of healthcare problems, particularly with multiple stakeholders, limited understanding of all variables, and communication challenges between professionals and consumers (Ven and Delbecq 1972). Use of the NGT has expanded to include pharmacy services and medication

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Knapp et al. (2020)

Author (year) Spinks et al. (2019)

To identify likely workforce issues impacting pharmacists in pharmacies with retail clinics and generate recommendations for optimizing outcomes in co-located facilities

Aim To establish clinical indicators to identify potentially preventable medication-related hospitalizations in Indigenous Australians

Pharmacist workforce and pharmacy practice researchers Invited n¼8, all participated Online via Qualtrics. Twoweek response time per round: two reminder emails (at 1 and 2 weeks) December 2017–October 2018

Participants, method, and timeframe Doctors and pharmacists with Indigenous healthcare experience or public health medication safety expert Invited via email n¼40; participants n¼13 Online via LimeSurvey. Two-week response time per round, one reminder email sent 1 week before closing May–November 2018

Literature review. Surveys pre-tested by research team but no specific details

Generation of Delphi statements Established indicators for Australian general population via RAND appropriateness measure (n¼45) and suggestions by experts (n¼56)

Consensus Methodologies and Producing the Evidence, Table 1 Delphi pharmacy-specific papers Rounds, voting, and consensus level Three rounds: 1 and 2 online voting; final round a face-toface meeting with experts (n¼3) Four-point scale: accept unchanged, reject indicator, specify alternative, and not sure. Allowed experts to provide rationale for rejection or alternative suggestion Indicator accepted or rejected at minimum 70% consensus level. Accepted or rejected indicators removed from subsequent rounds Panelists received written (anonymous, verbatim) feedback and links to guidelines or literature Delphi Survey 1: To identify impact on practicing in co-located facilities. Three rating rounds using fourpoint scale: none, little, moderate, and great. Expert comments permitted in rounds 1–2. Impact variable accepted if great or moderate rating at minimum 80% consensus level at the third round. All variables rank ordered on percent achieving consensus (first sort), the percentage ranked as great, and finally the percentage Two Delphi’s involving same experts. Consensus only determined in the last round. Rank ordering of items (results from Survey 1 presented as a bar graph). No attrition Unclear how experts were invited or if surveys were provided at the same time. Limited detail on literature review

Points of difference and limitations Experts interviewed individually to clarify Delphi process. No attrition. Faceto-face meeting with a subgroup of experts instead of a final anonymous round No reporting on whether experts provided with statistical results of previous round

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Alshakrah et al. (2021)

To develop a pharmaceutical care complexity screening tool for hospital admission

Delphi 1: Invited n¼49; participants n¼41; n¼33 Pharmacists, academics, and physicians Delphi 2: Invited n¼56; participants n¼43; n¼40 United Kingdom chief pharmacists and clinical service pharmacy managers SelectSuvery.Net Six weeks to respond per round, up to four reminders Delphi 1: December 2017 (round 1) to May 2018 (round 2). Delphi 2: October

Delphi 1: Informed by systematic review, phone interviews with chief pharmacists (published), research team, and Expert Reference Group discussions Delphi 2: Informed by previous interviews and reviewed by research team Surveys piloted with three clinical pharmacists

ranked as moderate (third sort). Items also rank ordered by summing impact scores (0, none; 1, somewhat; 2, moderate; 3, great). Delphi Survey 2: First round obtained expert feedback on recommendations for optimizing operation of co-located facilities. Ratings for rounds 2 (comments permitted) and 3. Threepoint scale: very, moderately, and not very important. Recommendation accepted if very important at minimum 80% consensus level at the third round, which were then rank ordered by percentage Panelists received a report based on responses, including comments and table of rating results of previous rounds Two rounds per Delphi Delphi 1: Develop a classification scheme for assigning pharmaceutical complexity levels to patients on hospital admission. Two rounds. Nine-point Likert scale: 1–3 unimportant; 4–6 uncertain; and 7–9 important. Delphi 2: Determine appropriate frequency of clinical pharmacist input for each complexity level and appropriate competency (continued)

Two separate and sequential Delphi studies. Researchers and Expert Reference Group reviewed results between rounds. Specified target sample sizes. Piloting of surveys. Results well reported Limited detail on systematic review. Attrition and demographics for each participant type not reported

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Jenghua et al. (2021)

Author (year)

To develop an extensive list of potentially inappropriate medications for patients with heart failure (PIMHF)

Aim

Cardiologists and hospital and academic pharmacists with expertise in heart failure Invited n¼40; round 1 participants n¼26 (only 9 responses eligible). Before the second round, researchers personally invited cardiologists who had not responded in round 1. n¼17 for the second and third rounds Email – no detail on

2018 (round 1) to December 2018 (round 2)

Participants, method, and timeframe

Specified data sources for initial questionnaire, which was pilot tested by two heart failure content experts

Generation of Delphi statements

Consensus Methodologies and Producing the Evidence, Table 1 (continued)

level of pharmacy staff to assign to each level. Ninepoint Likert scale: 1–3 low practicality or clinical appropriateness; 4–6 uncertain; and 7–9 high practicality or clinical appropriateness Consensus according to RAND appropriateness operational definition. No statements removed from surveys in second rounds. Delphi 1: consensus reached at second round. 2: Consensus achieved in both rounds for the same statements, therefore stopped at second round Panelists received summary of comments, overall median score, and panelists’ previous score Three rounds Round 1: To assess each medication for its effects on cardiac function (checklist scale of six options; participants could select more than one option and add other reasons) and a 3-point Likert scale about whether it was a PIMFH (yes, not, unable to make a decision). Further medications could be added.

Rounds, voting, and consensus level

Required experts to declare conflicts of interest if involved with any considered medication. Specified eligibility criteria based on years of practice, workplace, willingness to join study, and availability, which affected the data used. Jenghua et al. referenced the literature they reviewed to inform their methods. They specified the minimum

Points of difference and limitations

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questionnaire platform. Reminder email sent a month later and every 2 weeks after until all questionnaires were received (rounds 1 and 2) January–September 2019; each round took 3 months Medicines rated as not a PIMFH by a minimum of 60% of experts were removed from the questionnaire; remaining and newly suggested items kept. Rounds 2 and 3: Medication list did not change. Fivepoint Likert scale (strongly disagree, disagree, undecided, agree, strongly agree). Experts asked to give reasons for any different answers in round 3 Pre-determined criteria: convergence (minimum of median 3.5 and 1.5 IQR) and stability (marginal changes of medians between rounds 2 and 3 of less than 15%) Panelists received feedback as first or third quartile and previous answer for third round number of rounds they planned to undertake (n¼3). The stability of medians between rounds two and three, and convergence were used to determine consensus. Unclear if pilot testing of the first questionnaire involved panelists. No detail on participant incentives

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management (McMillan et al. 2014; Chen et al. 2018), general practice services (Gallagher et al. 1993), to identify patient preferences for treatment (Kremer et al. 2016), medical education (Reimer et al. 2019; Humphrey-Murto et al. 2017), to identify and refine items for health surveys (Harb et al. 2021), and to explore sensitive topics and promote a more inclusive approach for vulnerable populations historically excluded from health-related decision-making (Tuffrey-Wijne et al. 2007; Spassiani et al. 2016), for example, evaluation of healthcare tools and discussion of death and dying to identify priorities for end-oflife care among individuals with intellectual disability (Spassiani et al. 2016; Tuffrey-Wijne et al. 2007). The NGT has been described as accessible for different populations and inherently adaptable (Spassiani et al. 2016; Harb et al. 2021). The original components of the NGT are described with examples of modification (Table 2). Researchers initially welcome participants, outline the purpose of the group, and usually provide experts with a single question to consider. Silent generation of responses follows, with participants independently recording ideas in response to the question alongside each other without speaking. Observing others writing creates “social facilitation-tension” that encourages participants to generate and record their own ideas (Delbecq and Van de Ven 1970; Delbecq and Van de Ven 1971; Van De Ven and Delbecq 1974). The second round-robin phase aims to promote balanced input, increase idea richness, and limit arguments over wording semantics (Delbecq and Van de Ven 1971). A facilitator sequentially asks each participant to verbally share one idea with no further elaboration to ensure balanced input from all experts. Each idea is recorded by another facilitator verbatim where participants can see it, e.g., whiteboard or shared screen if online, until ideas are exhausted. Participants are encouraged to share any additional ideas prompted as other ideas are shared, known as “hitch-hiking” on ideas (Van De Ven and Delbecq 1974). Asking participants to raise their hand if they had the same item was noted by Delbecq and Van de Ven (1970). The round-robin process promotes sharing of less mainstream ideas when more secure

group members role-model early self-disclosure leading to less inhibition for others as does absence of distracting value-based commentary or individuals monopolizing group discussion (Delbecq and Van de Ven 1971; Delbecq and Van de Ven 1970). The subsequent clarification phase provides an opportunity for group discussion of rationale for ideas and to defend, elaborate on, or add items (Delbecq and Van de Ven 1971). Researchers were cautioned against removing or collapsing specific items into problem categories given that the aim was to comprehensively identify critical problem dimensions (Delbecq and Van de Ven 1971). The interactive component of groups encourages participants to reflect on alternative dimensions of the issue and provides valuable qualitative insights (Van De Ven and Delbecq 1971; Delbecq and Van de Ven 1970). Once a final list is agreed, there is individual voting for top priorities, typically the five items considered most critical with the highest number for the most important priority, e.g., five (Delbecq and Van de Ven 1971). Ven and Delbecq (1972) also describe ranking up to ten items and additional numerical rating of the relative importance of individual items to provide insight into the magnitude of difference between priorities. Rating perceived importance on a numerical scale has been used to select survey items, for example, an item could be ranked top priority yet be allocated 5 on a scale of 1 to 10 of relative importance (Harb et al. 2021). The application of the NGT varies across several dimensions, including research objectives, number of groups, participants per group, and group processes. Harb et al. (2021) identify 30 distinct decision points for researchers and discuss the advantages and disadvantages of modifications in a scoping review of 57 studies using the NGT in health survey research. Researchers are encouraged to make considered decisions for each point and document these thoroughly to promote transparency and validity (Harb et al. 2021). Key areas of modification are briefly discussed below. Although primarily run in person, the NGT has been used via teleconference (Edwards et al. 2019) and online (Kulczycki and Shewchuk 2008; Tseng et al. 2006). The number of groups

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Consensus Methodologies and Producing the Evidence, Table 2 Nominal group technique then and now

can vary widely and is influenced by the research objectives and stakeholder groups; multiple objectives may require increasing numbers of groups which can be more challenging to analyze (McMillan et al. 2014). Participants generally respond to a single question without being provided additional topic information. The NGT can be applied using multiple sequential questions which increases group intensity and may introduce fatigue (Spassiani et al. 2016). Priming information from the literature or data from prior groups has been presented when generating health survey items (Harb et al. 2021) or eliciting ideas on future ideal pharmacy services (McMillan et al. 2014). This can allow item confirmation but may narrow discussion; alternatively, participants can be asked to share ideas prior to information presentation (Harb et al. 2021). Modification to the round-robin process includes experts sharing all ideas at once which can allow individuals to dominate and limit contribution of conflicting ideas (Delbecq and Van de Ven 1970; Delbecq and Van de Ven 1971). If participants discuss their ideas among themselves or during the round-robin phase, this may be more time efficient but limits consideration of each item within the broader list (Duggan 1999; Harb et al. 2021). The clarification phase can reduce ambiguity, eliminate duplication, and group similar ideas together as concepts (Lomas et al. 1987; Kremer et al. 2016) to create a refined list according to group preferences. However, this relies on effective facilitation as it may be driven by more dominant group members, may disenfranchise participants if their idea is absorbed into a higher-level construct, and can make voting more challenging if ideas become broad concepts when merged or participants find it difficult to distinguish between items they view as equally important (Delbecq and Van de Ven 1970; Delbecq and Van de Ven 1971; Harb et al. 2021). Although consensus voting is usually individual, collaborative group evaluation can be conducted which relies more on facilitator interpretation than individual voting and is subject to social pressure (Delbecq and Van de Ven 1970; Delbecq and Van de Ven 1971). Final priorities can be decided by a combination of total voting

NGT stage as per original method (Delbecq and Van de Ven 1970; Delbecq and Van de Ven 1971; Ven and Delbecq 1972) Introduction: welcome and explanation of group aim (10 minutes)

Modifications of process * adapted from (Harb et al. 2021) Priming information provided from the literature or preliminary research findings Silent generation: The NGT is repeated for individuals independently additional questions write ideas in response to a Pre-determined list of ideas single question (15 to presented first 30 minutes) Silent generation then a list of ideas is presented Group brainstorming of ideas verbally Round-robin: individuals Individuals provide all contribute one idea at a ideas at once time, sequentially until Idea sharing is less there are no new ideas. No structured discussion or refinement of Concurrently clarify and ideas elaborate on ideas Ideas are refined during round-robin Coffee break Clarification: discuss, Consolidate or merge items elaborate, and add ideas as categories (30 minutes) Voting: individually Concurrent item ranking of top 5 to refinement and evaluation 10 priorities Evaluate all items for inclusion in survey* Rate importance for survey items* Post-group refinement by researchers without group input* Feedback presented to the Feedback and additional group on final preferences discussion with This can include opportunity to change spontaneous discussion as evaluation* individual votes are recorded to clarify the vote which may lead to further refinement (Ven and Delbecq 1972) Second vote plus item rating: individuals review and change their original ranking. Participants rate relative importance of re-ranked items allocating 100 to the most important item and values of 0 to 100 for the remaining items

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score and number of votes, or relative importance rating (McMillan et al. 2014; Ven and Delbecq 1972). Researchers present aggregate results as consensus of importance to highlight opportunities for further discussion, confirm understanding of items, and explore ambiguities revealed through the voting. Insights gained may provide more accurate final evaluation (Delbecq and Van de Ven 1970; Delbecq and Van de Ven 1971), although group judgement may influence this particularly if there is opportunity to change voting (Ven and Delbecq 1972).

The NGT has been modified a number of different ways, and Table 3 presents four diverse applications within a pharmacy context.

Using the NGT Online

The COVID-19 pandemic has facilitated online use of the NGT, and challenges include disjointed communication when people are unused to online meetings, technical challenges, and allowing extra time for participants to use online forms. The authors have conducted eight online sessions with health professionals and students to establish priorities for integrating pharmacists in primary care; educational resources to promote multidisciplinary practice; mental health training and medication safety. Groups ranged from three to ten participants and two facilitators were used in all cases. Key learnings are outlined here. Confirm consent prior to the meeting, particularly if obtained online, and allow time for technical checks and sign-in. Have a minimum of two facilitators, one focused on group dynamics and facilitation and the other to share their screen and document member contributions verbatim in view of the group. Encourage everyone to remain on camera, and let them know the order you will ask them for ideas to set expectations. Set up an online template for recording ideas, e.g., a table with an additional column to allocate a letter or number for consolidating ideas and sorting. Online voting can be easily achieved via online surveys or forms set up as a list of letters or numbers to reflect the final list and the ability to allocate 5 points, etc. The group can refer to the list via screen sharing and vote on their device, i.e., laptop, phone, or tablet. Lastly, online voting can provide immediate results, often visual, without the need to calculate scores which can facilitate additional discussion.

RAND/UCLA Appropriateness Method (RAM) The RAND/UCLA Appropriateness Method combines the best available evidence with the collective, usually clinical, judgement of experts to create statements regarding appropriateness, i.e., whether the expected benefits outweigh the expected negative outcomes of a particular intervention or action (Shekelle 2004; Fitch et al. 2001; Humphrey-Murto et al. 2017). Appropriateness is determined exclusive of cost (Brook et al. 1986). In pharmacy, RAM has most often been used to develop lists of medicines that are appropriate or inappropriate in certain patient populations (Basger et al. 2012; Foubert et al. 2021), but it has also been used to identify tasks that could be transferred to pharmacy technicians to free up pharmacist time (Kuhn et al. 2016; Ourth et al. 2019). In medicine, RAM is often used to determine the appropriate management of a condition or the appropriateness of performing a particular procedure in specific patients or patient groups. The RAM has also been used to develop guidelines and clinical decision aids (Fitch et al. 2001) and to identify professional competencies in nursing (Dijkstra et al. 2021). Ideal topics for studies implementing RAM are those where the procedure or medicine is common, associated with risk, or resource-intensive (e.g., expensive), or where the use of the procedure or medicine varies in different geographical areas or is controversial, or combinations of these (Fitch et al. 2001; Humphrey-Murto et al. 2017). The RAM combines elements of both Delphi (literature review to inform pre-defined items provided in an initial voting round that is undertaken by post or electronically) and the NGT (subsequent face-to-face meeting with item discussion). The RAM process involves synthesis of the evidence; development of a list of indications and specific definitions by researchers; anonymous individual rating of the indications by experts (round 1); a moderated panel meeting to discuss round 1 ratings, during or after which experts privately re-rate the

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Consensus Methodologies and Producing the Evidence, Table 3 NGT pharmacy-specific papers Author (year) McMillan et al. (2014)

Aim Evaluate priorities for healthcare and pharmacy services across multiple groups

Method and participants (n¼) n¼26 groups of 15 consumer and carer groups 11 professional groups (average 6 per group, range 2–14)

Generation of NGT items 2 questions Q1 203 ideas and 83 priorities Q2 276 ideas and 130 priorities

Fakih et al. (2016)

Attitudes toward pharmacy involvement in weight management and priorities for education resources

n¼4 groups 3 homogenous and 1 heterogenous groups to confirm (average 7 per group, range 4–10)

10 questions Original ideas list not reported, only priorities for each group per question

Hussainy et al. (2016)

To generate frameworks for pharmacy Objective Structured Clinical Examinations (OSCE)

n¼ 2 groups (average 8 per group, range 7-9)

Given five draft OSCE frameworks and asked to generate new framework ideas which were then ranked Process repeated

n¼1 group of 8 Heterogenous to maximize idea generation

1 question 63 ideas rationalized to 20 topics

Newlands Prioritizing et al. improvement for (2018) community pharmacy services

Voting and consensus level Identification of top 5 priorities for two questions Compared two methods of voting analysis across multiple groups to reflect the predominance of a theme more clearly as well as high scores Analysis of qualitative data to contextualize priorities Identification of top 5 priorities for ten questions in three homogenous groups – women, pharmacy assistants, and pharmacists Ideas re-ranked by researchers, aggregated for three groups, and presented to final heterogenous group to add new ideas and create a final ranked list using a 1 to 10 scale The two most highly ranked frameworks of the additional nine identified, informed the final OSCE framework

Points of difference and limitations Consolidation into themes may lose detail in priorities

Multiple questions used, possibly limiting idea generation Not all questions clear Researchers re-ranked and aggregated ideas without participant input

Presentation of frameworks may have narrowed focus Heterogenous groups created potential power imbalance and possible disparity in ranking NGT in 1 face-to-face Ideas limited to meeting used to one group generate Final list of 63 statements that topics given were rationalized to equal weighting 20 topics by the as there was no stakeholders for voting subsequent eDelphi Topics in the NGT formed the first round of eDelphi Two additional rounds conducted

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indications (round 2); and an analysis of round 2 data to determine appropriate indications (Fitch et al. 2001). A modification of this process is the addition of a third postal or electronic round where the “necessity” (whether it must be offered) of each appropriate indicator is rated (Fitch et al. 2001). Unlike Delphi, the aim of the literature review in RAM is to capture the best available evidence related to the topic rather than narrow down on a particular question(s). Subsequently, several reviews exploring several levels of evidence, not just randomized controlled trials, in different patient groups or a systematic review supplemented by additional evidence are more likely to achieve this goal than a lone systematic review (Fitch et al. 2001). A lesser-known style of review, the systematic quantitative literature review, takes a broader view of the literature and therefore may be a useful approach (Pickering and Byrne 2014). The literature review results are used to compile a list of indications (clinical scenarios) which categorize potential patients in terms of clinical variables that could influence decisionmaking, e.g., symptoms, history, and test results (Fitch et al. 2001). Fitch et al. (Fitch et al. 2001) provide an example of how indications can be grouped into chapters based on clinical variables to manage data when there are large numbers of indications. Indications should be “decidable,” i.e., able to be assessed as appropriate or not and apply to the majority of clinical situations or patients, preferably both (Shekelle 2004). To ensure that experts have the same understanding of the indications and optimize reproducibility, definitions of all the terms in the list need to be created (Shekelle 2004). Unlike NGT and some Delphi, experts do not generate ideas as the method assumes the evidence to create the questionnaire can be found in the literature (Fitch et al. 2001). The RAM extrapolates from this evidence to identify the appropriateness of more indications, and therefore it is not surprising that the reliability of RAM is influenced by the level of evidence (Shekelle et al. 1998b). The expert panel is usually comprised of representatives from all disciplines involved in making treatment decisions for the patient group being studied (Shekelle 2004; Fitch et al. 2001). Multidisciplinary panels are less likely

to suffer from bias when compared to unidisciplinary panels as those that perform a particular procedure are more likely to rate it higher (Kahan et al. 1994). Experts are provided with a summary of the literature, the indications, and the definitions and asked to rate the appropriateness of each indication (Fitch et al. 2001), which despite their expertise and practice context should be assessed based on the performance of an average clinician, in an average hospital, treating an average patient (Shekelle 2004; Fitch et al. 2001). Experts rate the indications on a scale of 1 highly inappropriate (expected harms greatly outweigh benefits) to 9 highly appropriate (expected benefits greatly outweigh harms) (Brook et al. 1986; Fitch et al. 2001). The rating of 5 can be interpreted as either unable to judge or harms and benefits are relatively equal (Fitch et al. 2001). In the analysis, median scores of 1–3 are classified as inappropriate, 4–6 as uncertain, and 7–9 as appropriate. If there is polarization of the results or they are spread across the scale, this indicates disagreement. If disagreement is present, irrespective of the median value, then the indication is classified as uncertain (Fitch et al. 2001). Several methods have been used to define disagreement. The RAM User’s Manual (Fitch et al. 2001) states that disagreement for an indication occurs when the Interpercentile Range (IPR) is greater than the Interpercentile Range Adjusted for Symmetry (IPRAS) with both calculated for the 30th to 70th percentile. The formula for calculating the IPRAS is (Basger et al. 2012; Fitch et al. 2001). IPRAS ¼ IPRr þ ðAsymmetry Index  Correction Factor for AsymmetryÞ IPRr is the IPR required for disagreement when perfect symmetry exists ¼ 2.35 The Asymmetry Index (AI) is the difference between the central point of the IPR and 5 (midpoint of the 1-9 scale) therefore AI ¼ 5 

ð30th percentile þ 70th percentileÞ  1:5 2

Correction Factor for Asymmetry ¼ 1.5

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After the responses from the first Delphi-like round have been analyzed and appropriateness and disagreement determined for each indication, individualized feedback is created for each expert. The facilitator is provided with a summary that contains the frequency distribution and the rating of each expert for each indication. This supports the facilitation of the face-to-face discussion in the second round. For example, if there is disagreement, a facilitator may ask the panel if anyone has a possible explanation for the variation or if it is due to the wording of the indication. While experts do not generate ideas, they can suggest amendments to the indications (Campbell and Cantrill 2001). Skill is needed to manage the discussion, to keep it focused on the evidence rather than personal opinion, and to sensitively word questions, especially when asking those with outlying ratings if they have any thoughts on the matter. Asking someone directly to explain why they voted differently from the others is to be avoided. Discussion proceeds on a chapter-bychapter basis with individuals re-rating the relevant indications after each chapter’s discussion. Depending on the number of indications, the entire meeting may take up to 2 days. If there is sufficient time left, and if necessity is an outcome of interest, items from round 2 that have been classified as appropriate may be rated for necessity at the end of the meeting (third round). Necessity can also be rated later, in a separate Delphilike round. To meet the definition of necessity, an indication must be appropriate and likely to be beneficial and the magnitude of benefit not small, and it would be improper not to recommend it (Kahan et al. 1994). Evaluation of necessity also uses a 9-point scale (1¼clearly not necessary to 9¼clearly necessary) (Fitch et al. 2001). A median rating of at least 7 without disagreement is required for an appropriate indication to be considered necessary (Fitch et al. 2001). In addition to the third round to measure necessity, other RAM variations include a round 0, where experts are either sent the list of indications for comment and to suggest modifications or invited to a meeting to discuss, clarify,

or modify indications and definitions, but no rating occurs (Fitch et al. 2001). Following the RAM with a Delphi involving potential users to validate the appropriateness indicators has also been undertaken (Dreischulte et al. 2012) (Table 4). Choosing a Consensus Method In a larger research program, consensus methods are not mutually exclusive and can also be used with other methods. For example, Dreischulte et al. (2012) used RAM to develop assessment criteria using ten experts and then tested applicability in a larger population through two rounds of Delphi (Table 5). Focus groups have been used to inform a Delphi questionnaire, as there was limited available literature (Koehler et al. 2019). When selecting consensus methods, first consider the level of existing knowledge in your area of interest (Campbell and Cantrill 2001) within a continuum from limited knowledge, to some knowledge, to significant knowledge. If there is limited knowledge, then the NGT may be a suitable choice as it is investigative and results in the generation of many (Humphrey-Murto et al. 2017) or diverse ideas (Manera et al. 2018; Harb et al. 2021). Van De Ven and Delbecq (1974) reported that the NGT generated two ideas for every 1.6 from Delphi. Specifically, the NGT explores what experts want or consider to be important through a combination of social processes and independent structured tasks; creating a tolerance for non-conformity in ideas; and greater participant satisfaction with the group process including more immediate results and associated sense of accomplishment (Van De Ven and Delbecq 1974). If there is some knowledge, held either in the literature or within experts or both, but gaps remain, a Delphi could be used to provide credible assumptions (Fink et al. 1984) as to what should be done. Idea generation can occur within a Delphi, but this is generally from a first round focused on brainstorming with experts contributing to questionnaire development. If a significant evidence base exists, then experts can combine this with their clinical experience to determine appropriateness using RAM

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Consensus Methodologies and Producing the Evidence, Table 4 RAM pharmacy-specific papers Author (year) Bell et al. (2014)

List developed Literature search of existing tools and guidelines to produce a 55-page evidence booklet

Basger et al. (2012)

Literature search of Australian medication information sources to identify appropriate and inappropriate management of common conditions. Focused on the 50 highest volume subsidized medicines in Australia and 40 most common reasons for older Australians to visit their general practitioner

Chartrand et al. (2018)

Quality indicators developed based on national guidelines and a literature review

Panel 9 members Multinational Multidisciplinary: 7 GPs, 1 pharmacist, 1 academic (implementation science) Expertise: patient safety in general practice Time commitment for panel members approx. 3 days 15 members Multidisciplinary: 5 geriatrician/ pharmacologists, 7 clinical pharmacists, disease management, 3 advisors to therapeutic publications 12 member panel for round 2 (loss of one person from each professional group)

9 experts: 2 community and 3 (anticoagulation or cardiovascular) hospital pharmacists, 2 primary care physicians, 1 cardiologist, and 1 researcher

Rounds Round 1 – email Rate clarity and necessity (and suggest changes to wording) Round 2 – 1-day faceto-face, group discussion Rate necessity and feasibility (and suggest changes to wording)

Agreement 80% within same 3-point region as the median Median 7 on necessity scale 2 people with scores outside the 3-point region around the median

Round 1 – email Rate appropriateness Round 2 – face-toface, group discussion Rate appropriateness and modify wording for clarity

Round 1 – agreement 4 or fewer experts rating outside the 3-point region of the group median. Disagreement 5 or more experts rating in each extreme, i.e., 1–3 and 7–9 Round 2 – agreement 3 or fewer experts voting outside the 3-point region containing the median or IPRAS>IPR. Disagreement 4 or more participants rating in each extreme Round 1 – median appropriateness score of  7 without disagreement retained and  3 without disagreement removed. Disagreement if “median score was  7 with at least 3 ratings (33% of the experts) of  3 or when the median score was  3 with at least 3 ratings (33% of the experts) of  7” Round 2 – only indicators with a median score of  7 without disagreement retained

Round 1 – emailed. Rate appropriateness and add to the list. Inclusion and exclusion criteria based on feasibility provided Round 2 – face-toface, group discussion Given modified quality indicator list, disagreements, and abstract of panel members’ comments. Rate appropriateness of indicators previously rated with a median between 4 and 6 or with disagreement and new or modified indicators Pilot test in 5 community pharmacies (2 patients each) removed if not

(continued)

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Consensus Methodologies and Producing the Evidence, Table 4 (continued) Author (year)

Dreischulte et al. (2012)

List developed

Targeted (common conditions and medicines in primary care) literature review plus clinical guidelines, prescribing advice, and safety alerts

Panel

Rounds

10 clinical, public health, or academic experts with knowledge relating to medication use in United Kingdom primary care: 4 GPs (2 with prescribing improvement roles), 6 pharmacists (2 academics, 2 from general practices, and 2 from medicines governance) Delphi: 23 (64% response) GPs and 13 (36% response) pharmacists

assessable, minor modifications before emailed to experts for approval Round 1 – email with supporting evidence summary Rate necessary and appropriate Round 2 – 1-day faceto-face, moderated discussion before re-rating. Unclear what a “summary” of the round 1 ratings comprised (i.e., did not specify whether median, numbers per rating, and/or own rating) Delphi study – 2 rounds by email Rating – agreement with “The described topic is a priority for collaborative quality improvement in primary care” Delphi round 1 – ratings summarized and fed back Delphi round 2 – re-rated and ratings used to finalize lists

Agreement

Appropriateness: median rating of 4 to 6 (“uncertain”) or disagreement (three or more ratings of 7 to 9 and three or more ratings of 1 to 3) rejected. Median ratings of 7 to 9 “appropriate” and median ratings of 1 to 3 “inappropriate” Necessary: median rating of 7 to 9 (¼ clearly necessary to do). Necessary to avoid median rating of 1 to 3. Median ratings of < 7 on the “necessary to do” and > 3 on the necessary to avoid scale and those showing disagreement (defined as above) rejected Delphi: second round median ratings of 7 to 9 without disagreement (30% or more 1 to 3 and 30% or more 7 to 9) “priority”; median ratings of 8 or 9 “high priority”

IPR, interpercentile range; IPRAS, Interpercentile Range Adjusted for Symmetry

(Fitch et al. 2001). In contrast to NGT and Delphi, this method is not designed for idea generation (Humphrey-Murto et al. 2017). Next, consider the key determinants of time and resource availability. The NGT is much faster than Delphi and RAM; however, this depends on the number of groups being held, the individual group size, and the number of questions asked. While recommended to improve study rigor (Humphrey-Murto et al. 2017), a literature review is not generally undertaken for the NGT (Campbell and Cantrill 2001), and participant

responses are collected immediately. With the Delphi, considerable amounts of time pass when waiting for experts’ responses to be returned, analyzing these, and providing controlled feedback. Therefore, depending on the number of rounds, the entire process can be lengthy and requires ongoing commitment from experts (Keeney et al. 2011). The other contributing factor is the literature review (if needed), although this is not as extensive as that required for RAM. To undertake RAM, significant researcher time is invested in undertaking a multifaceted review.

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Consensus Methodologies and Producing the Evidence, Table 5 Comparison of consensus methods Consensus type Characteristics Panel size

Delphi

NGT

RAM

Small to large number (n≈6–1000s) Yes

Small – 6 to 10

Small number (n¼7–15)

No

Timeframe

Each round takes weeks to months

1.5 hours or longer per question

Location

Anywhere

Anywhere (online) or local (face-to-face)

Cost contributors

Researcher time (months)

Initial item generation

Open-ended question for brainstorming or literature review Generally, 2–3 for voting. Ceased when consensus is achieved or stability of responses Anonymous. Scale of varying sizes, commonly 4–9 points Variable percentage (commonly 70–80%) of predefined threshold(s)

Venue, refreshments, travel (face-to-face); researcher time (days to weeks) Silent generation Round-robin sharing

Yes for round 1, No for round 2, if the optional round 3 is combined with round 2 No, if separate Delphi-like Yes Round 1 takes weeks to months; round 2 is 1–2 days; optional round 3 can be combined with round 2 but if separate weeks to months Round 1 anywhere; round 2 anywhere (online) or local (face-to-face); optional round 3 anywhere Venue, refreshments, travel (round 2 face-to-face); researcher time (months)

Expert anonymity

Rounds or groups

Voting

Consensus level

Generally one voting round Feedback and re-vote possible Anonymous

Higher voting score ¼ higher priority Relative importance and number of votes can also be used

Literature review and synthesis, tailored for population(s) 2 (first round for voting, second round meeting); 3 if necessity assessed

Anonymous. 9-point Likert scale for appropriateness and necessity Appropriateness ¼ median between 7 and 9 with no disagreement (see above for details)

Fitch et al. (2001), De Villiers et al. (2005), Humphrey-Murto et al. (2017), Drumm et al. (2021), and Keeney et al. (2011)

Furthermore, the face-to-face meetings can also result in significant costs as they may require up to 2 days to complete (Fitch et al. 2001). Therefore, the RAM depends on experts who may be timepoor being willing to participate. This may require the provision of an honorarium. While the first round is essentially a Delphi and thus requires a similar amount of time, the second round is akin to an NGT, and therefore responses are immediate. Digital platforms, e.g., Zoom or Microsoft Teams, can allow what have previously been face-to-face meetings for NGT and RAM to be held online, thereby reducing some costs and geographical

barriers. Online access rather than posting Delphi questionnaires has further reduced the time involved (Chalmers and Armour 2019). For studies focused on highly controversial or sensitive issues, where anonymity needs to be guaranteed for experts to be willing to participate or to avoid power imbalances, then Delphi is often first choice as this does not require a meeting component. However, the NGT can be used in vulnerable populations to discuss sensitive issues (Spassiani et al. 2016; Tuffrey-Wijne et al. 2007). Skilled moderation is needed to manage power imbalances in NGT and RAM.

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Lastly, if the number of experts available and potential attrition is a concern, the impact on bias and other quality measures must be considered. De Villiers et al. (2005) noted concerns with too few participants at the end if the pool of experts was small; therefore, attention needs to be made on the impact of additional rounds on loss of experts. A benefit of the NGT is that dropout is not a concern as it involves a single round. Consequently, the NGT has been used when few experts have been available (McMillan et al. 2016); however, smaller groups can limit analysis and skew the weighting of priorities. While justification of a consensus technique may be due to a gap in the literature, it is necessary to explain why a particular consensus method has been chosen. Careful consideration of the methods and impact on the outcomes as well as detailed reporting by researchers of what was done will help meet quality requirements.

credibility, transferability, dependability, and confirmability (Lincoln and Guba 1985), and for quantitative measures, this is based on reliability and validity. Therefore, quality is focused on the steps involved within each method: Literature review (Delphi and RAM): depending on the breadth of the review required, consideration should be given to PRISMA guidelines including the search strategy and reporting, be it a systematic or scoping review (Preferred Reporting Items for Systematic Reviews and Meta-Analyses. 2021). A systematic quantitative literature review does not have formal guidelines, although information can be found (Pickering and Byrne 2014). Fitch et al. (2001) provide further advice for the RAM review. Questionnaire (Delphi and RAM) or question (NGT) development: if Delphi or RAM questionnaire development is not based on a literature review, then how this was done needs to be reported. For example, if preceding exploratory work such as interviews was used and findings were not published elsewhere, then the researchers’ process of moving from the raw data to the questionnaire needs to be outlined. Furthermore, items generated by experts should use their wording with minimal editing or changes by researchers (Hasson et al. 2000). There are two main approaches to validate the questionnaire once developed: to pilot test by asking for feedback on the questions, either using Delphi participants or a separate group of experts, or requesting feedback on the questions and their responses in the initial Delphi round. Ultimately, questions must be clearly articulated (Humphrey-Murto et al. 2017) and support definitive responses. The Consensus-Based Checklist for Reporting of Survey Studies (CROSS) provides guidance (Sharma et al. 2021) on various elements that may apply to the Delphi or RAM. The NGT relies on an unambiguous question that facilitates identification of a range of diverse items (Tuffrey-Wijne et al. 2007), and broader exploratory questions can elicit a breadth of items that lack depth (Harb et al. 2021). For the NGT, question(s) should assist the generation of ideas, avoid directing experts to a particular or narrow way of

Optimizing Quality: Rigor and Robustness Inherent flexibility of these methods allows adaptation to diverse settings and research questions. However, subsequent variations in definitions, methods, and reporting may impact on rigor (Humphrey-Murto et al. 2017) or validity of results (Bourrée et al. 2008). There is no clear evidence to support the validity and reliability of Delphi and NGT, particularly with multiple adaptations. Hasson and Keeney (2011) stated that further research was required to test the accuracy of the Delphi. Delbecq et al. (1975) referred to a study by Van de Ven that found the Delphi and NGT generated more ideas than interacting (unstructured) groups, suggesting that these methods may be more robust in this context probably because of their structure and focus. There is some evidence of validity and reliability for RAM (Shekelle et al. 1998b; Fitch et al. 2001; Shekelle et al. 1998a). As the Delphi, NGT, and RAM combine qualitative and quantitative methods, each respective method needs to be executed and reported in accord with its underlying epistemology. For qualitative research, this focuses on trustworthiness, which requires the establishment of

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thinking, or ask participants to consider multiple issues within one question. When the NGT is used for multiple groups, question(s) are best asked in a standardized way. Question ambiguity can be limited by initial interviews and/or testing questions with a small sample of target stakeholders (Ven and Delbecq 1972; Gallagher et al. 1993). Maintaining silence during independent idea generation is important to socially facilitate a higher volume of unique, quality suggestions important to all individuals than is typical with collective brainstorming (Delbecq and Van de Ven 1971; Van De Ven and Delbecq 1971; Van De Ven and Delbecq 1974). This is further supported by verbatim listing ideas in the round-robin phase and “hitch-hiking” on other’s ideas (Van De Ven and Delbecq 1974) with no further elaboration which can prematurely narrow focus when stronger personalities dominate (Van De Ven and Delbecq 1971; Delbecq and Van de Ven 1971). Delphi can involve various scales to assess expert opinion; some include a neutral point. While compelling experts to choose options aligned with either disagreement or agreement (De Villiers et al. 2005) could assist in creating or forcing consensus, the exclusion of a neutral point(s) could force experts who are genuinely ambivalent to misrepresent their opinion, thereby creating a source of bias. Indeed, Weijters et al. (2010) hypothesized that ambivalent respondents may become frustrated and respond toward disagreement; therefore, forcing genuinely ambivalent participants to choose whether they disagree or agree requires careful consideration. Feedback could be sought to determine why an expert chose a neutral point and help to determine if the neutral point should be removed in future rounds. If researchers are looking for a general representation of opinion, then retaining a neutral point is suggested (Hohmann et al. 2018). RAM requires the inclusion of neutral points. Ultimately, different scales are associated with different types of bias, and consensus is affected not only by the chosen consensus threshold but also by the rating scale used (Lange et al. 2020). General information on the choice of scale format is reported elsewhere (Weijters et al. 2010).

Expert identification and selection: as the credibility and validity of the outcomes depend on the choice of experts, careful selection of these participants is necessary. Van de Ven and Delbecq (1972) highlight the need for diverse target groups with relevant experience in order to effectively explore problems. Researchers are encouraged to consider less obvious stakeholders to prevent omission of important insights, for example, including family members as well as primary caregivers of people with chronic illness (Harb et al. 2021). Engaging experts who are willing and able to commit to the entire process (de Meyrick 2003) avoids bias related to the loss of stakeholders. Reaching consensus in Delphi and RAM requires experts to be open to other points of view and not rigidly hold on to their own opinion; therefore, experts should possess these attributes. The number of participants in reported studies varies considerably, and the selection of experts is complicated due to the use of both qualitative and quantitative methods. For qualitative research, a purposive sample is generally sought to reflect a range (breadth) of opinions, whereas for quantitative research, a representative sample is ideal. Basic thematic analysis in qualitative research requires at least six participants (Hennink et al. 2016) and could therefore be considered as an acceptable minimum sample size for this element, whereas if generalizability is desired, then larger sample sizes are needed. If samples are too large, this requires multiple groups in NGT or increases the timing between rounds in Delphi. Increasing the number of rounds is likely to result in greater attrition or lack of engagement, a key concern in studies requiring participant involvement over a long time period (Belton et al. 2019). A separate consideration for the NGT is that of power imbalance, for example, patients and doctors. Homogenous groups limit power imbalance and allow focus on specific expertise. However, heterogenous groups may create additional, higher-quality solutions although diversity can limit consensus (Murphy et al. 1998; Humphrey-Murto et al. 2017; Van De Ven and Delbecq 1971) and a skilled facilitator is essential. An explanation of how experts were

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selected, i.e., inclusion and exclusion criteria, and the numbers of participants representing each stakeholder group are required. Analysis and feedback: except for RAM (Fitch et al. 2001), there is no defined numerical determination of consensus, and the empirical values vary between and within methods. Subsequently, the consensus level should be determined a priori (Diamond et al. 2014; Jünger et al. 2017) and clearly articulated before commencing rounds or, in the case the NGT, selection of the voting methods, e.g., top 5 priorities or more. The results of each round should be reported, including which items were retained, removed, or modified. Modifications are based on expert feedback, the analysis of which needs to be transparent. Expert comments as to why they rated an item as they did, can either be fed back as raw data or summaries (Hsu and Sandford 2007; Hasson et al. 2000). To reduce expert workload, a summary rather than raw data is preferred; however, researcher bias may be a problem if the analysis process (e.g., thematic analysis) is not outlined. Barrios et al. (2021) reported that there were no agreed guidelines about how feedback should be provided in a Delphi. The detailed reporting of the qualitative analysis is also required when combining data from multiple NGT groups, and researchers are encouraged to consider qualitative insights or themes across the entire group (Ven and Delbecq 1972) not just the top ranked responses (McMillan et al. 2014). While previously, word counts may have precluded these data from publication, the option of supplementary material now provides a way to demonstrate the trustworthiness of the analysis. Involvement of multiple researchers can limit bias, and review by stakeholders can improve credibility (Ven and Delbecq 1972). When the NGT is being used to inform more targeted exploration of a problem, content analysis of priorities or categories that can be easily measured or observed is recommended (Ven and Delbecq 1972). Rounds or groups: additional rounds may result in disengagement leading to attrition or experts conforming with others to end the process. The risk of attrition increases with additional Delphi rounds, further highlighting the importance of

sample size and sample selection. When attrition occurs, this can either be accepted or replacement sought. When expert panels are multidisciplinary, if attrition affects one discipline more than another, there is an increased risk of bias due to this loss of representation. Sometimes additional rounds use intentionally larger sample sizes involving additional or different experts. Due to the potential change in sample size with each round, it is important to report the number of experts invited, the corresponding response rate, and the participant characteristics for each round (Boulkedid et al. 2011; Humphrey-Murto et al. 2017). The addition of more experts in the final round may be used to increase the generalizability of the findings (Green et al. 1999); ideally, responses in this final round should be consistent with the previous round, thereby validating the original panel composition. For the NGT, the number of nominal groups has varied from 1 to 41 groups of 2 to 50 participants (Harb et al. 2021; Delbecq and Van de Ven 1971; McMillan et al. 2014) guided by study scope and diversity of stakeholders required. Between six and ten people limits time burden, ensures balanced participation, and creates a manageable list of items for voting (Delbecq and Van de Ven 1971; Tuffrey-Wijne et al. 2007). Larger or heterogenous groups are harder to manage, requiring a skilled facilitator, yet can generate more ideas or be managed as multiple subgroups that share item lists with the larger group (Chen et al. 2018; Delbecq and Van de Ven 1971). Smaller groups can inflate weighting placed on individual items (TuffreyWijne et al. 2007). Conducting more than one group will ensure that important ideas are not missed but has implications with respect to cost, logistics, and analysis. Reporting: there is specific guidance for Delphi (Jünger et al. 2017) called the Conducting and Reporting of Delphi Studies (CREDES); however, none for RAM and the NGT. There are however other useful checklists such as COREQ (Tong et al. 2007) and CROSS (Sharma et al. 2021), and the EQUATOR network has a useful repository of guidelines (https://www.equator-network.org/ reporting-guidelines/). Some advice as to what should be considered when undertaking these

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methods can be found (Keeney et al. 2011; Hasson et al. 2000; Fitch et al. 2001; Delbecq et al. 1975; Humphrey-Murto et al. 2017; Diamond et al. 2014; Belton et al. 2019); sufficient and clear reporting of all related processes undertaken by researchers is essential to ensure methodological rigor (Humphrey-Murto et al. 2017). The validity and reliability of a study can be influenced by the application of the methods and the reporting; however, the underlying methodology may also have intrinsic strengths and weaknesses.

of interest for a Delphi instead of a brainstorming round, and a Delphi can be used after an NGT or a RAM to test the representativeness of the findings. While anonymity is generally considered as a strength because it removes power imbalances, dominant personalities, and the influence of others (Campbell and Cantrill 2001), it could also result in experts feeling isolated or less engaged with the process. Concerns with an expert no longer feeling committed and simply conforming to the group position have been raised (de Meyrick 2003), and how to improve the experiences of Delphi experts warrants further exploration (Keeney et al. 2011). Comparatively, the NGT generates ideas through a combination of social processes and independent structured tasks, creating greater participant satisfaction with the group process. While discussion can help clarify the meaning and context of items (Campbell and Cantrill 2001) and increase participant engagement, this is not an option within the Delphi. Time is a constraint for any method that requires an in-depth literature review or uses multiple rounds, and the cost of face-to-face meetings, especially those where experts are geographically dispersed, can be a barrier in undertaking the NGT and RAM. However, ensuring clarity of instructions and minimizing rounds, as well as the use of online meetings (e.g., video conferencing), could mitigate some concerns and increase accessibility to experts. The NGT is time efficient and quiet reflection and non-interactivity of the first phase “stimulates creative tension” to enhance the generation of issue-focused ideas and increase participant commitment to the process when working silently alongside others (Delbecq and Van de Ven 1971; Van De Ven and Delbecq 1974). The roundrobin process promotes balanced input, reducing the influence of stronger personalities or those with perceived higher status (Van De Ven and Delbecq 1974).

Strengths and Weaknesses A structured approach to gathering the opinions of experts is assumed to result in a more valid outcome than the opinions of a single person (Keeney et al. 2011). It is important to remember that the outcomes from these methods are based on opinion, which can be biased (Schulick 2014), may or may not be correct (Keeney et al. 2006), and is subject to change. Different panel compositions, the passing of time, new evidence, and changing health policy are likely to impact on sustained generalizability of findings. For example, appropriateness criteria can become obsolete when safer and more effective medicines become available. Experts may not be able to agree on some topics; this should not be viewed as a negative as it may indicate the need for further research in that area or reflect more comprehensive problem definition. This may be preferable to agreement on the most obvious choice or lowest common denominator (Fink et al. 1984) which acts to reinforce current beliefs and does not clarify contentious issues. Most strengths and weaknesses are related to the quality of the implementation of the methods chosen, as described above. There are some overarching considerations that warrant specific attention. Firstly, the flexibility of these methods may be a strength in that they can be adapted to suit the research question; however, these variations may not always be valid, especially if the reporting has been inadequate. These methods can be combined to mitigate some associated limitations, for example, to save time, an NGT can be used to identify topics

Tips Facilitator selection: Researcher(s) or facilitator (s) require working knowledge of the method and an ability to manage emergent challenges. A minimum of two facilitators is needed for the NGT and possibly less for other methods. In the

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NGT, one organizes the group and facilitates discussion; the other documents group ideas and the consolidation phase using a whiteboard or shared screen if online, shares voting documents or associated link, and presents scores back to the group. Recruiting and retaining experts: The term “expert” is all-encompassing and should reflect diversity in stakeholders with relevant connection to the issue under investigation. Representatives from professional organizations and patient groups can assist with the identification of suitable experts, add credibility to the findings and facilitate dissemination. The research question should guide selection of experts with consideration given to the less obvious stakeholder who has valuable input. Personally contacting experts beforehand to inform them about the study and confirm interest is recommended. Expert location: Technology allows all consensus methods to be run online. This allows expert recruitment from geographically diverse locations. The NGT is also conducted in person and works best if all people are in the room or, if online, are all signed into individual devices. If multiple people use one sign-in, it can be difficult to hear and/or observe visual cues. Preparation: Written or online consent can be obtained. For all methods, send out calendar invitations in advance with participant information and anything else relevant, e.g., the NGT question. Offer participants an opportunity to test online platforms with the researcher, particularly if it is unfamiliar. Resend a reminder immediately before the activity. For the NGT, use a standard template or file for verbatim recording of ideas and online voting form. Questions and questionnaires: Use simple, unambiguous language, with clear and concise instructions, with the date and method of return clearly stated. Test NGT questions and Delphi questionnaires prior to use. Ideally, restrict Delphi questionnaire length to 30 min, and use a format that accommodates the skills and preferences of experts. For example, online questionnaires may present a barrier to those with limited digital literacy. Timing: Limit research burden where possible. A single NGT can be conducted in 1.5 to 2 hours, and increasing this can impact on the number of

quality ideas generated. For Delphi, allow experts 2 weeks to respond to the questionnaire with nonresponders followed up by a personal phone call or email. Incentives, thank yous, and stressing the importance of continued participation may support ongoing engagement when several rounds are required. Running an NGT: The facilitator initially provides an overview of the project aim and introductions from individual experts; when conducted online, everyone is invited to turn on their cameras. Experts are reminded of the NGT question on paper for silent generation or copied into the chat of an online meeting. Paper copies can be two-sided with voting on the reverse side. If online, have the standard template open ready to be screen shared for the round-robin phase and a link for the online voting form. Allow sufficient time for discussion, and watch for dominant personalities as there is a risk of collapsing specific ideas into general categories which are challenging to vote for. Limit ranking of priorities to a manageable number, e.g., top 5 priorities, to limit participant burden. Feedback: Use of online voting forms accessible via different devices provides quick feedback to participants in the NGT. For Delphi rounds, qualitative summary and personalized quantitative results should be provided on a question-byquestion basis as well as identifying items that have already reached consensus. Although feedback summaries reduce the workload of the experts, researchers need to ensure that they do not unintentionally introduce bias through wording or omission of items. Managing workload: Researcher workload and time to completion are significantly influenced by the scope of the research question, size and depth of literature review(s) (if performed), number of participants, and number of rounds or NGT groups. Using online platforms, e.g., Qualtrics, LimeSurvey, and REDCap, removes the need to enter data, saving time and avoiding data entry errors. There are also programs for undertaking Delphi; however, cost and whether the platform meets the ethics privacy and data security requirements in your country are important considerations.

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▶ Evidence Produced While Using Qualitative Methodologies Including Research Trustworthiness ▶ Surveys in Health Services Research in Pharmacy

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Continuous Education for Pharmacists: Documenting Research Evidence Faten Amer 1,2 and Fouad Moghrabi 3,4,5 1 Doctoral School of Health Sciences, Faculty of Health Sciences, University of Pécs, Pécs, Hungary 2 School of Pharmacy, Faculty of Medicine and Health Sciences, An Najah National University, Nablus, Palestine 3 Department of Chemistry, Faculty of Science, Bethlehem University, Bethlehem, Palestine 4 Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Butler University, Indianapolis, IN, USA 5 School of Pharmacy, Faculty of Pharmacy, Al-Quds University, Jerusalem, Palestine

Abstract

In this chapter, we aim to document the research evidence of continuing education (CE) and continuing professional development (CPD) for pharmacists, including their impact, design and development process, delivery methods, gaps and challenges, pharmacists’ attitudes and motivators, utilization of technology, and accreditation. Despite the effectiveness of CE/CPD in improving pharmacists’ knowledge in the short term, their long-term effectiveness and the impact on pharmacists’ skills and practices, as well as patient outcomes, still need further investigation. Developing CPD programs consists of five stages through which combined competency-based, demand-side, and supply-side standards are utilized. The use of technology in distance learning for pharmacists proved to be useful

and effective. However, the use and accessibility of the internet and technology and time limitations are among the challenges facing the successful delivery of CE/CPD. Gaps and challenges while developing and implementing CE/CPD programs were found to cause many issues in pharmacy practice and need to be considered. Improving pharmacists’ attitudes and engaging them in the development process were found to motivate them and increase their participation. Low-middle income countries (LMICs) are not yet implementing CPD programs efficiently, with a lack of accreditation and validation. Therefore, health policy makers in LMICs must consider imposing a compulsory CPD model, assessing pharmacists’ needs, and improving the quality of CPD programs that aim to improve patient-related outcomes. Keywords

Continuing education · CPD · Development · Pharmacists · Knowledge

Introduction The role of pharmacists has evolved significantly from dispensing, providing clinical care, offering immunizations, and screening for illnesses to health coaching. The effectiveness of pharmacists’ roles was investigated and proven for clinical pharmacists (Hatoum et al. 1988; Bond et al. 1999, 2001; Kane et al. 2003; Lee et al. 2019; Ahmed et al. 2021). Recently, researchers have also focused on describing the various roles of community pharmacists (Milosavljevic et al. 2018; Buss et al. 2018; Newman et al. 2020; Strand et al. 2020). The Centers for Disease Control and Prevention (CDC) acknowledges that pharmacists may contribute to public health outcomes in a wide variety of settings, including testing, antimicrobial stewardship programs, vaccines, and many others (Strand et al. 2020). Three measured outcomes encompass the pharmacist’s influence. First, pharmacists recorded important contributions in improving clinical and therapeutic outcomes, such as controlling blood pressure,

Continuous Education for Pharmacists: Documenting Research Evidence

glycemic control, hyperlipidemia, weight, renal functions, cardiovascular and respiratory diseases, in addition to reducing the number of adverse drug reaction events, medication errors, drug–drug interactions, smoking rates, lifethreatening infections, mortality, and patient length of stay (Bond et al. 1999, 2001; Kane et al. 2003; Buss et al. 2018; Lee et al. 2019). Second, pharmacists have a role in enhancing the humanistic patient outcomes that affect the function and well-being of patients, such as patient knowledge, patient compliance, and healthrelated quality of life (Kane et al. 2003; Milosavljevic et al. 2018; Ahmed et al. 2021). Additionally, pharmacists had evidence of improving the economic and financial benefits, such as considerable medication cost savings and cost avoidance (Hatoum et al. 1988; Bond et al. 2001; Kane et al. 2003). To maintain the vital role of pharmacists in the health sector, however, pharmacists’ knowledge, skills, and attitudes must be improved. Numerous studies (Yoo et al. 2014; Shawahna et al. 2017; Deepalakshmi et al. 2019; Albarrak et al. 2021; Ceballos et al. 2021; Byerley et al. 2022) have shown knowledge gaps among pharmacists, which often result in pharmacists providing unnecessary recommendations or missing vital recommendations and counseling for patients. For example, in research conducted in Colombia (Byerley et al. 2022), only 2.7% of patients received tramadol usage advice and counseling. In addition, 99% were not informed of the adverse effects of tramadol, and none of the simulated female patients were advised of the warnings associated with tramadol usage during pregnancy or breastfeeding. Additionally, the inappropriate use of antibiotics and the unrestricted availability of antibiotics in community pharmacies, in addition to the unregulated over-the-counter dispensing of antibiotics (Dameh et al. 2010), have caused antimicrobial resistance to be a serious challenge in many countries. In contrast, due to the developed health-care systems in developed countries such as Sweden and the important role of pharmacists, their professional competence, public awareness in the field, and optimal and regulated use of antibiotics, antimicrobial resistance was found to be

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minimized and more controlled in developed countries (Ung et al. 2018) compared to lowmiddle income countries (LMICs), where antimicrobial resistance control and strategy development remain a serious threat (Ayukekbong et al. 2017). These gaps highlight the importance of continuing education (CE) for pharmacists to improve their knowledge, attitudes, and practices (KAP) and patient-related outcomes.

Definitions Continuing education (CE) and continuing professional development (CPD) are utilized worldwide to accomplish lifelong learning for pharmacists, to remain up-to-date on current practice and standards, and to guarantee that they are giving the highest quality patient care (Rouse 2004; Micallef and Kayyali 2019). CE and traditional continuing pharmacy education (CPE) have existed longer than CPD on a global scale and are the backbone of post-qualification education in many countries. While both guarantee that learning is completed and documented, CE focuses on attendance at education or training events and documenting the number of hours of education gained. While CE is an important aspect of CPD, it does not guarantee improved professional practice or patient outcomes on its own but is a cyclical process driven to meet professional-specific requirements based on the work and area of expertise, plan learning, take action by completing learning, and then assess the influence of these activities on their practice (FIP 2002; Micallef et al. 2020). Lifelong learning is a learning activity undertaken throughout life (PSI 2010). See Table 1 and Fig. 1.

The Impact of CE/CPD Implementations According to the Pharmaceutical Society of Ireland (PSI), the ultimate goal of any CPD system for health professionals is to improve patient safety (PSI 2010). However, most available research has evaluated the effect of adopting CE, CPE, and CPD on enhancing the KAP of

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Continuous Education for Pharmacists: Documenting Research Evidence, Table 1 Definitions (FIP 2002; PSI 2010; Micallef et al. 2020) Continuing education (CE)

Continuing professional development (CPD)

Lifelong learning

Competency-based education (CBE)

A structured educational procedure meant to assist the ongoing growth of pharmacists in order to maintain and improve their professional competence. Continuing education should encourage problem-solving and critical thinking and be relevant to pharmacy practice A self-directed, continuous, systematic, and outcomefocused approach to professional growth and learning. CPD covers but is not limited to CE All activities conducted throughout one’s life with the objective of enhancing knowledge, skills, and/or competence from a personal, civic, social, and/or occupational perspective Education which focuses on objectives and competencies of graduates and encourages learner-centeredness via constructive alignment in the design of curriculum and evaluations that support learning and stimulate prospective learning after completion of the training program instead of focusing on time-based training accomplishments

pharmacists. There is limited research evaluating the impact on patient safety as a final measured outcome. A study in India (Deepalakshmi et al. 2019) aimed to educate and train community pharmacists in pharmacovigilance services through CPE and to analyze the KAP of adverse drug reaction monitoring and reporting. The CPE program was shown to have a beneficial effect on the enhancement of adverse drug reactionrelated KAP. Another Indian study indicated that the CPE program significantly improved pharmacists’ KAP for three diseases: diabetes mellitus, hypertension, and peptic ulcer disease

(Durai et al. 2016). Additionally, a study in California (Cheung et al. 2021) created a CPE curriculum to promote healthy pregnancies. The innovative continuing pharmacy education program substantially boosted pharmacists’ and student pharmacists’ understanding of optimal birth spacing, prevention and treatment of preeclampsia, and prevention of premature delivery. Participants also expressed improved comfort with identifying patients in need of particular pharmaceutical interventions and counseling them. Researchers have paid particular attention to evaluating the function of CE in enhancing pharmacists’ KAP to increase the usage of medications containing fiscalized ingredients. In a study conducted by Mauricio et al. (2022), pharmacists received a CE program consisting of a web-based social networking site, a virtual course, a dispensing information system, and face-to-face training. The intervention group’s self-efficacy skills and attitudes increased by 88%. However, the dispensing criteria examined through simulated patient methodology revealed no statistically significant variations in the skills and attitudes of the pharmacists across groups in actual practice. This may be due to the difficulty of demonstrating the impact of CPD on pharmacist practice (Kostrzewski et al. 2009). In contrast, a study (Trewet and Fjortoft 2013) showed that employing a CPD approach to create learning activities led to improvements in practice in 60% of the intervention group. Additionally, in another study (McConnell et al. 2012), more than 40% of intervention participants claimed that CPD significantly altered their learning practices compared to traditional CPE. However, this change was determined by the pharmacists’ self-evaluation. This finding matches those of a study (McConnell et al. 2010), which revealed that compared to pharmacists who participated in traditional CPE, those who engaged in CPD were more likely to say that their perspectives of various aspects of pharmacy practice had improved as a consequence of their education activities. Electronic-learning (E-learning) effectiveness was also evaluated by researchers. Two studies (Salter et al. 2014; Nesterowicz et al. 2014) demonstrated that e-learning in pharmacy education

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Continouing

CPD focuses on performing tasks and continuous improvement via reflection and lifelong learning

Knowledge

Do

& Skills

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Demonstrate

CE focuses purely on knowledge

Acquiring Knowledge & Skills

Apply Knowledge

Gather Knowledge Continuous Education for Pharmacists: Documenting Research Evidence, Fig. 1 The difference between continuing education (CE) and continuing

professional development (CPD). (Source: Adapted from PSI (2010)). Note: CPD, continuing professional development; CE, continuing education

boosted pharmacists’ knowledge effectively over the short term and was highly and widely accepted by pharmacists from a variety of working situations. However, the long-term effectiveness still needs further investigation (Salter et al. 2014). Additionally, despite the impact on pharmacists’ knowledge, there is minimal evidence that e-learning enhances skills or professional practice effectively (Salter et al. 2014). Moreover, it is recommended that the advantages of e-learning at the patient and organizational levels still require translational research (Salter et al. 2014). This finding matches a review (O’Hare and Girvin 2018) that concluded that although some studies found e-learning effective in improving pharmacist practices, there was no evidence for the role of e-learning effectiveness in benefiting patients or improving care.

CPD framework are an objective standards framework that explicitly states the CPD criteria, an accreditation framework that verifies the quality of CPD activity, and an assessment framework that verifies that professionals are adhering to the CPD requirements (see Fig. 2). According to the International Pharmaceutical Federation (FIP 2002) and the Pharmaceutical Society of Ireland (PSI 2010), the CPD cycle model generally encompasses five stages: (1) self-appraisal to identify the learning need, (2) personal plan on how to achieve improvement, (3) take action by engaging in appropriate CPD activities, (4) document action to record achievements, and (5) evaluating the outcomes (see Fig. 3). According to the Accreditation Council for Pharmacy Education (ACPE; Accreditation Council for Pharmacy Education 2015), the Pharmaceutical Society of New Zealand (Pharmaceutical Society of New Zealand), and the South African Pharmacy Council (South African Pharmacy Council 2016), the CPD cycle model constitutes four stages: (1) reflection on practice, which represents what the pharmacist needs to know or develop; (2) planning how this can be achieved; (3) implementation

The Design and Development of CPD Pharmacy professionals need to establish CPD systems to reach their professional development goals (Owen et al. 2020). According to the PSI (PSI 2010), the key components of a successful

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Continuous Education for Pharmacists: Documenting Research Evidence, Fig. 2 The key components of a successful CPD framework. (Source: Adapted from PSI (2010)). Note: CPD, continuing professional development

Continuous Education for Pharmacists: Documenting Research Evidence, Fig. 3 The five stages of a CPD cycle model. (Source: Adapted from PSI (2010)). Note: CPD, continuing professional development

1Self-appraisal

5-

2-

Evaluation

Personal plan

4Documentation

of action and activities; and (4) evaluation of what had been learned and how it is benefiting the practice. In the first step of self-appraisal and reflection on practice, the participants’ needs shall be decided to determine the pharmacological topics (Awad and Bridgeman 2014). Based on this study,

3Action

the top CE areas of interest for pharmacists and pharmacy technicians were in this order: sepsis and targeted antimicrobial therapy, common infections in critically ill patients, new drugs of abuse, institutional multidrug resistance, and fluid and electrolyte replacement therapy. Another example of this step is a study in Lebanon (Sacre

Continuous Education for Pharmacists: Documenting Research Evidence

et al. 2019), which found that the majority of pharmacists preferred topics in this order: new medications and guidelines for treating diseases, followed by medication therapy management and new drug dosage forms, preventive medicine such as nutrition and physical activity, soft skills and management, and technology, new quality and accreditation standards, and laws and regulations. This step was also referred to as competencebased education by an Australian study (Benson et al. 2020) licensed by the FIP. Competencebased education is becoming the main strategy for developing evidence-based curricula in the health sciences, and it is becoming more popular in pharmacy. PSI (PSI 2010) considered it crucial to combine competency-based standards with demand-side standards and supply-side standards to arrive at effective CPD system standards (see Fig. 4). In the same vein, researchers and health policy makers offered evidence-based suggestions for the development and implementation phases of CE:

3.

4.

5.

6. 1. To encourage engagement with other professionals and peers in the development process (PSI 2010). 2. To confirm that the courses are built based on patient needs (PSI 2010) and professional needs (Pharmaceutical Society of New Zealand;

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SUPPLY SIDE STANDARDS

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FIP 2002; PSI 2010; Accreditation Council for Pharmacy Education 2015; South African Pharmacy Council 2016; Sacre et al. 2019; Benson et al. 2020). Additionally, they are referenced against the best practices (PSI 2010). To confirm that the courses are self-directed with motivational and rewarding systems (PSI 2010). To confirm that the courses are accessible (Benson et al. 2020) and user-friendly (PSI 2010; Benson et al. 2020) by using online assignments and short in-person sessions that require the building of CPD infrastructure (Benson et al. 2020; Lorenzoni et al. 2021). However, difficulties in commuting and technology use itself can sometimes act as barriers (Sacre et al. 2019; Lorenzoni et al. 2021). The CPD system can either be voluntary, with activity undertaken as and when the professional demands it, or mandatory, where compliance is required by law or registering bodies (PSI 2010). A focus on the role of global health in overcoming the obstacles impeding formal program development (34) begins with tying local health to global health to promote a global perspective, expanding upon existing partnerships to provide international global health experiences, using technology and

An effective CPD system should ensure standards across all 3 spheres

DEMAND SIDE STANDARDS

Continuous Education for Pharmacists: Documenting Research Evidence, Fig. 4 Approaches to the settings of standards in CPD. (Source: Adapted from PSI (2010)). Note: CPD, continuing professional development; competency-based standards, standards related to the competencies maintained or developed as a result of

engaging in the CPD activity; supply-side standards, standards related to the quality and relevance of the CPD activities being delivered; demand-side standards, standards related to the engagement of the professionals in the CPD program

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simulation for virtual global health engagement, and emphasizing implementation science concepts to connect and translate local health interventions to a global scale. Despite the existence of frameworks for a global model, there is little evidence of progress, according to researchers (Micallef and Kayyali 2020), who urged pharmacy authorities worldwide to develop global standards with clear, measurable outcomes to exchange best practices and deliver consistent patient care globally. This also includes the international recognition of the certificates (PSI 2010). Additionally, the certificate must be designed to address the need for modification and ongoing enhancement (Benson et al. 2020). Benson et al. provided a good example of an evidence-based approach to postgraduate curriculum development steps (Benson et al. 2020). First, the authors began with the need analysis phase by conducting a comprehensive literature analysis for general practitioner pharmacist roles and a review of the National Competency Standards Framework for Pharmacists in Australia. Second, the mapping of these roles yielded seven subcategories and 48 unique activities. Third, an expert panel of Australian pharmacy practice educators was consulted via a Delphi validation process, a technique used to establish an evidence-based consensus by providing a systematic method for collecting and aggregating informed judgments from a group of experts over subsequent cycles of input. Fourth, the expert panel’s identification of the training requirements of GP pharmacists was utilized to shape both the program’s learning outcomes and learning objective. Fifth, learning activities have been designed to meet the learning objectives, with the requirements of the program’s participants. Sixth, the delivery of the learning activities was made flexible. Seventh, cases of problem-based learning with realistic real-world settings were offered. In the last step, learners were evaluated through online quizzes, participation in online discussion boards, and the preparation of an advanced practice portfolio, in which they submitted proof from their clinical

placement demonstrating their competency in performing the specified tasks. The last phase is crucial since standards can only operate effectively if they are linked to a system of monitoring and evaluation (PSI 2010). A study in Brazil (Lorenzoni et al. 2021) alluded to the last phase of assessment for the e-learning CE through the adaptation of sociotechnical systems, which provides a broader approach of analysis. Sociotechnical systems were built on a framework consisting of seven elements: goals, people, processes, culture, technology, infrastructure, and scenario. This research, for example, indicated that technology, culture, infrastructure, and scenario factors had the most impact on the processes defining pharmacists’ daily utilization of e-learning in their CE activities. Evaluation of the activities and the achievement of impact and goals, as well as the assessment of learning and feedback, are parts of the accreditation of CE, which will be discussed thoroughly in the subsequent sections.

The Learning Delivery Methods of CE/CPD Learning delivery is the method of passing on knowledge and educational materials to participants (Nesterowicz et al. 2014). Learning delivery methods in pharmacy may take one of the following forms. Face-to-Face (In-Person) Activities Face-to-face learning is the traditional way of learning in which the participants gather with the instructor/teacher in the classroom for a normal teaching session. Face-to-face activities may involve lectures, training and discussion sessions, coaching, and a variety of hands-on activities. Face-to-face learning is synchronous, meaning that participants and the instructor need to attend the training session in real time, which is advantageous in terms of providing real-time feedback to participants (Pharmaceutical Society of New Zealand). On the other hand, it limits the audience by time, as some individuals might not always be available for attending at the same time.

Continuous Education for Pharmacists: Documenting Research Evidence

Virtual and Online Activities Virtual activities have recently become a common alternative to in-person sessions, especially during the COVID-19 pandemic. In virtual activities, participants and instructors meet virtually using online platforms with a variety of useful features and simulation models (Mak et al. 2021). Virtual live conferences and events have also become a crucial method of CE that provides participants with an easy, rapid, convenient, and flexible way to join a variety of CE and CPD activities anywhere in the world. Virtual activities and conferences are also considered more affordable and less resource consuming than in-person activities that require more planning, resources, and time (PSI 2010). Despite the increased use of technology in virtual activities and online learning, face-to-face learning is still more acceptable among pharmacists (Micallef and Kayyali 2019). Face-to-face and virtual activities are considered synchronous activities in which participants and instructors are limited by time, which is beneficial in providing the audience with networking opportunities, real-time feedback, discussions, and communications (Pharmaceutical Society of New Zealand). On the other hand, it may be a drawback, as participants might not always be available to attend the training session at the same time. E-Learning Activities E-learning activities are asynchronous approaches in which participants use electronic and digital devices such as personal computers, tablets, and smartphones to access online content at different times and locations. Online content may be in the form of tutorial prerecorded videos, e-books, interactive online courses, podcasts, webinars, and many others (Martínez-Torres et al. 2011; Salter et al. 2014). While asynchronous activities may not provide participants with the same experience and communications as real-time synchronous activities, they are considered beneficial and useful, as they are self-paced. E-learning, as well as new digital technologies and expanding mobile apps, are becoming important sources of knowledge and information for pharmacists in particular and health-care providers in general, as these apps

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are helpful in keeping them updated with recent information in their field. Phones and laptops were found to be the most commonly used devices for accessing e-learning platforms. Google and YouTube were found to be highly used for CE and CPD, and apps such as PubMed, LexiComp, MedScape, and Epocrates were among the commonly used apps by pharmacists and health-care providers. Nevertheless, connectivity and internet access were found to be among the factors that might affect the overall e-learning experience (either positively or negatively), which needs to be considered (Curran et al. 2019). A review found that e-learning was an effective type of CE, had the ability to improve pharmacists’ knowledge and enhance their practice, and is considered an acceptable type of pharmacy CE in a variety of working environments, including clinical, academic, and industrial areas (Salter et al. 2014). Over 80% of practicing pharmacists in Poland have considered e-learning platforms for their continuing education in pharmacy (Nesterowicz et al. 2014). E-learning in pharmacy education has not only been used as continuing education for pharmacists but has also been incorporated into pharmacy colleges at many universities using many technologies and internet-based systems. This was found to be an effective, successful, and promising method for enhancing the knowledge of pharmacy students as well (Salter et al. 2014). Blended Activities Blended activities use a mixed approach of face-to face, virtual, and e-learning methods to deliver CE/CPD sessions (Micallef and Kayyali 2019; Manzini et al. 2020). They may use classrooms for in-person gathering, as well online access to online material. This allows the participants to study at their own pace while at the same time having real-time meetings and discussions with each other and their instructors (Manzini et al. 2020). According to the ACPE, the CE/CPD categories and activities may include academic or professional activities in the form of structured and unstructured learning, which can improve the current understanding of participants, enhance their

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competence in the profession, and provide them with new knowledge (Accreditation Council for Pharmacy Education 2015). Such academic and professional activities may include attending conferences, workshops, or even attending a postgraduate education. Completing certification courses, searching the literature for new research and studies, joining journal clubs, and related community services are also great examples of CE/CPD activities (Accreditation Council for Pharmacy Education 2015).

The Utilization of Technology in CE/CPD The use of technology in distance learning to develop CE/CPD activities for pharmacists has been proven to be useful and effective, as it allows pharmacists to expand their access to educational materials and stay involved in CE/CPD programs regardless of the differences in geographical regions (Lorenzoni et al. 2021). Several studies have been carried out to evaluate the effectiveness of e-learning in pharmacy, and a consensus was that e-learning is well accepted among pharmacists and was found to increase their knowledge (Salter et al. 2014; Nesterowicz et al. 2014). A study carried out in the South University School of Pharmacy reported that a mixed approach of in-person and online learning significantly improved students’ knowledge of cardiology pharmacotherapy over the long term (Crouch 2009). Similarly, the implementation of a multicomponent program in CE/CPD programs has been considered by researchers at the University of Toronto, Canada, which incorporated online sessions and in-person activities to enhance the ability of pharmacists to provide patient care in pharmacogenomics and was found to be successful and promising (Crown et al. 2020). The use of audience response systems, which are electronic tools or mobile applications used to facilitate communication between the audience and the presenters and improve CE, was found to be acceptable among pharmacists participating in CE activities and may be considered to improve

CE lectures for remote participants (Koval et al. 2020). Furthermore, using special internet platforms, a group of researchers were able to create an online program that has been used to train pharmacists as well as pharmacy students in providing medication therapy management to diabetic patients by conducting an online meeting between the pharmacists and a virtual diabetic patient, which was found to be successful and helped improve the pharmacists’ knowledge and skills in providing medication management to these patients (Battaglia et al. 2012). Another internetbased program to train pharmacists and improve their knowledge in making appropriate clinical interventions in chronic kidney disease patients was developed, assessed, and found to increase pharmacists’ knowledge in the field over the short term (Legris et al. 2011). Nevertheless, implementing this newly acquired knowledge in long-term practice must be evaluated (Legris et al. 2011; Nesterowicz et al. 2014; Crown et al. 2020).

The Evaluation, Gaps, and Challenges of CE/CPD A study that recorded CE challenges among pharmacists (Yoo et al. 2014) found that there was an academic gap between 4- and 6-year pharmacy programs in South Korea. This has caused many issues in pharmacy practice. Four-year pharmacy graduates were not as competitive as 6-year graduates, so they needed CE programs in clinical pharmacy to fill this academic gap. In Japan and the United States, experiential education and transitional CE programs for 4-year pharmacy graduates have improved patient care and pharmacists’ clinical understanding. To close the academic gap among the 4-year pharmacy degrees in other countries, translation and experiential programs should be explored under the support of governments (Yoo et al. 2014). The majority of pharmacists highlighted logistical hurdles as barriers to putting new information into practice. The implementation of CPD activities for pharmacists by their company/management, as well as time limitations, were reported

Continuous Education for Pharmacists: Documenting Research Evidence

to be the highest top obstacles for the implementation of CPD activities (Cheung et al. 2021). Another barrier encountered by North Carolina (NC) pharmacists who attended CPD trainings is the recorded learning and evidence-based data that demonstrate the usefulness of the different CE activities prior to submission to certification authorities (Tofade et al. 2013). This might be referred to as the CE model in NC, which does not require high requirements for the documentation of CE activities in the state. The Australian approach, on the other hand, was found to be effective, which resulted in advances in pharmacy practice and patient care, and a similar model was suggested to be applied in the NC (Tofade et al. 2013). Another obstacle is the time limitation. In Kuwait, a recent study found that more than half of the participating pharmacists do not have enough time to attend CE activities, in addition to the poor availability of scientific resources and inconsistency in scientific conference organizations (Aldosari et al. 2020). In Lebanon, despite the positive attitude and motivation among pharmacists, more than 75% of pharmacists reported that the lack of time, as well as job restrictions, were among the major obstacles that prevented them from attending CE activities (Saade et al. 2018). Another study carried out among Malaysian pharmacists also reported that job restrictions, in addition to how easily CPD programs are accessed (e.g., travel, lack of time, and participation costs), were among the top barriers to pharmacists’ participation in CPD activities (Aziz 2013). Additionally, since self-learning through the internet and mobile technologies has developed dramatically over the past decade, limitations in internet and technology access have also been identified as obstacles to pharmacy CE/CPD (Donyai et al. 2011; Curran et al. 2019). A recent study (Curran et al. 2019) revealed that restrictive policies of some organizations, such as limiting pharmacists from internet access on mobile and electronic devices, have been shown to reduce their ability to learn and improve their knowledge and experience, which could affect the overall health services provided to patients.

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Despite pharmacists’ favorable attitude toward CE activities in Ethiopia, they are not sufficiently aware of CE/CPD concepts owing to the limited opportunities available in the country. Therefore, additional help to adequately address the notion of CPD in Ethiopia is required (Gelayee et al. 2018). Furthermore, insufficient financial support from employers and leaders was shown to be a barrier to completing CPD activities (Donyai et al. 2011).

Pharmacists’ Attitudes and Motivators Toward CE/CPD It is essential to study and improve pharmacists’ attitudes regarding CE/CPD programs since their attitudes may play a crucial role in increasing their participation in these programs. Numerous studies have shown that pharmacists in general have a favorable view of CE/CPD programs since they assist them in developing their personal knowledge of the area while also meeting their career and professional requirements (Alhaqan et al. 2021). On the other hand, another study found that pharmacists were frustrated with the CPD procedure and its stringent paperwork requirements (Dopp et al. 2010). CPD activities were more accepted among hospital pharmacists than community pharmacists because hospital pharmacies had a more supportive, sociable, and friendly working atmosphere than community pharmacies (Alhaqan et al. 2021). Therefore, addressing the attitudes and beliefs of pharmacists and overcoming the various barriers and obstacles in CE is an important key for the success of CPD (Donyai et al. 2011). In a study carried out in Lebanon, obstacles such as lack of interest in CE programs, work obligations, and lack of time were reported (Sacre et al. 2019). Consequently, strategies and plans need to be considered to overcome these barriers in the future and achieve the desired outcome of CE/CPD activities. A study (Cheung et al. 2021) found that following the CE, pharmacists reported increased comfort and motivation to identify and advise patients in need of interventions supporting healthy pregnancies. Perhaps the best study to

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explain pharmacists’ motivation for CE was based on the self-determination theory theoretical framework (Tjin A Tsoi et al. 2016). This theory summarizes motivation into three types: autonomous motivation (AM), which represents motivation from an internal locus of causality; controlled motivation (CM), which originates from an external locus of causality; and relative autonomous motivation (RAM), which measures AM in an individual after correcting for CM. This research revealed a favorable correlation between RAM and involvement in CE but revealed that the existing CE system is likely not favorable to AM stimulation.

CPD in Developed Countries and LMICs The availability of CPD programs in LMICs has been explored, and compared to developed countries, it was found that many LMICs have poor or even no CPD protocols and programs, such as Pakistan, Trinidad, and Tobago (Shamim et al. 2021). In Palestine, pharmacists are still not required to undergo CPD programs to renew their license in the country as well (Hamouda et al. 2015). On the other hand, despite having poor standards, CPD is considered obligatory for pharmacists in Ghana, with a variety of CPD activities performed in areas such as drug management, medication safety, and treatment protocols and procedures (Shamim et al. 2021). In Lebanon, participating in CE activities has become compulsory in 2014 as well (Sacre et al. 2019), and a study to validate the motivation scales as well as value toward the different CE activities in Lebanon has been carried out, where motivation was found to be highly correlated with participation in CE activities (Tawil et al. 2020). In contrast, developed countries such as Canada, Australia, and New Zealand have mandatory CPD programs that are required to be taken by practicing pharmacists to be considered for license renewal (FIP 2002). In the United Kingdom, pharmacists need to complete at least nine CPD records every year as well (Donyai et al. 2011). Although CE has become compulsory in the United States for license renewal, the system

has not yet upgraded to CPD. Japan has an optional CPD program for pharmacists in practice but still has some challenges in implementing the program in the country (FIP 2002). To conclude, LMICs are not yet implementing CPD programs efficiently, with a lack of accreditation and validation of such programs, which needs further improvement and development to ensure high standards of CE (Rouse 2004). Health policy makers in LMICs also need to create a compulsory CPD model for pharmacists to improve their competency in the field (Chan et al. 2021; Shamim et al. 2021). However, they should be aware that pharmacists in LMICs could be more resistant to change than those in developing countries (Sacre et al. 2019). This highlights the importance of pharmacist engagement and motivation in this process in LMICs. Moreover, the impact of these CPD programs on improving patient care as well as their quality should be thoroughly evaluated with the lowest possible risk of bias (Ahmed et al. 2021).

Accreditation of CE/CPD Programs As the various CE and CPD programs continued to improve with time, there has been a need for accreditation standards and criteria to guarantee the development of competent, efficient, and high-quality programs. ACPE, which has been established for more than four decades, expanded its role to include the accreditation of CE programs and providers. ACPE has the responsibility of setting the standards and requirements that are needed to accredit CE programs as well as CE providers, who, in turn, plan and carry out CE and CPD programs (Accreditation Council for Pharmacy Education 2015). When needed, ACPE may provide accredited CE providers with feedback and reviews on their conducted CPD activities (Travlos et al. 2017). ACPE requires CE providers to submit complete documentation about their CPD program, including (but not limited to) the type of delivery of the program, its evidencebased content, expected learning outcomes, biographies, and other supporting resources (Kheir

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and Wilbur 2018). In a recent study carried out in the Portuguese Pharmaceutical Society (Batista et al. 2022), a significant increase in the submitted and accredited CPD activities was reported, indicating an increase in interest in requesting accreditation for CPD activities.

in LMICs should be thoroughly evaluated with the lowest possible risk of bias.

Conclusion

AM CBE CDC

To maintain the effectiveness of pharmacist roles, the knowledge, skills, and attitudes (KAP) must be improved. CE or CPD are utilized worldwide for this purpose. CPD proved to be more effective than traditional CE. Despite CE/CPD effectiveness in improving pharmacists’ KAP in the short term, a long-term effectiveness assessment and the impact on improving pharmacists’ skills and practices, as well as on patient-related outcomes, still need further investigation. Developing a CPD consists of five stages, starting with need appraisal and ending with evaluation. It is crucial to combine competency-based standards, demand-side standards, with supply-side standards to arrive at effective CPD system standards. Learning delivery methods in pharmacies may take different forms, such as face-to-face or distance learning. The use of technology in distance learning to develop CE activities for pharmacists proved to be useful and effective. However, the use and accessibility of the internet and technology as well as time limitations are among the challenges facing the successful delivery of CE/CPD. Gaps and challenges while developing and implementing CE/CPD programs were found to cause many issues in pharmacy practice and need to be considered. Improving pharmacists’ attitudes toward CE/CPD and engaging them in this process will motivate them and increase their participation. LMICs are not yet implementing CPD programs efficiently, with a lack of accreditation and validation of such programs. This draws the need for further improvement and development to ensure high standards of CE. A compulsory CPD model in LMICs may improve pharmacists’ competency, which must be considered by health policy makers. Finally, the impact of CPD programs on improving patient care as well as their quality

Glossary ACPE

CE CM CPD CPE E-learning FIP KAP LMICs NC PSI RAM

Accreditation Council for Pharmacy Education autonomous motivation Competency-based education Centers for Disease Control and Prevention continuing education controlled motivation continuing professional development continuing pharmacy education Electronic learning International Pharmaceutical Federation knowledge, attitudes, and practices Low-middle income countries North Carolina Pharmaceutical Society of Ireland relative autonomous motivation

Cross-References ▶ Behavioral Medicine/Behavioral Science in Pharmacy ▶ Health Education, Promotion, and Prevention in LMICs ▶ Healthcare Education and Training of Health Personnel ▶ Patient Safety from a Pharmacy Perspective ▶ Pharmaceutical Health Services Administration, Planning, Management, and Leadership: Lessons Learned for LMICs

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Continuous Education for Pharmacists: Documenting Research Evidence

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COVID-19 and Medicines Access N. Kheir1,2, Amy Hai Yan Chan2, S. Scahill2 and Kebede Beyene2 1 College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates 2 School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand

Abstract

The outbreak of COVID-19 (novel coronavirus) pandemic has had a huge impact on countless aspects in people’s lives since the pandemic started late 2019. The global lockdowns and the restrictions on travel movement had led to curtailing of imports of general and essential medicines and medical supplies with many pharmaceutical manufacturing firms shifting their focus to the production of medicines and medical equipment targeted at the fight against COVID-19, rather than general medicines production. This has left a huge gap for pharmaceutical imports that aim to treat other chronic diseases. As a result, access to medicines for both acute and chronic health conditions has been reduced, and this has led to an increased rate of health complications because of suboptimal management. The COVID-19 pandemic has highlighted the difficulties inherent in not having a reliable sustainable global medicines-supply chain that could reduce the negative impact of similar

COVID-19 and Medicines Access

crises on the lives of patients who rely of uninterrupted supplies of life-saving pharmaceuticals. This chapter provides an overview of the medicines issues facing the population from the COVID-19 pandemic, discusses the impact of the COVID-19 pandemic on access to vaccines, pharmaceuticals for acute and chronic conditions, and the role of pharmacists in the pandemic, and provides a discussion of the challenges, opportunities, and recommendations facing patients, health professionals, and policymakers now and in the future, as a result of the pandemic. Keywords

COVID-19 · Medicine access · Pandemic

Introduction Globally, as of July 05, 2021, there have been 183,560,151 confirmed cases of COVID-19 (novel coronavirus), including 3,978,581 deaths (WHO 2021). However, the actual death toll from COVID-19 is likely to be much higher than the number of confirmed deaths, due to underreporting. The pandemic forced a global “new normal” that has affected people’s everyday lives in numerous ways. The spread of the virus left economies and businesses crumbling, while governments frantically try to control the spread of the virus. One of the key areas impacted by the pandemic is access to medicines. Medicines are the most common therapeutic intervention with the greatest health impact of any health intervention. While medicines access is taken for granted in high-income countries, one-third of the world’s population have no regular access to essential medicines, particularly in Africa and Asia (Hogerzeil and Mirza 2011). Essential medicines availability is a major challenge for low- and middle-income countries (LMICs) and often also in developed nations due to a variety of factors including lack of dedicated resources to ensure the manufacture of medicines and other health supplies, poor healthcare infrastructure, and lack of workforce capacity. Narrowing this gap in medicine access between high- and low-income

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countries has been a global public health priority (Chan et al. 2020), along with achieving universal health coverage, a target in the United Nations’ Sustainable Development Goals (Kohler and Mackey 2020). The uncertainty brought about by COVID-19 has highlighted the difficulties inherent in having a reliable global drug-supply chain and has caused an increase in global demand for many essential and life-saving medications. The COVID-19 virus impacted the acquisition of raw material and caused pharmaceutical manufacturing shutdowns around the world. In response to the pandemic, over 30 pharmaceutical factories in China that manufacture active pharmaceutical ingredients for drug companies in the United States (US) were closed (Moore et al. 2020). Consequently, a domino effect took place and manufacturers in other parts of the world were forced to depend on existing stock or finding alternative sources of supply (Bookwalter 2021). India, the world’s largest producer of generic drugs, began to experience delays in receiving active ingredients from China and could not keep up with global demand (Moore et al. 2020). To illustrate the extent of shortages in medicine supplies due to the COVID-19 crisis, it had been reported that in a six-month period in 2020, drug shortages have been equivalent to 87% of the shortages reported for the entire year in 2019 (Sen-Crowe et al. 2020). The pandemic also exposed weaknesses in the drug supply chain, including poor communication between the different public and private health institutions about the status of drug shortages, overreliance on drug imports, and failure of local drug manufacturers to meet the local need for essential drugs and medical supplies (Alruthia et al. 2018). The direct impact of these shortages on the quality of care and patient outcomes included an increase in mortality, nonadherence to regular medicines, and hospitalizations (Phuong et al. 2019). In this book chapter, we will discuss the status and efforts taken globally to manufacture vaccines to counteract the pandemic, and we will explore issues related to affordability, distribution, societal expectations, and fears that have direct impact on access to vaccines. Ethical and equitable

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aspects of vaccine access and other factors that affect access to the available vaccines will also be discussed. Furthermore, impact of COVID-19 on access to medications for acute and long-term health conditions will be explored. Finally, the challenges and opportunities brought about by COVID-19 will be discussed, emphasizing impact on healthcare and pharmaceutical services, and a list of recommendations for key stakeholders will be provided.

Background on the COVID-19 Pandemic Since the identification of coronavirus in 1960 as the cause of the common cold, seven types of coronaviruses have been identified as capable of causing infection in humans, and three of them are highly pathogenic. These are the Severe Acute Respiratory Syndrome Coronavirus-1 (SARSCOV-1), the Middle East Respiratory Syndrome (MERS), and Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2 – i.e., the virus causing COVID-19). COVID-19, characterized by respiratory distress, was first reported in December 2019, in Wuhan, China (Belete 2021). The COVID-19 pandemic has driven many countries to join forces and come up with vaccines with the intention of stopping or slowing the spread of the infection and reducing global mortality. Due to its high pathogenicity, COVID-19 was able to rapidly spread across the world, and this placed a significant burden on healthcare services and reduced healthcare systems ability to respond the infection (Haq et al. 2020). However, the initial shock was followed by frantic efforts that resulted in an unprecedented global collaboration geared towards the very rapid creation of effective and safe vaccines. These efforts were crucial since the virus causing COVID-19 went through continuous mutations that produce different variants of itself, and this challenged the scientific community to continue their research on manufacturing, improving, and evaluating the safety and efficacy of the vaccine. The urgent need to develop COVID-19 vaccines and to scale up supply has resulted in significant co-operation among private,

COVID-19 and Medicines Access

governmental, and nonprofit organizations all invested in the building and expansion of production facilities and establishing contract manufacturing and distribution networks to enable the rapid roll-out of COVID-19 vaccines. COVAX (which will be mentioned below) was one such initiative (WHO 2020d). As a result, by July 2020, the WHO had documented over 160 different candidates of potential COVID-19 vaccines, and by February 2021, at least seven vaccines had been rolled out across several countries. These include those developed by AstraZeneca in partnership with Oxford University, BioNTech in partnership with Pfizer, Gamaleya, and Moderna (WHO 2020a). Another five vaccines (from China, India, Kazakhstan, and Russia) have either received approval or have been authorized for emergency use by other regulatory agencies (WHO 2020a). At the same time, more than 200 additional vaccine candidates were in development, of which more than 60 are in clinical development at the time of writing.

COVID-19 and Access to Vaccines Vaccine Hesitancy Although vaccination rates have been high or increasing in most countries, suboptimal coverage poses a threat to individual and population immunity and is a key barrier to individual access to COVID-19 vaccines. The phenomenon of vaccine hesitancy is thought to be a key driver of suboptimal vaccination coverage (Bedford et al. 2018). Vaccine hesitancy has been identified by the WHO as one of the top ten global health threats (WHO 2019). Vaccine hesitancy has been defined by the SAGE Working Group on Vaccine Hesitancy as “a behavior, influenced by a number of factors including issues of confidence, complacency, and convenience” (Larson et al. 2014). As such, vaccine hesitancy could be considered an issue hampering access to vaccination and one that is influenced by multiple factors, including low confidence (lack of trust on vaccines or providers), complacency (lack of perceived need to be vaccinated, lack of value in vaccination), and inconvenience (easy access to vaccines)

COVID-19 and Medicines Access

(MacDonald 2015; WHO 2014). Reports of vaccination administration by race and ethnicity indicate higher vaccine hesitancy among African Americans and Latino adults compared to nonHispanic White Americans, a reflection of inequitable access to vaccination (Hooper et al. 2021). Vaccine hesitancy, which ultimately leads to poor uptake of vaccines with proven effectiveness in preventing the disease among the target population, is worrying since the COVID-19 pandemic is unlikely to end until there is reasonable herd immunity. While it is well proven that the spread of the virus can be mitigated through physical distancing, face coverings, and testing and tracing, the risk of outbreaks and impact on economies and social life is likely to remain until effective vaccines are administered to large cohorts of the global population (Wouters et al. 2021). Hesitancy to receive a vaccine is not only exhibited by the general population, but also by healthcare workers. A qualitative study conducted in Turkey in 2020 reported that hesitancy to receive the COVID-19 vaccine was mainly due to poor trust in the vaccine and concerns about its side effects (Kose et al. 2021). A questionnairebased European study that recruited healthcare professionals also reported that vaccine hesitancy was found to be mainly related to concerns about the safety of the vaccine, lack of confidence in the vaccines, the pharmaceutical companies, or health authorities (Neumann-Böhme et al. 2020). Respondents in this study expressed concerns that the vaccine might still be experimental and that it might not be safe for specific groups, such as pregnant woman, people with preexisting conditions like multiple sclerosis (MS), and people with preexisting allergies to vaccines and the like. Vaccines Affordability and Equity The need to ensure the affordability of COVID-19 vaccines to allow equitable access during the pandemic is key, particularly to reduce disparities in vaccine access between HICs and LMICs. Simply licensing vaccines is not enough to achieve global control of COVID-19. Vaccines also need to be produced at large scale, equitably distributed, and be reasonably priced to support equitable access (Wouters et al. 2021).

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To speed the release of new vaccines following public health crises, such as pandemics, regulatory bodies have started to grant vaccine use authorizations in emergencies (Wouters et al. 2021). The intention of all governments and other agencies at a national and international level has been to promote mass vaccination through expanding vaccine production and distribution capacity. Because no single pharmaceutical company can supply the required volumes of vaccine to large populations, the industry has initiated a strategy involving alliances and partnerships. This has been illustrated by a tendency toward collaboration and in sharing knowledge, technology, and data between domestic manufacturers (Price et al. 2020). To illustrate this, some pioneer companies in COVID-19 vaccine development initiated collaboration agreements with manufacturers in middle-income countries (e.g., AstraZeneca with the Serum Institute of India, Fiocruz in Brazil, mAbxience Buenos Aires in Argentina, and Siam Bioscience in Thailand; and Johnson & Johnson has an agreement with Aspen Pharmacare in South Africa; and Novavax with the Serum Institute of India). Many LMICs, which receive little external funding assistance, have historically been requested to pay high vaccine prices that are unrealistic, considering the income levels in these countries (Herlihy et al. 2016). As most countries aim to vaccinate their entire population in order to restrict or halt COVID-19, the costs of such targets are simply beyond the financial capability of most LMICs. Funding COVID-19 vaccine programs does not only include the price of the vaccines, but also the cost of distribution, storage, training staff, vaccine administration, recordkeeping, and pharmacovigilance activities. This means countries will need financial support to procure, purchase, store, and deploy vaccine distribution and administration. As a result, by midMay 2021, only 0.3% of total doses of approved vaccines had been distributed to low-income countries, which represented a clear case of inequity at the time; however, the world has moved fast to bring some balance to the clearly tilted situation between LMIC and HIC. And in 2020,

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the Covax Scheme (run by a number of international organizations, including the WHO and the UN children’s charity, UNICEF) was created with the aim of making COVID-19 vaccines equitably available around the world, with high-income countries subsidizing costs for poorer nations. The scheme hopes to distribute enough vaccines to protect at least 20% of the population in 92 LMICs, starting with healthcare workers and the most vulnerable groups. The initial goal was to provide two billion doses of the COVID-19 vaccine worldwide in 2021 and 1.8 billion doses by early 2022. Vaccination: Freedom of Movement and Ethics To minimize the risk of infection with the COVID-19 virus, several countries have been planning the implementation of what has been termed “vaccine passports,” certificates linked to the passport holder confirming full vaccination. The intention of this was to “allow people to freely move around the globe, travel and access public venues, and travel without compromising personal safety and public health” (Osama et al. 2021). However, the idea of creating a vaccine passport carries complex and difficult ethical and practical problems and challenges, especially at an international level. There will be a need for international standardization of the passports, with clear and realistic measures to curb, fraud, forgery, and criminal abuse. Also, for vaccine passports to be credible certificates of protection and a true indicator of lack of infectiousness by the holder, more evidence about the long-term effectiveness of different types of vaccines is required. This is particularly the case with the regular emergence of new variants of the coronavirus. Making COVID-19 vaccine passports mandatory for international travel will also impact on traveling to seek treatments abroad (travel tourism), which may affect access to affordable medicinal interventions. This could be seen as an infringement on personal liberty and human rights, where restricting the movement of those who pose minimal risk of spreading COVID-19 by being immune presents ethical infringement.

COVID-19 and Medicines Access

However, COVID-19 vaccine passports could also incentivize vaccination (Brown et al. 2020).

COVID-19 and Access to Pharmaceuticals Access to Pharmaceuticals in Acute Conditions Delays in treatment of health conditions that require immediate medical attention can be lifethreatening. The COVID-19 pandemic significantly affected access to pharmaceuticals needed for treating acute health conditions, for example, antibiotics and analgesics in addition to many other medications. During the first phase of the pandemic, such acute conditions and treatments were classified as low priority in health care systems that were stretched beyond expectation. Patients and their families had to navigate difficult healthcare systems trying to access health facilities for acute health conditions and elective surgeries. Patients were returned back from clinics and were triaged to over-populated waiting clinic rooms. It had been reported that health care systems started to delay elective care to mitigate the spread of COVID-19 in health care settings, and by May 2020, the US national surveillance data found that emergency department visits had declined by over 40% during the early months of the pandemic (Hartnett et al. 2020). All these restrictions and deficiencies affected access to medications required for acute health conditions. The lockdown that had to be mandated in most countries at different times of the pandemic has also changed the practices and dynamics of the healthcare system. For example, in the UK, there had been reports of an unexpectedly high rate of antibiotic prescribing during COVID-19, suggesting potential for inappropriate antibiotic use in telephone consultations (Armitage and Nellums 2021). Antibiotic prescribing rates were observed to be higher in remote consultations than during in-person appointments, which raises concerns that COVID-19 might be contributing to antimicrobial resistance (AMR) due to greater diagnostic uncertainty that results from an inability to perform proper investigations to ascertain

COVID-19 and Medicines Access

the need and rationale of antibiotic treatment during telephone appointments. However, in the absence of a system that allows for distant antibiotic prescribing, and where antibiotics can only be dispensed on prescription, one would expect a reduction on the rate of antibiotic use, with potential impact on AMR (Monnet and Harbarth 2020). The changes in antibiotic prescribing, and the effect of this on AMR as a consequence of the COVID-19 pandemic, still need to be explored through research. Access to Pharmaceuticals for Chronic Health Conditions The COVID-19 pandemic had a significant impact on populations of patients with noncommunicable diseases (NCD) in many ways. Due to lockdown, patients with long-term conditions were less able to exercise and move around, which affected their general wellbeing and their ability to access their regular medications (Palmer et al. 2020). Self-isolation recommendations that particularly targeted older people with chronic health conditions exposed them to higher healthrelated risks as these measures had long-term impact on the management of chronic conditions and on their medication management. While these preventative measures helped reduce the risk of the transmission of the infection, they also represented a major problem for the routine care (including pharmaceutical access) of patients with chronic conditions (Kretchy et al. 2021). NCDs rely heavily on strict adherence to lifestyle changes and on medications for adequate therapeutic outcomes. Patients with HIV or those with tuberculosis, for example, are required to adhere to their medications to ensure optimal outcomes and treatment success. The disruptions caused by the pandemic affect an individual’s routine, and ultimately the pharmaceutical management of their health and wellbeing, and of long-term conditions. For example, significant curtailing of healthcare services took place, where priority was given to services that focus on limiting the spread of infection and treating acute COVID-19 cases. This had affected routine management of NCD cases,

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outpatient visits for long-term health conditions, and elective surgeries (Palmer et al. 2018). As a result, access to pharmaceuticals decreased for millions of people with NCD due to difficulties in accessing prescription repeats of medication and in arranging follow-up care especially in LMICs. This was compounded by a significant shortage of skilled healthcare workers including pharmacists (see section “The Role of Community Pharmacy in Access to Pharmaceuticals During the COVID-19 Pandemic”) due to sudden increased need to relocate medical teams to COVID-19 patients and due to heavy losses among frontline healthcare workers (including doctors) who got infected with the virus themselves and many lost their lives as a result (Iyengar et al. 2020). LMICs rely heavily on pharmaceutical manufacturing capacity in countries or imports from outside international firms. The impact of the pandemic may be felt when essential medicines are unavailable and inaccessible to meet the needs of all, especially those with chronic diseases. In Nigeria, one of the LMICs, a study reported that around 35% of the patients with chronic illnesses had difficulties accessing essential medicines during the COVID-19 lockdown, with around 84% experiencing deteriorating chronic health conditions as a direct result of difficulties accessing medicines (Emmanuel Awucha et al. 2020). In this study, over 75% of patients with chronic health conditions could not afford for medicines, and around 80% of those living with chronic illness reported that their income was negatively affected by the pandemic. With limited supplies of pharmaceuticals to meet the increased demand created as a result of the global shutdown caused by the pandemic, the market values of medicines for chronic diseases have escalated. This has made several medicines unaffordable for patients in LMICs who require continued supplies of these essential medicines (Kretchy et al. 2021). Moreover, the shift in focus of healthcare and the pharmaceutical industry towards COVID-19 cases undermined the pharmaceutical care provided to vulnerable groups such as frail and older people with

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government(s). However, this role was compromised by the interrupted drug supplies and unavailability of many medicines as a result. Despite this, pharmacies were quick to purchase, and sell, large numbers of hand alcohol-based sanitizing products, antipyretic drugs, disposable gloves, and other personnel protective items. Some pharmacies started home delivery services, although these services were limited by logistical issues related to safety restrictions, staffing requirements, and cost. Unlike in developed countries, prescriptions are still collected in person by the patients and taken to pharmacies in developing countries and LMICs. This practice presents many risks to the patients, and governments have to make plans to introduce electronic prescribing to reduce the associated risks (Bahlol and Dewey 2021). Many countries have already been navigating and considering new ways to serve their patients at times of restricted movements and lockdowns. These strategies include telehealth, restricting patient visiting times as well as remotely seeing the patients (WHO 2020b). The WHO has also published interim guidance on public health and social measures (WHO 2020c).

NCDs. The delays, cancellations, and postponement of routine medical follow-up visits of patients with long-term NCDs compounded with policies of physical distancing and restriction of movement all disrupted the medicines supply process for millions of patients suffering from chronic health conditions and requiring longterm management, mostly with pharmaceuticals (Kluge et al. 2020). Millions of patients on long-term drug treatments across the globe rely on the available public health systems for medication refills. However, patients from low socioeconomic background were the most negatively affected and were always at higher risk of poor adherence to their medicinal treatment due to poor or nonexistent supplies of pharmaceuticals and to poor outcomes as a consequence. For example, some reports showed that over 40% of the patients indicated that the COVID-19 pandemic has posed negative impacts on the availability of medications and their follow-up visits, whereas a similar number believed that it caused an unaffordable or increased price of medications (Shimels et al. 2021). In most of these cases, any additional out-of-pocket costs imposed on such patients were out of the question, and this sort of unintentional poor adherence to therapy is proof of failure in the healthcare system of any given country.

Challenges, Opportunities, and Recommendations

The Role of Community Pharmacy in Access to Pharmaceuticals During the COVID-19 Pandemic The role of pharmacists and pharmacies during the pandemic was crucial as pharmacies remained a key point of contact with the public in issues related to their health. In most LMICs, community pharmacies are well established and provide first point of contact for healthcare services to large portions of the community. The important value of community pharmacies was further highlighted during the COVID-19 pandemic crisis as pharmacies were one of the few places that remained open regardless of the level of lockdown restriction implemented by the

Challenges Previous crises related to supplies and availability of pharmaceuticals have highlighted the high proportion of patients prescribed medicines for long-term conditions and the lack of medicines reserves in disasters (Carameli et al. 2013). People panic and stockpile; medicines become an essential “commodity” mirroring the same demand behaviors seen with items such as water and toilet paper (Scipioni 2020). This unprecedented demand drives inequity of access, where those people from the lower power strata in the society also lack the resources to access medicines where indicated (Shadmi et al. 2020). The COVID-19

COVID-19 and Medicines Access

disproportionally affects the poor, minorities, and a broad range of vulnerable populations who have poor access to high-quality public health and medical care. While the extent of medicines shortages is currently unknown, public fear and anxiety caused an increased demand for medicines and this is fueled by myths, misinformation, and the social media. Clinician behavior also changes with anecdotal evidence suggesting general practitioners (GPs) and nurse practitioners freely supply large volumes of additional prescriptions and pharmacies dispense them (Knaus 2020). Community pharmacy resources are strained with a call from professional bodies for intermittent pharmacy closures to protect an already stretched workforce (Wickware 2020b). Manufacturer supply chains are being interrupted due to transport restrictions and country lockdowns. WHO data shows that many medicines are imported and few countries have the capacity to manufacture locally (Cameron et al. 2004), for example, 40–50% of all US generic medicines come from India (Scipioni 2020). Medicines production has reduced or stopped altogether; with China being the world’s largest supplier of active pharmaceutical ingredients (PHARMAC 2021; Scipioni 2020), and country recovery just beginning, the impact of this on the supply chain remains to be seen. Many governments do not invest in the pharmacy sector as with the medical and nursing workforce. In LMIC, the number of pharmacists per 10,000 population may be less than 0.5 per 10,000, for example, Uganda, Pakistan, compared to over 20 pharmacists per 10,000 population in higher income countries, for example, Japan, Malta (Bates et al. 2016). As an increasing number of health workers become exposed to COVID19, there is a real risk that this will impact on the pharmaceutical workforce and the ability of pharmacy to remain viable. Many governments have not put systems in place to regulate the supply of pharmaceuticals and plan for a potential shortfall in the pharmacy workforce.

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Opportunities and Recommendations Reducing Duration of Supply

Governments have a significant role to play in ensuring ongoing medicines supply and distribution. Some governments are in a strong position to regulate and have the infrastructure in place to immediately do so. In some countries, there have been moves by governments to curtail medicines oversupply to ensure future availability (PHARMAC 2021). To sustain ongoing medicines production and supply, there is also a need to ensure air freight routes remain open. Improving Equity

Lockdown restrictions in many countries result in those 70 years or older remaining at home for months at a time (Public Health England 2020). In such vulnerable populations, it may be advisable to increase the duration of supply to reduce pressures to travel to obtain medication, support social distancing, reduce anxiety about medicines access, and ensure these populations equitable access to medicines (Mahtani et al. 2020). However, this needs to be weighed up against potential risks such as the use of expired medication, medication wastage, and risks of “hoarding.” The impact of government interventions to support medicines access in vulnerable communities need to be considered to ensure this does not negatively impact on health equity. Mesolevel Influencing Drug Supplies

Supplying smaller medicine volumes with increased frequency increases the burden on pharmacists and their staff, GPs, and nurse practitioners with prescriptions re-issues. There have been calls by professional organizations such as the UK National Pharmacy Association to allow original pack sizes of medicines to be supplied to reduce dispensing time (Wickware 2020a). How this impacts on individuals and the primary care workforce needs consideration; regardless, primary care will need funding allocation to allow mobilization of the workforce to undertake

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medicines rationing and other medicines-related service delivery. Microlevel Responsibilities

Primary health providers have a key role to play in communicating information and managing medicines access and the public response during emerging threats. There are clear opportunities for our primary care providers to intervene and manage anxieties about medicine supply and to help with rationing medication supplies while ensuring equitable access. They are also in the position to counter misinformation to reduce fear, anxiety, and stigma (Rust et al. 2009). Crisis is the greatest lever for multilevel change (Ferlie and Shortell 2001); as individuals, organizations, and governments, we need to think carefully, but rapidly, about measures across the whole system to ensure ongoing medicines access for societies around the globe. Expanding the Role of Pharmacy and Pharmacists

The dire situation associated with difficult access to health care meant that pharmacists had to take on expanded roles, sometimes without being appropriately prepared for them. Community pharmacists had to ensure continuity of provision of medications at times where medicines’ supplies were scarce and inconsistent, and many pharmacies took health promotion and disease preventive services and delivered health advice much more than they regularly did. Some pharmacists in countries where pharmacy practice is progressive and expanded services were provided by suitably qualified pharmacists started providing much more medication management services, infectious disease mitigation, point-of-care testing, and vaccinations (Strand et al. 2020). During the pandemic, pharmacists have been able to reach out for patients who had access to health care problems and suffer health equity issues to maximize patient access to essential medications through medicine home delivery services, issuing larger or more frequent refill medicines, and satellite pharmacy outlets (CDC 2020; Vaduganathan et al. 2020). The pandemic has highlighted the crucial role of pharmaceutical services in times of health

COVID-19 and Medicines Access

crises. The community pharmacy remains to be the first point of contact to millions of people who need accessible health care services that do not require prebooking or complicated scheduling of visits. For decades, pharmacy leaders, regulators, academics, and practitioners were calling for the wide adoption of patient-centered services that move the pharmacist away from dispensing tasks and adopt the philosophy and practice of pharmaceutical care and medicines management (Hepler and Strand 1990; George et al. 2010). In combating a pandemic like the COVID-19, this change of focus of the pharmacist will be directed to the prevention, identification, and/or resolution of drug therapy problems, addressing issues around inappropriate use of medicines, promoting health lifestyle and rational use of medicines. Pharmacists will continue to provide patient-centered drug information and counseling on long-term medications, ensuring availability of essential medicines, proposing alternative treatments, addressing polypharmacy and medication safety concerns, assessing medication needs, promoting adherence, and providing follow-up services (Kretchy et al. 2021). Pharmacists also need to move towards ensuring continuity of care, receive training and licensure to administer vaccination, and offer disease-specific consultation, screening services, and referral where applicable. Re-structuring the Scope of Practice of Pharmacists

The COVID-19 pandemic drew attention to an ongoing problem that had been facing pharmacy and pharmacists in developing countries for decades – the limited role of pharmacists to medicines supply roles due to traditional, often outdated, regulations and traditional curricula in pharmacy schools. Expanding the (legal) scope of community pharmacy practice to maximize their contribution to patients and health systems in ensuring continuity of care and treatments, increasing vaccination coverage and offering evidence-based patient-tailored consultations must be seriously considered by departments of pharmacy and pharmacy regulatory bodies in all countries. This however requires a shift in pharmacy education from traditional, science-focused

COVID-19 and Medicines Access COVID-19 and Medicines Access, Fig. 1 Remunerated pharmacy interventions on COVID-19 (ISBE 2020)

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Stock and supply essential medicines (as defined at national level)

10

Preparing alcohol-based hand sanitizer formulations

9 5

Use of disposable masks by pharmacy staff Increased demand/change to home delivery of medicines

4

Stock and supply of protective devices

2

Supply of medicines usually supplied in hospital setting (e.g. oncology,…)

2

Management of medicines shortages

2

courses to more clinically oriented and contemporary curricula. This, we argue, is an important opportunity and not only a challenge, to push boundaries and extend the role of pharmacists. Many developed countries, like in Europe, had passed legislations to empower pharmacists to provide extended services in their fight against COVID-19. For example, the following services had been remunerated by governments or health payers in Europe (Fig. 1).

Conclusions The COVID-19 pandemic has stretched healthcare systems worldwide and forced countries to alter their priorities in medical treatments, leading to huge impact on access to vaccines and to medicines for both acute and chronic health conditions leading to an increased rate of health complications because of suboptimal management. The global lockdowns and the restrictions on travel movement had led to curtailing of imports of general and essential medicines and medical supplies with many pharmaceutical manufacturing firms shifting their focus to the production of medicines and medical equipment targeted at the fight against COVID-19, rather than general medicines production. The COVID19 pandemic has drawn attention to the need for urgent actions to establish policies and actions to ensure sustainable, equitable, and ongoing

medicines supply (including safe and effective vaccines), so that in future epidemics and pandemics, the risk of drug shortages and access to adequate treatments to all those who need them are minimized.

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Deprescribing Lalitha Raman-Wilms1, Barbara Farrell2,3 and Wade Thompson4,5 1 College of Pharmacy, Rady Faculty of Health Sciences and Centre on Aging, University of Manitoba, Winnipeg, MB, Canada 2 Bruyère Research Institute, Department of Family Medicine, University of Ottawa, Ottawa, ON, Canada 3 School of Pharmacy, University of Waterloo, Ottawa, ON, Canada 4 Department of Anesthesiology, Pharmacology, and Therapeutics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada 5 Research Unit of General Practice, University of Southern Denmark, Odense, Denmark

Abstract

Deprescribing refers to reducing or stopping medications that may no longer be needed, may be causing more harm than good, or may not align with a patient’s healthcare goals and treatment preferences. This chapter provides a historical perspective on the problem of too much medication among older persons, describes practice and research initiatives in the field over the last two decades, and outlines healthcare provider education, public engagement approaches, and policy strategies that

have been used to promote deprescribing. Each section concludes with recommendations for future work. The chapter uses a narrative approach and shares how we, as authors, have been involved in some of the initiatives by sharing some examples from our work. Keywords

Deprescribing · Polypharmacy · Potentially inappropriate medication (PIM)

Deprescribing in Practice and Research Polypharmacy: An Emerging Issue with an Aging Population The latter half of the twentieth century saw many new medication developments intended to prevent complications from and to treat disease. People were living longer. Older people began to take multiple medications, a situation initially known as “polypharmacy,” often defined as more than five medications. Clinicians began relating the use of some medications to adverse drug reactions, including hypoglycemia, falls, fractures, and confusion leading to emergency room visits and hospitalizations (Beard 1992; Beyth and Shorr 1999; Meador 1998; Tay et al. 1987). As people took more medications, nonadherence and medication management errors became more prevalent. A concurrent increase in therapies available to treat disease meant that people

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began taking even more medications, leading to a shift in the definition of polypharmacy to one of taking more medications than clinically indicated or that were unnecessary (Masnoon et al. 2017). The use of multiple medications is particularly challenging for older people who have declining organ function (leading to altered pharmacokinetics), altered receptor sensitivity, changes in homeostasis, and multiple comorbidities (Sloan 1992; Goldber and Roberts 1983). Studies evaluating the impact of medications have rarely included older or frail individuals, making it difficult for clinicians to extrapolate benefit/harm information to this population (Schmucker and Vesell 1999). Another challenge is that medications are often initiated by several different prescribers; however, there is rarely a central coordinator to manage multiple prescribers/medications for individual patients. Calls to action emerged internationally from the physician, nursing, and pharmacy communities with clinicians advocating for individualized treatment plans for older people, lower medication doses, using non-pharmacologic therapies when possible, and careful and frequent review of the anticipated effects and unanticipated effects of medications, including reassessing the need for a medication before adding another to treat an adverse effect (Colley and Lucas 1993; Stewart and Cooper 1994; Cohen 2000). Tools to support the identification of potentially inappropriate medications in older people were published but used primarily in a research context (Beers et al. 1991; McLeod et al. 1997). Pharmaceutical care emerged as both a philosophy and process to guide responsibility for, and identification of, medication-related problems and outcomes (Hepler and Strand 1990a, b; Cipolle et al. 1998a, b). Challenges remained in helping healthcare providers balance incomplete evidence for medication efficacy in frail, older people against risks of adverse drug reactions without denying older people potentially valuable interventions (Le Couteur et al. 2004). In the early 1990s, two of the chapter authors (BF and LRW) began applying the Pharmaceutical Care process in geriatrics care and teaching the

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process to other pharmacists. In 1999, BF joined the Geriatric Day Hospital at Bruyère Continuing Care. Literature intended to help with the stopping of medications guided this early practice (Graves et al. 1997). Individual patient care and two small observational studies of targeted medication withdrawal at Bruyère showed that medications could be stopped, reduced, or changed to safer alternatives without negatively impacting patients, and resulting in improved cognition, reduced falls, and improved quality of life (Farrell et al. 2003, 2004, 2006). Together with other pharmacist and family physician colleagues, BF co-led a project integrating pharmacists into Ontario, Canada, primary care practices with a goal of stimulating interprofessional care to identify and address medication-related problems prior to and at the onset of frailty (Dolovich et al. 2008). This model has since emerged as an approach to manage increasingly complex polypharmacy in many other countries. In 2003, the word “deprescribing” was published by an Australian author to describe the process of (1) reviewing medications, (2) identifying those to be stopped, (3) creating a deprescribing plan with the patient, and (4) providing frequent review and support to them (Woodward 2003). Approaches to addressing polypharmacy continued during this decade with interventions such as computerized decision support, multidisciplinary or pharmacist-led regular medication reviews, and patient education. A 2012 Cochrane review found that multifaceted pharmaceutical care interventions reduced inappropriate prescribing and medication-related problems but did not demonstrate clinically significant improvement in patient outcomes (Cochrane 2012). However, withdrawal of specific classes of medications was demonstrated to help resolve adverse drug reactions known to be caused by those classes (e.g., stopping benzodiazepines led to improvement in cognitive and psychomotor abilities (Habraken et al. 1997; Curran et al. 2003), stopping nonsteroidal antiinflammatory drugs led to improvement in blood pressure (McKellar et al. 2011a, b)). Additionally, a systematic review of studies of withdrawal of antihypertensive therapy showed many patients

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remained normotensive, did not necessarily require starting therapy again, and had no increase in mortality (Iyer et al. 2008). Despite at least partial success of these approaches in trials, the prevalence of multiple medication use and potentially inappropriate medications continued to grow in many developed nations (Liu and Christensen 2002; Hajjar et al. 2007). There remained little widespread guidance as to how primary care providers could assess and manage polypharmacy in practice (Fulton and Allen 2005). Calls for additional tools and approaches began to emerge (O’Mahony and Gallagher 2008; Gallagher et al. 2008a, b; Garfinkel et al. 2007). By 2010, the word “deprescribing” was used only twice (by the same French author) as a keyword in Medline (Queneau 2004; Queneau et al. 2007). Deciding What to Do Next: Beginning to Address Polypharmacy and Deprescribing Throughout the 2010s, publication of articles about polypharmacy rose exponentially. Several systematic reviews concluded that interventions used in studies tended to be complex and did not necessarily demonstrate improvements in what was determined to be clinically relevant endpoints (e.g., hospitalization) (Johansson et al. 2016; Cooper et al. 2015). Yet, in BF’s practice, patients who had medications reduced or stopped appeared to feel better, to be less confused and to stop falling; a series of case reports was published to illustrate the process of team-based deprescribing and outcomes (Farrell et al. 2013a, b, c, d, e, f, 2014a, b, c, d). Many others began publishing practical approaches and examples of how deprescribing processes could be implemented in practice (Kwan and Farrell 2013; Lemay and Daziel 2012; Hardy and Hilmer 2011; Scott et al 2015; Cassels 2017; Reeve et al. 2014a, b; Frank 2014; Frank and Weir 2014; Scott et al. 2013; Hilmer et al. 2012; Bain et al. 2008). Deprescribing research began advancing over the late 2000s and early 2010s with publication of reviews and commentaries on the importance of safely reducing medication use where appropriate. While there was a relative paucity of evidence on deprescribing to this point (Iyer et al. 2008; Gnjidic 2012), early trials (Beer et al.

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2011; Garfinkel et al. 2007) suggested that deprescribing appeared feasible and safe. Systematic reviews of factors facilitating or challenging deprescribing also began to be published (Reeve et al. 2013; Anderson et al. 2014b). These are outlined in Table 1 along with more recent analyses of barriers and facilitators. In 2012, the Bruyère Deprescribing Guidelines Research Team was formed, in collaboration with the OPEN: the Ontario Pharmacy Evidence Network (open-pharmacy-research.ca). This group aimed to address one of the chief concerns of healthcare providers – that clinical guidelines advised on how to start medications, but not on how to safely stop them. From 2013–2016, this team, including the three authors of this chapter (BF, LRW and WT), developed four evidencebased deprescribing guidelines according to priorities identified by Canadian clinicians, and published a methodology paper for those wanting to develop similar guidelines (Farrell et al. 2016, 2017a, b; Pottie et al. 2018; Bjerre et al. 2018). Knowledge mobilization tools and activities included decision-support algorithms, patient educational pamphlets, infographics, whiteboard videos; a broad stakeholder engagement and social media campaign utilizing Twitter (@deprescribing); and open access to tools through a dedicated website Deprescribing.org Optimizing Medication Use and app. A fifth guideline was developed in collaboration with an Australian team (Reeve et al. 2018). A Period of Synthesis and Investigating Approaches to Deprescribing While our group worked on developing evidencebased deprescribing guidelines, other researchers focused on synthesizing deprescribing literature and began concentrating efforts on addressing patient and provider barriers to deprescribing. Narrative and systematic reviews in the late 2010s described a growing evidence base for deprescribing interventions, particularly relating to withdrawal of individual medications and approaches to reducing potentially inappropriate medications (Page et al. 2016; Reeve et al. 2017). Deprescribing interventions have included

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Deprescribing, Table 1 Examples of barriers and facilitators to deprescribing Barrier Lack of skills/ knowledge/selfefficacy Lack of evidence, guidance, or tools Fear and uncertainty

Description

Facilitator

Provider level Difficulty deciding whether deprescribing is appropriate (balancing benefits/harms), developing a deprescribing plan, monitoring Limited available resources or guidance to help implement deprescribing in practice

Evidence-based tools and resources such as decision support tools, decision aids, communication tools Evidence syntheses to inform practice

Fear about stopping Communication and planning

Concern about the consequences of stopping a medication and/or concern about patient/caregiver reaction to stopping Patient level Concern about withdrawal symptoms or return of underlying condition Not knowing the rationale for deprescribing and not being involved in deprescribing plan

Time, effort, and resource constraints

Health system Concern that deprescribing is time-consuming and does not fit well into existing workflows or pathways

Fragmented care

Culture

Difficulty communicating and collaborating with other healthcare team members and unclear roles/ responsibilities Guidelines focused on initiation of medications and treatment of single diseases; desire to maintain status quo

Resources to educate patients and involve in decisions, such as patient decision aids Public engagement to raise awareness about deprescribing Mechanisms to provide support and monitoring throughout deprescribing process Reimbursement for deprescribing Improved, user-centered integration of deprescribing into care processes and workflows Organized, multidisciplinary care teams Improved coordination of care and communication between sectors Integration of multi-morbidity and deprescribing in treatment guidelines

Anderson (2014b), Reeve (2013), Lundby et al. (2019), Paque (2019), and Doherty (2020)

deprescribing tools, patient/provider education, medication reviews, or combinations thereof (multifaceted interventions). Deprescribing tools have been highlighted in 2020 reviews by Reeve and Michiels-Corsten et al (Reeve 2020; Michiels-Corsten 2020). Tools have been categorized as: general deprescribing guides, generic deprescribing frameworks, drug or drug class-specific deprescribing guides and guidelines, electronic decision support systems, criteria lists for identifying potentially inappropriate medications (PIM), and patient education material. An overview of these deprescribing tools and resources is in Table 2. Systematic reviews have summarized the effectiveness of computerized

deprescribing decision support tools (Monteiro et al. 2019), reporting that such tools appear to generally reduce the mean number of PIMs but the clinical impact of the tools was unclear. Evidencebased deprescribing guideline implementation has been studied and suggests their use decreases targeted drug usage and cost, increases selfefficacy for deprescribing, and that their content is relevant, educational, and applicable to patient care (Thompson et al. 2016a, b; Farrell et al. 2018a, b, c, 2020a, b). The effect of using PIM identification tools alone to improve outcomes is uncertain (O’Mahony et al. 2020; Blum et al. 2021), and most do not contain any specific deprescribing advice (Curtin et al. 2019). New

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Deprescribing, Table 2 Deprescribing tools and resources Part of deprescribing process Identify potential deprescribing targets (i.e., potentially inappropriate medications)

Overall frameworks or approaches for deprescribing in an individual patient (including electronic decision support)

Medication-specific deprescribing guidance

Communication and patient engagement

Example of tools or resources • Screening Tool for Older Peoples Prescriptions (STOPP; O’Mahony et al. 2015) • STOPPFrail (Lavan et al. 2016) • LESS-CHRON (Rodríguez-Pérez et al. 2017) • Beers Criteria (Beers et al. 1991; American Geriatrics Society 2020) • Medication Appropriateness Index (Hanlon and Schmader 2013) • Systematic review of medication appropriateness tools (Masnoon 2018) • 5- and 10-step deprescribing approaches (CEASE) (Scott et al. 2014, 2015) • Generic deprescribing algorithms (Garfinkel et al. 2007; Frank 2014) • Scottish Government Polypharmacy Model of Care Group. Polypharmacy Guidance, Realistic Prescribing 3rd Edition, 2018. Scottish Government. Available from: http://www.polypharmacy.scot.nhs.uk/polypharmacy-guidancemedicines-review/for-healthcare-professionals/ • Deprescribing: A Practical Guide: NHS North Derbyshire CCG, NHS Erewash CCG, NHS Hardwick CCG, NHS South Derbyshire CCG; 2017. Available from: http://www.derbyshiremedicinesmanagement.nhs.uk/assets/Clinical_ Guidelines/clinical_guidelines_front_page/Deprescribing.pdf • MedStopper (https://medstopper.com/) • PIMSPlus (https://www.pimsplus.org/) • TaperMD (https://tapermd.com/) • Goal-Directed Medication Review Electronic Decision Support System (G-MEDSS) (https://gmedss.com/) • Evidence-based guidelines and algorithms produced by or in collaboration with the Bruyère Deprescribing Guidelines Team (https://deprescribing.org/resources/ deprescribing-guidelines-algorithms/) • Palliative and Therapeutic Harmonization Program (PATH) Clinic guidelines (https://pathclinic.ca/education/clinical-practice-guidelines/) • Primary Health Tasmania resources (https://www.primaryhealthtas.com.au/ resources/deprescribing-resources/) • NSW Therapeutic Advisory Group deprescribing tools (https://www.nswtag. org.au/deprescribing-tools/) • Patient education and decision-making materials produced by or in collaboration with the Bruyère Deprescribing Guidelines Team (https:// deprescribing.org/resources/deprescribing-information-pamphlets/) • Patient educational brochures from the Canadian Deprescribing Network (https://www.deprescribingnetwork.ca/patient-handouts) • Review on patient education materials (Fajardo et al. 2019a, b)

Adapted from Farrell et al. (2019a, b), Thompson et al. (2019), and Reeve (2020)

PIM-identification tools have recently been created and also include deprescribing advice such as STOPPFrail (Lavan et al. 2016) and LESSCHRON (Rodríguez-Pérez et al. 2017). Deprescribing interventions have also included patient education materials (Fajardo et al. 2019a, b), such as patient decision aids and patient information pamphlets. These materials have mostly addressed medications used for symptom control (such as benzodiazepines or proton pump inhibitors) and were mostly assessed at above-average reading levels. Despite the

existence of several patient education materials, there has been relatively little evaluation of education materials in clinical studies. Two RCTs have shown that pharmacist-led education of patients (via a direct-to-patient brochure about PIM use) leads to a higher discontinuation rate of PIMs compared to usual care (Tannenbaum et al. 2014a, b; Martin et al. 2018). A 2020 scoping review on deprescribing interventions identified nine studies involving patient education around polypharmacy and deprescribing (Isenor et al. 2021a, b).

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Medication reviews by pharmacists, physicians, and/or other healthcare professionals have been studied in the context of reducing inappropriate polypharmacy. Contemporary syntheses of studies evaluating the effectiveness of medication reviews suggest they may modestly reduce the number of medications people are taking, and measures of inappropriate prescribing, while their effect on clinical outcomes is less clear (Christensen et al. 2016; Thillaindadesan et al. 2018). A central challenge with medication review-based interventions continues to be that they are often employed by one, or a small number of, expert clinicians in a highly specific context such as an academic clinic. The most commonly evaluated deprescribing interventions have been multifaceted interventions involving combinations of different strategies such as tools, medication reviews (pharmacist-led, physician-led, or collaborative), and education (Isenor et al. 2021a, b; Hansen et al. 2018a, b, c, Rankin et al. 2018; Dills et al. 2018). These deprescribing interventions are feasible, safe, and might reduce the number of medications people are taking as well as various measures of inappropriate prescribing. Overall, evidence syntheses during recent years have confirmed that deprescribing interventions likely reduce the use of potentially inappropriate medications, though their effect on clinical outcomes is uncertain. Available evidence suggests that deprescribing might reduce mortality risk but may not have an effect on quality of life, falls, or hospitalization (Bloomfield et al. 2020; Ulley et al. 2019; Pruskoswki et al. 2019; Page et al. 2016). One of the challenges with the majority of deprescribing interventions developed and tested to date is that they have not been evaluated in routine clinical practice and thus their real-world effectiveness, usability, and sustainability are unclear (Thompson et al. 2019; Reeve 2020). Even those that have been evaluated are often studied in ideal conditions by people involved in developing them, and/or are tested in small, short-term, uncontrolled studies. Further, the rigor of the development process for many tools is variable (and in some cases unclear).

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A Patient-Centered Approach to Deprescribing Emerges While targeted medication deprescribing (e.g., targeting individuals taking a specific medication or class of medications) continues, many have realized the importance of a more holistic approach in which an individual person’s experience, values, and preferences for medication therapy are taken into account in a shared decisionmaking model which helps patients weigh risks and benefits of continuing with a medication or deprescribing it (Jansen et al. 2016). Recent research has provided greater insight into patient attitudes towards deprescribing. Patients appear to have varying preferences and attitudes related to deprescribing, with some being more hesitant to deprescribing, others ambivalent, and others actively wishing to reduce medication burden (Weir et al. 2018). However, trust in prescribers plays an important role in patients’ decisions to consider deprescribing (Lundby et al. 2019). Following the creation of the revised patient attitudes towards deprescribing (rPATD), research groups from around the world have examined patient willingness to engage in deprescribing together with their healthcare team. A recent meta-analysis of 29 studies (n¼11,049) involving the rPATD reported that 88% of patients surveyed were willing to have a medication deprescribed if suggested by their doctor (Lin Chock et al. 2021). Recent reviews have collated updated evidence on patient attitudes to deprescribing (Burghle et al. 2020; Doherty et al. 2020). Despite a patient’s willingness to consider deprescribing when suggested by a healthcare provider, key challenges remain to implementing deprescribing in routine practice from the patient’s perspective (Table 2). Hope for future benefit from medications, belief in the beneficial effects of medications, and fear of the effects of stopping can serve as patient barriers to deprescribing. Patients also express a lack of knowledge about their medications and fear of upsetting a prescriber if they ask questions (Galley et al. 2021). Additional barriers include poor communication from healthcare providers,

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lack of coordination of care, and unfamiliar healthcare providers. Patient enablers of deprescribing include clear communication with a healthcare provider (e.g., explaining the reason for deprescribing), a clear plan (close monitoring and follow-up), and a good relationship with the provider suggesting deprescribing. Patients may also be motivated to pursue deprescribing if they feel they are taking too many medications. Patients’ desired level of involvement in deprescribing decisions has also been examined (Weir et al. 2018; Thompson et al. 2020a, b). As with most healthcare decisions, some patients prefer to leave decisions to prescribers while some want to actively participate in planning and shared decision-making around deprescribing. Deprescribing decisions and decision-making should therefore be individualized. Healthcare Providers Highlight Needs to Support Deprescribing Contemporary reviews have underscored how primary care physicians feel reluctant to deprescribe medications started by specialists and that there can be pressure from guidelines as well as by patients/families to continue to prescribe rather than deprescribe (Lundby et al. 2019; Doherty et al. 2020). Healthcare providers have described a lack of self-efficacy around deprescribing as a potential barrier – this includes lack of knowledge and skills and lack of confidence due to uncertainty in the effects of deprescribing (and consequently fear around the consequences of stopping a medication). Healthcare providers endorse a need for additional training and mentoring on deprescribing (Anderson et al. 2014a, b; Doherty et al. 2020). Fragmented care and unclear roles and responsibilities around deprescribing also continue to be reported as barriers. Finally, constraints such as lack of time and poor integration of deprescribing into existing workflows and processes continue to be reported as barriers to deprescribing from a healthcare provider perspective. While healthcare providers acknowledge life expectancy, quality of life, patient preferences, and well-being as important considerations for deprescribing,

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these concepts can be difficult to communicate with patients and caregivers in a manner that facilitates shared decision-making. Recent reviews suggest that improved communication between patients and healthcare providers would enable deprescribing, along with improved communication between healthcare providers across sectors (i.e., improved continuity of care) (Doherty et al. 2020). Finally, healthcare providers report that mechanisms that allow for tailored approaches, shared decision-making, and individualized care may help providers better grapple with uncertainty (Doherty et al. 2020). Healthcare providers face myriad health system and organizational barriers to deprescribing, as well as training, education, and resource needs (Doherty et al. 2020). For example, reviews have highlighted how a “prescribing culture” has limited uptake of deprescribing to date. Further, lack of reimbursement or billing codes for deprescribing mean there may be lack of incentive for physicians to pursue deprescribing. Siloed healthcare, poor coordination of care, and limited access to multidisciplinary teams mean that deprescribing can be difficult to implement successfully in practice. Since health system challenges may differ depending on the care environment, patient population, and setting, it has been suggested that deprescribing interventions may need to be tailored to the specific context they are being implemented in (Sawan et al. 2020a, b). Thus, there needs to be greater consideration of the existing workflows and processes (i.e., process evaluation) when implementing deprescribing. Moving Deprescribing Research into Practice Knowledge translation to move research into practice continues as an area of focus in the deprescribing field. Various initiatives have been carrying out this work over the last several years, including: RxFiles (Canada), Therapeutics Initiative (Canada), NPS MedicineWise (Australia), Scottish Government Effective Prescribing and Therapeutics (Scotland), and others. Future research on deprescribing should address the limitations and challenges identified in the

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literature to date, and harness knowledge of deprescribing facilitators. An implementation science approach to enhancing uptake of deprescribing in clinical practice has been advocated as a key focus area for deprescribing. The ultimate aim is to foster practical and sustainable deprescribing practices in routine care (Isenor et al. 2021a, b; Ailabouni et al. 2021). To realize this objective, it has been suggested that multifaceted approaches are needed, which include various tailored and useable tools and resources (e.g., guidance, tools for communicating with patients that address known barriers), and environments/systems that allow for them to implemented and integrated into care effectively (Reeve 2020). Deprescribing studies should incorporate evidence-based interventions, clear implementation strategies (specific approach to translate intervention to real-world setting), and consider contextual determinants (factors that affect the ability to implement interventions such as known barriers and facilitators at the health system and patient/provider level) (Ailabouni et al. 2021). A greater focus on cost-effectiveness of deprescribing interventions, and more exploration of patient engagement strategies is also needed (Thompson et al. 2019; Turner et al. 2021). Finally, future research studies on deprescribing should focus on clinically important outcomes, such as quality of life or patient function, rather than strictly focusing on numbers of medications or medication appropriateness indices (Aubert et al. 2020).

Healthcare Provider Education on Deprescribing Development of Curricula in the Care of Older Adults In the late 1980s, with a recognition that contemporary curriculum did not prepare health professionals adequately for the complex care of older individuals, Geriatric Education training centers were established within universities in the United States. The focus of the centers was on faculty development to support geriatric curriculum development and training of pre-licensure learners, including physicians, pharmacists, and other health

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professionals, with the goal of improving health care of older adults (Kahl et al. 1992). With a call to better prepare health professionals in the care of older persons, by the 2000s healthcare professional curricula in North America, especially in medicine, nursing, and pharmacy, integrated geriatrics content within courses (Misiaszek et al. 2001; Dutta et al. 2005; Bardach and Rowles 2012). By late 2000s, all Canadian medical schools had a requirement to include mandatory geriatric content in the preclinical years, with limited availability of geriatric clerkships (Gordon 2011). Mandatory postgraduate medical competencies in geriatrics were determined in the 2000s (Bardach and Rowles 2012; Charles et al. 2014). The American Society of Consultant Pharmacists recognized the importance of pharmacists being trained in the care of older adults, and in 1997, established the Certified Geriatric Pharmacist (CGP) credential. More recently, this certification has moved to a Board Specialty (2017) (Ref: https://www.bpsweb.org/ bps-specialties/geriatric-pharmacy/). Parallel to the development of geriatric curriculum, the development of Pharmaceutical Care practice started to emerge in the 1990s. Pharmacy educators including two of the authors (LRW and BF) started to incorporate the new practice within pharmacy education (Cipolle et al. 1998a, b, 2012). This model enables pharmacy learners to develop a framework for consistent review of patients’ medications. The patient care process emphasizes the importance of understanding the patient’s medication experience, including their beliefs, cultural influences, preferences, and values related to medication use. The first phase of this process starts with the question, if a sign or symptom being experienced by a patient could be caused by a medication they are taking. This essential first question can identify a drug-induced cause and could potentially prevent or identify a prescribing cascade. This is followed by an assessment of indication for drug therapy, addressing both overtreatment (when there is no clear indication that a drug is required) and undertreatment (any indications for a new drug to be started). This is followed by an assessment

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for effectiveness and safety of a drug and patient’s adherence to therapy. This comprehensive medication assessment process (2012) started to be taught within pharmacy programs. In 2013, one of the authors (LRW) developed and administered a geriatrics course for pre-licensure pharmacy students incorporating Pharmaceutical Care with an emphasis on holistic approach to patient care within an interprofessional collaborative care model. Students were taught how to carry out comprehensive geriatric assessments, including assessment of polypharmacy. Similarly, other pre-licensure pharmacy programs in Canada and in other disciplines were including aspects of polypharmacy management within geriatric content, such as the use of screening tools to identify potentially inappropriate medications (personal communication; Zolezzi et al. 2018). Now, well into the twenty-first century, as the population continues to age and live longer, there continues to be a call for learners in medicine, pharmacy, and nursing from pre-licensure, postgraduate to continuing professional development, to be trained in the management of polypharmacy and deprescribing in order to decrease the consequences of “medication overload” (The Lown Institute 2020 report, CaDeN, Mackey and Bornsterin 2020). The Need for Deprescribing Content in the Curriculum Routine teaching of deprescribing is not yet part of the current healthcare education culture. Prelicensure students from pharmacy and medicine programs indicate inadequate training in prescribing, which is seen as a continuum to deprescribing (Woit et al. 2020). Amongst Canadian pharmacists, less than 50% were aware of the prevalence of polypharmacy and inappropriate prescribing in older adults (Zou and Tannenbaum 2014). Physicians, pharmacists, and nurses agree that they have a clear role in polypharmacy and medication management, including appropriate prescribing, medication monitoring, and effective communication with patients (Farrell et al. 2018a, b, c). Other healthcare professionals have typically viewed their role as being supportive in

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managing polypharmacy, and recommending alternative therapies that can reduce reliance on medications (Farrell et al. 2018a, b, c). Therefore, there is a need to ensure all healthcare professional students are aware of the reasons for and general approaches to deprescribing, with additional emphasis on deprescribing and monitoring approaches for those who are able to prescribe medications. As inadequate knowledge and skills are often viewed as a barrier to deprescribing, consistent and effective teaching of deprescribing can enable learners to undertake this task routinely and increase their self-confidence in practice (Anderson et al. 2014b). Teaching of Deprescribing Curriculum development in health professional programs is often guided by standardized national educational outcomes and competencies, providing a planned approach to teaching (AFPC 2017, CanMEDS, CASN 2014, NAPRA 2014). Polypharmacy management and deprescribing is not consistently included within national competencies at the pre-licensure level or within more recently developed guides for entry-to-practice geriatric education in pharmacy, medicine, and nursing (ASCP website, Masud et al. 2014; CASN 2017; Misiaszek et al. 2001; Dutta et al. 2005; Gordon 2011). Deprescribing taught in the early part of prelicensure programs can prepare learners to apply concepts during their practicums and later in practice as clinicians. Consideration should be given to teaching within an interprofessional education framework, enabling students from different disciplines to learn from, and with each other. This can facilitate an understanding of the role that each health professional brings in working with patients to effectively manage deprescribing. As outlined in Table 3, polypharmacy management and deprescribing requires a comprehensive understanding of patient-related, health-related, and drug-related information, as well as effective interpersonal and team skills. Also important are knowledge and skills related to tools and strategies to identify inappropriate medications; evidence-based information to assess benefits

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Deprescribing, Table 3 Highlights of knowledge and skills required to engage in effective deprescribing activities Patient (family/caregiver) related • Develop a therapeutic (trusting) relationship with patients • Gather information related to patient’s medical conditions, indications for drug therapy and medication use information, as well as their beliefs related to their health and treatment • Determine patient’s medication experience, preferences, and values and understand how these should influence decisions • Provide accurate and relevant information to patients on their health and medication use, including benefits and harms of medications • Engage patients in shared decision-making • Identify patient groups who may benefit from deprescribing (e.g., older adults, those receiving palliative care) • Understand the healthcare needs of patients across a life span • Use good communications skills to effectively engage patients throughout the medication use process, including monitoring and follow-up of deprescribing activities Medication/therapeutics-related • Polypharmacy and its consequences • Physiological changes with age and their impact on drug therapy • Pharmacokinetics and pharmacodynamics of drugs • Understanding of common geriatric syndromes • Understanding of common adverse drug events • Prescribing cascades • Tools to identify potentially inappropriate medications (e.g., Beers Criteria, STOPP) • Evidence for deprescribing, including drug-specific guidelines • Tools to understand the benefits of treatment and risks of harms (e.g., number needed to treat (NNT), number needed to harm (NNH)) • Medications that require tapering to deprescribe and recommended tapering schedules • Role of nonpharmacological options and interventions for specific conditions when deprescribing • Tools that facilitate deprescribing (e.g., electronic decision supports) Other knowledge and skills • Professional identity • Roles and responsibilities of other health professionals in the medication use process and deprescribing • Interpersonal skills • Medication-related discrepancies related to transitions in care • Ethical aspects of decision-making • Approach to including medication indication in clinical documentation General approaches to teaching deprescribing • Teach that deprescribing is a continuum of good prescribing • Use a framework or set of deprescribing competencies to guide curricula • Provide earlier, formal, structured learning about deprescribing with practical applications • Provide longitudinal learning opportunities (longer experiential rotations) that allow implementing a deprescribing plan • Provide a variety of clinical areas to support student learning of deprescribing • Emphasize a holistic approach to patient care • Provide simulations for deprescribing that are authentic patient cases • Teach learners how to navigate grey areas that require clinical judgment • Target learning to address barriers and enablers of deprescribing Adapted from Raman-Wilms et al. (2019a, b)

and harms of continuing and stopping medications; and steps involved in deprescribing. At the core of deprescribing and optimization of medication use is the ability to undertake a comprehensive medication review to identify and prioritize drug therapy problems, and to implement a deprescribing plan, including monitoring and

follow-up. A common understanding of terminology is also important amongst healthcare learners as terms such as “medication review” can be perceived differently. In a survey, pre-licensure pharmacy students perceived this as relating to the patient experience with a focus on adherence, while medical students considered more the

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clinical aspects such as interactions and medication indications (Poots et al. 2020). Teaching should start with the end in mind by identifying learning objectives or competencies required to effectively engage in deprescribing practice. The content and assessment strategies should be aligned with the objectives. Miller’s framework for clinical assessment can be used as a guide to teach and assess clinical knowledge and skills (Miller 1990). This framework can be useful in structuring deprescribing activities and in guiding the learner on the required expectations. Adult learning principles should be utilized in teaching so that the required knowledge and skills can be learned in a relevant, applicable manner (Birkenholz 1999; Brundage and MacKeracher 1980). Especially in continuing professional development, factors such as learner readiness, prior experience, and motivation to learn should be considered as these can enhance the effectiveness of the program or activity. Table 3 highlights important knowledge and skills required for effective deprescribing, as well as some general approaches to teaching. Within pharmacy curricula in Canada, many programs address aspects of deprescribing within clinical therapeutics or medication management courses (personal communication). However, there is a lack of comprehensive competencies that guide the teaching of deprescribing and variation between programs exists. When taught, concepts are introduced early in programs, with integration within clinical cases in later years. Topics are taught using didactic, case-based, problem-based approaches, and seminar formats. Many students engage in deprescribing during their experiential education. Teaching within pharmacy programs is often dependent on having faculty with experience in geriatrics and deprescribing (personal communication). Technical skills related to identifying inappropriate medication prescribing can be taught through the use of structured medication review tools such as STOPP to students in early pre-licensure programs (Keijsers et al. 2014). As part of medication optimization, an understanding of tools to identify potential under prescribing (START) should also be taught (Keijsers et al. 2014).

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Practice in using these tools to identify PIMs and undertreatment can be helpful prior to students working directly with patients. Teaching deprescribing theory and skills through partnerships between programs such as pharmacy and nursing using simulated workshop cases and patient actors can be effective in understanding the role of deprescribing in optimizing medication use (undergraduate level) and in addressing polypharmacy (use of screening tools at the postgraduate level) (Poots et al. 2017). Introducing evidence-based approaches to deprescribing with use of decision support algorithms and patient education materials can be practiced in simulated settings within the program, and applied to patient care through experiential education. A survey of polypharmacy management in European countries indicate that deprescribing education is not consistently included within programs, with some countries like Scotland and Sweden integrating some level polypharmacyrelated education at the undergraduate level in pharmacy and in medical training (McIntosh et al. 2018a, b). Deprescribing for clinicians may be more effective when undertaken within interprofessional collaborative practice settings (CIHC 2010; Schmidt-Mende et al. 2018). Training provided to a healthcare team or to a group of health professionals facilitates their practice and can address contextual barriers. Information on educational resources and approaches to clinician education of deprescribing is included in Table 4 (Zimmerman et al. 2020). In March 2018, educators, researchers, and practitioners from across Canada and internationally met at a symposium to discuss how deprescribing is taught and assessed within health professional programs at all levels (Raman-Wilms et al. 2019a, b). Deprescribing was discussed as a continuum of good prescribing practice. The complexity of tasks for prescribing and deprescribing that involve cognitive processes such as critical thinking was acknowledged and it was noted that these are not routinely or consistently taught. With respect to prescribing, the focus often is on the technical aspects. The role of heuristics or cognitive shortcuts with respect to prescribing was

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Deprescribing, Table 4 Strategies to teach deprescribing and to provide supports to health professional learners Strategy Structured medication review process

Use of screening tools in teaching

Polypharmacy cases with interprofessional case discussion instructions and worksheets

Interprofessional team-based deprescribing workshop

Online polypharmacy and deprescribing module Peer and faculty support; coaching

Educational Outreach (EO) An example of EO is academic detailing, which involves one-on-one education by and for healthcare professionals.

Drug Information Service

Audit and feedback

Benefits Use of a stepwise approach to deprescribing can enable learning of technical skills related to deprescribing Tools such as Beers Criteria, START/ STOPP tool, etc. can enable learners to identify potentially inappropriate medications when reviewing patient’s medications Use of cases can enable uni- or multiprofessional learners to understand the cognitive process required to undertake deprescribing and the role of the various health professions in the process of deprescribing An interprofessional workshop can be structured to address specific barriers to deprescribing, an opportunity to learn with other professionals, and facilitate a process for addressed contextual issues related to deprescribing An interactive module for healthcare providers and learners developed by authors BF and WT In addition to education and training, oneon-one coaching with other clinicians, and support through clinical and quality experts can be effective in deprescribing initiatives Educational outreach can provide the required knowledge and support to clinicians. EO has demonstrated some change in prescribing behavior if tailored and specific. Academic detailing, in combination with medication reviews, particularly when patients are involved, can reduce potentially inappropriate prescribing (Clyne et al. 2016a, b). The feasibility of using academic detailing strategies to support deprescribing in nursing homes has also been demonstrated (Pruskowski et al. 2021). Several organizations incorporate deprescribing advice and strategies amongst their tools and resources that provide drug information.

To influence general professional practice This entails a review of current professional practice against set standards or targets, with specific feedback provided to clinicians. This has demonstrated to influence general professional practice and can support deprescribing

Reference Scott et al. 2017 Reeve et al. 2014a, b (American Geriatrics Society 2012, 2019) (Gallagher et al. 2008a, b)

https://deprescribing.org/ resources/publications/ (Farrell et al. 2013a, b, c, d, e, f, 2014a, b, c, d)

Zimmerman et al. 2020

Bruyère - Polypharmacy and Deprescribing (bruyere.org) (Hirdes et al. 2020a, b)

(Hansen et al. 2018a, b, c; Kunstler et al. 2019; Schmidt-Mende et al. 2018) Ontario, Canada-based Centre for Effective Practice (Clyne et al. 2016a, b; Pruskowski et al. 2021)

Canadian Pharmacists Association eCPS Saskatchewan Canadabased RxFiles The British Columbiabased therapeutics initiative (Ivers 2012)

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highlighted; this may increase the reliance of prescribers on previous patterns, making it additionally challenging to incorporate active decisionmaking that is required for deprescribing. Also, it was felt that the latter is often learned during practicum, with learning dependent on the experience and patterns of the preceptor or supervisor. It was noted that few learners may feel well prepared to undertake independent prescribing decisions, considered “a qualitatively different cognitive and emotional activity.” Important factors on how deprescribing should be taught within health professional programs was shared (see Table 3). There was agreement that both prescribing and deprescribing should be taught in a formal, structured manner within a framework. Formal assessment of prescribing and deprescribing (beyond the technical components) was considered important to enable the development of skills in practice. Miller’s assessment of clinical performance can help guide this process (Miller 1990). The Future of Deprescribing Education: Competencies to Guide Educational Approaches In pre-licensure curricula, deprescribing needs to be taught as a continuation of good prescribing. Similar to having agreed upon competencies for prescribing (e.g., Royal Pharmaceutical Society’s (RPS) Competency Framework for All Prescribers), a framework of deprescribing competencies can guide education of health profession learners (RPS 2015, RCPS 2018, CNA 2015). The Prescribing Competency Framework for prescribers (assess patient, identify treatment options, reach a shared decision, prescribe, provide information, monitor and review; prescribe safely, prescribe professionally, improve prescribing practice, prescribe as part of a team) has been effectively used to teach prescribing to trainee pharmacists and can be mapped to existing medical and other health professional undergraduate curricula (Picton et al., 2016; RPS Competency Framework Sep 2021). In the latest revision of the RPS Competency Framework published in September 2021, the competency of “Identifying Evidence-Based treatment options available for

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clinical decision making,” consideration is also given to “stopping treatment (appropriate polypharmacy and deprescribing).” This is an important step in ensuring that learners understand that deprescribing is a continuum of good prescribing. A framework can ensure a structured and comprehensive education approach. Several interprofessional members of the healthcare provider group of the Canadian Deprescribing Network, including two of the authors (BF and LRW), have worked on the development of proposed deprescribing competencies. This framework is intended for health professional curriculum developers in Canada to use as a guide in developing deprescribing content and assessment within all levels of learners (Farrell et al. 2023). In addition to teaching, assessment of prescribing and deprescribing knowledge and skills should be embedded within programlevel assessments and within national licensing exams. Ultimately, program accreditation standards and requirements should have expectations of prescribing and deprescribing curriculum and assessment, which will increase the momentum and quality of teaching and evaluation of deprescribing within health professional programs.

Patient, Public, and Stakeholder Engagement for Deprescribing The Need for Public Awareness About Polypharmacy and Deprescribing The issue of polypharmacy and proposed approaches to deprescribing are widely discussed within healthcare provider circles. However, the public, including individual patients and their caregivers, are less knowledgeable about these concepts (Turner and Tannenbaum 2017). As described in the section on the emergence of a patient-centred approach to deprescribing, much needs to be done to engage people on an individual level, and to educate the general public to facilitate deprescribing. People are often hesitant to initiate conversations about their medications with their healthcare providers (Belcher et al.

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2006; Galley et al. 2021). When knowledgeable about their medications, including reasons for use and harms associated with them, people may be more willing to approach their healthcare providers with questions that may lead to discussions about deprescribing. Engaging Patients in Shared Decision-Making About Their Medications Shared decision-making involves ensuring people are aware that they have options, discussing their benefits and harms, and exploring patient preferences (Jansen et al. 2016). Frameworks for medical care increasingly acknowledge the importance of informed decision-making (Elwyn et al. 2017a, b; Lehman 2017; Hoffmann et al. 2018), and this is no different when it comes to making the decision to reduce or stop a medication. In Table 5, we share how shared decisionmaking tasks can be carried out for deprescribing decisions.

Aside from determining general public interest and attitudes toward deprescribing, little has been published explicitly describing how shared decision-making for deprescribing might be feasibly integrated into clinical care. Ouellet et al.’s approach to rational deprescribing for older adults with multiple chronic conditions includes three steps which support such shared decisionmaking: 1) gathering information about patient’s goals and care preferences, current medication and health trajectories in order to 2) mutually explore potential trade-offs of various options and 3) to carry out trials of deprescribing to reach individualized goals (Ouellet et al. 2018). They also suggest an order in which medications may be targeted for deprescribing discussions: first, medications that may have more harm than benefit, then those that offer little benefit given the patient’s health trajectory or given their care preferences. Todd et al have also published a conceptual framework highlighting different

Deprescribing, Table 5 Process for shared decision-making with patients for deprescribing Step Create awareness that options exist

Discuss options and their harms and benefits

Explore patient preferences for different options

Make the decision

Adapted from Jansen et al. (2016)

Considerations in deprescribing • When to initiate discussions about deprescribing (i.e., identify triggers for medication review) • Older people’s attitudes toward medicine (which may be internally contradictory, e.g., feeling medications are working but wanting fewer of them) • Cognitive biases (e.g., for the status quo) • Multidisciplinary decisions (e.g., multiple specialists involved) and companion involvement (i.e., needing to take a triadic approach) • Ability to understand options (e.g., affected by cognitive processes and comorbidity) • Difficulty understanding potential benefits and harms of different options (e.g., considering numeric and literacy skills) • Difficulty communicating uncertainty (especially with little evidence for benefits or harms) • How to distinguish between different types of medicine (i.e., preventive vs. managing symptoms) • Preferences in older people vary and are unstable (i.e., people’s preferences may change as they acquire more information; they may perceive their healthcare provider already knows their preferences) • Weighing benefits and harms is more complex in older people (e.g., life expectancy difficult to predict) • Preferences for involvement and patient autonomy (e.g., there is variation in how much people want to participate in final decision-making) • Deprescribing is an ongoing process (i.e., with careful monitoring, benefits or harms may emerge and prompt different decision-making)

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aspects of patient context (clinical, psychological, social, financial, and physical) that should be taken into account in making decisions about deprescribing with individual patients (Todd et al. 2018). Both articles illustrate these approaches with patient cases. The Shed-MEDS hospital-based patientcentered deprescribing framework included, as part of its pilot intervention, a standardized patient preference interview which elicited patient preferences for each medication with the potential for deprescribing (covering adherence, possible side effects, perceived benefit/harm, cost and level of interest in stopping or reducing the medication) (Petersen et al. 2018). Subsequent reductions in medication use suggests that patients were willing to reduce or stop medications when engaged in discussion about effectiveness, side effects, and cost though the intervention itself was found to be timeconsuming; however, it is unclear to what extent patients were involved in the discussion of various options regarding each medication decision. Thompson et al explored how family physicians approach statin deprescribing with older people and highlighted continuing challenges in communicating uncertainty and life expectancy that may impact on a person’s preference for a given treatment and emphasizes the need for better approaches or frameworks to promote shared decisions around deprescribing (Thompson et al. 2020a, b). Education for the Public A national Canadian survey in 2016 revealed that less than 7% of the public had heard the term “deprescribing” though up to two-thirds of respondents indicated that they were aware some medications could cause harm (Turner and Tannenbaum 2017). They also found people who were aware of the term “deprescribing” were much more likely to initiate a conversation with their healthcare provider about their medications and concluded that more public awareness about when and how to talk to healthcare providers about deprescribing is needed (Turner and

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Tannenbaum 2017). Public awareness, education, and action regarding deprescribing has therefore been a focus for the Canadian Deprescribing Network established in 2015 (Turner et al. 2018). To support the patient’s role in shared decisionmaking about their medications (including deprescribing), the network has purposefully engaged public members in its planning and key messaging, disseminated information about deprescribing through the media, newsletters, and its website, provided tools to support deprescribing discussions and decisions, hosted public deprescribing fairs, conducted public lectures, engaged community champions and hosted national stakeholder summits (Turner et al. 2018). The Institute for Safe Medication Practices in Canada also hosts a widely distributed pamphlet encouraging people to ask questions about their medications (ISMP). Internationally, calls to raise public awareness of the potential harms of medication use and the importance of deprescribing have emerged in recent years. In the United States, the Lown Institute published a 2020 national action plan on eliminating medication overload emphasizing the importance of raising awareness about medication overload and encouraging people to participate in prescription checkups (The Lown Institute). The WHO “Medication Safety in Polypharmacy” report also highlights the importance of raising patient awareness and encouraging active participation in medication reviews (WHO). Helping the public understand the scope and impact of polypharmacy, how they might improve their knowledge of their own medications, and how to have conversations about them has also been the focus of research groups in Canada and the United States as outlined below. Engaging the Public in Research and Advocacy There has also been increasing focus on involving patients and members of the public in deprescribing research and advocacy in recent years. Patient engagement in research contributes

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to study design, selecting relevant study outcomes, tailoring interventions and enrolling participants, with many researchers reporting better feasibility, acceptability, rigor, and relevance of research (Forsyth et al. 2019). The Canadian Bruyère Deprescribing Guidelines Research Team works with patient advocates to produce public-oriented knowledge mobilization materials and to guide discussion at collaborative research events. Using a community-based participatory research approach with a local advisory group , the team confirmed that older individuals do not have access to easy to understandable information about their medications; they are fearful of asking medicationrelated questions of their physicians; older individuals are unfamiliar with the “circle of care” and are unsure of their role within it; and that communication challenges both between healthcare providers and patients remain (Galley et al. 2021). Based on these findings, the team developed interactive public education workshops (“Talking about your medications”) to enable older individuals to start conversations about medications with their care providers and to participate in decisions related to their medication-related care (Farrell et al. 2022). Open-access resources include information pamphlets, workshop materials, facilitator guides, and participant workbooks https:// deprescribing.org/talking-about-medicationsworkshop-materials/. The Team Alice project in Western New York State, USA, is also exploring strategies for community-based participatory research in deprescribing, as well as patient-driven deprescribing which involves community outreach, engagement, and partnerships (NIHR). (Team Alice) Teams in Canada and the United Kingdom are now involving patients in designing deprescribing guidelines and interventions, ensuring that patient voices and experience are incorporated in deprescribing research and advocacy efforts (Swinglehurst and Fudge 2019; personal communication). The recently published NHS plan to reduce overprescribing included interviews, focus groups, and co-design workshops with members of the public to guide its recommendations (Good for us 2021).

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Engaging Stakeholders The problem of polypharmacy is not easily solved. Some have described it as a “wicked” problem, one that involves patients (with shifting contexts, values, and goals), medications, and diseases (with varying balances of benefits and harms), clinicians (with varying interpretations of the latter), and a myriad of other social and environmental factors (Armstrong and Swinglehurst 2018). Wicked problems arise when people agree or disagree about priorities and approaches; there is no single solution; what might work in one context may not work in another (Rittel and Webber 1973). Stakeholder engagement has increasingly been used as a strategy to involve audiences of varying types to help everyone potentially involved in some aspect of polypharmacy management understand the issues and participate in activities that might ultimately contribute to addressing the problem of polypharmacy and deprescribing strategies. In particular, working with like-minded organizations that include those aiming to support evidence-based care, promote patient safety, and educate healthcare professionals on emerging approaches to quality care is seen by many working in the deprescribing field as vital to moving forward. The Bruyère Deprescribing Guidelines Research Team used a stakeholder engagement approach in developing its guidelines, firstly by including representation from different healthcare provider groups on its development teams, then by inviting national Canadian professional organization review and endorsement of the guidelines (including the Canadian Pharmacists Association, College of Family Physicians of Canada, Canadian Nursing Association, Choosing Wisely among others). This engagement was expanded to the United States through partnership with the Institute for Healthcare Improvement and the Commonwealth Fund (McCarthy 2017). International partnerships grew with a strong social media presence and the hosting of a 2018 international symposium bringing together 130 individuals from 10 countries to discuss how deprescribing guideline recommendations could be implemented in practice, as well as future

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directions for education and research (Farrell et al. 2019a, b; Moriarty et al. 2019; Raman-Wilms et al. 2019a, b; Thompson et al. 2019; Conklin et al. 2019). This meeting provided the impetus for the development of additional collaborations and contributed to development of new networks (e.g., the English Deprescribing Network, the United States Deprescribing Research Network) further supporting the goal of wide stakeholder engagement. Next Steps for Engagement Encouraging patient-driven deprescribing as well as increasing patient engagement in deprescribing have both been highlighted as important future areas for deprescribing. Having patients advocate for themselves and drive the deprescribing process may be one strategy to increase uptake of deprescribing in practice. Despite the efforts outlined in the section above, there remains scant literature and work in this area to date, thereby representing an important area for future work. Since patient preferences around deprescribing are variable, and health literacy and knowledge about medications differs, efforts in patient engagement around deprescribing need to accommodate differences between patients. Tailored strategies may be one opportunity, along with codesign of engagement and outreach strategies to ensure optimal reach and effectiveness.

Policy Changes to Support Deprescribing The Role of Policy in Facilitating Deprescribing Contextual factors related to the healthcare delivery site (community, clinic, hospital, residential care) and the political and economic environment (regulatory, financial, payment regimens) can influence the success of deprescribing initiatives (Reid et al. 2005). As changes within health systems are complex, policies supporting deprescribing should address barriers and facilitators identified at the various levels of healthcare (Table 2).

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Most deprescribing interventions target healthcare providers (e.g., restricting use of a drug) or patients/consumers (e.g., education) with variable effectiveness, sometimes with unexpected negative outcomes (Shaw et al. 2019; Table 6). Policies need to consider the multifaceted aspects of deprescribing and the type of interventions that can be effective. Designing effective interventions requires an understanding of the specific behaviour(s) that should be targeted and the types of policies that would be supportive of the behaviour change. The Behaviour Change Wheel (BCW), an evidence-based theoretical framework that links a comprehensive model for understanding behaviour (COM-B system), with intervention domains (Theoretical Domains Framework [TDF (v2)]) can be used to design and evaluate interventions, with the aim of changing targeted behaviors (Michie et al. 2011; Cane et al. 2012; Atkins et al. 2017). This framework illustrates how Behaviour is influenced by an individual’s Capability, Opportunity and Motivation (COM-B system) and how these relate to the 14 domains (TDF (v2)) such as knowledge, skills, social/professional role and identity, beliefs, etc. that relate to the individual’s capability and motivation (Michie et al. 2011; Cane et al. 2012; Atkins et al. 2017). Based on the contextual situation, interventions addressing specific domains targeted to change specific behaviors of individuals, can be successful. Examples of Policy Interventions that Support Deprescribing The BCW also links behaviour elements to intervention types and their relationship to specific policy categories; these include fiscal measures, guidelines, environmental/social planning, communication/marketing, legislation, service provision, and regulation (Michie et al. 2011). Mechanisms facilitating deprescribing within these policy categories include delisting of medications, changes in drug schedule, influence over prescribing (such as limiting use of drug, restriction of quantity), monitoring usage, financial incentives, educating healthcare providers and/or the public, etc.

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Deprescribing, Table 6 Examples of deprescribing policy strategies Policy category: Legislation Strategy Delisting of chlorpropamide (Canada 2001) (Sketris et al. 2004)

Rescheduling of alprazolam (Australia 2014) (Shaw et al. 2019; Schaffer 2016)

Policy category: Regulation Strategy Delisting of oxycodone (Canada 2012) (Fischer et al. 2014, 2016) (Fischer et al. 2016) Prescriber Monitoring, (US, 1989) (Shaw et al. 2019, Wagner et al. 2003)

Policy category: Fiscal Strategy Financial incentive to general practitioners (France, 2012) (Fisher et al. 2012; Rat et al. 2014)

Intervention description Intervention type: Restriction by delisting. Chlorpropamide not being covered the provincial drug plan for older adults. Restriction of availability of medication. Medication scheduling change from regular prescription status to a controlled drug status that required monitoring by health authorities Intervention description Restricting use by delisting. Oxycodone removed from several provincial formularies; no longer subsidized by the government. Monitoring of use. Triplicate Prescription Program Benzodiazepine prescriptions to be written on triplicate-copy-pad (a copy provided to each of, prescriber, pharmacist, and the State Department of Health)

Intervention description Incentivization by paying for performance. One of four priorities of quality improvement program. Financial payment for not prescribing longacting benzodiazepines and for not using benzodiazepines long term Policy category: Regulation, guidelines, communication/marketing Strategy Intervention description Restriction of driver’s license Restriction of use. (Denmark, 2008) Policy restricted driver’s license for (Steentoft et al. 2010) Eriksen and older persons on benzodiazepines; Bjerrum 2015 part of Denmark’s overall public safety legislation that considered drugs that can impair driving. Policy category: Service provision, communication/marketing Strategy Intervention description Public and health provider Education. education Healthcare providers received (Australia) education related to treatment (Dollman et al. 2005) guidelines for insomnia; the public was also provided information on insomnia.

Results Chlorpropamide use decreased. Replaced by other antihyperglycemics, with glyburide being the most commonly used. Alprazolam use decreased in 6 months; a significant increase in use of other medications such as benzodiazepines, antidepressants, and antipsychotics

Results Prescribing of oxycodone decreased; increase in prescriptions for other potent opioids such as fentanyl and hydromorphone Use of benzodiazepine prescriptions decreased at one year following policy implementation; an increase in use of other medications

Results A year after implementation, resulted in a slight decrease in use of longacting benzodiazepines, but also a slight increase in new and use of longterm benzodiazepine prescriptions.

Results Resulted in a decrease in the use of long-acting and short-acting benzodiazepines at 5 years.

Results Resulted in a decrease in benzodiazepine and antidepressant dispensing. (Dollman et al. 2005)

(continued)

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Deprescribing, Table 6 (continued) Policy category: Environmental/social planning, service provision Strategy Intervention description Payment for medication education Incentivization to educate patients. Programs that support pharmacist led medication reviews such as MedsCheck through community pharmacies and in long-term/ residential care (Canada) Payment for recommendations to Pharmacists can complete prescribers on medication changes Pharmaceutical Opinions and make recommendations related to medications to physicians and educate patients (Canada) Payment for Comprehensive and/or Incentivization to identify PIMs and structured Medication Reviews in for pharmacists to communicate and community pharmacy and longcollaborate with prescribers term care/ residential care • High-risk medications in the elderly and potentially harmful drug-disease interactions in the elderly (United States) • Medicare Part D prescription drug program, medication therapy management service (United States) • Structured medication reviews (UK) • Home medicines review (Australia) • Medicine use reviews (Australia) • Residential medication management reviews (Australia) • Medicines use review (New Zealand) • Medicines management in care homes (UK)

Results This program primarily supports patient education (Chen et al. 2019; Sawan et al. 2020a, b).

Use of Pharmaceutical Opinions can decrease PIMs and have been shown to be cost-effective (Tannenbaum et al. 2015; Martin et al. 2018; Ontario Pharmaceutical Opinion Program) Comprehensive medication reviews can decrease the use of PIMs and can facilitate deprescribing (Chen et al. 2019; Sawan et al. 2020a, b).

Ref: Deprescribing by Policy Canadian Deprescribing Network

Examples of deprescribing policy strategies are provided in Table 6. Unfortunately, few policy interventions focus on providing safer nondrug alternatives (e.g., deprescribing benzodiazepines and providing funding for cognitive behavioral therapy for insomnia) which may be less expensive options when treating adverse drug events is considered (Tannenbaum et al. 2015). Multifaceted approaches can address a combination of intervention types such as education and monitoring. Limitations of policies that target a single intervention type such as restricting use of a drug by delisting it, without appropriate patient education and availability and access to

alternative nondrug measures, can lead to use of other similar drugs as replacement and with a potential for an increase in out-of-pocket cost to patients. When provided with appropriate education, patients are willing to discuss deprescribing and can be empowered to participate in shared decision-making (Tannenbaum et al. 2014a, b; Martin et al. 2018). An approach taken to decrease inappropriate antipsychotic use within long-term care homes in Canada included education and training of healthcare providers, support through one-on-one coaching, monitoring of progress and sharing of results, with engagement of the interdisciplinary team, resulting in a decrease in use of

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antipsychotics, without worsening of residents’ behaviour-related symptoms (Hirdes et al. 2020a, b). To support effective deprescribing in long-term care, policies designed to support these types of multifaceted interventions can be effective. The success of strategies to promote deprescribing is influenced by the context where care is provided and other factors such as time for the intervention and payment for services. Comparative effectiveness of policies and interventions in trials are often difficult to assess due to the heterogeneity of studies, and variability in healthcare settings and programs. Developing policy that addresses specific barriers and enablers related to contextual factors can support successful interventions. The interventions described elsewhere in this chapter can be grouped into supportive policy categories. Isenor et al have begun this work in their recent scoping review of deprescribing interventions in primary healthcare settings (Isenor et al. 2021a, b). They indicate that commonly targeted intervention domains include: environmental restructuring (e.g., such as providing evidence-based checklists like STOPP/START), enablement (e.g., providing social support), training (e.g., instructions to patients on how to taper medications; training clinicians on deprescribing and on how to communicate with patients), and persuasion (e.g., patient education on consequences of polypharmacy). Most studies in this review demonstrated a small decrease in the use of medications or successful deprescribing of specific drugs. Use of the BCW can enable determination of effective deprescribing initiatives that address specific behaviour changes techniques (Isenor et al. 2021a, b). Similarly, a better understanding of intervention domains to be targeted in other care contexts such as residential care or acute care can support identification of effective intervention types and respective supportive policies. The European Commission is funding several projects targeted at improvement of medication safety and prevention of harm to patients. Initiated in 2015, a consortium of stakeholders from eight countries across the EU with interdisciplinary teams of clinicians, including pharmacists, policymakers, health economists, and researchers, came together

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to promote and support “management of appropriate polypharmacy and adherence in the elderly” project SIMPATHY (Stimulating Innovative Management of Polypharmacy and Adherence in THe ElderlY) (SIMPATHY website). This consortium aims to create effective policies supporting community-based interventions to address inappropriate medication use (Stewart et al. 2017). Future Considerations for Policy Long-term sustainable changes in managing polypharmacy and potentially inappropriate medication use require broader systems-level approaches that influence policy development. Policies should be patient-centered and aim to reduce adverse drug events, hospitalizations, mortality, and cost to the system and to patients. Policies to promote deprescribing should be collaborative across jurisdictions, provide support for alternative nondrug options, and address patient’s social determinants of health that can impact on medication use. Consideration should be given to the specific behaviour changes, the context where care is provided, and the interventions that would support these. Evidence for cost-effective deprescribing strategies is likely to support sustainable policy changes. To facilitate optimization of medication use, a multipronged, multilevel approach is required, with policy interventions targeting specific behaviors for change.

Deprescribing: The Role of Networks During the 2010s, healthcare providers, researchers, and members of the public began to work more closely together on the problem of polypharmacy and deprescribing by establishing geographically linked networks. These networks brought interested stakeholders together with goals to increase awareness and to build capacity for policy, education, and research related to deprescribing. Examples of these are provided in Table 7.

Conclusion Current research provides us with a good understanding of barriers to deprescribing and enablers

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Deprescribing, Table 7 Deprescribing networks Network Australian Deprescribing Network (ADeN) (founded 2014)

Canadian Deprescribing Network (CaDeN) (founded 2015)

English Deprescribing Network (EDeN) (founded 2019)

United States Deprescribing Network (USDeN) (founded 2019)

Northern European Researchers in Deprescribing (NERD) (founded 2019)

Description and contact information ADeN is a group of clinicians, academic researchers, policymakers, students, and consumers working together to develop evidence-based, clinical guidance and knowledge translation to facilitate deprescribing. The network aims to promote research and awareness for the safe and appropriate use of medication for all Australians https://www.australiandeprescribingnetwork.com.au/ Twitter: https://twitter.com/DeprescribeAU CaDeN is a group of healthcare leaders, clinicians, decision-makers, academic researchers, and patient advocates working together to mobilize knowledge and promote deprescribing. The network aims to raise public awareness around the use of potentially inappropriate and harmful medications for older Canadians, building resources to address safe drug and nondrug approaches https://www.deprescribingnetwork.ca Twitter: https://twitter.com/DeprescribeNet EDeN is a group of healthcare professionals, researchers, and policy makers with an interest in promoting appropriate prescribing and shared decision-making. The network aims to raise awareness and skills around deprescribing among healthcare professionals and highlight the need for it to be part of appropriate patient care. It aims to contribute to the development of a national strategy to avoid harm caused by inappropriate polypharmacy. Twitter: https://twitter.com/EDeprescribeN USDeN is a group of investigators and stakeholders focused on improving research on deprescribing for older adults in the United States. The network aims to support and grow research on deprescribing that is scientifically rigorous and responsive to the needs of the people who are the end users of that research – patients, caregivers, clinicians, and health systems leaders. https://deprescribingresearch.org/ Twitter: https://twitter.com/DeprescribeUS NERD is a group of academic researchers and clinicians that aims to support collaboration between deprescribing researchers and increase the visibility of deprescribing in Northern Europe. The network’s goal is to strengthen collaborations and partnerships in deprescribing across countries by addressing research gaps and spreading knowledge and implementation of deprescribing. Twitter: https://twitter.com/NDeprescribing

of deprescribing. Deprescribing interventions demonstrate a decrease in PIMs but the clinical impact and real-world effectiveness is unclear. Ongoing research should address impact of deprescribing practices on patient outcomes such as quality of life, symptom control, hospitalizations, nursing home admissions, mortality, and health system-level outcomes (e.g., costs, system utilization), and explore practical and sustainable approaches to implementing deprescribing in routine care. A competency framework for deprescribing can help guide curricula for pre-licensure,

postgraduate, and continuing professional development. Deprescribing should be taught as a continuum of good prescribing, and both should be assessed within health professional programs and through national competency exams for licensure. Engaging the patient in shared decisionmaking related to their medications and deprescribing is important. Educating the public about medication use can enable them to have meaningful conversations with their health-care team about their health and medications.

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A patient-centered approach should be used in developing deprescribing policies and interventions to ensure sustainable, long-term changes. Theoretical behaviour frameworks such as the Behaviour Change Wheel can support the development of effective interventions and policies. Deprescribing networks play an important role in many countries in bringing together and engaging stakeholders, to increase their awareness and to support the work in research, clinical practice, education, and policy development.

Cross-References ▶ Evidence of the Impact of Interventions to Decrease Polypharmacy ▶ Geriatric Health Services: Evidence and Impact in Pharmacy and Pharmaceutical Public Health in Low-to-Middle-Income Countries ▶ Healthcare Education and Training of Health Personnel Acknowledgments We thank Dr. Justin Turner PhD, BPharm, MClinPharm for his expertise in reviewing the chapter.

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Deprescribing Education, LLC. Printed in the United States of America; 2012. ISBN 978-0-07-175638-9 Clyne B, Cooper JA, Hughes CM, Fahey T, Smith M. A process evaluation of a cluster randomized trial to reduce potentially inappropriate prescribing in older people in primary care (OPTI-SCRIPT study). Trials. 2016a;17:386. CNA Canadian Nurses Association RN Prescribing Framework 2015. https://cna-aiic.ca/en/professionaldevelopment/rn-prescribing-framework Deprescribing website https://deprescribing.org/resources/ publications/ Dutta AP, Daftary MN, Oke F, et al. Geriatric education in US schools of pharmacy: a snapshot. Consult Pharm. 2005;20(1):45–52. https://doi.org/10.4140/tcp.n. 2005.45. eCPS. Farrell B, Thompson W, Black C, et al. Operationalizing the Canadian Interprofessional Health Collaborative (CIHC) Competency Framework for team management of polypharmacy in older persons: a modified Delphi validation. Can Pharm J. 2018b;151(6):395–407. https://doi.org/10.1177/2F1715163518804276. Gallagher P, Ryan C, Byrne S, Kennedy J, O'Mahony D. STOPP (screening tool of older Person's prescriptions) and START (screening tool to alert doctors to right treatment) consensus validation. Int J Clin Pharmacol Ther. 2008b;46(2):72–83. Gordon J. Updated survey of the geriatrics content of Canadian undergraduate and postgraduate medical curricula. Can Geriatrics J. 2011;14(2):34–9. Hansen CR, O’Mahony D, Kearney PM, Sahm LJ, Cullinan S, Huibers CJA, et al. Identification of behaviour change techniques in deprescribing interventions: a systematic review and meta-analysis. Br J Clin Pharmacol. 2018b;84:2716–28. Hirdes JP, Major J, Didic S, Quinn C, Mitchell L, Chen J, et al. A Canadian cohort study to evaluate the outcomes associated with a Multicenter initiative to reduce antipsychotic use in long-term care homes. JAMDA. 2020a;21:817–22. Ivers N, Jamtvedt G, Flottorp S, et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2012;6: CD000259. Kahl A, Blandford MA, Krueger K, Zwick DI. Geriatric education centres address medication issues affecting older adults. Public Health Rep. 1992;107(1):37–46. Keijsers CJPW, van Doorn ABD, van Kalles A, de Wildt DJ, Bouwers JRBJ, van de Kamp H, et al. Structured Pharmaceutical analysis of the systematic tool to reduce inappropriate Prescribing is an effective method for Final-year medical students to improve polypharmacy skills: a randomized controlled trial. J Am Ger Soc. 2014;62(7):1353–9. Kunstler BE, Lennox A, Bragge P. Changing prescribing behaviours with educational outreach: an overview of evidence and practice. BMC Med Educ. 2019;19(1): 311. https://doi.org/10.1186/s12909-019-1735-3.

121 Mackey S, Bornsterin S. Deprescribing medications: barriers and enablers. NLCAHR May 2020. https://www. nlcahr.mun.ca/CHRSP/RER_Deprescribing_May_ 2020.pdf Masud T, Blundell A, Gordon AL, Mulpeter K, Roller R, Singler K, et al. European undergraduate curriculum in geriatric medicine developed using an international modified Delphi technique. Age Ageing. 2014:1–8. McIntosh J, et al. A case study of polypharmacy management in nine European countries: implications for change management and implementation. PLoS One. 2018a; https://doi.org/10.1371/journal.pone.0195232. Miller GE. The assessment of clinical skills/competence/ performance. Acad Med. 1990;65(9):S63–7. Misiaszek BC, Borrie MJ, Grymonpre RE, Brymer CD, Crilly RG, Viana L. Canadian university faculties of pharmacy: undergraduate curriculum survey of geriatric. 2001. NAPRA National Association of pharmacy regulatory authorities, professional competencies for Canadian pharmacists at entry to Practice 2014. https://napra.ca/ sites/default/files/2017-08/Comp_for_Cdn_PHARMA CISTS_at_EntrytoPractice_March2014_b.pdf Picton C, Loughrey C, Webb A. The need for a prescribing competency framework to address the burden of complex polypharmacy among multiple long-term conditions. Clin Med. 2016;16(5):470–4. https://doi.org/10. 7861/clinmedicine.16-5-470. Poots AJ, Jubraj B, Barnett NL. Education around deprescribing: ‘spread and embed’ the story so far. Eur J Hosp Pharm Jan. 2017;24(10):7–9. Poots AJ, Jubraj B, Ward E, et al. Education around medication review and deprescribing: a survey of medical and pharmacy students’ perspectives. Ther Adv Drug S a f . 2 0 2 0 ; 11 : 1 – 9 . h t t p s : / / d o i . o rg / 1 0 . 11 7 7 / 2042098620909610. Pruskowski JA, Kane-Gill SL, Kavalieratos D, Wilson BK, Arnold RM, Handler SM. Feasibility of an academic detailing intervention to support deprescribing in the nursing home. JAMDA. July 27 2021. Raman-Wilms L, Farrell B, Sadowski C, Austin Z. Deprescribing: an educational imperative. Res Soc Adm Pharm. 2019a;15(6):790–5. https://www. sciencedirect.com/science/article/pii/S155174111 8307745) RCPS Royal College of Physicians and Surgeons of Canada The Prescribing Safely Canada Physician Prescribing Competencies 2018. file:///Users/lraman/ Downloads/psc-prescribing-competencies-e% 20(1).pdf Reeve E, Shakib S, Hendrix I, Roberts MS, Wiese MD. Review of deprescribing processes and development of an evidence-based, patient-centred deprescribing process. Br J Clin Pharmacol. 2014b;78(4):738–47. Royal Pharmaceutical Society Competency Framework 2021. https://www.rpharms.com/Portals/0/RPS% 20document%20library/Open%20access/Prescribing %20Competency%20Framework/RPS%

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122 20Competency%20Framework.pdf? ver¼AlHRKuior3ef_fNnaMd3iA%3d%3d RPS Prescribing Competency Framework 2015. https:// www.rpharms.com/resources/frameworks/prescriberscompetency-framework RxFiles. Schmidt-Mende K, Hasselström J, Wettermark B, Andersen M, Bastholm-Rahmner P. General practitioners’and nurses’ views on medication reviews and potentially inappropriate medicines in elderly patients – a qualitative study of reports by educating pharmacists Scandinavian. J Primary Health Care. 2018;36(3):329–41. https://doi.org/10.1080/ 02813432.2018.1487458. Scott I, Anderson K, Freeman C. 2017 review of structured guides for deprescribing. Eur J Hospital Pharm. 2017;24(1):51. The Lown Institute, Eliminating Medication Overload: A National Action Plan January 2020. https:// lowninstitute.org/reports/eliminating-medicationoverload-a-national-action-plan/ Therapeutics Initiative Therapeutics Initiative | Better prescribing. Better health. – Better prescribing. Better health. (ubc.ca) Woit C, Yuksel N, Charrois TL. Competence and confidence with prescribing in pharmacy and medicine: a scoping review. Int J Pharm Pract. 2020;28:312–25. Zimmerman KM, Linsky AM, Donohoe KL, Hobgood SE, Sargent L, Salgado TM. An interprofessional workshop to enhance de-prescribing practices among health care providers. JCEHP. 2020;40(1):49–57. Zolezzi M, Sadowski C, Al-Hasan N, et al. Geriatric education in schools of pharmacy: students’ and educators’ perspectives in Qatar and Canada. Curr Pharm Teach Learn. 2018;10:1184–96. https://doi.org/10.1016/j. cptl.2018.06.010. Zou D, Tannenbaum C. Educational needs, practice patterns and quality indicators to improve geriatric pharmacy care. Can Pharm J. 2014;147(2):110–7.

Policy Section References Atkins L, Francis J, Islam R, O’Connor D, Patey A, Ivers N, et al. A guide to using the theoretical domains framework of behaviour change to investigate implementation problems. Implement Sci. 2017;12:77. https://doi.org/10.1186/s13012-017-0605-9. Canadian Deprescribing Network – Deprescribing by Policy https://static1.squarespace.com/static/5836f01fe6f2e1fa6 2c11f08/t/5ae9cc67f950b7f1080bbccd/1525271655720/ Policy_infographic_13April2018_FINAL_web.pdf Canadian Deprescribing Network – Deprescribing by Policy. https://static1.squarespace.com/static/5836f01 fe6f2e1fa62c11f08/t/5ae9cc67f950b7f1080bbccd/1 525271655720/Policy_infographic_13April201 8_FINAL_web.pdf Cane J, O’Connor D, Michie S. Validation of the theoretical domains framework for use in behaviour change and implementation research. Implement Sci. 2012;7:

Deprescribing 37. http://www.implementationscience.com/content/7/ 1/37 Chen EYH, Kn W, Sluggett JK, Ilomäki J, Hilmer SN, Corlis M, et al. Process, impact and outcomes of medication review in Australian residential aged care facilities: a systematic review. Australas J Ageing. 2019;38(Suppl 2):9–25. Dollman WB, Leblanc VT, Stevens L, O’Connor PJ, Roughead EE, Gilbert AL. Achieving a sustained reduction in benzodiazepine use through implementation of an area-wide multi-strategic approach. Clin Pharm Ther. 2005;30:425–32. Farrell B, Conklin J, Dolovich L, Irving H, Maclure M, McCarthy L, Moriarty F, Pottie K, Raman-Wilms L, Reeve E, Thompson W. Deprescribing guidelines: an international symposium on development, implementation, research and health professional education. Res Soc Adm Pharm. 2019a;15(6):780–9. Fischer B, Jones W, Rehm J. Trends and changes in prescription opioid analgesic dispensing in Canada 2005–2012: an update with a focus on recent interventions. BMC Health Serv Res. 2014;14:90. Fischer B, Jurgen R, Tyndall M. Effective Canadian policy to reduce harms from prescription opioids: learning from past failures. CMAJ. 2016;188:17–8. Fisher J, Sanyal C, Frail D, Sketris I. The intended and unintended consequences of benzodiazepine monitoring programmes: a review of the literature. J Clin Pharm Ther. 2012;37:7–21. Hansen CR, O’Mahony D, Kearney PM, Sahm LJ, Cullinan S, Huibers CJA, Thevelin S, Rutjes AWS, Knol W, Streit S, Byrne S. Identification of behaviour change techniques in deprescribing interventions: a systematic review and meta-analysis. Br J Clin Pharmacol. 2018c;84:2716–28. High-Risk Medications in the Elderly and Potentially Harmful Drug-Disease Interactions in the Elderly (United States). https://www.ncqa.org/hedis/measures/ Hirdes JP, Major J, Didic S, et al. A Canadian Cohort Study to Evaluate the Outcomes Associated with Multicenter Initiative to Reduce Antipsychotic Use in Long-Term Care Homes. J Post-Acute Long Term Care Medicine (JAMDA). 2020b;21(6):P817–22. https://doi.org/10. 1016/j.jamda.2020.04.004. Home Medicines Review (Australia). https://www. healthdirect.gov.au/home-medicines-review Isenor E, Bai I, Cormier R, Jhelwig M, Reeve E, Whelan AM, Burgess S, Martin-Misener R, Kennie-Kaulbach N. Deprescribing interventions in primary health care mapped to the behaviour change wheel: a scoping review. RSAP. 2021b;17(7):1229–41. Martin P, Tamblyn R, Benedetti A, Ahmed S, Tannenbaum C. Effect of a pharmacist-led educational intervention on inappropriate medication prescriptions in older adults: the D-PRESCRIBE randomized clinical trial. JAMA. 2018;320(18):1889–98. https://doi.org/10. 1001/jama.2018.16131. Medicare Part D prescription drug program, Medication Therapy Management service (United States). https://

Deprescribing www.cms.gov/Medicare/Prescription-Drug-Coverage/ PrescriptionDrugCovContra/MTM Medication Management Reviews (Australia). https:// www1.health.gov.au/internet/main/publishing.nsf/Con tent/medication_management_reviews.htm Medicines management in care. homeshttps://www.nice. org.uk/guidance/qs85/chapter/quality-statement-5medication-reviews Medicines management in care homes (UK). https://www. nice.org.uk/guidance/qs85/chapter/quality-statement5-medication-reviews Medicines Use Review. https://www.psnz.org.nz/Cate gory?Action¼View&Category_id¼449 Medicines Use Review (New Zealand). https://www.psnz. org.nz/Category?Action¼View&Category_id¼449 MedsCheck: MedsCheck - Health Care Professionals MOHLTC (gov.on.ca) Michie, et al. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implementation Sci. 2011;6(1):42. http://www.implementationscience. com/content/6/1/42 Ontario Pharmaceutical Opinion Program - Health Care Professionals - MOHLTC (gov.on.ca) Rat C, Penhouet G, Gaultier A, Chaslerie A, Pivette J, Nguyen JM, et al. Did the new French pay-forperformance system modify benzodiazepine prescribing practices? BMC Health Serv Res. 2014;14:301. Reid PP, Compton WD, Grossman JH, et al. Chapter 2, A Framework for a Systems Approach to Health Care Delivery. In: Building a Better Delivery System: A New Engineering/Health Care Partnership. Institute of Medicine. Washington, DC: The National Academies Press; 2005. https://doi.org/10.17226/11378. Residential Medication Management Reviews https:// www1.health.gov.au/internet/main/publishing.nsf/Con tent/medication_management_reviews.htm#:~: text¼The%20Residential%20Medication%20Manage ment%20Review,benefit%20from%20such%20a% 20service Residential Medication Management Reviews (Australia). https://www1.health.gov.au/internet/main/publishing. nsf/Content/medication_management_reviews.htm#: ~:text¼The%20Residential%20Medication%20Man agement%20Review,benefit%20from%20such%20a% 20service Sawan M, Reeve E, Turner J, Todd A, Steinman MA, Petrovic M, et al. A systems approach to identifying the challenges of implementing deprescribing in older adults across different health care settings and countries: a narrative review. Expert Rev Clin Pharmacol. 2020b;13(3):233–45. Schaffer A, et al. Interrupted time series analysis of the effect of rescheduling alprazolam in Australia: taking control of prescription drug use. JAMA Int Med. 2016;176(8):1223. Shaw J, Murphy AL, Turner JP, Gardner DM, Silvius JL, Bouck Z, et al. Policies for deprescribing: an international scan of intended and unintended outcomes of

123 limiting sedative-hypnotic use in community-dwelling older adults. Healthcare Policy. 2019;14(4):39–51. SIMPATHY. http://simpathy.eu/vision-and-mission Sketris IS, Kephart G, Frail DM, Skedgel C, Allen MJ. The effect of deinsuring chlorpropamide on the prescribing of oral antihyperglycemics for Nova Scotia seniors’ Pharmacare beneficiaries. Pharmacotherapy. 2004;24(6):784–91. Steentoft A, Simonsen KW, Linnet K. The frequency of drugs among Danish drivers before and after the introduction of fixed concentration limits. Traffic Inj Prev. 2010;11(4):329–33. https://doi.org/10.1080/ 15389581003792783. Stewart D, Mair A, Wilson M, Kardas P, Lewek P, Alonso A, McIntosh J. MacLure K and SIMPATHY consortium. Expert Opin Drug Saf. 2017;16(2): 203–13. https://doi.org/10.1080/14740338.2017. 1265503. Structured Medication Reviews (UK). https://www. england.nhs.uk/publication/structured-medicationreviews-and-medicines-optimisation/ Tannenbaum C, Martin P, Tamblyn R, Benedetti A, Ahmed S. Reduction of inappropriate benzodiazepine prescriptions among older adults through direct patient education: the EMPOWER cluster randomized trial. JAMA Intern Med. 2014b;174(6):890–8. https://doi.org/10. 1001/jamainternmed.2014.949. PMID: 24733354 Tannenbaum C, Diaby V, Singh D, Perreault S, Luc M, Vasiliadis HM. Sedative-hypnotic medicines and falls in community-dwelling older adults: a costeffectiveness (decision-tree) analysis from a US Medicare perspective. Drugs Aging. 2015;32(4):305–14. Wagner AK, Soumerai SB, Zhang F, Mah C, SimoniWastila L, Cosler L, et al. Effects of state surveillance on new post-hospitalization benzodiazepine use. Int J Qual Health Care. 2003;15(5):423–31.

Other References Armstrong N, Swinglehurst D. Understanding medical overuse: the case of problematic polypharmacy and the potential of ethnography. Fam Pract. 2018;35(5): 526–7. Belcher VN, Fried TR, Agostini JV, Tinetti ME. Views of older adults on patient participation in medicationrelated decision making. J Gen Intern Med. 2006;21(4):298–303. Blum, et al. Optimizing therapy to prevent avoidable hospital admissions in multimorbid older adults (OPERAM): cluster randomised controlled trial. BMJ. 2021;374 https://doi.org/10.1136/bmj.n1585. Clyne B, Cooper JA, Hughes CM, Fahey T, Smith M. A process evaluation of a cluster randomized trial to reduce potentially inappropriate prescribing in older people in primary care (OPTI-SCRIPT study). Trials. 2016b;17:386. Elwyn G, Durand MA, Song J, et al. A three-talk model for shared decision making: mulitstage consultation process. BMJ. 2017a;359:j4891.

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124 Elwyn G, Cochran N, Pignone M. Shared decision making – the importance of diagnosing preferences. JAMA Intern Med. 2017b;177(9):1239–40. Fajardo MA, Weir KR, Bonner C, Gnjidic D, Jansen J. Availability and readability of patient education materials for deprescribing: an environmental scan. Br J Clin Pharmacol. 2019b;85 https://doi.org/10.1111/ bcp.13912. Farrell B, Richardson L, Raman-Wilms L, de Launay D, Alsabbagh MW, Conklin J. Self-efficacy for deprescribing: a survey for health care professionals using evidence-based deprescribing guidelines. Res Soc Adm Pharm. 2018c;14(1):18–25. Farrell B, Conklin J, Dolovich L, Irving H, Maclure M, McCarthy L, Moriarty F, Pottie K, Raman-Wilms L, Reeve E, Thompson W. Deprescribing guidelines: an international symposium on development, implementation, research and health professional education. Res Soc Adm Pharm. 2019b;15(6):780–9. https://www. sciencedirect.com/science/article/pii/S155174111 8307447) Farrell B, Grad R, Howell P, Quast T, Reeve E. Deprescribing guidelines: value of an interactive mobile application. PRiMER. 2020b;4:26. Forsyth LP, Carman KL, Szydlowski V, Fayish L, Davidson L, Hickam DH, Hall C, Bhat G, Neu D, Steward L, Jalowsky M, Aronson N, Anyanwu CU. Patient engagement in research: early findings from the patient-centred outcomes research institute. Health Aff. 2019;38(3):359–67. Good for you, Good for us, Good for everybody. https:// assets.publishing.service.gov.uk/government/uploads/ system/uploads/attachment_data/file/1019475/goodfor-you-good-for-us-good-for-everybody.pdf Hepler CD, Strand LM. Opportunities and responsibilities in pharmaceutical care. Am J Hosp Pharm. 1990b;45(3):533–43. Hoffmann T, Jansen J, Glasziou P. The importance and challenges of shared decision making in older people with multimorbidity. PLoS Med. 2018;15(3): e1002530. ISMP 5 Questions to Ask - ISMP Canada (ismp-canada. org) Lehman R. Sharing as the future of medicine. JAMA Intern Med. 2017;177(9):1237–8. McCarthy D Reducing Inappropriate Medication Use by Implementing Deprescribing Guidelines. Cambridge, Massachusetts: Institute for Healthcare Improvement; 2017. (Available at ihi.org Reducing Inappropriate Medication Use by Implementing Deprescribing Guidelines | IHI - Institute for Healthcare Improvement McIntosh J, Alonso A, MacLure K, Steward D, Kempen T, Mair A, et al. A case study of polypharmacy management in nine European countries: implications for change management and implementation. PLoS One. 2018b;13(4):e0195232. McKellar GD, Hampson R, Tierney A, et al. Nonsteroidal antiinflammatory drug withdrawal in patients with stable rheumatoid arthritis. J Rheumatol. 2011b;38: 2150–2.

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Developing, Implementing and Evaluating Complex Services/Interventions, and Generating the Evidence Cathal Cadogan School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin, Ireland

Abstract

A robust process of development and evaluation is required to generate evidence to support implementation of new pharmacy services and pharmacist-led healthcare interventions into clinical practice. The process of developing, evaluating, and implementing healthcare interventions has grown considerably and is increasingly incorporating a scientific approach to ensure that resulting interventions are fit for purpose and have a greater likelihood of being effective. This chapter will examine and discuss the methods of developing, evaluating, and implementing healthcare interventions and generating relevant evidence to inform healthcare decisions. The chapter shows that there is no single correct approach to use during any of these processes. However, a systematic approach that uses rigorous research methods is critical to producing high-quality research findings. Applying the learnings from existing frameworks and previous research, as presented in this chapter, will help researchers to generate evidence to support the implementation of new pharmacy services and pharmacist-led interventions going forward.

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Keywords

Health services research · Intervention development · Evaluation

Introduction Health services offer a wide variety of health interventions which the World Health Organization defines as “any act performed for, with or on behalf of a person or population whose purpose is to assess, improve, maintain, promote or modify health, functioning, or health conditions” (World Health Organization 2022). These can encompass screening services to detect diseases, drug treatments for chronic conditions (e.g., asthma, diabetes) and medication reviews. The demand for health services is continually increasing due to a variety of factors including population growth and ageing, as well as an increasing global burden of disease (European Commission 2020). For example, it is predicted that by 2070, those aged 65 years and older will account for 30.3% of the population across Europe (compared to 20.3% in 2019) (European Commission 2020). These population level demographic changes will have a profound impact on future healthcare needs. Health services and health professionals working within them are continually looking to develop new ways of working or to optimize existing practices in response to changing population healthcare needs. This applies to pharmacy practice which encompasses three core elements: medicines use, patient-centered care, and health services provision (Hasan et al. 2019). The role of the pharmacist has evolved considerably over time from traditional compounding and dispensing activities to a more patient-centered practice that increasingly makes use of the full range of pharmacists’ skills and competencies. For example, pharmacists in various jurisdictions (e.g., Canada, United Kingdom) now have a more active role in the prescribing process as independent and supplementary prescribers (Cope et al. 2016; Zhou et al. 2019). In parallel with the development and expansion of pharmacists’ role, there has been an increasing emphasis on research to generate

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evidence to support the implementation of new pharmacy services and pharmacist-led interventions (Tsuyuki and Bond 2019). Health services research in pharmacy (also termed “pharmacy practice research”) which focuses on the assessment and evaluation of pharmacy practice has also grown considerably in recent years (Dolovich and Tsuyuki 2016; Bond and Tsuyuki 2019). A number of systematic reviews also provide evidence that shows the potential benefit of extended roles for pharmacists (Weeks et al. 2016; de Barra et al. 2018). For example, a Cochrane systematic review that examined pharmacist services in outpatient settings found that such services can achieve improvements in blood pressure control in patients with hypertension (de Barra et al. 2018). It is now widely accepted that a robust process of development and evaluation is required to generate evidence to support implementation of new interventions and services into clinical practice. The process of developing, evaluating, and implementing healthcare interventions has grown considerably and is increasingly incorporating a scientific approach to ensure that resulting interventions are fit for purpose and have a greater likelihood of being effective. This has resulted in increased focus on areas such as: the use of theory in intervention development; the use of standardized terminology to describe interventions and facilitate replication; the use of feasibility/pilot testing before proceeding to definitive evaluations, and; the use of process evaluations to help understand an intervention’s implementation and mechanisms of action. This chapter will examine and discuss the methods of developing, evaluating, and implementing healthcare interventions, and generating relevant evidence to inform healthcare decisions. Illustrative examples will be incorporated throughout.

Intervention Development, and Evaluation: Available Guidance and Frameworks The term “complex intervention” is often used when discussing the development and evaluation

of healthcare interventions for which there are various definitions. For example, the UK Medical Research Council considers interventions to be complex as a result of “either the characteristics of the intervention itself (e.g., multiple interacting components) and/or the dependence on external factors to generate outcomes (e.g., characteristics of intervention recipients, and/or the context within which it is implemented)” (Skivington et al. 2021). However, the value of categorizing interventions as “simple” or “complex” has been contested (Petticrew 2011; Moore et al. 2017). For example, Moore et al. propose that “all interventions are complex, but some are more complex than others” (Moore et al. 2017). Consequently, the term “healthcare intervention” will be used in place of “complex intervention” throughout the remainder of this chapter. Over the last two decades, several frameworks and guidance documents have been published relating to the development and evaluation of healthcare interventions. The focus of individual frameworks encompasses a range of areas including behavior change (Michie et al. 2011; Cane et al. 2012), public health (Wight et al. 2016), and digital health (Mummah et al. 2016; Kowatsch et al. 2019). For example, the Behavior Change Wheel was developed from 19 behavior change frameworks and can be used in systematically developing behavior change interventions (Michie et al. 2011). The Behavior Change Wheel consists of three layers (Fig. 1). At the core of the wheel is the COM-B (Capability, Opportunity, Motivation- Behavior) model of behavior change. COM-B posits that human behavior is a result of interactions between capability, opportunity, and motivation (Michie et al. 2011). Each of the model’s three components can be further subdivided. For example, capability can be subdivided into “physical capability” (ability/skills to perform the behavior) and “psychological capability” (capacity to engage in essential decisionmaking processes). Surrounding the Behavior Change Wheel’s central hub is a layer of nine intervention functions which are linked to specific COM-B components and aimed at addressing deficits in one or more of the model’s components. The outer layer comprises seven policy categories

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Intervention Development Historically, the intervention development process has received relatively little attention in the literature and guidance in this area has been lacking. For example, a notable limitation of some of the existing guidance documents and

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that can support delivery of the associated intervention functions (Michie et al. 2011). Table 1 provides a summary of different frameworks including published examples of their application in the context of health services research in pharmacy. Across different frameworks, commonalities exist in terms of key stages, namely, that; (1) the intervention is developed to address a key problem, structure/process, or behavior; (2) the intervention undergoes evaluation; (3) the intervention is then implemented on a wider scale. Within different frameworks each of the stages may be further broken down into a series of smaller steps. Insufficient evidence exists to advocate that a specific approach or set of actions is essential to develop an effective intervention (O’Catháin et al. 2019a; Turner et al. 2019). Nevertheless, a structured and transparent approach is important. The proceeding sections will examine each of the key phases of intervention development, evaluation, and implementation in turn.

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Developing, Implementing and Evaluating Complex Services/Interventions, and Generating the Evidence, Fig. 1 The Behavior Change Wheel (Source: Michie et al. 2011)

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frameworks outlined in Table 1 is that they do not provide explicit direction in relation to each step of the intervention development process (Wight et al. 2016). This is further exemplified by the fact that detailed descriptions of the intervention development process are often lacking in published study reports. Consequently, the intervention development process has been referred to as a black box (Hoddinott 2015). A further challenge to gaining insight into the development process is that intervention developers use a variety of different approaches and take a range of different actions during the process without always fully outlining the rationale for their decisions (Croot et al. 2019). Increasingly, funding organizations and medical journals are willing to support and publish intervention development studies which have been defined as studies that “describe the rationale, decision-making processes, methods and findings that occur from an intervention’s inception up to the point that it is ready to proceed to pilot/feasibility testing prior to a definitive trial or evaluation” (Hoddinott 2015). For example, a substantial body of work has been completed as part of the INDEX Study (IdentifyiNg and assessing different approaches to DEveloping compleX interventions) which was funded by the UK Medical Research Council to produce

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Developing, Implementing and Evaluating Complex Services/Interventions, and Generating the Evidence, Table 1 Examples of guidance documents and

frameworks for the development and/or evaluation of healthcare interventions

Guidance/Framework 1. Behavior Change Wheel (Michie et al. 2011)

Summary overview Focus: Behavior change Key steps/phases: (1) Define problem in behavioral terms; (2) Select target behavior; (3) Specify target behavior; (4) Identify what needs to change to achieve the target behavior using COM-B (inner hub of wheel); (5) Identify intervention functions (middle hub of wheel) to achieve the target behavior; (6) Identify policy categories (outer hub of wheel) that support the intervention functions; (7) Identify behavior change techniques; (8) Identify mode of delivery. 2. Integrate, Design, Focus: Digital health Assess, and Share Key steps/phases: (1) Integrate (empathize, specify, (IDEAS) Framework ground); (2) Design (ideate, prototype, gather, (Mummah et al. 2016) build); (3) Assess (pilot, evaluate); (4) Share. 3. Intervention mapping Focus: Behavior change framework (Eldredge Key steps/phases: (1) Conduct a needs assessment et al. 2016) or problem analysis; (2) Develop matrices of change objectives for the intervention to target; (3) Select theory-based intervention methods and translate them into practical applications; (4) Develop the intervention program; (5) Develop plan for adoption, implementation and sustainability; (6) Develop an evaluation plan. 4. Medical Research Focus: Complex interventions Council framework for Key steps/phases: (1) Development (Identifying the developing and evidence base; Identifying/developing theory; evaluating complex Modelling process and outcomes); (2) Feasibility/ interventions (Craig Piloting (Testing procedures; Estimating et al. 2008) recruitment/retention; Determining sample size); (3) Evaluation (Assessing effectiveness; Understanding change process; Assessing costeffectiveness); (4) Implementation (Dissemination; Surveillance and monitoring; Long term follow-up) 5. PRECEDE– Focus: Population health PROCEED model Key steps/phases: (1) PRECEDE - Assessment (Crosby and Noar 2011) Phase (Social assessment; Epidemiological assessment; Educational and ecological assessment; Administrative and policy assessment); (2) PROCEED - Implementation Phase (Program implementation; Process evaluation; Impact evaluation; Outcome evaluation) a

Examples relating to health services research in pharmacy Intervention to support appropriate antibiotic prescribing by pharmacists and nurses (Courtenay et al. 2019) Intervention to improve information exchange during community pharmacy based over-the-counter consultations (Seubert et al. 2018)

None identifieda

Community pharmacy-based intervention to improve medication adherence among breast cancer survivors (Labonté et al. 2020) Community pharmacy-based intervention to optimize antidepressant use (Santina et al. 2018) Community pharmacy-based intervention to promote smoking cessation (Steed et al. 2017) Coach-led intervention to ease novice community pharmacists’ transition to practice (Magola et al. 2022)

Community pharmacist-led intervention program for control of hypertension (Chabot et al. 2003) Ear health intervention for rural community pharmacy (Taylor et al. 2021)

Based on searches of PubMed and backward citation tracking up to June 2022

guidance for researchers on how to develop complex interventions to improve health and healthcare outcomes (INDEX study 2016). This project has highlighted the wide variety of approaches to the intervention development process that exist. Based on a systematic methods

overview of intervention development processes, Ó’Catháin et al. (2019b) developed a taxonomy comprising eight categories: (1) Partnership; (2) Target population-centered; (3) Theory and evidence-based; (4) Implementation-based; (5) Efficiency-based; (6) Stepped or phased;

Developing, Implementing and Evaluating Complex Services/Interventions Developing, Implementing and Evaluating Complex Services/Interventions, and Generating the Evidence, Table 2 Taxonomy of intervention development approaches (O’Cathain et al. 2019b) Category Partnership

Target populationcentered Theory and evidence-based

Implementationbased

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Stepped or phased Interventionspecific Combination

Definition Intervention development involves an equal partnership between the research team and those who will ultimately use the intervention. Intervention development is based on the views and actions of those who will use the intervention. Intervention development is based on a combination of published research evidence and formal theories. Intervention development is focused on ensuring that the intervention can be used in real world settings. Intervention development involves testing the various intervention components using experimental design to aid intervention component selection and to optimize efficiency. Intervention development involves a systematic set of processes/ phases. The approach to intervention development is constructed for a specific intervention type. Intervention development is based on a combination of different approaches.

(7) Intervention-specific; (8) Combination (Table 2). The taxonomy highlights the range of different approaches and possible actions involved in developing interventions. Further work undertaken as part of the INDEX study has integrated the above taxonomy with findings from a qualitative interview study and a consensus exercise involving those with experience in developing healthcare interventions to produce guidance for researchers on actions to take during the intervention development process (O’Catháin et al. 2019a). This guidance outlines a comprehensive set of actions to guide the intervention development process. However, as noted by the authors, it may not be possible or desirable for those involved in developing an intervention

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to address all the actions during the development process, as some may not be relevant to the specific problem or context (O’Catháin et al. 2019a). Therefore, the relevance of each action to a particular intervention in a specific context should be reviewed at the start of, and throughout, the development process. A distilled summary of selected key actions from this guidance document is discussed further below.

Plan the Development Process In planning the intervention development process, it is important to clearly identify the problem or issue that needs to be addressed and the key stakeholders that need to be involved. A team then needs to be assembled that includes researchers with relevant methodological expertise, as well as other relevant collaborators such as clinicians, patients and/or members of the public. The nature of the problem to be addressed and the approach to be used to address it should be used to guide the choice of team members. For example, a behavior change focused intervention may benefit from the expertise of a health psychologist, whereas a digital health intervention may require the input of a computer scientist. The team should then decide on the approach to intervention development (Table 2) and generate a protocol to identify the relevant time and resource requirements and ensure a systematic and transparent approach to the entire process. As outlined in Table 1 above, several of the frameworks that can be used for intervention development purposes comprise a series of stages or steps. During the planning process, it is important to recognize that intervention development is a dynamic process. Consequently, even the most detailed and well thought plans need to open to being adapted and refined as data are collected and new insights into the problem emerge that needs to be addressed. However, this can present challenges for intervention developers particularly in situations where a need arises to deviate from original grant application protocols in order to incorporate knowledge and evidence acquired during the development process (O’Catháin et al. 2019a).

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Identify and Review Relevant Evidence and Theory Before proceeding with the intervention development process, it is important to identify and consider relevant evidence and theory. In identifying relevant evidence, systematic reviews of existing or related interventions that are relevant to the specific challenge to be addressed can often be a useful starting point. Systematic reviews can assist with the identification, appraisal and synthesis of available evidence, and aid intervention developers in selecting intervention components and outcomes measures to include as part of the intervention’s overall evaluation. This can help to provide useful information about previously evaluated interventions, their components and any underpinning theory, as well as any deficiencies in previously evaluated interventions and the methods by which they were evaluated. For example, in developing a community pharmacy-based intervention to improve medication adherence for older adults prescribed polypharmacy in primary care, Patton et al. started by conducting a systematic review of adherence interventions targeting older adults and identified a deficit of relevant literature, as well as limitations with the design and evaluation of existing interventions (Patton et al. 2017). The findings of this systematic review helped to inform the intervention’s future development and evaluation by highlighting the need for a robust approach and the types of outcome measures that should be considered (Patton et al. 2018, 2019). Increasingly, it is advocated that intervention development should be underpinned by relevant theory or a theoretical framework (Stewart and Klein 2016). A theory represents a systematic way of understanding events or situations through “a set of concepts, definitions, and propositions that explain or predict these events or situations by illustrating the relationships between variables” (Glanz and Rimer 2005). A “good theory” offers a clear explanation of why and how specific relationships between variables lead to specific events (Nilsen 2015). In contrast to identifying relevant evidence using established methodologies (e.g., systematic reviews), the methods

involved in identifying and/or developing appropriate theory have been less clear (Hughes et al. 2016). Moreover, the multitude of existing theories can pose difficulties for researchers in selecting one theory over another. Consequently, various theoretical frameworks have been developed which can represent a composite of various theories. In contrast to theories, theoretical frameworks only describe empirical phenomena by using a set of categories (Nilsen 2015). The Theoretical Domains Framework is one such example that features prominently in the development of behavior change interventions (Atkins et al. 2017). The Theoretical Domains Framework is an integrated framework of 33 behavior change theories that was developed in order to simplify psychological theories relating to behavior change and enhance their accessibility and use by researchers from non-health psychology disciplines (Michie et al. 2008; Cane et al. 2012). The most recent version of the Theoretical Domains Framework which was refined following a validation exercise comprises 14 theoretical domains: “Knowledge,” “Skills,” “Social/Professional Role and Identity,” “Beliefs about Capabilities,” “Optimism,” “Beliefs about Consequences,” “Reinforcement,” “Intentions,” “Goals,” “Memory, Attention and Decision Processes,” “Environmental Context and Resources,” “Social Influences,” “Emotions,” and “‘Behavioral Regulation” (Cane et al. 2012). Each domain represents potential mediators (i.e., barriers and facilitators) of behavior change that can be targeted as part of a behavior change intervention. The individual domains within the Theoretical Domains Framework map to the COM-B model and within the Behavior Change Wheel, both frameworks have been used concurrently in developing healthcare interventions (Atkins et al. 2017). However, as a standalone framework, the Theoretical Domains Framework is primarily focused on understanding mediators of behavior change and is not a standalone reference source to guide researchers through the entire process of intervention development and evaluation. Advocates of the use of theory during the intervention development process contend that

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it can help in overcoming inherent limitations and biases of a purely pragmatic approach that is guided by researchers’ implicit assumptions as to the type of intervention that is likely to be effective (Improved Clinical Effectiveness through Behavioral Research 2006; Grol et al. 2007). For example, theories generate testable hypotheses and be used to explore the potential causal mechanisms that underpin the intervention’s effect. However, the use of theory in the intervention development process is contested and evidence to support this type of approach over non-theory-based approaches is lacking (Oxman et al. 2005; Bhattacharyya et al. 2006). This is exemplified by the findings of a recent systematic review of systematic reviews of published randomized controlled trials and meta-analyses that examined whether theorybased interventions were associated with more effective health behavior change interventions compared to non-theory-based interventions (Dalgetty et al. 2019). The review found that theory-based interventions were not associated with more effective behavior change compared to non-theory-based interventions (Dalgetty et al. 2019). Moreover, it is important to note that non-theory-based interventions can be effective in achieving the desired outcomes. For example, the Bridge-It study evaluated a community pharmacy-based intervention involving a bridging supply of emergency hormonal contraception and rapid access to a participating sexual and reproductive health clinic (Cameron et al. 2020). There was no formal underpinning theory reported in the study protocol or main evaluation report; however, the intervention was found to be effective in achieving its primary outcome (i.e., the use of effective contraception at follow-up) (Cameron et al. 2019, 2020). Despite the current lack of evidence to favor theory-based intervention over non-theory-based intervention, it is recognized that theory can provide an explicit and coherent framework for the development, evaluation, and optimization of interventions (Dalgetty et al. 2019). The potential ways in which theory could advance the intervention development process should be considered on a case-by-case basis.

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Undertake Primary Data Collection In undertaking primary data collection, the focus is on using appropriate research methods to collect relevant data that will help to inform the intervention’s development and ultimately enable the intervention to address the particular healthcare challenge or issue that has been identified. Intervention development studies are often underpinned by qualitative interviews which allow in depth exploration of key stakeholders’ perceptions and experiences of a particular health care issues and rich data to be collected. For example, in developing an intervention to improve the prescribing of appropriate polypharmacy in older adults in primary care, Cadogan et al. conducted semi-structured interviews with GPs and community pharmacists (Cadogan et al. 2015). The interview questions were underpinned by the Theoretical Domains Framework which enabled key theoretical domains to be identified and mapped to behavior change techniques as part of the subsequent intervention (Cadogan et al. 2016). There is no single correct approach to undertaking data collection for the purpose of intervention development. Data collection need not necessarily be restricted to qualitative methods; quantitative methods and mixed methods approaches may also be applicable. In making any decisions, researchers should consider the strengths and limitations of different research methods, as well as available time, resources and expertise within the research team.

Design and Refine the Intervention The terms development and design are often used interchangeably in the literature. Development refers to the entire process of developing the intervention, whereas design tends to refer to the specific parts of the development process where decisions are made about intervention content, format and delivery (O’Cathain et al. 2019a). It has been posited that the tendency to conflate

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development and design may have contributed to the latter receiving insufficient attention (Rousseau et al. 2019). There is no single approach for designing or refining interventions. In many cases, the approach will depend on the type of intervention and intervention development process. For example, in the context of behavior change interventions, the process typically involves identifying and targeting behavioral determinants using behavior change techniques. This is core to the development of interventions underpinned by the behavior change wheel (Michie et al. 2011) and the intervention mapping approach (Eldredge et al. 2016). However, there is as yet no guidance on how best to translate behavior change techniques into actual intervention components or decide on the overall content, format and delivery of the intervention. During the design process, it may be the case that multiple plausible intervention approaches are initially identified. In such cases, a structured approach is needed in balancing up the strengths and weaknesses of different approaches, as well as any other pertinent issues, and in making decisions between them. Different approaches exist to

facilitate this process (Table 3). For example, the SWOT analysis originated as a business strategy tool for organizations to compare themselves against their competitors (Teoli et al. 2022). In the context of intervention design and refinement, it can be used compare interventions in terms of their respective strengths, weaknesses, opportunities, threats. Another approach involves the application of the APEASE criteria (Affordability, Practicability, Effectiveness/cost-effectiveness, Acceptability, Side-effects/safety, Equity) which have been developed to assist with the process of designing and evaluating interventions (Michie et al. 2014). Further refinement may be necessary following any preliminary evaluation of the intervention (as discussed below).

End the Development Process The intervention development process can be concluded when the research team is satisfied that the intervention should proceed to the evaluation stage. However, there are no established criteria for arriving at this point. Nevertheless, it is

Developing, Implementing and Evaluating Complex Services/Interventions, and Generating the Evidence, Table 3 Approaches for assessing interventions during the design phase Approach SWOT analysis (Teoli et al. 2022)

APEASE criteria (Michie et al. 2014)

Components Strengths Weaknesses Opportunities Threats Affordability

Practicability Effectiveness/ cost-effectiveness Acceptability

Side-effects/ safety Equity

Questions to consider What are the intervention’s key strengths/advantages? What are the intervention’s key weaknesses/disadvantages? What are the key facilitators/enablers to the intervention’s implementation? What are the key barriers to the intervention’s implementation? Can the intervention be delivered and/or accessed by all those to whom it would be relevant or of benefit to within an acceptable budget? Can the intervention be delivered as designed? Are there substantial resourcing/training requirements to deliver it in routine practice? Will the intervention produce the desired effect in a real-world context (as opposed to being delivered under optimal conditions)? Does the intervention show a favorable ratio of effect relative to cost? Will the intervention be considered to be appropriate by relevant stakeholders (e.g., patients, the public, healthcare professionals, policy makers)? Could the intervention result in unwanted side-effects or unintended consequences? Will the intervention reduce or increase disparities (e.g., standard of living, wellbeing, health) between different sectors of society?

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recommended that the intervention and its components are described in sufficient detail to facilitate replication, preferably using standardized information or terminology (O’Catháin et al. 2019a). For example, the Cochrane Collaboration’s Effective Practice and Organization of Care (EPOC) taxonomy of healthcare intervention comprises four overarching categories of interventions (i.e., “Delivery arrangements,” “Financial arrangements,” “Governance arrangements,” “Implementation strategies”) (Effective Practice and Organisation of Care (EPOC)). In the context of behavior change interventions, the Behavior Change Technique Taxonomy (version 1) has been developed to aid the specification of intervention components and currently consists of 93 behavior change techniques (Michie et al. 2013). A number of reporting tools have also been developed to enhance the completeness of reporting at the end of the intervention development process, and ultimately enhance the replicability of interventions (Albrecht et al. 2013; Hoffmann et al. 2014). For example, the Template for Intervention Description and Replication (TIDieR) checklist comprises 12 items to improve intervention reporting and make the information more accessible to readers (brief name, why, what (materials), what (procedure), who provided, how, where, when, and how much, tailoring, modifications, how well (planned), how well (actual)) (Hoffmann et al. 2014).

Evaluation and Implementation Once a healthcare intervention has been developed, evaluation is critical to determine whether it should be implemented on a wider scale. The randomized controlled trial design is widely considered the gold standard in terms of evidencebased medicine and the hierarchy of evidence (Akobeng 2005). This is because the study procedures are considered to provide the most reliable evidence regarding the effectiveness of interventions whereby they minimize the risk of confounding factors (e.g., systematic differences between intervention groups) influencing the

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results (Sibbald and Roland 1998). However, trial evaluations of healthcare interventions are inherently complicated. Ensuring that all aspects of the trial such as recruitment and retention, intervention delivery, data collection and outcome evaluation proceed as intended requires careful planning and regular monitoring. Consequently, pilot and feasibility studies play an increasingly important role in the process of developing and evaluating healthcare interventions. Upon proceeding to a definitive trial evaluation, innovative evaluation designs may be needed to address problems such as contamination bias that can occur in clinical practices settings. Process evaluations can help to provide further information on the intervention’s implementation and mechanisms of impact, as well as the context in which they are conducted. This section will look at each of these areas in turn. Preliminary Evaluations There has been a considerable growth in the use of preliminary evaluations in the form of pilot and feasibility studies in the last decade. In recognition of this, a dedicated journal, Pilot and Feasibility Studies, was launched in 2015 (Lancaster 2015). To enhance the quality of reporting, an extension of the Consolidated Standards of Reporting Trials (CONSORT) guidelines that is specific to pilot and feasibility studies was published the following year (Eldridge et al. 2016a). Similar to terminology regarding intervention development and design, the terms pilot and feasibility study have often been used interchangeably (Arain et al. 2010). This prompted efforts to develop more definitive and differentiating definitions of the terms. Eldridge et al. developed a conceptual framework (Fig. 2) for defining pilot and feasibility studies in preparation for randomized controlled trials (Eldridge et al. 2016b). This framework considers feasibility to be an overarching concept within which three distinct study types can be distinguished: randomized pilot studies, nonrandomized pilot studies, and feasibility studies that are not pilot studies (discussed further below). As opposed to pilot and feasibility studies being seen as mutually exclusive, pilot studies are

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Developing, Implementing and Evaluating Complex Services/Interventions, and Generating the Evidence, Fig. 2 Conceptual framework for pilot and feasibility studies. (Source: Eldridge et al. 2016b)

considered as a subset of feasibility studies. Both study types ask similar questions in terms of whether something can be done, is it sensible to proceed with it, and if so, how. A key differentiating factor is that pilot studies also have a specific focus on study design whereby a future study, or part thereof, is conducted on a smaller scale (Eldridge et al. 2016b). It is important to note that pilot and feasibility studies are not designed or powered to assess effectiveness. This is an important consideration as the misuse of pilot and feasibility studies in evaluating an intervention’s effects can lead to inaccurate estimations (i.e., overestimation or underestimation) of an intervention’s true effects which can ultimately result in inappropriate decisions on how to proceed with future research or the implementation of an intervention in clinical practice (Kistin and Silverstein 2015). Randomized pilot studies involve conducting a future randomized controlled trial, or parts thereof, on a smaller scale to determine whether the trial can be done. This can provide useful

insights into the feasibility of the practical aspects, such as randomization of participants and data collection, that are critical to the success of a future full-scale trial. For example, a cluster randomized pilot trial of the Smoking Treatment Optimisation in Pharmacies (STOP) intervention was undertaken in the UK whereby 12 community pharmacies were randomly allocated to intervention or control groups (Madurasinghe et al. 2017). The STOP intervention sought to optimize community pharmacy-based smoking cessation services through education and training. The specific objectives of the pilot study included: estimating likely participation rates of pharmacies and smoking cessation advisors; establishing retention rates of patient participants; establishing potential impact on smoking cessation outcomes; assessing the feasibility of data collection methods, and; identifying methods of optimizing trial logistics for a future definitive trial. The study found that recruitment proceeded as intended with the required numbers of pharmacies recruited. However, areas that needed to be refined included

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the logistics of how the intervention was delivered. For example, only certified smoking cessation advisors were trained in intervention delivery. However, on recognizing that counter assistants were the first point of contact with pharmacy customers, the researchers concluded that any future trial would also include training for counter assistants to help target and engage more smokers in the intervention (Madurasinghe et al. 2017). Non-randomized pilot studies are largely similar to randomized pilot studies except that there is no randomization of participants. These studies can look at the feasibility of the practical aspects of conducting a future trial as outlined under randomized pilot studies above. However, they can also look solely at the piloting of the intervention. For example, using a non-randomized pilot study design, Patton et al. assessed the feasibility of study procedures (e.g., recruitment, data collection) for a future randomized controlled trial of the Solutions for Medication Adherence Problems (S-MAP) intervention in community pharmacies to support older adults adhere to multiple medications (Patton et al. 2021). This study assessed the acceptability of the interventions and feasibility of methods of recruitment, intervention delivery and data collection across 12 community pharmacies. The study found that the intervention was considered acceptable by pharmacists and patients and that key procedures (e.g., pharmacy recruitment and retention, intervention delivery) were feasible. However, the researchers noted that issues with patient recruitment, retention and missing data would need to be resolved before progressing to a definitive evaluation (Patton et al. 2021). Feasibility studies that are not pilot studies seek to answer questions as to whether some element of a future trial can be done but do not involve implementing the intervention to be evaluated or other processes that will be undertaken in a future trial. Feasibility studies are distinct from pilot studies (outlined above) in that no part of a future randomized controlled trial is being conducted on a smaller scale. These types of studies can examine a range of areas, such as, potential participants’ views about the acceptability of an intervention or study procedures. For example, a feasibility study of a pharmacist-led medicines

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review intervention in community-dwelling Māori older adults in New Zealand, assessed patient acceptability of the intervention using structured qualitative interviews (Hikaka et al. 2021). The study found that the intervention was viewed positively by participants and considered acceptable to them and that they would engage with it in the future if available. Irrespective of the specific objectives or design of the pilot or feasibility study, it is important to think through how decisions will be made on progressing further with the healthcare intervention in advance of the study being conducted. Progression criteria are increasingly advocated as a way of ensuring transparency around decisions to progress to a larger definitive trial (Mbuagbaw et al. 2019; Mellor et al. 2021). These involve outlining a list of feasibility criteria, including how they will be interpreted, and how their interpretation will inform progression to a larger trial (Mbuagbaw et al. 2019). For example, Avery et al. have proposed a traffic light system: continue/green (no issues of concern that would threaten the success of a future trial); amend/ amber (issues that need to be addressed but potential to proceed to a future trial with caution) or stop/red (issues that cannot be resolved and would threaten the success of a future trial) (Avery et al. 2017). Examples of progression criteria from the S-MAP pilot study (Patton et al. 2021) are outlined in Table 4 below. Definitive Evaluations Traditionally, the evaluation of healthcare interventions has focused on whether or not an intervention works in achieving its intended outcome and randomized controlled trials (RCTs) have been considered the gold standard for conducting such evaluations. The characteristic feature of a properly conducted RCT design is that by randomizing study participants to different groups, it enables groups to be compared whereby the only difference between them relates to the intervention received (Sibbald and Roland 1998). RCT evaluations of interventions can be placed on a continuum that progress from efficacy to effectiveness (Singal et al. 2014). Efficacy focuses on the performance of an intervention under ideal

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Developing, Implementing and Evaluating Complex Services/Interventions, and Generating the Evidence, Table 4 Sample progression criteria from the Feasibility parameter Pharmacy recruitment

Data source(s) Research team records

Pharmacy retention

Research team records

Missing data

Questionnaires, dispensing data

Intervention acceptability to patients

Feedback survey (postintervention delivery)

Solutions for Medication Adherence Problems (S-MAP) pilot study. (Source: Patton et al. 2021)

Progression criteria Stop Amend If 5 If 6–9 pharmacies pharmacies recruited and/or it recruited takes longer than within first expected 8 months (>4–6 months) If 49% of If 50%–79% of pharmacies pharmacies retained for retained for required required period period If 50% of If 21–49% of main main outcome outcome data are missing data are missing If 50–79% of If 49% of patients patients report report that that intervention intervention was acceptable was acceptable

and controlled circumstances, whereas effectiveness focuses on the intervention’s performance under “real-world” conditions (Singal et al. 2014). Efficacy studies evaluate interventions under highly controlled conditions resulting in high internal validity (i.e., freedom from error). However, these highly controlled conditions often necessitate substantial deviations from clinical practice (e.g., highly prescriptive study protocols) which affects the external validity (i.e., generalizability) of such studies. Conversely, effectiveness studies tend to sacrifice some internal validity, and have higher external validity than efficacy studies as they are more reflective of real-life settings (Treweek and Zwarenstein 2009). This means that effectiveness trials can often be of greater interest to clinicians and policymakers in informing decision making. The conventional RCT design involves randomization to intervention groups at the level of individual participant. However, this type of design may not always be feasible or appropriate,

Go If 10 pharmacies recruited to take part in 4 months If 80% of pharmacies retained for required period If 20% of main outcome data are missing If 80% of patients report that intervention was acceptable

Final decision (Stop, Amend, Go) Go: 12 pharmacies recruited within 3 months.

Go: 92% of pharmacies retained.

Amend: 30% of primary and secondary outcome data were missing.

Go: 89% of patients who completed the feedback survey reported the S-MAP intervention was completely acceptable (n ¼ 22) or acceptable (n ¼ 17).

particularly in the context of health services research and evaluations of healthcare interventions. For example, if an intervention is delivered in such a way that the individuals randomized to different groups are in frequent contact with one another (e.g., patients attending the same pharmacy), there is a high risk of contamination bias whereby one group may be influenced (“contaminated”) by the other group (Moberg and Kramer 2015). In the context of a community pharmacy based smoking cessation intervention, this could involve patients within an individual pharmacy who have been allocated to the control group (involving usual care) hearing about the intervention from intervention group participants and deciding to seek help with smoking cessation. This in turn would affect the overall outcome of the evaluation. In order to overcome the challenge of contamination with the conventional RCT design, cluster RCTs have been developed as a specific trial design to evaluate interventions delivered at

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group level. In cluster RCTs, entire groups of individuals are randomly allocated to receive interventions, and these groups are referred to as clusters. These clusters often comprise groups of patients within individual healthcare settings (e.g., community pharmacies (Rodrigues et al. 2021), general practices (Wallis et al. 2022)). Cluster randomization can help to reduce the potential for treatment contamination between intervention and control groups. However, compared to conventional RCTs where participants are randomized at an individual level, the design of cluster RCTs is typically more complex whereby they require a larger sample size to obtain equivalent statistical power, as well as more complex statistical analysis (Campbell et al. 2004). For example, outcomes of individuals within the same cluster tend to be correlated and this needs to be accounted for in the analysis of cluster RCT evaluations. A further variation of the cluster RCT is the stepped wedge cluster RCT which is increasingly used in health services research. This form of study design involves an initial period whereby no clusters receive the intervention (Hemming et al. 2015). Following this period, clusters are randomized to move from control conditions to the intervention in a stepwise manner at fixed intervals. This process proceeds until all clusters have been exposed to the intervention. Data collection is a continuous process so that each cluster provides data during both intervention and control periods. With the stepped wedge cluster RCT design, there will be a period at the end of the study when all clusters are exposed to the intervention. This type of design enables comparisons to be made within and between clusters, which can maximize statistical power and ultimately reduce the number of clusters that would be required using a parallel trial design (Hemming and Taljaard 2020). It can be particularly suited for providing rigorous evaluations of organizational level interventions to improve quality and safety. For example, using a stepped wedge cluster RCT design, Hau et al. examined the effectiveness of a pharmacist-led deprescribing intervention to reduce falls in nursing homes (Kua

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et al. 2021). This study was conducted across four nursing home sites (i.e., the clusters). Control conditions consisted of usual care with sequential stepwise crossover of nursing home sites to the intervention group at three-month intervals with repeated measurements up to the 12-month time point. Similar to cluster RCTs, the stepped wedge design is inherently more complicated than conventional RCTs and requires relevant methodological and statistical expertise within the research team (Hemming and Taljaard 2020). Regardless of the specific study design used to evaluate an intervention, it is important to note that a lack of observed intervention effect may not necessarily be due to an ineffective intervention. Given the complexities of running a RCT evaluation, or variation of a RCT design, there are multiple other factors that could account for a lack of observed intervention effect. For example, if an intervention or the associated study procedures are not considered acceptable to patients or healthcare providers, then any lack or observed effect may be due to implementation issues. This further illustrates the importance of preliminary evaluations (discussed above). In addition to this, effect sizes from RCT evaluations do not provide policy makers with the required information in terms of how an intervention could be implemented and replicated in a specific context (Moore et al. 2015). Hence, the importance of process evaluations as outlined below. Process Evaluations Process evaluations seek to provide more detailed understanding of healthcare interventions by examining their implementation, mechanisms of impact, and context, with a view to informing policy and practice (Moore et al. 2015). Implementation looks at what was implemented and how, often with a particular focus on intervention fidelity (whether it was delivered as intended) and dose (the quantity of intervention implemented). Mechanisms focus on how the interventions produces change. This can involve testing hypothesized causal pathways underlying the intervention’s effect. Context includes anything external to the intervention that could act as a

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barrier or facilitator to either its implementation or effects. Understanding the context within which an intervention is delivered is critical to the interpretation of the findings of an evaluation and whether the findings can be generalized to other settings (Moore et al. 2015). There is no universal structure for the design of a process evaluation as due to the diversity of healthcare interventions, the aims and methods of process evaluations will vary according to individual interventions and settings. However, this also creates challenges in identifying process evaluations within published reports involving definitive RCT evaluations of healthcare interventions. This is exemplified by the findings of a systematic review which found varying and inconsistent use of the term “process evaluation,” as well as poor visibility of the process evaluation component within trial reports (French et al. 2020). However, there are some common considerations for researchers in terms of developing and planning process evaluations (Table 5), and the UK Medical Research Council has produced useful guidance on this topic (Moore et al. 2015). As part of the Bridge-it study (Cameron et al. 2020), a process evaluation was conducted in parallel with the cluster RCT evaluation of the community pharmacy-based intervention that involved a bridging supply of emergency hormonal contraception and rapid access to a participating sexual and reproductive health clinic (Patterson et al. 2022). The process evaluation was guided by the UK Medical Research Council guidance on process evaluations and used a combination of quantitative and qualitative methods to examine the intervention’s implementation and mechanisms of change, as well as the impact of contextual factors on study outcomes. For example, qualitative interviews with a sample of pharmacists and study participants across different study sites explored the intervention’s implementation and found that the community pharmacybased intervention was acceptable to pharmacists and patients and largely delivered as attended. These findings were corroborated by researcher field notes and meeting records. A combination of data from the qualitative interviews and screening logs provided insights into the mechanisms of

Developing, Implementing and Evaluating Complex Services/Interventions, and Generating the Evidence, Table 5 Examples of key considerations for process evaluations. (Adapted from Moore et al. 2015 a) Planning Ensure that there is appropriate research expertise within the research team Balance the need to allow close observation and data collection, against the need to remain credible as independent evaluators Develop a plan for integrating process and outcome data from the outset Design and conduct Describe the intervention in detail and clarify any underlying causal assumptions in terms of its implementation and its mechanisms of action in a specific context Identify key uncertainties and carefully select the most important questions that need to be addressed Use a combination of research methods that are appropriate to the research questions (e.g., quantitative methods to measure key process variables, qualitative method to explore emerging changes in implementation and experiences of the intervention) Consider collecting data at multiple time points to ensure that changes to the intervention over time are captured Analysis Analyze fidelity, dose, and reach using descriptive quantitative information Ensure that qualitative and quantitative analyses complement and build upon one another (e.g., qualitative data used to explain quantitative findings) Undertake initial analysis and reporting of process data before trial outcomes are known where possible to avoid bias Reporting Publish (using relevant reporting guidance) a full report that includes all components of the evaluation and/or a protocol paper describing the evaluation, which can be referenced in all subsequent publications Highlight the contributions of the work undertaken to intervention theory or methods development to appeal to the interest of a wide audience Disseminate findings to relevant stakeholders (e.g., clinical, policy makers) a

See original reference source for a more extensive list

change underpinning the intervention which included the ease of access of the intervention and the increased awareness that it raised of contraception and sexual and reproductive health services. However, the context within which the

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intervention was delivered revealed additional challenges that could act as a barrier to future implementation of any such intervention on a wider scale, such as competing priorities and staffing issues within community pharmacies (Patterson et al. 2022). This example illustrates how process evaluations can provide more detailed insights into healthcare interventions by examining their implementation, mechanisms of impact, and context. This type of information is not achieved through trial evaluation that purely focus on quantitative estimates of intervention effect size. Enhancing Future Research In optimizing the process of intervention development, evaluation and implementation, there are a number of key areas where careful attention should be paid to improve the rigor and robustness of the work undertaken, starting with the composition of the research team. It is important to ensure that there is a sufficient breadth of knowledge and expertise within the research team. In addition to relevant clinical expertise, it is also important to include those with relevant methodological and/or statistical expertise, as appropriate, according to the stage of the intervention development/evaluation process. For example, in developing and evaluating an intervention to improve medication adherence in older adults prescribed polypharmacy, the S-MAP research team included health psychologists with expertise in behavior change (Patton et al. 2018; Patton et al. 2021). Trial evaluations of interventions should also look to include members with clinical trial expertise as well as dedicated methodological support from a clinical trials unit. The role of Patient and Public Involvement (PPI) within the project should be considered at the outset, from decisions about the research team’s composition, to the design and conduct of individual study components. PPI has been defined as “research carried out ‘with’ or ‘by’ members of the public rather than ‘to’, ‘about’ or ‘for’ them” (Health Research Board (Ireland)). It is advocated in both clinical and pre-clinical research in order to improve the quality,

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efficiency, and impact of research (Fox et al. 2021; Modigh et al. 2021). PPI is now increasingly featuring in health services research relating to the development and evaluation of healthcare interventions. For example, as part of a randomized controlled trial of an intervention involving telephone health coaching to support selfmanagement in a primary care-based population of patient with mild symptoms of chronic obstructive pulmonary disease, PPI contributors collaborated with the researchers in developing a multimedia resource to assist with patient recruitment (Jolly et al. 2019). This included generic information about the trial (e.g., confidentiality, randomization), as well as study-specific information (e.g., study aim, associated risks). PPI input can help to optimize study procedures and associated documentation by ensuring that they are more acceptable and accessible to potential participants. PPI contributors can also play an active role in data analysis and interpretation, as well as dissemination of study findings. High quality reporting is also vital to promoting rigorous research. Protocols for studies relating to each key stage of the intervention development and evaluation process should be made available prospectively. Researchers should look to explain any deviations from planned procedures in final reports so that others can learn from their experiences. A key challenge with healthcare interventions is that sufficient details are often not reported in study reports to enable replication. For example, a systematic review of pharmacy-based interventions reported that most trials lacked adequate reporting of the interventions which ultimately detracted from both the scientific and applied value of their research (de Barra et al. 2019). As previously outlined, taxonomies of standardized terminology (e.g., Behavior Change Technique Taxonomy (Michie et al. 2013)) and reporting checklists (e.g., TIDieR (Hoffmann et al. 2014)), can help to address this. However, deficits in the quality of reporting are not limited to intervention descriptions. A lack of detailed reporting has also been reported with a range of different study types. Hence, dedicated reporting guidance has been developed to improve the quality of reporting of different

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study types including intervention development studies (Duncan et al. 2020), pilot and feasibility studies (Eldridge et al. 2016a), and trial evaluations (Campbell et al. 2004; Moher et al. 2010; Hemming et al. 2018). Similarly, reporting checklists have also been development to improve the quality and consistency of reporting of PPI in research (Staniszewska et al. 2017). Finally, it is worth remembering that there is no single correct approach to the development, evaluation and implementation of healthcare interventions. It is important that researchers are flexible in their approach and remain open to using new and innovative methods informed by best available evidence and guidance, as well as practical experience. As illustrated in Table 2 and noted in the literature, some frameworks have reportedly been underutilized in pharmacy practice research to date (Sabater-Hernández et al. 2016; Handyside et al. 2021). Reporting on the lessons learned from a particular approach can help to inform future research.

Conclusion This chapter has examined and discussed the methods of developing, evaluating, and implementing healthcare interventions relating to the practice of pharmacy. It is important to note that there is no single correct approach to use during any of these processes. However, a systematic approach that uses rigorous research methods is critical to producing high quality research findings. Applying the learnings from existing frameworks and previous research, as presented in this chapter, could help researchers to generate evidence to support the implementation of new pharmacy services and pharmacist-led interventions going forward.

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Digital Health and Pharmacy: Evidence Synthesis and Applications Rabia Hussain1, Hadzliana Zainal2, Dzul Azri Mohamed Noor2 and Sadia Shakeel3 1 Discipline of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia 2 Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia 3 Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Dow College of Pharmacy, Dow University of Health Sciences, Karachi, Pakistan

Abstract

Globally, many healthcare institutions have adopted interventions related to digital health technologies to ensure the seamless experience

for both patients and clients. Digital health is a field that utilizes artificial intelligence, big data, and robotics to carry out several functions in the field. The entry synthesizes the evidence about the use of digital health technologies for patients, healthcare professionals, healthcare institutions, and academics. Moreover, the entry also discusses the recommendations for the future of digital health in the delivery of health services. Keywords

Digital health · Pharmacy · Telehealth · Health innovation · Health technology

Introduction The pharmaceutical field has been leveraging on the close intertwine between biology and technology for the past several decades, and the advancement seems unstoppable. The famous quote by Steve Jobs, “the biggest innovations of the 21st century will be at the intersection of biology and technology,” has been thought to manifest in the field of drug discovery. This close connection has been proven worthy and indispensable in the recent COVID-19 pandemic. Artificial intelligence (AI) and computer-aided drug discovery (CADD) tools were pertinent in discovering vaccines and in the search for new drugs for the treatment of COVID-19 (Floresta et al. 2022). Apart from the drug discovery field, health services in pharmacy and pharmaceutical public health have also adopted innovations and health technologies to ensure seamless experience for the patients and clients. This entry covers the following: 1. Scientific innovations using virtual approach for public access to health and patient care in the comfort of their homes 2. Digital health innovations/initiatives assisting pharmacists in delivering quality and timeless care for patients and clients/patients/public 3. Different health technologies used in academia to equip the pharmacists of the future

Digital Health and Pharmacy: Evidence Synthesis and Applications

4. Scientific innovations that augment the health service capabilities in prescribing and monitoring of medication use 5. Future research direction to provide more evidence-based innovations and use of technology in delivering health services in pharmacy and pharmaceutical public health

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drugs, devices, and screening tools), quality of service (i.e., home medication review, digital health record, and telemedicine), and quality of life (i.e., vaccines, detection of tumor, new treatment guidelines, and self-monitoring mobile applications). Some of the advancement of technologies in health are mentioned in the time line in Fig. 1.

The Evolution of Scientific Innovation in Healthcare

Digital Health for Patients

The major milestone in the past concentrated in looking for a cure or drug discovery which then sprouted into further innovations for greater use or improved services. For example, the isolation of morphine in 1827 paved way for the development of injectable morphine through hypodermic syringe in 1852. Later, it kick-started the discovery of newer generation of painkillers to date. Scientific innovations in healthcare generally aim at improving the quality of care (i.e., new

Digital health services are changing how people manage their well-being and contribute to selfcare. Digital health could be an excellent platform to assist patients in actively managing their own health needs by raising awareness and fundamental medical knowledge about health and disease (Atasoy et al. 2019). It is further strengthened by innovative technologies like mobile phone or web-based applications, which motivate patients to follow their doctor’s recommendations for

Digital Health and Pharmacy: Evidence Synthesis and Applications, Fig. 1 Time line for some of the healthcarerelated technologies invented over the centuries

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medication and lifestyle changes more closely. Online healthcare platforms rapidly expanded to thousands of people, thereby enhancing patient access to updated healthcare information (Alwashmi 2020). Through digital platforms, it is possible to conveniently connect patients with healthcare experts while they are continuing their normal life routines (Shan et al. 2019). Accordingly, it might support patients in adhering to doctors’ advice and encouraging a healthy lifestyle, which would improve the prevention and control of chronic diseases. Many consumer technology companies have entered the healthcare business, including Apple, Google, and Fitbit (Schuhmacher et al. 2022). Besides, health or fitness mobile applications are available on Google Play and the Apple App Store, although only a small percentage have been certified by organizations like the United States Food and Drug Administration (FDA) (Schuhmacher et al. 2022). Blood glucose meter connectivity is a common feature of mobile applications, which are frequently created by device manufacturers (Jeffrey et al. 2019). Roche’s commercially available Accu-Chek Connect application contains a photographic meal diary and an insulin bolus calculator to help with carbohydrate counting. It receives self-monitoring of blood glucose (SMBG) values from the AccuChek Connect blood glucose meter (Drincic et al. 2016). In a three-arm randomized controlled trial, the Diabeo plus telemedicine arm had a larger HbA1c reduction at 6 months compared to the usual care arm (p < 0.001), with no difference in hypoglycemia (Charpentier et al. 2011). In a review conducted by Shah et al., the included studies have shown that digital health interventions for patient education, physical activity, and exercise were successful for persons with knee osteoarthritis and knee replacement. When compared to conventional treatments, all digital strategies were observed to produce significant improvements in individuals (Shah et al. 2022). A systematic review, including 19 studies, was conducted to observe pharmacist-led digital interventions for improving patients’ health-related clinical outcomes. The most frequently employed digital interventions were phone calls, websites,

and mobile applications. Among them, the latter two interventions had generally beneficial outcomes, whereas the effects of telephone-based interventions were decidedly variable on patients’ outcomes (Park et al. 2022). Telephone Based Different healthcare professionals have widely used telephone consultations as it is a highly accessible method of telehealth delivery due to the high rates of telephone ownership around the world (Funderskov et al. 2019). A randomized comparison involving 224 patients with coronary artery disease and depression indicated that a telephone-based care program was a useful tool for treating depression, and by treating depression in these patients, it may improve overall survival (Yang et al. 2019). A Dobkin-led randomized controlled trial was aimed at assessing the effectiveness of a ten-session T-CBT (telephone-based cognitive behavioral treatment) for treating depression in patients with Parkinson’s disease. The results showed that T-CBT is an intervention that effectively addresses a critical unmet Parkinson disease treatment requirement and gets around access limitations to evidence-based multidisciplinary care (Dobkin et al. 2020). A systematic review was conducted to describe the existing research on screening of distress and methods for referral to supportive care in telephone-based services for patients with chronic conditions like cancer. The results have indicated that established distress-screening techniques are utilized efficiently over the phone to detect discomfort, especially in relation to cancer (Taylor et al. 2020). A study demonstrated that an international core capability framework for physiotherapists providing telephone-based care was helpful for professional development activities in telehealth delivery and offered direction for physiotherapists (Davies et al. 2022). A prospective, randomized, open-label trial examined the effectiveness of telephone-based weekly follow-up in Egyptian patients with stomach cancer or metastatic colorectal cancer compared to a control group. According to the findings, pharmacist-led telephone follow-up (TFU) was helpful in assisting and establishing a strong rapport, based

Digital Health and Pharmacy: Evidence Synthesis and Applications

on trust between the patient and the caring pharmacist. It also showed the ways TFU might benefit patients’ adherence and tolerance (Eldeib et al. 2019). Web Based Web-based healthcare systems enhance patient care quality, safety, engagement, and coordination for better health outcomes. Different healthcare websites are intended to improve patients’ adherence to medication, understanding of medical issues, and active participation in the healthcare system (Sadasivaiah et al. 2019). MyHealthPortal keeps patients well-versed in all aspects of healthcare services. For instance, it makes use of an encrypted email delivery infrastructure to enable secure messaging for tasks like processing requests for appointment cancellation, informing patients of the progress, confirming cancellations of appointments, and receiving patients’ feedback. There is no direct communication between patients and medical staff; instead, secure messaging occurs on behalf of the patient, between the portal and the healthcare professionals (Tanbeer and Sykes 2021). Similarly, PalliCOVID is an illustration of a digital health technology that can help professionals to have access to palliative care resources (Lai et al. 2020). A study was conducted to assess usage patterns of PalliCOVID to understand how users interacted with this platform for palliative care during the localized spike of COVID-19 infection in Massachusetts. The findings showed its competency to guide future dissemination tactics and suggested platform improvements that will better serve the platform’s user (Lai et al. 2020). The findings of another study, conducted to evaluate the practicability of a web-based cardiac rehabilitation intervention for those who dropped out from usual cardiac rehabilitation, have revealed that web-based interventions had the opportunity to provide people access to the rehabilitation services who would not otherwise go to cardiac rehabilitation (Houchen-Wolloff et al. 2018).

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provision of health services for improving health outcomes through wireless devices and mobiles (Sapsin 2020). mHealth interventions frequently use tools like wearable technology, smartphone applications, and short message service (SMS) text messaging (Neubeck et al. 2015). Likewise, mHealth encompasses telemedicine, personalized medicine, and health information technology (Knitza et al. 2019). Numerous categories of mobile applications, or mobile apps, may be downloaded via the Internet from online application stores like Google Play and iTunes. Mobile applications and wearable sensors provide patients more control, offer individualized support, and produce better results than those with the conventional therapy. Symptom checkers and referral tools are just a few of the many mHealth options available to patients with chronic conditions. After a diagnosis has been made, mHealth solutions allow patients to more effectively track their symptoms, both passively through sensors and actively through data entry. Furthermore, electronic medication reminders can also improve drug compliance (Knitza et al. 2020). Rheuma Auszeit was developed by the German rheumatic and musculoskeletal disease patient organization Rheuma-Liga, and it offers rheumatic patients self-managementbased recommendations that are useful to support patients dealing with rheumatic pain (Knitza et al. 2019). Similarly, Rheuma Auszeit was well-accepted by German patients suffering from psoriatic arthritis, rheumatoid arthritis, and ankylosing spondyloarthritis (Lambrecht et al. 2021). Through the FLUency program from Kinsa Health, schools received linked smart thermometers that can be used to measure risk and identify gradelevel patterns for viral illness outbreaks among students. More than two million people used the Kinsa thermometers, and the software has enhanced real-time tracking of influenza-like sickness (Pandit et al. 2022). Some common mobile apps with their function are listed in Table 1 (Balapour et al. 2019).

Electronic Health Record Mobile Based The United States Food and Drug Administration describes “mobile health” (mHealth) as the

The term “Electronic Health Record” (EHR) refers to real-time, patient-centered records that

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Digital Health and Pharmacy: Evidence Synthesis and Applications, Table 1 Some common mobile applications with their function Mobile applications Siilo

Health

MyFitnessPal

Lifesum

Medical ID

CardMedic

Purpose Help healthcare professionals work together more effectively on challenging cases, enhance patient care, and share information in a compliant manner Enabling users to perform a wide range of activities, such as calling a doctor or being reminded of their daily step goal Helps customers set attainable objectives, understand how behaviors can be improved, and keep track of their daily food diaries Assist customers in monitoring their fitness, counting steps, and even analyzing their sleep patterns Users can send an alarm SMS with their estimated position in only one click. Marketed as “The app that could save your life” Intended to facilitate patient-doctor communication

securely and quickly provide information to authorized individuals. Even though it does contain the treatment and medical histories of patients, an EHR system is created to go beyond the traditional clinical information collected in a healthcare provider’s location and can be inclusive of a larger perspective of a patient’s care (Hossain et al. 2019). Through advancements in blockchain technology, medical record, insurance billing, and smart contract transactions have improved. By enabling increased access to patient medical information, device tracking, prescription databases, hospital assets, and the entire life span of a device within the blockchain infrastructure, blockchain technology can substantially improve the connectivity of healthcare databases (Tanwar et al. 2020). Raket et al. developed and validated the Dynamic Electronic Health Record Detection (DETECT) which was the first individualized risk-prediction model that used extensive EHR screening to identify people at risk of first episode of psychosis. It was found that DETECT has

potential clinical advantages for large-scale screening (Raket et al. 2020). Another study revealed that by using the new OpenSAFELY platform and pseudonymously linking primary care electronic health records to patient-level data from the COVID-19 Patient Notification System (CPNS) for hospital inpatient deaths with confirmed COVID-19, researchers were able to quantify a number of clinical risk factors for COVID-19-related mortalities (Collaborative et al. 2020).

Digital Health for Healthcare Professionals Digital health serves numerous advantages including reduced medication error, improved resource allocation, reduced turnaround times, adherence to clinical guidelines, and improved resource allocation by the healthcare professionals (Jarva et al. 2022). The term digital health refers to the use of digital technology to enhance the safety, quality, and efficiency in delivering healthcare services to the patients (Jimenez et al. 2020). For the implementation of a digital healthbased technology, it is very important to understand patient’s interest and reasons for the use of such technology. Moreover, to understand the full scope and impact of a digital health intervention, it is crucial to consider factors such as social aspects, human-technology interaction, privacy concerns, security issues, and technology infrastructure (Wang et al. 2020). Telehealth The term telehealth refers to the interactive communication between healthcare professionals and patients from a distant site by the use of audio and video equipment. It aims to deliver healthcare services remotely, by the use of information technology and telecommunication. The term telehealth can be interchangeably used as remote health care, virtual care, mobile health, or e-health referring to the range of solutions and paradigms that are utilized for continuous and remote monitoring (Sana et al. 2020).

Digital Health and Pharmacy: Evidence Synthesis and Applications

Telehealth has the potential to make health care more effective, organized, and available. It is more acceptable in those specialties where it involves minimum physical examinations including radiology and dermatology, whereas less popular in the areas where extensive physical examinations are required, such as cardiology and neurology (Singh et al. 2021). It can improve access to healthcare for patients in remote or underserved areas, as well as for those with mobility or transportation challenges, decreasing the overall healthcare costs and hospitalization (Haleem et al. 2021). However, telehealth may also have limitations such as data security, access to Internet technology, and lack of physical examination, which could underpin the utility of this technology (Haleem et al. 2021). Examples of telehealth services include virtual consultations, remote monitoring of patients, and electronic prescribing and are discussed below. Telemedicine or Virtual Consultations

Telemedicine is the use of information and communication technology, whereby patients can communicate to their healthcare professionals about their health concerns through video conferencing and audio calls (Zuccotti et al. 2023). Telehealth includes online consultations, remote monitoring, remote physical and psychiatry rehabilitation, and telehealth nursing. It allows patients to have better healthcare service choices, enhanced performance, and quality of services, moreover better cost saving for patients, both by reducing the travel expenses and optimizing the treatment procedures (Weinstein et al. 2014; Parimbelli et al. 2018). Telemedicine is considered to cover three main domains, namely, functionality, application, and technology. The functionality part explains the various forms, which the telemedicine could take while managing patients, that include consultation, diagnosis, monitoring, and mentoring. The application part can be divided based on medical specialty, disease, site of care, and modality of treatment. The technology domain covers the network, connectivity, and synchronicity (Bashshur et al. 2011). Medline, a telehealth firm, has created a telehealth program for Walgreens, to provide an

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avenue, whereby the healthcare professionals could interact with the patients through video chat (Walgreens 2023). The increase in telemedicine was witnessed especially during COVID-19. Such as an innovation team at the University of Pennsylvania Health System demonstrated an automated textmessaging system which could remotely monitor patients with confirmed or suspected Covid-19 at home under COVID Watch. Initially, 3000 patients participated and approximately 83% of patients were managed by the program without escalating to human care (Morgan et al. 2020). An analogous term to telemedicine is tele pharmacy, which refers to the provision of pharmaceutical services using telecommunication and information technologies to patients remotely. Tele pharmacy aims to deliver services such as prescription dispensing, medication order review, therapeutic drug monitoring, and patient counseling through audio or video sources (Poudel and Nissen 2016). Omboni and Tenti (2019) have devised and validated a web-based telemedicine service, “The Tholomeus,” in several community pharmacies in Italy for the screening and monitoring of most common chronic disorders. The tele pharmacy program has further led to the development of TEMPLAR Study (TEleMonitoring of blood Pressure in Local phARmacies) in the region. The preliminary evidence from the TEMPLAR study has indicated that a telemedicine service providing medical reporting and counseling may prevent the cardiovascular related complications among suspected individuals (Omboni and Tenti 2019).. Moreover, in Pakistan, a tele pharmacy-based pilot project “Guddi Baji Program” was initiated to deliver quality health and wellness services for one million women across 3000 villages in Pakistan. The project utilized the services of female health workers in rural parts of the country to connect rural female patients to a nationwide network of female doctors through a high-definition (HD) video consultation, and positive impacts were observed (Bukhari et al. 2021). Remote Monitoring

The practices related to the use of electronic devices and communication are already

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established in the healthcare field. Moreover, with the advancement of traditional Internet to Internet of Things, having people and objects sharing, and interaction with the information, further opportunities have been developed for improved and costefficient healthcare (Boric-Lubecke et al. 2014). Patients can use wearable devices or other technologies to transmit data such as vital signs or blood glucose levels to their healthcare providers. Moreover, the modern-day smartphones and smart watches are fitted with a number of sensors, which helps in sensing and detecting several health parameters and health conditions. The data collected through smart devices regarding device and mobile application usage, call logs, and messages could provide valuable information about an individual’s physical and mental health over a long period of time (Majumder and Deen 2019). An interesting study about wearable devices has indicated that a wearable device was capable of monitoring both physical and emotional health. The device was designed in a way that it could measure beat by beat blood pressure and could detect the changes in the mood state of the patients (Murali et al. 2015). Wearable devices and mobile phone health apps have changed the whole scenario of healthcare and has made an individual more empowered about controlling the healthcare status on their own. Marketing Services Institute for Healthcare Informatics has indicated that around 110,000 of 165,000 health-related mobile phone applications were for health and fitness (Gay and Leijdekkers 2015). This shows that enormous amount of data is being produced on health and fitness perspective. However, these types of data usually remain in silo and mostly remain separated. To provide better health outcomes and better patient engagement, an integration of patient health and fitness data is required (Gay and Leijdekkers 2015). A study by Guy et al. about myFitnessCompanion has summarized that health- and fitness-related mobile applications can offer a better holistic view of health and fitness data of the user. Moreover, such data can then be analyzed to offer better and more personalized advice and care (Gay and Leijdekkers 2015).

Electronic Prescribing

Electronic prescribing involves the use of health information technology to send electronic prescriptions from the prescribers directly to pharmacies using specific software (Hincapie et al. 2014). E-prescribing results in better formulary management, smooth refill process, reduced medication errors, and improved efficiency of both the pharmacists and prescribers (Hincapie et al. 2014). A mixed methods study was conducted regarding effects of implementing E-prescribing on ward pharmacists’ activities. It was found that pharmacists may spend less time with patients having e-prescription and valued the safety features of the electronic prescribing and medication administration (ePA) system (McLeod et al. 2019). Another study from New Zealand has documented the patients’ experience regarding E-prescribing and access to prescription medicines during lockdown. The study found that patients received their e-prescriptions either through direct pick-up from pharmacies or occasionally through home delivery. Further participants have highlighted that they wanted e-prescribing to continue post lockdown and recorded their concerns over the cost and communication part of this service (Imlach et al. 2021). Artificial Intelligence (AI) and Big Data for Healthcare Professionals Although advances in information technology in the past decade have covered almost all aspects of our lives including the healthcare field (Thimbleby 2013). The transition to electronic medical records and availability of patient data have been associated with increases in the volume and complexity of patient information, as well as an increase in medical alerts, and increased expectations for rapid and accurate diagnosis and treatment (Thimbleby 2013). The role of artificial intelligence is expanding to the several areas of healthcare field including medical imaging analysis for the diagnosis and treatment of cancers (Davenport and Kalakota 2019). The involvement of automation and robotics have evolved in the surgeries, predictive

Digital Health and Pharmacy: Evidence Synthesis and Applications

analytics, and management of population health in order to identify the possible public health concerns among population (Cresswell et al. 2018). Moreover, natural language processing helps with the management of electronic medical records and clinical decisions by the healthcare professionals. This is because these healthcare technologies help generate the data that could further help to improve patient care (Davenport and Kalakota 2019). In an international evaluation of AI on breast cancer screening by McKinney et al. (2020), it was confirmed an absolute reduction of 5.7% and 1.2% (United States and United Kingdom) in false positives and 9.4% and 2.7% in false negatives interpretation of mammogram compared to those done by the experts (McKinney et al. 2020). Another recent United Kingdombased study has developed a biomarker derived from digital readouts of daily-life movement behavior of the subjects; the biomarker could help in predicting disease progression in patients with muscular dystrophy and can potentially track the response to therapy (Ricotti et al. 2023). Similarly, researchers from the United States developed a deep learning model to detect infarct in noncontrast head computerized tomography. They have found that the model substantially outperformed on a test set of 150 computerized tomography scans of patients who were potential candidates for thrombectomy compared to the opinions of three expert neuroradiologists. Moreover, the results indicated sensitivity of 96% (specificity 72%) for the model contrary to the sensitivity of 61–66% (specificity 90–92%) for the experts (Gauriau et al. 2023). A retrospective cohort study was conducted among type 2 for glucose forecasts on logging behavior and clinical outcomes for a period of 3 months. The outcomes have indicated that patients who received glucose forecasts had low level of glucose compared to those who did not receive any (Imrisek et al. 2022). Below is the list of different AI-based service providers delivering services for the clients in Tables 2, 3, and 4 (Daley 2023).

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Digital Health and Pharmacy: Evidence Synthesis and Applications, Table 2 AI supporting healthcare professionals AI services providers Viz.ai

PathAI

Regard

Enlitic

Caption Health

Functions Viz.ai helps care teams react faster with AI-powered healthcare solutions It can detect issues and notify care teams quickly, whereby providing alternative solutions, and discuss treatments to save lives PathAI utilizes machine learning technique to assist pathologists in decision-making by reducing errors in cancer treatment and improving individualized therapy Regard uses AI to diagnose patients while being connected to the patients’ electronic medical records. Moreover, it also provides recommendations to the healthcare providers about patient care Enlitic utilizes deep learning technology to assist doctors in radiology diagnoses. Moreover, it analyzes radiology images, unstructured medical, blood tests, EKGs, genomics, and patient medical history to help getting insight into a patient’s real-time needs Caption Health utilizes ultrasound technology and AI for early disease identification. The AI guides help providers to interpret and assess by producing diagnostic quality images

Digital Health for Healthcare Institutions Medication error and patient adherence are two mostly challenging issues faced by the healthcare system worldwide (Keers et al. 2013; Stirratt et al. 2018). Realizing these issues are preventable most of the time due to human factors (Keers et al. 2013); the following are some of the innovations that are being developed and utilized to eliminate this error and subsequently improve patient safety. Barcode Technology Barcode medication administration (BCMA) utilizes barcode technology as an inventory system control with the goal of ensuring patient receiving

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Digital Health and Pharmacy: Evidence Synthesis and Applications, Table 3 AI services related to patient experience AI services providers Kaia Health

Spring Health

One Drop

Twin health

CloudMedX

Functions Kaia Health operates a digital therapeutics platform that features live physical therapists to provide people care within the boundaries of their schedules Spring health offers mental health solutions by using deep learning technology. It helps referring the employees to a right specialist for both inpatient or telehealth care for mental health One Drop provides a discreet solution for managing chronic conditions like diabetes and high blood pressure, as well as weight management Twin Health combines Internet of things technology, artificial intelligence, data science, medical science, and healthcare to address and reverse chronic conditions like type 2 diabetes mellitus CloudMedX uses machine learning to help hospitals and clinics manage patient information, clinical history, and payment information throughout patient experience in healthcare system

right medication at the right dose and time. This system requires an introduction of a unique code for each medication. Once pharmacist receives an order, medication is dispensed in barcoded packages and delivered to the ward. When the drug is ready to be administered, clinician or nurses will scan the packaging barcode and the barcode at the patient’s wristband. A notification will be given in case of any error detected during the process that requires nurses’ attention and rectification (Wideman et al. 2005). By having this notification, theoretically medication error during the drug administration can be prevented. A study conducted in Switzerland that employed BCMA showed that BCMA implementation is able to reduce medication preparation time at both per individual medication dose and within a 24-h period (Küng et al. 2021). In the same study, the incidence of medication error was found to reduce medication preparation error by

Digital Health and Pharmacy: Evidence Synthesis and Applications, Table 4 AI-enhanced robotic surgery AI-enhanced robotic service providers Vicarious Surgical

Auris Health

Carnegie Mellon University

Accuray

Intuitive

Functions Vicarious Surgical utilizes AI-enabled robots coupled with virtual reality to assist surgeons in performing surgeries Auris Health employs data science, AI, endoscope design, and micro instrumentation to develop robots for improved endoscopic procedures The Heartlander, a miniature mobile robot developed by Robotics Institute at Carnegie Mellon University, is designed to facilitate therapy on the heart The Accuray CyberKnife system provides AI-based robotics to track and assist the healthcare provider to treat cancerous tumors Intuitive’s da Vinci platform is specialized in developing cameras, surgical tools, and robotic arms to aid in minimally invasive procedures

half, after implementing BCMA. Similar findings were found in a 6-year retrospective data analysis after BCMA implementation with reduction of medication administration error by 43.5% and decrease of actual patient harm event by about 55% (Thompson et al. 2018). While the use of BCMA system has shown to reduce medication error gradually over the years, a study conducted in Canada suggests that integration of BCMA with computerized physician order entry (CPOE) and automated dispensing machine may be able to have an immediate absolute decrease in medication error and adverse drug events (Burkoski et al. 2019). However, given that BCMA still relies on human control, it is still bound to not being used as intended or instructed. This will lead to a policy deviation and a so-called work-around, a temporary way to conduct a certain task when the usual or planned method is not working (Cresswell et al. 2017). Examples of policy deviation and workaround that have been reported include failure to

Digital Health and Pharmacy: Evidence Synthesis and Applications

scan, medications not being dispensed, malfunction of scanner, mislabeled or unlabeled medications, unreadable or missing patient wristband barcodes, and others (Mulac et al. 2021; van der Veen et al. 2018; Wideman et al. 2005). Suggestions to improve these issues include staff training to improve compliance and organizational leadership that endorse and support the implementation (Wideman et al. 2005). In 2011, the Falsified Medicine Directives was published which aims to combat fake medicines and ensure safe medicines and a more controlled trade of medicines in Europe. One of the processes included in the directives is the medicine verification and decommissioning system. Both processes employ the use of a barcode technology in the form of 2D data matrix codes (Merks et al. 2020) in which medication was scanned and decommissioned. During these processes, a popup notification will inform the authenticity of the drug and whether it has been recalled or expired (Naughton, 2019). This decommissioning process can be done either at the point of receiving the goods or prior to administering drug to patient. While this may ensure safety of the patients, many studies suggested that both medication verification and decommissioned process may increase operational time, and patient’s waiting time, and reduce the interaction with patients (Dalton et al. 2022; de CN et al. 2017). Given that this directive is still in the early stages of implementation, further studies may still need to be conducted to determine its benefit in the healthcare setting. Automated Dispensing Automated dispensing has been one of the solutions suggested to improve medication errors, previously encountered in the hospital and community setting. It incorporates software to manage the storage, and dispense and track the drug. The automated dispensing systems available that have been studied include Pyxis (Roman et al. 2016), Omnicell (McCarthy and Ferker 2016), Rowa Vmax (Skalafouris et al. 2015), MedCarousel (Temple and Ludwig 2010), Rowa Speedcase (James et al. 2013), and ScriptPro Sp-200 (Lin et al. 2007).

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A systematic review on the use of automated dispensing system in hospital wards and pharmacy suggested that automated dispensing was able to reduce dispensing error and it has increased the dispensing accuracy (Batson et al. 2021). In terms of manpower, automatic dispensing was noted to reduce the time spent and the need for full-time equivalent pharmacy technician and other staff in terms of counting, filling, and checking medication expiry dates (Chapuis et al. 2015; Noparatayaporn et al. 2017). Regarding financial implication following automatic dispensing implementation, almost all studies reviewed have suggested a reduction of cost in terms of labor saving, reduced stock and inventory costs, and reduced wastage (Chapuis et al. 2015; De-Carvalho et al. 2017; McCarthy and Ferker 2016; Noparatayaporn et al. 2017). A systematic review looking into the impact of automated dispensing in the community pharmacy settings suggested almost similar findings, with majority of studies reviewed showing a reduction in dispensing incident and medicationrelated occurrences (Sng et al. 2019). Most of the studies also reported a reduction of time spent in filling and packing of medication, but one study suggested that the time saved by following the introduction of the automated dispensing is not significant (Lin et al. 2007). In addition, studies conducted at the community setting also investigated the job satisfaction or perception among the staff with one of the studies suggesting that there is reduction in workload and work stress following the automatic dispensing implementation (James et al. 2013). Nevertheless, there is a concern that the use of automatic dispensing machine may bypass the pharmacist’s review and introduce error during the process (Repella et al. 2022). One way to overcome this issue is by having pharmacist to screen and verify the order prior to dispensing. However, such practices may increase the number of pharmacists required to conduct such process, as shown by a study in Thailand (Noparatayaporn et al. 2017). Meanwhile, another study suggests a framework whereby a hospital pharmacy board is created to identify in advance list of medications that can be overridden and directly dispensed

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through automatic dispensing cabinet (Rhodes et al. 2022). This may reduce the workload of the pharmacists without compromising patient’s safety. Adherence Monitoring Patient adherence is a crucial aspect in medication process, and it includes adherence to both medication and lifestyle adjustment. Nonadherence has been shown to have a negative impact on patient outcomes as it may lead to treatment failure and even death (Chimeh et al. 2020). With the advent of technology, many innovations and tools have been created to monitor and improve medication adherence. However, most of them are still in their early phase of development or not fully utilized (Virella Pérez et al. 2019). Thus, not many of them have been studied in terms of their outcome to patient adherence. Among tools that are being developed to improve patient adherence is a wearable electronic device either in a form of a smartwatch or apps downloaded on a smartphone. Currently, the most common wearable electronic device focuses on an activity tracker that consists of a sensor and algorithm to calculate physical activity measure. Measurement included in the physical tracker includes step count, sedentary activity, energy expenditure, and activity intensity (Henriksen et al. 2018). There is currently a consumer-based and research-grade activity tracker brand available. Among them are the Fitbit, Garmin, ActiGraph, and SenseWear. Multiple studies utilizing activity tracker have been conducted among population of all ages (Paolillo et al. 2022; van Ekris et al. 2020) which suggest the acceptability of population toward the devices. In fact, in one study participants using activity tracker have shown to have much higher physical activity and they were able to achieve 10,000 steps or more as compared to those with standard intervention (Kim et al. 2022). However, the limitations of studies using activity tracker mostly relate to the different adherence definitions being employed in the study with some using number of hours the participants wear the device while some calculated them using the step count. In addition,

differences in the study period also slightly affected the result interpretation (Chan et al. 2022). In terms of medication adherence, innovation in the form of smart pillboxes or bottles has also been studied. Among them are Pillsy, WisePill, and smart pill bottle by AdhereTech. These smart pillboxes or bottles incorporate alarm or notification system via voice or text messages to remind patients on their medication schedule. This notification usually is given few minutes before their medication schedule, and for some notification will be provided up to 2 hours after missing doses (Ellsworth et al. 2021; Mauro et al. 2019; Orrell et al. 2015). In two of the studies that utilized smart pill boxes, adherence was measured either by capturing the time stamp for each bottleuncapping event (Mauro et al. 2019) or by using a drug-level measurement (Ellsworth et al. 2021). Both studies suggest that the use of smart pill boxes or bottles will be able to improve patient’s medication adherence. However, both studies are conducted in a small sample size. Furthermore, none of the participants were followed up; thus, the question whether an increased adherence observed will translate into a better therapeutic outcome remains unanswered. While smart pill boxes did show some benefit in improving medication adherence, there is a concern that smart pill boxes do not really reflect the true drug-taking by the patient (Klugman et al. 2018). To address this concern, an innovation was made in the form of an ingestible sensor that is embedded within a drug formulation such as capsule. This smart pill also known as digital pill system comes together with a wearable reader (Chai et al. 2022; Frias et al. 2017). Upon contact with the gastric acid, the digital pill will be dissolved, thus releasing the ingestible sensor, and activating it. The data captured from signal emitting from the sensor will be captured by the reader. The ingestion time stamp captured and stored by the reader can be accessed via the user’s smartphone and physician online system using a cloud-based server. Two studies that have employed digital pill system suggest that overall patients are comfortable and find digital pill is easy to use (Chai et al. 2022;

Digital Health and Pharmacy: Evidence Synthesis and Applications

Frias et al. 2017). There are none or minimal adverse events reported in both studies with mostly relating to skin irritation from the patch use. Only one study looked into the difference in systolic blood pressure between hypertensive patient in the intervention and control group while the intervention group showed a greater reduction in systolic blood pressure (Frias et al. 2017). However, this reduction is only significant at 4-week but not at 12-week follow-up periods. While these results look promising, more studies with higher number of sample size are needed to further validate the benefit of the digital pills.

Digital Health for Academia The use of digital technology has been around for a while now, but the use has rapidly evolved ever since the COVID-19 pandemic (KawaguchiSuzuki et al. 2020). Students are forced to stay at home, and pharmacy educators used the opportunity to convert teaching methods using technologies including, but not limited to, augmented reality, virtual reality, and simulations, as mentioned in this section. These technologies have evolved, and the use is widespread for all areas of pharmacy curriculum. Augmented Reality The term “augmented reality” (AR) refers to a technology that integrates the actual world with the virtual one. AR application is the employment of software and hardware into a real environment by providing interactive and dynamic content, as a supplement in the actual world to further optimize the aims to be reached in principle. As a result of advances in technology, smartphone platforms that were previously solely utilized for communication are now able to handle AR applications, particularly those that aid in learning. With augmented reality, it is believed that students would gain new knowledge and a greater grasp of the subject matter. If instructors or lecturers and students connect with one another, the learning process can be deemed effective and of high quality. One of the keys to the success of the learning process is increasing student motivation for

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learning. It is envisaged that augmented reality would be able to address issues with low student motivation to study (Silvi 2022). There are two ways to implement augmented reality: marker-based AR and marker-less AR. In the marker-based AR, target recognition such as 3D object, text, picture, QR Code, or human face are utilized. One can embed the virtual object on the target once it has been identified by the AR engine and display it on their mobile screen. Model-based approaches using the 2D characteristics are used to identify the image taken. On the other hand, a marker-less AR uses the GPS on mobile devices to record the device position and displays data pertaining to that location (Amar et al. 2013). In pharmaceutical education, there are many disciplines that have adopted teaching and learning methods using augmented reality. However, it is still in its infancy stage whereby most of the applications are institutional based and implemented at small scales as a proof of concept and to test the acceptance of learners. In developing competencies in counseling, several pilot studies have been carried out, for example, utilizing HP Reveal ® software, an AR learning tool focusing on competencies required for counseling and supply of naloxone in community pharmacies (Schneider et al. 2020) and counseling for contraception and hormone replacement therapies (Salem et al. 2020) in an undergraduate Bachelor of Pharmacy (Honours) program to evaluate the acceptability, usability, and effectiveness for student learning. To bridge the gap of varied chemistry background training among pharmacy students, an AR in medicinal chemistry was developed with majority of the students preferring AR to be adopted in the curriculum (Smith and Friel 2021). Moreover, in the laboratory setting, AR guided the student to insert correct volumes of reagents for critical steps in the workflow of antimicrobial susceptibility testing (Kapp et al. 2020). To reduce the number of medication errors related to lack of information, integration of Augmented Reality (AR) glasses during the phase of injectable drug preparation was developed which allows the operator to have both of his hands

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free while preparing the correct injectable doses (Ben Othman et al. 2016). In terms of acceptance and motivation toward the use of AR in teaching and learning, there are mixed feedbacks. For example, after using the “Pharma Compounds AR” (PCAR) educational tool looking at complex molecules, there was an increase in motivation and perceived usefulness after the implementation of the AR tool (Essel et al. 2022). In contrast, after completing the role-play AR micro modules on poststroke management and chronic obstructive pulmonary disease, there was no discernible difference in how clinical knowledge and confidence in patient counseling improved after the modules. This was probably due to the technical issues students encountered while using the AR modules (Li et al. 2021). The acceptance toward the headmounted display (HMD) and augmented reality (AR) HMD-AR warfarin counseling guide to assist pharmacists in counseling declined after the implementation of the counseling guide. This accounts for more understanding of the end user’s needs. In a nutshell, although much needs to be explored and fine-tuned on the technical contents, AR in education has a potential to move forward and be one of the indispensable tools in pharmacy education. There is plenty of room for institutions and software developers to develop new virtual experiential learning which can also be marketed as a start-up like the famous AccuVein ® that uses AR and a handheld scanner that projects over the skin and shows nurses and doctors where veins are in the patients’ bodies. Virtual Reality Virtual reality (VR) is the use of digital data to build 3D computer-generated environments that the user may interact with. Since the 1990s, the virtual reality simulation using audio-visual experience automatic virtual environment (CAVE) has been the in-thing to create a real-life environment (Cruz-Neira et al. 1992). However, the use of CAVE is only possible to be used for a small group of people at any one time due to the spatial requirement. Now, a variety of equipment, including personal computers, smartphones, tablets,

gaming consoles, and head mounting devices, can be used to create VR. In the patient setting, a controlled trial comparing VR with other distraction techniques found that VR was more effective in reducing subjective pain severity in children undergoing burn treatment, than interacting with a childcare worker or listening to music (van Twillert et al. 2007). In the academic laboratory setting, VR use has changed the research landscape and reduces costs while exploring new knowledge. For example, users may explore within cells in VR’s realistic environment, which gives scientists a fresh viewpoint (Johnston et al. 2018). Additionally, the study of drug diffusion, uptake, and efficacy can be done in silico using the eBrain, a virtual reality platform that models drug administration to the brain, while identifying optimal treatments from a multitude of treatment protocols (Setty 2019). With this approach, screening of new drugs can be accelerated using VR while providing information for Investigational New Drug (IND) applications (Holbein 2009). A 3D simulation of a radiopharmacy lab was developed and put into action through a virtual learning community in radiopharmacy (VirRAD) European project. In this lab, students who are portrayed by 3D avatars may experiment with radiopharmacy equipment by completing certain learning situations (Alexiou et al. 2004). Another example is utilizing virtual patients to provide simulated scenarios. The responses to care vary based on students’ input and recommendations. For example, if an abnormal laboratory value such as elevated potassium is not addressed and corrected, complications may be encountered. The application of this virtual patient is to emulate pharmaceutical care in practice whereby students need to search in databases and other resources to gather the information needed to make informed decisions about their patients (Hussein and Kawahara 2006). Immersive 360 videos share many characteristics with VR, including audiovisual and the use of a head-mounted display to view content. However, notably 360 videos are known to have a lack of interactivity when compared with VR. The content maker handpicks the video progress, and

Digital Health and Pharmacy: Evidence Synthesis and Applications

viewers have no control over the interaction. Nevertheless, it facilitates students’ learning about a real-world situation that they would not have been able to access through other means. Immersive 360 videos may help educators to introduce a sense of realism and high fidelity within teaching that is difficult to replicate in a classroom setting – this, in turn, may help equip the workforce to learn how to manage more complex scenarios or presentations. Simulated Patient Learning In pharmacy teaching, there will be times where students are unable to physically be at the site of practice due to time constraint or lack of space or manpower to oversee the teaching and learning process. Hence, educators need to improvise the method to ensure all students receive the knowledge and skills seamlessly. In addition to conventional teaching modalities, simulation-based education (SBE) is a vital teaching strategy. Like conventional teaching techniques, SBE has enhanced students’ knowledge, understanding, and many crucial abilities during their undergraduate studies in pharmacy. Moreover, SBE is now essential for helping students improve their cooperation, decision-making, and communication abilities (Korayem et al. 2022). The best way to perform SBE is by simulation remotely through role-playing or computer-based simulation (CBS). CBS in healthcare education enables users to mimic the role of a health practitioner in a specific context (Gharib et al. 2023). An example of a successful CBS tool is called MyDispense, made to enable users to increase their knowledge, sharpen their cognitive abilities, and form professional attitudes and values. Students utilize the system work in a setting where they must examine the facts at hand to decide what to do and how to do it in a way that is safe and helpful for the patient. Interacting with patients and other healthcare professionals helps students improve their interpersonal skills. Instant feedback enables students to reflect on their performance and how they could advance (Costelloe 2017). Evidence has shown that implementing SBE using Manikin-Based Simulations (MBSs) within

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the pharmacy curriculum has improved students’ learning, critical thinking, problem-solving, clinical skills, and information retention since it is designed to also expose students to high-risk or rare medical diseases (Seybert et al. 2012). However, due to the nature of the simulation, it is probably more applicable to advanced pharmacy learners (Beck 2000). To apply such a simulation, extensive resources are needed, including equipment, facilities, and advanced technical skills, which may be a drawback to widely implement this SBE method. Another method is through Serious Gaming (SG), designed for educational purposes whereby real-world events or processes are simulated to solve problems and not the usual entertainment associated with the name. Escape rooms are a popular simulation gamification technique used to improve learning outcomes for pharmacy students (Caldas et al. 2019). The rooms are used by teams or individuals who have a set amount of time to “escape” from simulated settings by solving a series of clues. Students can learn about pharmacotherapy topics such as diabetes, heart failure, cancer, toxicology, nonsterile compounding, and geriatrics, with the help of simulated gamification (Seybert et al. 2012).

Conclusion Advancement in digital health is on a continuous rise and so is the acceptability and implementation of digital health in the delivery of healthcare services to both the patients and the clients. Be it telehealth, electronic prescribing, electronic health record management, automated dispensing, and adherence monitoring, digital health is encompassing the whole healthcare world. On the other hand, the academia is also adopting the use of digital health tools to train future healthcare professionals in the provision of health services. Besides, digital health may bring challenges such as privacy issues, lack of data transparency, and restriction on the availability of data, and availability of limited resources may lead to serious concerns over the acceptability of digital health. Nonetheless, addressing such issues to avoid

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malpractices, data breaches as well as development of digital health technology infrastructure may seem necessary for full adoption of the digital health services.

Future Research Directions Advancement of technology has provided us with the opportunity to enhance healthcare services and improve patient safety. While many innovations have shown a promising result, there are still some areas that need to be investigated. For example, the implementation of digital health services to the patients and healthcare professionals require careful consideration in terms of evaluation of resources, privacy, availability of data and technology infrastructure on the utilization of telehealth, patient monitoring, e-prescribing, and role of artificial intelligence and big data in the provision of healthcare services. Thus, to understand the full scope and impact of such innovations in the healthcare system, more studies are needed from the viewpoint of diverse populations as well as from both developed and developing healthcare systems. In terms of automation and barcode technology, more studies are needed to further improve staff compliance and avoiding new errors from being introduced into the systems. Meanwhile, studies with a bigger sample size and a longer study duration are still required to further validate the tools that have been developed to improve patient adherence. In addition, studies looking into the tools’ impact toward patient’s therapeutic outcome are crucially needed to further strengthen its applicability in the medical and health fields. With the emergence of machine learning and deep learning, it is of interest to see whether such technology can be employed to further personalize the wearable devices, thus keeping the users motivated to continually use the devices and further increase their adherence. Regardless of the positive outcomes such as leadership, teamwork, problem-solving, and communication skills reported in SG, for example (Cain and Piascik 2015), these new concepts or technologies used by pharmacy educators need to

be evaluated for the use in pharmacy curricula. At the moment, there are mostly pilot studies (Korenoski et al. 2021; Caldas et al. 2019; Eukel et al. 2017; Salem et al. 2020; Schneider et al. 2020) to support the use of technologies in teaching and learning. This is mostly due to the costs involved and the inaccessibility of materials for the use in a large group. Further recommendation would be to carry out a multicenter, multiuser assessment of the technology implementation using AR, VR, and /or simulation-based learning experience. This should also involve costeffectiveness studies especially when involving high-cost equipment (Seybert et al. 2012).

Cross-References ▶ Impact of Digital Healthcare Technology and Services on LMICs

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Disaster Management and Emergency Preparedness in Low- and Middle-Income Countries Binaya Sapkota 1, Sunil Shrestha 2, Bhuvan K. C. 2 and Amir Khorram-Manesh 1 Faculty of Health Sciences, Department of Pharmaceutical Sciences, Nobel College, Sinamangal, Kathmandu, Nepal 2 School of Pharmacy, Monash University Malaysia, Subang Jaya, Selangor, Malaysia 3 Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden

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Abstract

Disaster is an event that causes significant damage, destruction, and human suffering. The imbalance between needs and available resources necessitates national or international assistance based on the severity of the damage. Disasters can be caused by nature (e.g., hurricanes, floods, tsunami, landslides, and earthquakes) or human-inflicted (e.g., bioterrorism, cyberattacks, armed conflicts, civil war, chemical, biological, nuclear, and radiological hazards). Over the last decade, 4777 natural disasters occurred worldwide, taking the lives of more than 880,000 people and causing economic losses of USD 685 billion. Since disaster effects are disproportionate, there is a need for effective pre-event, event, and post-event plans. Disaster resilience can be achieved by learning and developing skills and resources at the individual, community, and operational level to respond to and recover from disasters. The practice of community-based disaster risk reduction and the concept of build back better,

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and management of post-disaster complications are gaining momentum. Effective disaster management demands a multidisciplinary response team to address medical and health needs; mental/psychological health and rehabilitation; disaster management education; foods, shelter, and essential logistics; sanitation, hygiene, and nutrition during relief activities. Regular healthcare operations can suddenly be hampered due to disruptions in the chain of supply, particularly. Disasters usually hamper uninterrupted flow of medical and pharmaceutical supplies and hence necessitate a competent logistics manager. Pharmacists play a crucial role in providing pharmaceutical supplies during disasters. Keywords

Disaster · Emergency preparedness · Pharmacist

Introduction Disasters and Disaster Risk Reduction (DRR) Introduction and Types

Although there is no unanimously accepted definition of disaster (Lopez-Ibor 2005), the term “disaster” is derived from the Latin word “disastro” or “bad star,” meaning catastrophe due to the unfavorable position of the planet (Khan et al. 2020). The World Health Organization Collaborating Centre for Research on the Epidemiology of Disasters (WHO CRED) defined disaster as an unforeseen and often sudden event that causes great damage, destruction, and human suffering, and necessitates a request to national or international level for external assistance (Guha-Sapir et al. 2012; Hossain et al. 2019). The United Nations International Strategy for Disaster Reduction (UNISDR) (2009) defined disasters as “a serious disruption of the functioning of a community or a society involving widespread human, material, economic or environmental losses and impacts, which exceeds the ability of the affected community or society to cope using its own resources” (UNISDR 2009).

The Asian Disaster Preparedness Center (ADPC) defined disaster as the loss of human lives and assets destroying economy, society, and others (Siriporananon and Visuthismajarn 2018). Multiple disasters are either defined as two or more hazards affecting a vulnerable population in the same region singly, or combined, at varying magnitude. Such disasters require prompt and timely government interventions, such as developing local-level infrastructure to manage complex crises (Mishra et al. 2012). Disaster can be expressed as the combination of coexisting hazard and vulnerability per existed capacity, that is, Disaster ¼ (Vulnerability + Hazard) / Capacity (IFRC 2021; Islam and Chik 2011), where the sum of vulnerability and hazards results in outcomes or effects [vulnerability + hazard ¼ effect (Cash et al. 2013)]. Similarly, Li et al. (2016) also developed an equation to depict the disaster risk of flood as flood risk ¼ function (hazard, exposure, and vulnerability) (Li et al. 2016). Hazards such as floods, earthquakes, cyclonic storms, landslides create a dangerous situation that threatens life, damages property or the environment (Islam and Chik 2011). A disaster causes material, economic, social, or environmental losses, casualties, and people’s deaths (Islam and Chik 2011; Barrimah et al. 2016; Kittu et al. 2019). The Sendai Framework for Disaster Risk Reduction (DRR) emphasized the following four prime areas for disaster risk management (DRM): i) understanding disaster risk; ii) strengthening disaster risk governance to manage the risk; iii) investing in DRR for resilience, and iv) enhancing disaster preparedness for effective response (Rivera et al. 2020). Currently, disaster risk reduction (DRR) is one of the global concerns as it helps prevent or avoid the disasters-induced great loss in the future (Watson et al. 2008). Some Major Disasters In Turkey, the Marmara earthquake on August 17, 1999, resulted in 17,127 deaths and 43,953 injuries (KoÇak 2018). It became the turning point for the Turkish crisis management in its exploration of strengths, weaknesses, and inefficiencies of the previous disaster management

Disaster Management and Emergency Preparedness in Low- and Middle-Income Countries

strategies and policies (Unlu et al. 2010). On January 26, 2001, India had faced the great Bhuj earthquake (6.9 Richter scale) in Gujarat Province, which killed 20,005 people from 7906 villages in 21 districts of the province (Pawar et al. 2005). On August 29, 2005, the hurricane Katrina severely disrupted the health services in the U.S. Gulf Coast (Millin et al. 2006). In 2005, rainfall of more than 944 mm inundated the entire region of Mumbai, India, taking more than 780 people’s lives, and causing direct economic burden of USD 120 million (Shaw 2015). In Jakarta, Indonesia, 750 mm of rainfall took many people’s lives and livestock and rendered significant economic losses in 2007 (Shaw 2015). The incredible 9 Richter earthquake and subsequent tsunami and explosion of the Fukushima Daiichi nuclear power plant triggered nuclear leakage in Japan’s northeast area, taking the lives of more than 14,358 people on March 11, 2011 (Wang et al. 2020). In 2015, two great earthquakes in Nepal with magnitudes of 7.8 (April 25) and 7.3 (May 12) Richter scales took the lives of 9000 people, injured 22,000, displaced 2,000,000 and, destroyed 90% of health facilities in the affected areas (Giri et al. 2018). Categories of Disasters One way to categorize a disaster is by its causes: (Khan et al. 2020; Siriporananon and Visuthismajarn 2018; Islam and Chik 2011; Kittu et al. 2019; Aburas and Alshammari 2020; American Society of Health-System Pharmacists 2003; Benis et al. 2018; Leaning and Guha-Sapir 2013; Mew 2013; Moe and Pathranarakul 2006; Moore 2008; Murthy and Christian 2010; Walsh et al. 2012). 1. Disasters caused by nature (e.g., hurricanes, floods, tornados, tsunami, landslides, earthquakes, volcanic eruptions, extreme heat). 2. Human-inflicted disasters (e.g., bioterrorism, cyberattacks, armed conflicts, civil war, vehicle accidents, fires, chemical, biological, nuclear, radiological (CBRN) hazards, structural collapses, air pollution, climate change and its repercussions, disasters caused by nuclear power failures).

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Similarly, emergencies may be classified as natural (e.g., earthquakes, tsunamis), technological, or man-made (e.g., conflicts, chemical crises). The WHO categorizes emergencies from grade 1 (events with minimal public health impacts) to grade 3 (events in one or more countries and have significant public health impacts that demand regional, national, and/or international response) (Garritty et al. 2017). There has been an increasing number of disasters caused by nature, probably, among others, due to climate change. Earthquakes, floods, droughts, bushfires, soil erosion, endemics, pandemics, landslides occur more frequently due to the climatic variation (Khan et al. 2020; Curnin et al. 2015; Khatri et al. 2019; Pandey 2019; Rehman et al. 2019; Siriwardana et al. 2018; Khorram-Manesh and Burkle Jr. 2020). They may strike both developed and developing countries with significant loss of human resources and devastation of physical and economic infrastructure (Islam and Chik 2011; Moe and Pathranarakul 2006; Guha-Sapir and Hoyois 2015). However, the effects are more prominent in underdeveloped countries, rural areas, and underserved communities with no emergency preparedness plan (Cordero-Reyes et al. 2017). Human-made disasters can also occur in distinctive new forms, making it difficult to predict their occurrence (Moore 2008; Lee 2020). Effects of Disasters Although disasters are inevitable, some can be prevented to some extent (e.g., floods, storms, and landslides) and/or their complications can be assessed and managed (e.g., earthquakes) (Kavota et al. 2020). Both nature-caused and humaninflicted disasters and their victims have exponentially risen over the past decades (Djalali et al. 2012). Disasters damage the physical and mental health of the victims and the responders, ultimately affecting their quality of life (QoL) (Shultz 2014; Thorpe et al. 2015). Between 2000 and 2017, disasters affected 193,312,310 people worldwide (Hosseini et al. 2020). Following the great 2015 earthquake of 7.9 Richter scale in Nepal, the devastation of healthcare centers in rural areas led to the unavailability of even

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essential health services to the disaster-affected populations (KoÇak 2018). Each year floods affect over 85 million people, making floods one of the deadliest natural disasters. Between 2000 and 2012, Vietnam faced 32 major floods, causing 2100 deaths and affecting over 32 million people. Floods lead to death and physical injuries as direct health effects; infectious diseases, malnutrition, and the spread of noncommunicable diseases are indirect effects. Indirect health effects may also result from the disrupted access to health care caused by infrastructural damage (e.g., roads, logistics, health facilities, and others). Therefore, many countries focus on flood preparedness (i.e., pre-disaster planning) to minimize the untoward health effects and ease or facilitate the post-disaster management efforts primarily at the community level. Flood preparedness covers situational action plans, maintenance of essential resources, including power supply, vehicles, foods, and medicines (Älgå et al. 2018). Disasters can create acute and chronic public health impacts, especially among the more vulnerable groups such as the elderly and the children and those with lower socioeconomic status. Acute health impacts of disasters include acute exacerbations of chronic medical conditions, including cerebrovascular events, long-term complications of injuries, and mental health problems (e.g., post-traumatic stress disorder (PTSD), depression, anxiety) (McCourt et al. 2020). People often become deprived of their medications during and in the aftermath of disasters (Watson et al. 2020).

• Tornadoes randomly affect the victims, striking one house, and skipping the next.

Characteristics of Disasters Each disaster has its unique characteristics and mode of destruction: (Jackson et al. 1999).

Vulnerable Populations in Terms of Disaster Effects The following population subset is especially more vulnerable to the disasters’ effects (IOM 2015; Khorram-Manesh et al. 2017a):

• Floods can lead to the evacuation of whole communities. • Earthquakes occur without prewarning, and immediate and sustained aftershocks further create dreadful situations. • Hurricanes cannot be predicted and lead to the sudden course change, causing large territories’ evacuation within their pocket areas.

Disaster Victims There are four categories (Taylor 1999; Young et al. 1998): • Primary victims: People directly exposed to the attack of the disaster events. • Secondary victims: People with close family and personal ties to the primary victims. • Tertiary victims: People who respond to disasters by their occupations/professions. • Quaternary victims: People who are the caring members of communities beyond the disaster-affected area. Disaster Effects on Health The health outcomes affected by disaster can be classified as the following (IOM 2015): (a) Short-term health outcomes: • Physical and mental trauma or injuries and illness. • Lack of access to life-saving logistics, including medications, devices, and equipment. • Disruption of critical medical services. (b) Intermediate- and long-term health outcomes: • Effect on environmental or behavioral health with trauma or injuries and physical stress. • Lack of access to health and human services. • Environmental pollution.

• Poor group. • People with language and literacy barriers. • People with medical conditions or disabilities (physical, mental, cognitive, or sensory). • Culturally, geographically, and socially isolated group.

Disaster Management and Emergency Preparedness in Low- and Middle-Income Countries

• People with extreme age (children and the elderly). Efforts to Mitigate Disasters Effective disaster management targets to mitigate the negative and enhance the positive impacts of disaster through efficient and timely mobilization of resources. Communication during a disaster event and the overall management process is a complicated process that should include sending, receiving, interpreting, and responding to disaster-related information by all the concerned personnel (Ha 2016). The UNDP developed a disaster risk index (DRI) to cover three-stage processes of mitigating a disaster: (Mishra et al. 2012). (i) Estimating the physical exposure: number of people exposed to hazards within the disaster-affected area. (ii) Relative vulnerability: ratio of number of deaths to the number of people exposed. (iii) Vulnerability indicators: socioeconomic and environmental variables that influence risk. Disaster Risk Reduction (DRR) Framework The Sendai Framework for Disaster Risk Reduction (SFDRR) 2015–2030, the landmark UN agreement adopted in March 2015 by 187 UN member states, targeted to minimize the disaster-related losses in terms of livelihoods, health, and property. This agreement created an environment to strengthen international cooperation in disaster management by ensuring the practical applicability of scientific knowledge during emergencies (Aitsi-Selmi and Murray 2016). SFDRR emphasized DRR and resilience at all levels via the implementation of integrated structural, socioeconomic, institutional, health, educational, environmental, techno-political, and legal measures to minimize or avoid hazard exposure and vulnerability and ultimately achieve sustainable development goals (SDGs) (Marzi et al. 2019). SFDRR includes four priorities and seven targets – four priorities within and across local, regional, national, and global levels are: (Goniewicz and Burkle 2019).

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(i) Understanding disaster risk. (ii) Strengthening disaster risk governance to manage the risk. (iii) Investing in DRR for resilience. (iv) Enhancing disaster preparedness for effective recovery, rehabilitation, and reconstruction. The SFDRR seven Global Targets are: (i) To reduce global disaster mortality by 2030. (ii) To reduce the number of victims globally by 2030. (iii) To reduce direct disaster-induced economic loss by 2030. (iv) To reduce disaster damage to infrastructure and disruption of basic services by 2030. (v) To enhance disaster management capacity by 2030. (vi) To enhance international cooperation to the developing countries for the effective implementation of the framework by 2030. (vii) To increase access to multi-hazard Early Warning Systems (EWS) by general people by 2030. Disaster Resilience Disaster resilience is a globally emerging process that enables resistance and response to disasters, while maintaining essential health services (e.g., rescue, prehospital care, triage, emergency, and critical care facilities). Such a process aims at improving or restoring existing systems to their original state (Zhong et al. 2014a). For instance, hospital resilience has four key domains: hospital safety, disaster preparedness and resources, continuity of essential medical services, recovery, and adaptation (Zhong et al. 2014b). Information and Communication Technology (ICT) in DRR A disaster management plan has a defined set of a chain of commands to be followed during the disaster (Sakurai and Murayama 2019). Risk communication is an interactive process of information dissemination and exchange among individuals, groups, and stakeholders regarding risk to health and the environment to make better

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and organized decisions during disasters (Lim and Nakazato 2020). Disaster Management and Emergency Preparedness Initiatives The term “disaster management” has often been replaced by the terms “disaster risk reduction” (DRR) and “disaster risk management” (DRM). Similarly, disaster management is also synonymously used with “emergency management” (Ahmed 2019; Baas et al. 2008). Disaster management aims to minimize the negative consequences of the event on health and the economy immediately and/or on a long-term basis. Effective disaster management emphasizes leadership and coordination across multidisciplinary professions, agencies, organizations, and even nations (Khorram-Manesh et al. 2016; Khorram-Manesh et al. 2015) Disaster management involves a continual and dynamic process of responding to disastrous situations (Wang 2016). Disaster Management Cycle All disasters usually follow a cyclical pattern of four reactionary stages: mitigation, preparedness, response, and recovery – collectively termed the “disaster cycle” (Moe and Pathranarakul 2006; Wang 2016; Beach 2010; Ciottone 2006). All the phases are complementary to each other. Among these phases, pre-disaster plans include preparedness, risk reduction, prevention, and mitigation; peri-disaster operations are conducted during the response phase, and post-disaster actions emphasize recovery and reconstruction (Wang 2016). (i) Mitigation/Prevention This stage emphasizes improvement of the physical infrastructure, necessary food stockings, safe potable water and other essential commodities, and other essential routines, and organizational planning for future events (Ciottone 2006); a set of comprehensive measures to be implemented before the event prevents mortalities, morbidities, structural and economic losses (KoÇak 2018).

(ii) Preparedness Disaster preparedness comprises the preparation or planning phase to minimize the repercussions of any disaster. It strengthens the overall management process (both short term and long term) and stakeholders’ technical and logistics capacity to respond to disasters. The program focuses on the implementation of disaster management policy, adopting Early Warning Systems (EWSs), mechanisms and procedures of response to the events, public empowerment with relevant training on coping techniques, and procurement of essential commodities for relief. These are mainly targeted to save and/or prolong the quality of lives (focusing primarily on psychological hazards on victims), minimize structural damage and economic burden posed by disasters, and facilitate disaster response by the concerned (Atanga 2020). Bioterrorism, disease outbreaks and pandemics, mass casualties, and other disasters have necessitated public health systems to strengthen emergency preparedness protocols (Leider et al. 2017). (iii) Response Disaster response represents the actions (unorganized or organized) taken to protect or save life and property before, during, and after disasters. Providing basic and advanced life support (BLS and ALS) activities are the prime focus during this phase. The response approaches include plans for evacuation during disasters, arrangement of short- and long-term shelter, food, and medical facilities (Atanga 2020). Psychological first aid (PFA) is widely used during the response phase by the first responders (i.e., affected people in the community) and disaster workers (IOM 2015). Hart’s Inverse Care Law (ICL) indicates that people who require the most care receive the least standard care, especially in low- and middle-income countries (LMICs) (Cookson et al. 2021; Hart 1971; Phibbs et al. 2016). Therefore, the exponentially rising global disasters and public health emergencies have necessitated many countries to revisit their disaster preparedness and response strategies to reduce

Disaster Management and Emergency Preparedness in Low- and Middle-Income Countries

mortality, morbidity, the severity of devastations (Sultan et al. 2020). (iv) Recovery This phase indicates the effort to return the victims’ livelihoods to normalcy after an event to achieve sustainable solutions to future disaster events, and it is usually a tedious process. It also may involve the reconstruction of physical infrastructure damaged by the disasters. Sometimes, the recovery phase may transition directly from the response to the mitigation phase (Atanga 2020). After every disaster, there usually comes new opportunities to address the prevailing inequities (if present) and recovery efforts (IOM 2015) (Fig. 1). Disaster risks vary from facility to facility, location to location, and operation to operation. Therefore, there is no single and unanimously accepted basic disaster management plan that fits all facilities and operations (Schneid and Collins 2001). Disaster management requires strong cooperation between federal and state governments, local agencies, and public and private sectors to mitigate the untoward impact of emergencies during disasters (Khan et al. 2020; Moe and Pathranarakul 2006). Disaster resilience can be achieved by learning and developing skills and resources at the individual, community, and operational level to respond to and recover from

Disaster Management and Emergency Preparedness in Lowand Middle-Income Countries, Fig. 1 Disaster management cycle (Ciottone 2006) [Figure credit: publisher of the book, that is, Elsevier Limited on May 31, 2021]

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disasters (Crawford et al. 2013). Balance between the requirements and resources (e.g., human, material) mobilized in the disaster sites can be maintained with the flexible surge capacity (FSC), which focuses on the mobilization of additional resources (e.g., even the nonprofessional but educated people) to respond to the disasters (Khorram-Manesh 2020).

Integrated Organizational Approach to Disaster Management The integrated approach covers proactive (i.e., identifying risk) and reactive strategies (i.e., assessing impacts of disaster and its level) (Moe and Pathranarakul 2006). Disaster management activities require active engagement of both internal and external organizations, different sectors, and disaster management network (Zaw and Lim 2017). There is an emerging concept of applying system thinking and complexity theory to disaster preparedness and disaster risk reduction (DP/DRR) as it leads to better understanding of the dynamics and interconnectedness of the disaster risk and DRR. Networks DP/DRR system can be formed by the active participation of institutions and voluntary groups, including the flexible surge capacity (FSC), which dynamically and proactively interact, sharing the related

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information and ideas. While applying the system thinking, the disproportionate and realistic interconnectedness of the repercussions of the disasters can be explored with the Risks Interconnections Map (RIM), potential disaster management strategies can then be formulated and implemented based on the model developed (Uusikylä et al. 2020). Prevention and Early Responding to Disaster and Its Complications Effective disaster preparedness and response actions require timely and efficient pre-event (preparedness), event (crisis phase), and postevent (consequence phase) planning. Pre-event activities include risk assessment, risk communication, and hazard prevention strategies; eventphase includes disaster risk communication and medical interventions (e.g., post-exposure prophylaxis (PEP) and treatment, psychological counseling, and screening/quarantine strategies); and the post-event involves mitigation and treatment of physical and mental health consequences (Barnett et al. 2005). Preparedness helps to develop a response plan and train the community people (i.e., the first responders) to save lives and reduce disaster events (Unlu et al. 2010). Early Warning System (EWS)

Early warning system (EWS) scores are tools used to explore the early signs of situational deterioration to initiate early intervention, such as enhancing health professionals’ response to disaster events and the victims (Smith et al. 2013). The EWS is good to be coupled with the Critical Care Team (CCT), Rapid Response Team (RRT), Patient At Risk Teams (PART) to provide timely assistance to the victims and improve their outcomes (Alam et al. 2014; Kramer et al. 2019). Therefore, establishing an efficient EWS is one of the integral components of disaster response, which mainly focuses on four elements: i) hazard exploration and recognition, ii) hazard monitoring and evaluation, iii) alerting the victims and the first responders, and iv) strengthening disaster response capacity (Moradian et al. 2015). For example, landslide EWSs were widely used to prevent its harmful effects and associated losses

(Wu et al. 2020). Flood forecasting and EWSs are cost-effective in reducing their untoward impacts with timely alerts on the tentative location and timing of floods with their strengths (Alfieri et al. 2019). Availability of efficient EWSs for disasters (e.g., earthquakes, floods/landslides, forest fires, etc.) that emphasize the need for prompt and authentic transmission of information about the potential disaster event helps avoid or minimize death toll and financial burden (Khan et al. 2020). However, the prevention paradox usually applies to disaster management, which states that the population-centered risk reduction approach may have little benefit for the individual. Inversely, the individual or small group-focused approaches may have a miniature effect at the population level. Therefore, disaster risks can be minimized by focusing on the whole population with the population-based management teams (PBMT) pre-event, rather than focusing solely on the at-risk individuals or small group post-event (Phibbs et al. 2016; Burkle Jr. et al. 2021). Community pharmacists have played an important role in monitoring medication misuse and organizing EWSs during normal time, which can be extrapolated in case of disasters also. Pharmacists can also exercise the EWS via software during inventory management of pharmaceuticals, which play a pivotal role at the time of disasters. Pre-disaster, Peri-Disaster, and Post-disaster Initiatives All Nature-caused calamities cannot always be predicted and prevented, and therefore, timely and efficient preparedness and response mechanisms can mitigate the loss of life, finance and restore normalcy (Kittu et al. 2019). For instance, flood disasters can be predicted before their occurrence (unlike earthquakes and volcanic eruptions) and can also be controlled with timely and appropriate measures (Li et al. 2016). Disaster Management Policy and Procedures: Prevention, Preparedness, and Mitigation A practical disaster medical response requires a well-planned and coordinated effort with many

Disaster Management and Emergency Preparedness in Low- and Middle-Income Countries

trained and experienced professionals, who can apply specialized knowledge and skills in critical situations, since skills and training are essential for increasing the willingness to work during diverse types of incidents and to take care of the casualties during emergencies (Walsh et al. 2012; Sultan et al. 2020). Disaster Management Clinics and Hospitals Local clinics or health facilities serve as the first responding point-of-care for the community people help mitigate the consequences of disasters (Kaufman et al. 2020), and therefore, should be equipped with disaster preparedness strategies. For example, residents consult the local clinics or hospitals at the time of disasters in Japan (Ochi et al. 2017). Hospitals are vital for providing emergency and intensive health care to the affected people as these follow the Hospital Incident Command System (HICS) during disasters. Concept of the Incident Command System (ICS) was first introduced in 1970 to manage the disasters posed by wildfires in Southern California, USA, and aimed to simplify the communication channel, establish lines of authority and command, and provide effective utilization of resources (Djalali et al. 2012). Hospital preparedness initiatives should address the immediate and long-term mass casualties and all hazards, including infectious disease pandemics.Hospitals should be provided with the proper machinery for disaster preparedness and subsequent resolution of past mistakes and failures, and improvement of the current strengths for better disaster management in the future (Seyedin et al. 2021). Disaster Management Academia (DMA) and Curricula Development Several universities and academic institutions worldwide have implemented competency-based education. The American Medical Association Center for Public Health Preparedness and Disaster Response also gave a comprehensive set of competencies by integrating all health specialties focused on disaster medicine and public health (Gallardo et al. 2015). Disaster preparedness and overall disaster management concepts can be

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cultivated in youths through training, personal experiences, and belief (Wang 2016; Brewer et al. 2020; Khorram-Manesh et al. 2018). These concepts can then be applied in real-life disaster responses and subsequently contribute and enhance disaster management capacity of societies and nations. Disaster management education and risk reduction are gaining momentum worldwide by three key domains: curriculum, educational infrastructure, and academic safety preparedness and operational plans. Schoolbased risk reduction education is also being popular to facilitate the optimum utilization of both internal and external school resources to minimize or avoid negative consequences of disasters (Wang 2016). Disaster Management Guidelines and Guidance Standard operating procedures (SOPs) and guidelines guide healthcare providers in better medical evaluations (Seyedin et al. 2021). Guidelines are useful, especially when there is uncertainty about decision-making during clinical practice, and should be developed by the Guideline Development Group, which should include expertise in clinical, social, ethical, and legal issues (Garritty et al. 2017). At the time of global emergencies like Ebola virus disease (EVD), coronavirus disease (COVID), the WHO plays global leadership through emergency (rapid response) guidelines developed by applying rigorous methods within 1 to 3 months’ period (Garritty et al. 2017). Community-Based Disaster Preparedness (CBDP) Community-based disaster management (CBDM) concept was popular in the late 1980s and 1990s and gradually evolved to CBDRM (communitybased disaster risk management), and then to CBDRR (community-based disaster risk reduction). CBDRM and CBDRR are often used synonymously with the prime focus on risk; however, these two terms are not precisely the same as CBDRR focuses mainly on the pre-disaster activities of the communities to achieve risk reduction, and CBDRM focuses more on the broader perspective of risk reduction by communities and

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covers pre-, peri-, and post-disaster periods (Shaw 2015). Community-based DRR is an essential component of disaster management in which communities at risks of disasters show active participation in identifying, monitoring and evaluating, and overall management of risks. Community people actively participate during the decision-making process and implementation of disaster management approaches (Pandey 2019). Hence, effective disaster management activities focus on community-capacity building and health promotion strategies by their concerted and organized efforts (Cordero-Reyes et al. 2017; Lee 2020). Negative consequences of disasters can be minimized by empowering and engaging the local community people, and receiving commitment and contribution from the sociological, organizational, professional, and political sectors to make them capable of adopting preparedness, response, recovery, mitigation, and overcoming the hazardous impacts (Islam and Chik 2011; Barrimah et al. 2016; Khorram-Manesh and Burkle Jr. 2020). Multidisciplinary response team focuses on addressing medical and health needs; mental/psychological health and rehabilitation; disaster management education; foods, shelter and essential logistics; sanitation, hygiene, and nutrition during relief activities (Cordero-Reyes et al. 2017). A case in point was the 1995 Kobe earthquake where the local communities helped 98% of the affected, and other rescue operations helped the remaining 2%. After that earthquake, Japan further focused more on enhancing community preparedness, establishing, and promoting professional networks at the time of disasters (Shaw 2015). Similarly, the Cyclone Preparedness Program (CPP) of Bangladesh focuses on the residents’ capacity enhancement as volunteers because they are the first responders during disaster events (Shaw 2015; Jeong et al. 2020; Khorram-Manesh et al. 2019). These days social resilience in disaster management is gaining momentum from researchers and practitioners and focuses on the comprehensive social resilience framework. Such a framework emphasizes consistent resilience across geographies by adapting it to the context.

However, there are multitudes of theoretical and practical hurdles in conducting a prompt but rational, accurate, and efficient evaluation of social resilience to disaster events. The “hard-to-reach” population is the group that is difficult to involve in public health initiatives or campaigns due to geographical adversity, social and economic hardships, social isolation, and cultural exclusion (Phibbs et al. 2016). On the other hand, unplanned or haphazard rapid urbanization has increasingly made the communities prone to disasters and pandemics, leaving no country immune to their repercussions. Still, each country may have its own coping strategies with its resources, competences, and capabilities (Khorram-Manesh et al. 2020). Therefore, community people should proactively and efficiently mitigate disaster risks to minimize or avoid the severity of impacts of disasters, develop resilience, and recover rapidly from the harmful impacts (Saja et al. 2019). Theories and Practice Relevant to Community Involvement in Disaster Management “All disasters begin locally” is an interesting philosophy for any disaster preparedness and response that emphasizes the role of the first responders (i.e., community people) to disaster management (Jeong et al. 2020; Barnett et al. 2012). Durkheim’s theory of social solidarity also stated that the increased interdependence of community people is directly related to increased social cohesion (Barnett et al. 2012). Thus, the more the community people are motivated and trained to respond to local disasters and save the needy, the more social cohesion will be. Nevertheless, community empowerment does not preclude that the state and federal governments have no role in disaster management. If local people and officials cannot tackle the consequences of a disaster, then the state and federal governments should support the victims, and assist or empower the community people to develop their capacity to respond to and recover from the impacts of disasters (Jeong et al. 2020). Witte’s Extended Parallel Processing Model (EPPM) (popularly known as Threat Management or Fear Management Model) helps explore the

Disaster Management and Emergency Preparedness in Low- and Middle-Income Countries

determinants of willingness to respond (WTR) to public health risks. The EPPM model states that people become more proactive during uncertain risks if they perceive the threats of hazard as legitimate (Barnett et al. 2012; Popova 2012; Rintamaki and Yang 2013). The model further described that the integration of perceived risk and perceived efficacy influences or drives risk reduction behavior (Barnett et al. 2012). The degree to which an individual feels threatened by an event determines his/her motivation to act, and his/her actual confidence in reduction or prevention of the event determines the action itself. EPPM is also helpful in social and behavior change communication (SBCC) approaches when a risk poses a real or perceived threat to individual’s health. Paton et al. developed the community engagement theory of hazard preparedness (CETHP) in New Zealand, and this was later tested in Australia and the USA as well (Paton et al. 2008), as it applied to all hazards, including earthquake, volcano, wildfire, flood and pandemics (Jang et al. 2016). Negative outcome expectancy (NOE) indicates the belief that the earthquake impacts are too disastrous for individuals to make any difference in ensuring personal safety and property security. Thus, NOE predicts that the community people of such belief or opinion will not show interest in hazard preparedness. In contrast, if people believe that hazard preparedness can minimize risk and enhance personal safety, they will develop positive outcome expectancy (POE), which helps develop vibrant human force for the future disaster management (Paton et al. 2008; Jang et al. 2016). Disaster Medicine and Public Health (DMPH) Common health problems at the time of disasters are (PAHO 2012): (a) High priority (acute phase): (i) Communicable diseases (CDs): acute respiratory infections (ARIs), fever, diarrhea, malaria, meningitis, encephalitis. (ii) Noncommunicable diseases (NCDs): acute asthma.

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(iii) Gastrointestinal tract problems: nausea, vomiting, and gastrointestinal (GI) muscle spasm. (iv) Injuries: physical trauma, wound infections, tetanus, rabies, and venom toxicity. (v) Surgeries: coagulation problems. (vi) Mental health problems: anxiety, depression, epilepsy. (vii) Reproductive health problems: sexually transmitted infections (STIs) (e.g., gonococcal infections: gonorrhea, chlamydia; nongonococcal infections: trichomoniasis and bacterial vaginosis). (viii) Skin infections.

(b) Medium priority (post-disaster management): (i) CDs: tuberculosis (TB). (ii) NCDs: diabetes, cardiovascular diseases (CVDs), blood problems (e.g., anemia), respiratory diseases (e.g., asthma, COPD). (iii) Reproductive health problems: syphilis, post-exposure prophylaxis (PEP) for HIV infection. Public health interventions that are useful during disaster management are (PAHO 2012): (i) Insecticides with bed nets. (ii) Water purifier (e.g., chlorine). (iii) Disinfectants.

Pharmaceuticals Issues in Disaster Management During the great earthquake 2015 in Nepal, the Nepalese pharmacists contributed to manage logistics of medicines and health items, and maintain smooth supply system in the affected areas, provided minor health services (including mental health) and dispensed medicines via locally built temporary health clinics (Shrestha et al. 2019).

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Disaster Medicines and Pharmaceuticals Disaster medicine is the area of medical specialization that aims to provide medical support during disaster preparation, planning, response, and recovery phases of the disaster life cycle and provide health care to the victims (Kittu et al. 2019). It is a discipline formed with the integration of emergency medicine, and public health management and disaster management (Barrimah et al. 2016; Ciottone 2006) and focuses on overall health (primarily psychological) concerns of the casualties (Barrimah et al. 2016). Its introduction in the medical curriculum is relevant to the students and practitioners (Barrimah et al. 2016). During public health emergency (e.g., infectious disease pandemics, terrorist attack involving chemical or biological agents), timely administration of medications or vaccines to the mass of people is very much important (Stewart and Cordell 2007; Matteson 2006) because disruption of routine treatment during disasters deteriorates chronic diseases (e.g., diabetes, hypertension, and other). Hence, prior preparation of the emergency packs may be an effective preparedness strategy to deliver medications to the disaster sites within the maximum possible period (Tomio et al. 2010). For example, 12-h push packages of large quantities of pharmaceuticals (e.g., both solid and liquid dosage forms of oral antibiotics, injectable medicines, select analgesics, and emergency medications), equipment, and medical supplies have been implemented in the USA to supply and deliver a broad-spectrum support to the disaster victims within 12 hours of the federal decision (Stewart and Cordell 2007). Uninterrupted supply of pharmaceuticals is also essential for the CDC category A, B, and C bioterrorism agents/diseases. Maximum level of supply is essential in the order of C category threats (e.g., emerging infectious diseases such as Nipah virus, hanta virus), B category threats (e.g., brucellosis, salmonellosis, shigellosis, psittacosis, Q fever, typhus fever, viral encephalitis, Vibrio cholerae, Escherichia coli, Staphylococci, and Clostridium-induced threats), and A category threats (e.g., anthrax, botulism, plague, smallpox, tularemia, viral hemorrhagic fevers caused by Ebola, Lassa viruses) (Stewart and Cordell 2007; CDC 2021).

Developing Drug Use Guidelines and Dosing Charts for Disaster Management

Pharmacists should counsel the patients not to take their old and new medicines simultaneously to avoid therapeutic duplication and potential health hazard. They should encourage the patients to continue their pre-disaster regimens (Montello and Ames 1999). Providing Drug Counseling During Disaster Pharmacotherapy is usually delayed for at least 48 h, utilizing the individual or group debriefing period. However, rapid initiation of pharmacotherapy (usually with the short-acting anti-anxiety agents such as lorazepam) is indicated in severe health problems for patients who are agitated, psychotic, and non-adherent. Patients who fail to respond to lorazepam are psychotic rather than anxious and require aggressive treatment with antipsychotic haloperidol. Beta-adrenergic antagonist propranolol can also be helpful to antagonize excessive adrenergic activity associated with post-traumatic stress disorder (PTSD) (Young et al. 1998). Pharmaceuticals Procurement, Storage, Inventory for Disaster Management Pharmacists usually assume a crucial role in the planning and executing distribution and control of both medicines and surgical items and medication therapy management (MTM) during disasters. Pharmacists’ expertise is helpful in the following areas (American Society of Health-System Pharmacists 2003): (a) Developing guidelines for diagnosis and treatment of the casualties. (b) Selecting pharmaceuticals (i.e., medicines and surgical items) for national and regional inventories. (c) Ensuring efficient storage, handling, packaging, labeling, and dispensing of emergency medicines and surgical items. (d) Ensuring the timely and appropriate distribution of emergency supplies of pharmaceuticals. (e) Empowering community people via education and counseling at the time of disasters.

Disaster Management and Emergency Preparedness in Low- and Middle-Income Countries

Pharmaceutical Policy Coordination for Disaster Management Medicine donations by the pharmaceutical companies, hospitals, government supply mechanisms, and others should be appropriately coordinated to facilitate their timely delivery to and utilization by the victims (Montello and Ames 1999). In the USA, in response to the pharmaceutical policy coordination after the disastrous Hurricane Katrina in 2005, Jefferson County Department of Health-approved pharmacists were allowed to issue a refill prescription for 30 days of medications falling under the prescriptions of (Hogue et al. 2009): • • • • • • • • • • • • • • • •

Asthma Cardiovascular disease Chronic dermatologic disorders Chronic ocular disorders Congestive heart failure (CHF) COPD Diabetes Depression GI disorders Hepatic disease Hypertension Neurological disorders Pancreatic insufficiency Psychiatric disorders Renal disease Thyroid and parathyroid disorders

Similarly, after the same hurricane, pharmacists were engaged more proactively in the public health activities in Birmingham, Alabama, especially focusing their expertise in medicationrelated activities, triage activities, and communication/administrative activities with the development of standard operating procedure for the same (Hogue et al. 2009).

Challenges Any disaster may create challenges to the usual real-time documentation practice (e.g., medical and medication records), either manual or

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electronic, which may ultimately disrupt the case-based reasoning (CBR)'s smooth functioning (Zoraster and Burkle 2013). Other potential threats to emergency preparedness include a lack of clarity on individual and organizational responsibilities related to emergency preparedness and response (EP&R), resource allocation, collaborative coordination, and synchronization of response efforts (VanVactor 2011). Government authorities in the federal or local levels are responsible for the regulation of the disaster management policies, including pharmaceutical distribution and transportation of the affected, from one to other facilities in collaboration with the health professionals (e.g., physicians, pharmacists, and other). However, it must be clear and clarified how the emergency treatment of patients, some of them under chemotherapy treatment for cancers should be guided. One such agreement was reported from the flooding of Bangkok in 2011. Hospitals evacuating their patients to other hospitals provided them with 1 week treatment and medication. The receiving hospitals could then take over the responsibility after 1 week. Such a good practice allows the evacuating hospital to utilize all its resources, while giving the receiving hospital time to purchase the drugs that they may not have at the time of transportation. The winner is the patient who experiences maximal safety (Khorram-Manesh et al. 2014). Documentation During Disasters Prevention and protection from probable criticism or litigation, the institutionalization of the CBR approach are essential reasons for documentation at the time of any disaster. During disasters, patients need rapid response and are quickly evacuated from one facility to another, making the written record quite problematic. Therefore, clinical documentation is not gaining much more priority during disasters, but adequate documentation ensures continuity of quality care, and improves patient outcomes. Paper documentation are especially the quickest mode of documentation at the time of disasters, which can later be transcribed to the electronic system. Components of both the paper-based and digital documentations are the same; they only differ in the modality

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of documentation. Another good example can be reported from hospital evacuations in Bangkok and during flooding in 2011. All information regarding patients were written on a paper-based report version in three copies, consisting of diagnosis, medical treatment, etc. The first copy remained in the evacuating hospital, the second one in the ambulance with prehospital crew, and the third was delivered to the receiving hospital (Khorram-Manesh et al. 2014). Post-disaster Complications (PDCs) and Management Wounds are a major cause of morbidity and mortality during any disaster event and get contaminated by various microorganisms present in the surroundings. These may compromise airway, ventilation, cardiac function, lead to hemorrhage, and may need to be managed by applying pressure with a tourniquet. Therefore, lifethreatening injuries (e.g., neurovascular injury) should first be managed, and the injuries to the extremities (e.g., bone and soft tissue injury) should be addressed later. Poor wound management makes infection more complicated and causes tissue necrosis necessitating excision or even amputation, and preventable sepsis, gangrene, and mortality. Therefore, proper wound management by the first responders can prevent future complications. Wound irrigation can be done with isotonic saline, distilled/sterile water, and/or dilute antiseptic solution (e.g., 1% povidone-iodine) or simply with boiled and cooled water, provided there is no provision of the former. Since the untreated river water or seawater have contaminants dissolved, these should not be used for wound irrigation (Wuthisuthimethawee et al. 2015). Medical, technical, and administrative assistance should be supplemented with psychosocial support to the affected to prevent the propensity of post-traumatic disorders (PTSDs). Following four areas are vital for psychosocial response (Purtscher 2005): (i) Planning an intervention (e.g., acute and long term).

(ii) Staff training to respond to traumatic disorders and mass emergencies. (iii) Evaluating the interventions. (iv) Managing disaster information. Challenges During Pharmaceutical Stockpiling All disaster management agencies want stringent inventory system of pharmaceuticals but in reality, it is quite complicated. Nature of the challenges may vary country-wise, region-wise, or disaster-wise. For example, in case of COVID crisis, PPE (e.g., facemasks), sanitizers, oxygen, ventilators, etc., were lacking in many parts of the world. In case of earthquakes, countries may not fac the same challenges. However, major challenges faced during stockpiling pharmaceuticals can be enumerated as (Stewart and Cordell 2007): • Budget arrangement. • Centralized versus decentralized stockpiles with their organization. • Vendors’ timely and efficient delivery. • Allocating inventory holding sites. • Preparing unit dose prophylactic regimens. • Ensuring effective management during bulk demands at the time of disasters. Detecting and Resolving Drug-Related Issues for Patients with Moderate, Chronic, and Acute Conditions Managing the injuries and healthcare problems of people with moderate, chronic, and acute conditions might lead to drug therapy-related problems (DTRPs) due to drug diseases or drug-drug interactions, adverse effects of certain drugs, or use of a drug that is contraindicated in the person with these problems. Furthermore, drugs used without careful review of the current medication might lead to inappropriate use of medicines. Providing healthcare service during disaster is done hastily because of the need of immediate health/medical service, and this can contribute to DTRPs in people with both moderate and acute chronic health problems. Community pharmacists are usually easily accessible experts to detect, prevent, and resolve such problems (Williams et al. 2011).

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Pharmaceutical Distribution and Logistics Management During Disaster When any major disaster strikes, many institutions or health systems find themselves illprepared, ill-equipped for sustainable delivery of services and smooth operations during and immediately after the event, despite having prior sound written plans and procedures for routine operations. Normal healthcare operations can suddenly be hampered with the nonavailability of supply, necessitating a competent logistics manager during crisis management planning, and pharmacists play a crucial role in pharmaceutical management during disasters (VanVactor 2011). A case in point of the critical drug shortages was that during the coronavirus disease 2019 (COVID-19) pandemic, when essential logistics of personal protective equipment (PPE), such as facemasks and sanitizers, were out of stock in almost all healthcare facilities all over the world for a prolonged period. Supply chain disruptions leading to medication shortages must immediately be tackled for their smooth delivery during disasters (Burry et al. 2020). It is however, important to emphasize the significance of intact infrastructure for pharmaceutical delivery. In Sweden, for instance, the recommendation to the population is to last at least 72 h before any help can reach them. A study in Gothenburg however shows that many people living at their own home, relying on delivery of their medication or other pharmaceutical items such as oxygen could not last that long, especially if the distance to the closest hospital was long or infrastructure for transport through land and water was damaged (Khorram-Manesh et al. 2017a).

Economic Implications of Disaster Management Services Natural disasters all over the world caused economic losses of USD 210 billion in 2016 (Siriwardana et al. 2018). Over the last decade, 4777 natural disasters occurred worldwide, taking the lives of more than 880,000 people, affecting health, economy, and jobs of about 1.9 billion

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people and causing economic losses of USD 685 billion (Kavota et al. 2020). Each year from 2000–2005, various disasters took 80,000 lives and USD 80 billion economic losses (Baas et al. 2008). Little is known about the fiscal costs of natural disasters due to the nature of devastations these may cause. Still, significant economic loss due to extreme weather has been estimated with the increasing population surge, ecosystem imbalance, and climate change all over the world. All these may lead to high economic loss of disasters exceeding the national gross domestic product (GDP) as well. A case in point of the economic loss (USD 4.8 billion per hurricane or USD 8.1 billion per year between 1970 and 2005) incurred by the physical damage from hurricanes in the USA can be taken. In the period from 1979 to 2002, the USA spent USD 19 billion and USD 67.7 billion on hurricane-related disasters and other disasters, respectively (Deryugina 2017), USD 28 billion on recovery, and USD 2.6 billion on mitigation between 1988 and 2001 (Ganderton 2005). The World Trade Center attacks in the USA in 2001 posed USD 11.6 billion loss (Deryugina 2017).

Pharmacists’ Contribution to Disaster Management Community and hospital pharmacists have been working in EP&R, such as screening, vaccinations, testing, ensuring medication safety, and smooth delivery and access to pharmaceuticals during natural disasters and pandemics such as COVID-19 (Aruru et al. 2021). Community pharmacists provided health services, medicines, and health supplies during COVID-19 pandemic in many countries such as USA and Qatar (ElGeed et al. 2021; Johnston et al. 2003). They were also involved in disseminating much required health information regarding COVID-19 safety measures in Korea (Vega and Nyarko 2021), vaccinating people against COVID-19 in UK and USA (Thayer 2021). They also served people with medicines during

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the great Australian wildfire (in New South Wales, Australia) (Winkle 2021). Pharmacists are an essential member of pharmaceutical and health logistics supply system and can play important role in overall medicines and health supply distribution. The significant roles of pharmacists during disaster management can be enumerated as (Ford et al. 2013): • Collaborate with other healthcare providers, policymakers, and the victims on medication management. • Educate the victims and their caretakers about the importance of therapies. • Help to prevent panic and fear caused during and after disasters. • Discourage personal drug stockpiles during disasters to avoid the unequal distribution of the resources among the needy. • Monitor disease (e.g., post-disaster stress, mental disorders and other) outcome or progression during disasters and their management process. • Engage in one-on-one patient counseling during medication management process. • Maintain proper inventory control and supply chain of drugs. • Develop and implement first-aid skills at the time of disasters. • Assist in patient triage and cardiopulmonary resuscitation (CPR) to ease the patients’ grief. • Maintain documentation of the services with the outcomes.

Lessons Learned Management of hurricanes Katrina and Rita in the USA showed that lack of human and material resources can have devastating impacts as many evacuees need special attention and management during and after disasters. Disasters are inevitable incidents, but their negative impacts can be mitigated by better planning, education and training, and learning from the past disasters of any kind. National, regional, and local command centers response to the needs of the victims immediately can effectively help manage the problems of the

victims. Post-disaster complications (both physical as well as psychiatric) should be the major concerns for the individual level and building back better (BBB) for the community and national levels (Khorram-Manesh et al. 2014). Every disaster is a great lesson itself for future preparedness and mitigation activities, though every time their impacts are disproportionate and sometime unavoidable. Following are the main lessons to be learnt from the experiences of disasters all over the world (Khorram-Manesh et al. 2017b): (i) Command and control are the utmost priority in all disasters to mobilize the human resources including FSC, material to the affected population and to make critical and decisive medical and nonmedical decisions. (ii) Safety of the affected as well as the disaster responders (such as primary or health professionals providing care to the affected) should be the prime concern of disaster mitigation. (iii) Communication among the disasters responders, supporting agencies, and population at different levels about the potential hazard prior to their occurrence and even after the disasters occurred should be arranged properly. (iv) Assessment of the disasters’ impacts (be it human or animal loss, economic damage, and other) among the deceased or missing also need to be considered. (v) Medical triage of the victims and sorting of what measures or contributions comes next must also be addressed properly. For example, following modality of triage with different colors, which is generally used by military and disaster medical services, can also be used as guidance for sorting various actions (Baxter and Casady 2020): (a) Blue: patients not needing urgent medical care or hospitalization. (b) Green: patients with minor injuries or illnesses (i.e., optional/supportive). (c) Yellow: more serious but nonthreatening cases (i.e., important or urgent).

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(d) Red: patients may die if they do not get immediate medical support and treatment (i.e., essential or critical). (e) Black: deceased or those with no probability of survival. (vi) All victims do not need treatment; some can be treated with palliative or nonmedical care and some may require psychological treatment as well. (vii) Transport and logistics management are the greatly hampered areas, which need immediate attention to become smoothly operational in case of any disaster.

Conclusions Although disasters are inevitable, some can be prevented to some extent (e.g., floods, storms, and landslides) and their complications can be assessed (e.g., earthquakes). Each disaster has its unique characteristics and mode of destruction. However, all disasters usually pass through a cyclical pattern of four reactionary stages: mitigation, preparedness, response, and recovery that should be tackled by a well-defined and tested disaster management plan in all phases. The exponentially rising global disasters and public health emergencies have necessitated many countries to revisit their disaster management plans to minimize mortality, morbidity, and severity of devastations. Pharmacists have a significant role in the multidisciplinary management of disaster and should exercise their knowledge in planning and executing distribution and control of pharmaceuticals and MTM during disasters in collaboration with other stakeholders.

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Disease Surveillance in Low- and Middle-Income Countries

Disease Surveillance in Lowand Middle-Income Countries Binaya Sapkota 1, Smriti Maskey 2, Rajeev Shrestha 3 and Sunil Shrestha 4 1 Faculty of Health Sciences, Department of Pharmaceutical Sciences, Nobel College, Sinamangal, Kathmandu, Nepal 2 Department of Epidemiology and Population Health, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA 3 Department of Pharmacy, District Hospital Lamjung, Besisahar, Lamjung, Nepal 4 School of Pharmacy, Monash University Malaysia, Subang Jaya, Selangor, Malaysia

Abstract

Disease surveillance is an integral component for preventing and controlling any disease or healthcare-associated infections. Surveillance helps clinical practitioners and decision-makers formulate priorities for effective interventions and evaluate their impacts. It helps to answer five important questions related to the diseases of interest: the event itself, event site, time of event, victim/patient, and causal agents. Surveillance can be carried out on active, passive, participatory, or syndromic basis by following eight important steps starting from setting goals to implementing the outcomes. Public health, infectious disease, and chronic disease surveillance are popular these days. Still, most disease surveillance activities in low- and middle-income countries are confined to humans, despite widespread detection and transmission of more than 60% of the emerging diseases by zoonotic pathogens. Pharmacistmaintained pharmaceutical databases have been useful in monitoring and determining the incidence, prevalence, and pattern of both druginduced and non-drug induced diseases. Keywords

Disease surveillance · Health informatics · Public Health Pharmacists

Disease Surveillance in Low- and Middle-Income Countries

Introduction Background to Disease Surveillance Disease surveillance is an important and integral component for preventing and controlling any disease or healthcare-associated infections (HAIs). Disease surveillance systems provide information about disease etiology, distribution, severity, and treatments (e.g., vaccines) (Mandyata et al. 2017). Health systems, including hospitals, use surveillance data to explore the disease trends within wards at a specified period (Lin and Trick 2016; Wise and Lovell 2013). Surveillance helps clinical practitioners and decision-makers with timely and useful information to formulate priorities for effective interventions and evaluate their impacts (Chiolero and Buckeridge 2020). For instance, surveillance in tuberculosis (TB) management supplemented with adequate TB focal persons, program officers, and the relevant health professionals are crucial to eradicate the disease (Sergeant et al. 2017; Yusuf et al. 2018). Definition The word “surveillance” is derived from two French words sur meaning over and veiller meaning to watch; hence, surveillance means being watched in simple terms (Brachman 2009). The World Health Organization (WHO) has defined “surveillance” as the perpetual systematic collection and analysis of the outcome-specific data for public health benefits and timely dissemination of findings to help the target population to avoid or prevent infections or epidemics (Aliabadi et al. 2020; Aronson et al. 2012; Calain 2007; Collier 2017; Davgasuren et al. 2019; Drewe et al. 2012; Dureab et al. 2020; Durigon et al. 2013; Groseclose and Buckeridge 2017; Lewis et al. 2011; Petrini and Ricciardi 2015; Petrini 2013; Robertson and Nelson 2010; Xiong et al. 2010). The WHO and the Centers for Disease Control and Prevention (CDC) have defined public health surveillance as systematic and continuous collection, analysis, interpretation, and dissemination of data about causative agents/hazards or risk factors, exposures, and health events which are essential to the planning, implementation, and

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evaluation of public health practice to prevent and control morbidity and mortality and to improve health (Chiolero and Buckeridge 2020; Aronson et al. 2012; Aiello et al. 2020; Barros et al. 2020; Berkelman and Buehler 1990; Guerra et al. 2019; Herida et al. 2016; Hirshon 2000; Hussain et al. 2016; Jajosky and Groseclose 2004; Klingler et al. 2017; Lee 2019; Lee and Thacker 2011; Memon et al. 2020; Pilot et al. 2019; Sosin 2003; Soucie 2012; Thacker 2007; Thacker et al. 1996; Thacker et al. 1995; Thacker and Stroup 1994; Visa et al. 2020; Willis et al. 2019; Zhang et al. 2014). Traditional surveillance practices include selective reporting of infectious diseases by health professionals, laboratories, and clinics (Lewis et al. 2011). Surveillance is one of the public health problem-finding processes (Hirshon 2000) and serves as the foundation of public health practice in planning, implementing, and evaluating disease control measures (Groseclose and Buckeridge 2017; Petrini and Ricciardi 2015; Xiong et al. 2010; Adokiya and AwoonorWilliams 2016; Adokiya et al. 2015; Cutts et al. 1993; Garg et al. 2020). Surveillance activities focus mainly on monitoring endemics, epidemics, and pandemics and control measures and exploring emerging, re-emerging, and exotic diseases with significant public health impacts (Drewe et al. 2012). Brief History of Public Health Surveillance Historically, disease surveillance had been significantly focused on infectious diseases. Hippocrates (460 B.C.- 370 B.C.), also known as the father of medicine and first epidemiologist, developed the concept of collecting and analyzing data (Merrill 2017; Morabia 2004). He coined the terms endemic (constant presence of diseases or illness in certain places) and epidemic (sudden increase in the incidence of diseases or illnesses in certain places). Public health surveillance (PHS) was first used during the bubonic plague in 1348, when public health guards were appointed to detect and quarantine the ships with infected people (Declich and Carter 1994). In 1532, London, England, started to systematically collect the mortality data, which was later used by

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Disease Surveillance in Low- and Middle-Income Countries

John Graunt (1620–1674) to study the cause of disease, analyze the disease patterns, and attribute the deaths to specific causes. In 1741, Rhode Island in the United States was the first to pass an act for surveillance requiring all their tavernkeepers to report infectious diseases among their patrons. This law was broadened in 1743, mandating the reporting of cholera, yellow fever, and smallpox (Thacker and Berkelman 1988). William Farr (1807–1883) spent 41 years of his life in England collecting and analyzing vital statistics data to describe how diseases impact the different population in different ways. For his valuable contributions, he is well-known as the founder of the modern concept of surveillance (Langmuir 1976). With the epidemiological transition from infectious to non-communicable diseases (NCDs), PHS has also broadened its horizon to encompass the NCDs. The first chief epidemiologist for the CDC Alexander Langmuir is also known as the present-day PHS founder. In 1963, he described surveillance applications to larger populations rather than confining it to individuals with infectious diseases in his landmark publication. In 1968, the twenty-first World Health Assembly recommended using surveillance principles to NCDs, including cancer, atherosclerosis, and behavioral issues like drug addiction (Langmuir 1963; WHO 1968). Surveillance system has been successfully implemented for diabetes, hypertension, and mental illness (e.g., anxiety and mood disorders), chronic respiratory diseases (e.g., chronic obstructive pulmonary disease (COPD), asthma) and cardiovascular conditions (e.g., ischemic heart disease (IHD), acute myocardial infarction, and heart failure), osteoporosis and fracture, Parkinson’s disease, multiple sclerosis, stroke, epilepsy, dementia (including Alzheimer’s disease), schizophrenia, osteoarthritis, gout, rheumatoid arthritis, and many other (Lix et al. 2018).

Characteristics of an Ideal Surveillance System

Principles of Surveillance Foege et al. (1976) stated that surveillance principles are to timely provide information on a disease to the decision-makers at an affordable cost (Foege et al. 1976).

• To describe the disease trend and to relate with public health actions. • To study the epidemiology of the disease. • To provide baseline data on disease trend (Declich and Carter 1994).

An ideal surveillance system has the following characteristics (Dawson et al. 2016; Powe et al. 2009): • Structural and operational simplicity of the surveillance system. • Flexibility to respond to new questions generated by research and address necessary changes in technology or reporting accordingly. • Quality, completeness, and validity of data. • Timely operation to recommend interventions and make necessary public health changes. • Stability and reliability in operation and dissemination of information. • Representativeness for the individuals to ensure generalizability of the findings. • Public and practitioners’ acceptability towards the system. • Sensitivity towards collecting all events and disease trends over a while. • Positive predictive value (PPV) for the true cases. Objectives of Disease Surveillance Systems Disease surveillance helps to answer five important questions about the diseases of interest, which is abbreviated as the 5Ws: what (event itself), where (event site), when (time of event), who (victim/patient), and why/how (causal agents) (Magumba et al. 2018). Surveillance systems for infectious diseases help early detection of outbreaks, trend analysis, and hypotheses generation that may be the cornerstone for future research and advancement (Green and Kaufman 2002). PHS targets to collect data on demographic and health-related characteristics of a population in a location (Chiolero and Buckeridge 2020). Important objectives of the surveillance system can be enumerated as follows:

Disease Surveillance in Low- and Middle-Income Countries

Types of Surveillance There are mainly two types of surveillance. (a) Active Surveillance of Diseases Langmuir (1963) defined active surveillance as the continued monitoring of distribution and incidence patterns via systematic collection, synthesis, and evaluation of morbidity, mortality, and other relevant data (Aronson et al. 2012; Langmuir 1963). The Council for International Organizations of Medical Sciences (CIOMS) defined active surveillance as “a surveillance method that ascertains the number of all adverse events (numerators) in patient populations exposed and unexposed to a medicinal product (denominators) followed by the use of observational epidemiological methods for the purposes of signal detection” (CIOMS 2010). Active exploration of HAIs by the concerned practitioners can be considered active surveillance (Aronson et al. 2012; Magumba et al. 2018). Similarly, conducting diagnostic surveys to aggregate valid information on people’s disease status at a given time is also an active surveillance mechanism (Hadorn and Stärk 2008). Although active surveillance generates real data on disease prevalence or incidence, this approach is costly, especially for rare diseases where a large sample size is necessary due to low expected prevalence (Hadorn and Stärk 2008). (b) Passive Surveillance of Diseases The CIOMS (2010) defined passive surveillance as “a surveillance method that relies on healthcare providers (and consumers in some countries) to take the initiative in communicating suspicions of adverse drug reactions that may have occurred in individual patients to a spontaneous reporting system” (CIOMS 2010). It is the reporting of the solicited cases or clinically suspect cases (i.e., reports sought by advertisement rather than by direct survey) (Aronson et al. 2012; Hadorn and Stärk 2008), and health workers wait for cases during patient care (Magumba et al. 2018). It helps to identify microorganisms and both infectious and non-infectious diseases in a

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population via pathological examinations (Lawson et al. 2015). Disease surveillance can also be indicatorbased or event-based. Indicator-based surveillance includes traditional systems with regular, predetermined reporting, whereas event-based surveillance is real-time and ad hoc and usually covers loose case definitions (Magumba et al. 2018). Disease surveillance helps to prospectively monitor disease patterns in the community and specific organizations (e.g., hospitals, long-term care facilities (LTCFs)). Traditional surveillance activities (e.g., laboratory-based surveillance, sentinel surveillance at in-patient and out-patient settings) are routinely conducted in the developed countries. These days, relatively newer syndromic surveillance is also popular to provide disease information for making sound and timely public health decisions in the form of triage, over-thecounter (OTC) medicine sales, and others (Cheng et al. 2013). (c) Other Types of Surveillance Except for the active and passive surveillance systems, other types of surveillance are gaining popularity. Some of them are discussed as follows: (i) Syndromic Surveillance The Centers for Disease Control and Prevention (CDC) defined syndromic surveillance (SS) as the surveillance system using healthrelated data that precede diagnosis and indicate the probability of an outbreak to alert for the timely public health response (Aronson et al. 2012; Davgasuren et al. 2019). It is an evolving scientific discipline and is a dynamic practice of continuous monitoring of disease patterns based on historical data (Yoon et al. 2017). It monitors indicators for the early detection of outbreaks, including patterns of medication sales and Internet-based health queries (Angelo et al. 2020). It helps to detect public health risks and evaluate population health based on the patients’ perceptions of illness, health-seeking behaviors, chief complaints, clinicians’ observations, and

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judgment (Davgasuren et al. 2019; Chapman et al. 2010). SS usually depends on the automated electronic reporting system, and algorithms (May et al. 2009) can be used to explore reports of syndromes of known or unresolved etiology with visible characteristics (Lawson et al. 2015). Integrating SS with laboratory tests increases the probability of proper detection and assessment of the disease (Magumba et al. 2018; Vlieg et al. 2017). SS is more efficient than the case reporting to track common illness (e.g., influenza) or rapidly detect the disease clusters (Birkhead et al. 2015). Purposes of Syndromic Surveillance System (SSS) • To detect infectious disease (ID) outbreak within time, search demographics of the affected and onset of symptoms, and explore the previous diagnosis (Davgasuren et al. 2019; Ziemann et al. 2016). • To raise situational awareness during mass gatherings, droughts, or other events of public health concerns (Ziemann et al. 2016).

Disease Surveillance in Low- and Middle-Income Countries

(ii) Participatory Disease Surveillance Participatory disease surveillance (PDS) is widespread in many developed countries to mitigate the burden of pandemics. It is an innovative mechanism applicable especially for communicable diseases in which people self-report their symptoms or events to facilitate the public health professionals collect and analyze data for relevant interventions (Garg et al. 2020). Similarly, integrated disease surveillance combines active and passive systems to collect data from multiple diseases and is the most practical and applicable surveillance system in resource constraint settings (Phalkey et al. 2015). PDS collects health-related data for necessary public health actions by directly involving the population at risks in submitting relevant data via mobile apps, hotlines, or other allocated procedures (Smolinski et al. 2017). Some examples of active PDS for infectious diseases are Influenzanet, FluTracking, and Flu Near You (Wójcik et al. 2014).

Surveillance Process Benefits of Syndromic Surveillance • It is useful for early epidemic control of vectorborne diseases and other diseases of public health concerns (e.g., pandemics) (May et al. 2009). • SS helps to strengthen and broaden the traditional PHS by providing real-time data on public health effects of syndromes when there was no such previous information (Ziemann et al. 2016; Elliot et al. 2020). • It can quickly provide information about the impacts of communicable and noncommunicable diseases (CDs and NCDs) and other public health threats (Elliot et al. 2020). • It helps collect the early warning information with known symptoms or provisional diagnoses along with the laboratory-confirmed diagnoses (Elliot et al. 2020). • It provides situational awareness during disease progression in the population (Elliot et al. 2020).

Syndromic or disease surveillance has two phases: detection and monitoring phases. The detection phase helps detect any disease outbreak soon, and the monitoring phase helps identify, track, and prevent disease spread. SS is easy to implement for the known diseases but may become difficult in case of new or emerging diseases (e.g., SARS, COVID-19, and others) (Bellika et al. 2007). Core Steps or Components of a Public Health Syndromic Surveillance Important steps of public health syndromic surveillance are as follows (Groseclose and Buckeridge 2017; Soucie 2012; Powe et al. 2009): • • • •

Step 1: Setting goals and objectives Step 2: Data collection Step 3: Sources of surveillance data Step 4: Data management with electronic health records

Disease Surveillance in Low- and Middle-Income Countries

• • • •

Step 5: Analysis of surveillance data Step 6: Interpretation of results Step 7: Dissemination of the results Step 8: Implementation

Step 1: Setting Goals and Objectives There must be clearly defined objectives for the surveillance from the very beginning stage. Measurement standards and indicators are critical, and criteria for case detection must be clear and validated to collect and not miss the data to meet the surveillance objectives. Highly complex data collection tools usually increase the burden of data collection and may negatively affect the amount and quality of data collected (Soucie 2012). Step 2: Data Collection Demographic, socioeconomic, and clinical data, data on disease complications and mortality, and data on risk factors or comorbidities from the population under surveillance are vital during the SS. Standardization and validation of data collection instrument and data quality are essential to compare population, locality, or disease patterns over long periods (Soucie 2012). Once the data are collected, secure data management systems should be operated to evaluate the accuracy, consistency, and completeness of data and enhance the data quality, integrity, and safety from disasters, virus attack, theft, and other potential threats (Soucie 2012). Step 3: Sources of Surveillance Data Data can be collected from population-based or healthcare provider-based sources. The former sources help collect data from local or national representative populations, whereas the latter sources help collect data from the beneficiary populations who receive healthcare services being delivered at their levels (Soucie 2012). Vital Statistics Vital statistics or disease and exposure registries provide longitudinal data via recording multiple events about the affected and general people over time (Lee and Thacker 2011).

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Disease Registries Disease registries help to respond to public health concerns to prevent or control disease spread and promote population health (Lee and Thacker 2011). These help to track and extract information about the care and economic, clinical, and humanistic outcomes (ECHO) of the population with chronic diseases. For example, National Program of Cancer Registries, Surveillance, Epidemiology, and End Results program and registries of hepatitis, immunization, traumatic brain injury (TBI), HIV, and others yield valuable results for the public health benefits (Lyerla and Stroup 2018). Disease registries provide timely, complete, and longitudinal data on quality of care and population-based disease incidence and prevalence. However, these may not always represent the whole population due to variations in the locations or inclusion criteria for the affected and may not record all the comorbidities that may confound the ultimate outcomes (Lyerla and Stroup 2018). Types of Disease Registries Data can be conveniently collected using different registries (e.g., individual patient registry, population-based, medical-based, and ministrybased registries), provided a sound registry system. (i) Patient registries generally have minimal health-related data and are useful to facilitate communication and dissemination of public awareness material (Soucie 2012). (ii) Population-based registries help extract data on public health importance cases and develop information from hospitals or laboratories (Thacker et al. 1995). (iii) Medical registries are useful to collect information on diseases (e.g., occurrence, type, extent, treatment) and are beneficial for PHS of rare conditions. These help monitor disease patterns among the population over time, guide planning and evaluation of disease control programs (e.g., prevention, screening and treatment measures), set

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priorities and strategies to allocate available resources, and conduct clinical, epidemiological, and health services research (HSR). (iv) Ministry-based registries are more public health-oriented, cover the national needs, represent the entire population, and are the best for common diseases in the society (Soucie 2012). Step 4: Data Management with Electronic Health Records Timeliness and completeness of PHS were ensured by implementing electronic reporting of laboratory results for diseases from the mid-1990s (Birkhead et al. 2015). Electronic health records (EHRs) provide real-time data on patient demographics, diagnoses, laboratory, and radiologic results and medications during the disease management process. The pooled EHR data across the healthcare providers can facilitate longitudinal analyses and wider comparisons across geographic areas and diverse populations to generate better case-based reasoning (CBR) (Lyerla and Stroup 2018). EHRs also contribute to expanding the vision of current surveillance activities by facilitating timely and efficient surveillance on disease prevalence, healthcare utilization, treatment modalities, and outcomes (Birkhead et al. 2015). EHRs augment better and easy access to clinical and public health guidelines, reminders about screening needs, and graphical interpretation and dissemination of disease trends among the patients. EHRs also enable the patients to extract their individual health reports remotely and use the information to manage their health from their own home or nearby health facility (Birkhead et al. 2015). Administrative Data Administrative data sets refer to the computerized information maintained by public or private healthcare facilities and insurance providers (Zito and Safer 1997). These can be used mainly for payment tracking or claims for health services (e.g., Medicaid and Medicare), accounting, and

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other patient care-related fiscal functions, healthcare utilization tracking, and quality of care for medical audit (Zito and Safer 1997; Hennessy 2006). These data also help in pharmacoepidemiological research as these are readily accessible and inexpensive and avoid selection and recall biases during surveys (Zito and Safer 1997; Hennessy 2006). Few examples of the useful administrative database in pharmacoepidemiological research are Canadian Provincial Databases, Kaiser Permanente Medical Care Program, UnitedHealth Group, Medicaid, and Health Maintenance Organization Research Network (Hennessy 2006). Step 5: Analysis of Surveillance Data Analyses of surveillance data include surveys of population, disease patterns and their risk factors, and outcomes over a period of time. These are useful to estimate and evaluate disease burdens in the population with their trends (Soucie 2012). Analysis involves comparing current data with the previous and exploring differences among data with the implications of such difference (Declich and Carter 1994). Analysis of PHS data helps to explore the presence or absence of signals (i.e., unusual patterns in data compared to the historical data) to decide on public health interventions. It also helps to detect the association (if any) of population characteristics with their health outcomes and develop a better idea about effective prevention or control measures (Groseclose and Buckeridge 2017). Surveillance data analysis should align with the objectives and processes of the surveillance system. Structure and content of the surveillance system should be defined from the very initial phase of surveillance to ensure that quality data are generated for analysis purpose (Groseclose and Buckeridge 2017). Step 6: Interpretation of Results After analyzing the surveillance data, we look for associations between disease outcomes and risk factors (if any). Such associations help to formulate interventions to reduce the spread of CDs or

Disease Surveillance in Low- and Middle-Income Countries

risk of complications (Soucie 2012). Interpretation also helps to find whether apparent increases in disease occurrence within a population truly represent the increases on the broader arena or not (Declich and Carter 1994). Steps 7: Dissemination of the Results Results generated from the analyses should be shared with the concerned stakeholders such as healthcare providers; public health authorities; local, federal, and national governments; policymakers; and the public (Soucie 2012). This is an integral component of the entire surveillance system (Declich and Carter 1994). Steps 8: Implementation PHS data can be used to project or evaluate the magnitude of the problem, detect the groups at risks of poor outcomes, explore the potential relationships between risk factors and outcomes, and formulate, implement, and monitor the impacts of interventions (Soucie 2012).

Various Surveillance Systems Surveillance system mainly includes routine reporting, sentinel surveillance, and communitybased reporting (Cutts et al. 1993) and is beyond collecting health- and disease-related data and producing reports (Birkhead et al. 2015). The WHO has regarded PHS as the foundation for public health promotion and well-being at the population level (Collier 2017). One of the most common examples of PHS is disease surveillance conducted by surveys or reporting by healthcare providers (Memon et al. 2020). PHS is how public health agencies and authorities collect data from in- and out-patient sections of healthcare facilities (HCFs) and monitor and evaluate the population’s health status (Guerra et al. 2019; Birkhead et al. 2015). PHS helps to detect trends in incidence and prevalence of diseases of public health concern and identify any irregularities in trends (e.g., abnormal IDs, adverse health events), followed by dissemination and use of the information to

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formulate and execute necessary public health interventions (Lee 2019; Zhang et al. 2014; Lyerla and Stroup 2018; Nagbe et al. 2019). National PHS helps to disseminate reliable and timely health-related information to facilitate the operational and strategic decision-making at different levels of HCFs (Phalkey et al. 2015; Sheikhali et al. 2016). It helps to estimate the magnitude of public health problems, detect disease history and spread, explore outbreaks, conduct pharmacoepidemiological research, and monitor and evaluate public health practice (Sosin 2003; Bauer et al. 2014). Based on the priority public health concerns, the surveillance types can be primarily divided into the following two types: Infectious Disease Surveillance Infectious disease surveillance (IDS) provides an early warning for disease outbreaks and depends on laboratory reports, syndromic sources, and clinical diagnoses (Vlieg et al. 2017). Concept of PHS emerged with the IDS in the late 1800s, mainly from the outbreaks of smallpox, cholera, typhoid, and TB (Birkhead et al. 2015). IDS is gaining momentum these days, primarily due to the rapid transmission of pathogens, epidemics, and pandemics and the rapid emergence of unprecedented antimicrobial resistance cases (Phalkey et al. 2015). A case in point was diarrheal surveillance conducted in an urban site in Kolkata, India, from 2003 to 2004 to assess cholera burden, explore its epidemiology, search for risk factors, and devise suitable public health strategies to tackle the problem (Ramamurthy and Sharma 2014). Chronic Diseases Surveillance Chronic disease surveillance was developed mainly for cancers and lead poisoning once the IDS emerged in public health practice. Surveillance of common chronic diseases such as cardiovascular disease (CVD), diabetes, and hypertension depends mainly on the populationlevel administrative databases (Birkhead et al. 2015). Chronic disease surveillance is also rapidly changing with the emergence of new chronic problems and priorities and novel health data

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sources with new technologies (e.g., EHRs) (Calanan et al. 2018). Currently, non-communicable chronic diseases (e.g., diabetes, hypertension, stroke, respiratory diseases, arthritis, obesity, cancer, and others) have enormous health (about 63% of all deaths worldwide) and economic impacts all over the world, and their impacts are more prominent in the developing countries as these affect and even kill the working group of population (Allegrante et al. 2019; Kim et al. 2013; Lyu et al. 2020; Oh et al. 2014; Raghupathi and Raghupathi 2018; Yach et al. 2005). Some of the major chronic diseases, including diabetes, hypertension, and hypercholesterolemia, can be managed via community- and clinical-oriented prevention strategies that focus on the root cause analysis (RCA) of chronic conditions (e.g., physical environments, behavioral, psychological and social conditions, and other). Some of the prevention strategies can be lifestyle modifications (e.g., food habits, exercise), smoking cessation, alcohol abstinence, and community water fluoridation and help the patients and caregivers manage their health even at their community level (Bauer et al. 2014; Airhihenbuwa et al. 2021). Examples of Other Surveillance Systems Some of the other widespread surveillance systems based on priority public health topics are listed as follows (Declich and Carter 1994; Aavitsland et al. 2001): • • • • • • • • • • • • • •

Accident surveillance Adverse drug event (ADE) surveillance Cancer surveillance Child nutrition and growth surveillance Cholera surveillance Congenital malformation surveillance Environmental surveillance Epidemic surveillance HIV/AIDS surveillance Injury surveillance Mental health surveillance Nosocomial infection surveillance Occupational health surveillance Poliomyelitis surveillance

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• Sexually transmitted disease surveillance • Smallpox surveillance • Tuberculosis (TB) surveillance • Vaccine surveillance

(STD)

Evaluation of Surveillance Systems Evaluation of a surveillance system is the systematic investigation of benefits, strengths, or public health significance of the system to detect and manage public health concerns. For example, evaluation of the malaria surveillance system helps to formulate interventions and monitor malaria eradication programs (Visa et al. 2020). Evaluation of a PHS system provides evidencebased information to strengthen disease reporting and implement public health actions (Hussain et al. 2016; Heidebrecht et al. 2011). Disease surveillance programs should be periodically evaluated to ensure and standardize the continuity and consistency of their functioning (Drewe et al. 2012).

Applications and Importance of Surveillance Systems Surveillance system helps to monitor and assess the incidence of the particular infection or event by collecting relevant data (e.g., route or mode of transmission, place of infection, and patients’ demographics) (Aavitsland et al. 2001). Surveillance helps public health professionals, clinical practitioners, and policymakers implement problem-specific interventions to mitigate disease progression among the population and address inequalities in resource allocation for effective disease management (Elliot et al. 2020). Disease surveillance is the principal means by which information of the public health relevance is gathered and disseminated (Groseclose and Buckeridge 2017; Robertson and Nelson 2010). For example, during infectious disease outbreaks, timely dissemination of information on their spread can facilitate appropriate action by public health authorities to prevent and mitigate their negative impacts (Robertson and Nelson 2010).

Disease Surveillance in Low- and Middle-Income Countries

Disease surveillance helps assess, predict, and mitigate infectious disease outbreaks based on data (e.g., demographics, individual case reports, morbidity and mortality, laboratory reports, and other) collected by HCFs (Salathé 2016). Surveillance is critical in disease prevention and control as it provides a foundation for public health actions such as interventions, policy changes, and evaluation of their impacts (Heidebrecht et al. 2011). Electronic Integrated Disease Surveillance System (EIDSS) is an electronic system intended to collect, notify, share, and analyze surveillance data within the “One Health” concept by integrating animal and human surveillance with both active and passive approaches (Wahl et al. 2012). PHS helps monitor, evaluate, and characterize the burden of adverse health events, prioritize public health actions, assess their impacts, and explore emerging health implications to the population (Groseclose and Buckeridge 2017). For example, influenza surveillance includes laboratory-based virological surveillance, SS, and monitoring the mortality of pneumonia and influenza (KassHout and Alhinnawi 2013). Regular monitoring and evaluation of surveillance systems help to explore the accurate application of surveillance data generated to protect health and the environment (Calba et al. 2015). Surveillance helps to detect diseases, support interventions implemented by HCFs, and estimate or predict public health impacts of diseases (Hussain et al. 2016). Uses of Public Health Surveillance Public health surveillance can be used for the following (Thacker et al. 1995; Catchpole 1996; German 2000): • Detecting frequency, distribution, and trends of emergent health problems, epidemics, and pandemics. • Estimating or predicting the magnitude of health problems. • Documenting the geographic spread of adverse health events among the population and monitoring change in risk factors.

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• Testing hypotheses and intervention planning and detecting any modifications in health practices. • Promoting epidemiologic research. • Depicting the course and progression of diseases and evaluating control and prevention strategies. Uses of Surveillance Data Important uses of surveillance data can be listed as follows (Catchpole 1996): • Setting of priorities for interventions • Planning and allocating resources for interventions • Exploring risky behaviors for the interventions • Directing public health policy and actions • Evaluating the effectiveness of interventions • Facilitating further epidemiologic research

Challenges of Surveillance Systems Traditionally, surveillance system was considered an isolated technique and was implemented vertically in most low- and middle-income countries (LMICs), creating multiple challenges to the entire healthcare process. The main drawback of the vertical programs is the transmission of data directly to the central levels with little or no coordination between the channels, causing the disrupted healthcare delivery and ultimately collapse of the smooth functioning (Phalkey et al. 2015). Main problems of the emergency department (ED)-based surveillance systems are as follows: • Economic burden to the public health authorities, EDs, hospitals, and other HCFs. • Problem in validation and standardization of data collection instrument and data collected. • Data security and confidentiality. • Problem in acceptance and ownership from emergency medicine (EM) practitioners (Hirshon 2000). • Ethical problems of public health importance (Petrini and Ricciardi 2015).

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Ethical and Legal Considerations of Surveillance Systems Most widely followed ethical principles are available in the Belmont Report published in 1979. The Belmont Report is a summary document, which includes the three basic ethical principles identified by the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (USDHEW 1978). These ethical principles and guidelines are briefly discussed as follows: 1. Respect for Persons: This includes two distinct requirements. First, individuals should be treated as autonomous agents who have their own opinions and choices. This means the individuals have the right to participate in research activities voluntarily. The second requirement elucidates that those individuals with diminished autonomy have to be protected based on the risk of harm and the chance of benefit offered by the research activities. 2. Beneficence: This includes not harming the individuals by maximizing the benefits and reducing the risks involved while participating in the research. 3. Justice: This addresses the “fairness in distribution.” This principle states that the burden of the research should not fall on one population while the other population gets the benefits of the research. Though surveillance activities should follow ethical principles, modern-day surveillance activities include the “population” and not just the individuals. Hence, the ethical guidelines for researching with human subjects may not always apply to public health surveillance. The WHO provides the following guidelines on ethical issues for conducting public health surveillance: (WHO 2017). • All surveillance systems must have a clear objective and a detailed plan for collecting, analyzing, using, and disseminating data. In addition, surveillance systems include prioritized public health issues.

Disease Surveillance in Low- and Middle-Income Countries

• Surveillance systems must be developed effectively and appropriately to minimize the risks and maximize the benefits. Furthermore, the countries should develop surveillance systems that are equitable and respect the rights of the individuals. • Surveillance systems should collect data only for valid public health issues. • Surveillance data collected by the counties should achieve public health goals and thus should be timely, reliable, and valid. • Countries should have a transparent prioritysetting process for planning surveillance systems. • There should be a global alliance to help lowresource countries undertake surveillance efforts. • Surveillance data planning, collection and dissemination should include community values and concerns. • While implementing surveillance systems, there should be an ongoing process of monitoring for any risks. When risks are identified, immediate and appropriate mitigation actions should be taken. • Surveillance of special and sensitive groups or individuals like those at higher risks of disease or any injustice should be conducted with special scrutiny to avoid unnecessary problems. • All surveillance data with personal identifiable health information should be kept confidential and secure. Collecting personal identifiable health information is only justified under certain circumstances like case investigations, contact tracing, and follow-ups in an outbreak. • Informed consent is an important part of the data collection process. However, under special circumstances where complete reliable and valid data sets are required and are placed under relevant protection, ethically, informed consent is not always required. • Surveillance data results must always be effectively communicated with the relevant target audiences. • Public health surveillance data can be shared with national and international public health organizations with appropriate justification and data protection.

Disease Surveillance in Low- and Middle-Income Countries

• During a public health emergency, it is crucial to share the surveillance data promptly to all the relevant stakeholders. • Public health organizations may use the surveillance data for research with appropriate justification and data protection. • Personal identifiable health data from surveillance should not be shared with organizations that could potentially use the data against individuals or use the data in a way not related to public health. Drug Surveillance and Public Health Drug resistance such as multiple drug resistance (MDR) and extensive drug resistance (XDR) poses a serious global challenge to TB treatment (Mbugi et al. 2012). Drug safety monitoring (DSM) is an all-time concern for health authorities and practitioners (e.g., physicians, pharmacists), pharmaceutical companies, and the general public (Faillie et al. 2016) and is determined by the rational use of drugs. Traditionally, DSM has been applied for prescription-only medicines (POMs), although it can also be extrapolated for over-the-counter (OTC) medicines (Bond and Hannaford 2003). DSM starts from the early stages of the drug development process to the consumption by the end-users. For example, organ toxicity, teratogenicity, mutagenicity, and carcinogenicity are frequently studied in preclinical trials of many medications (Faillie et al. 2016).

Transition/Translation of Disease Surveillance into Pharmacy Practice Pharmacy is a widely distributed and accessible healthcare center to the public. Community people are closely attached to the pharmacy practitioners. They visited pharmacies and pharmacists to collect medicine and healthcare advice to solve any medical problems they encountered (Tsuyuki et al. 2018). Pharmacists are responsible for responding and countering the patient’s health problem. They are accessible to identify, collect, and analyze the early detection of people’s health-related problems and possible causes associated with them. The properly

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integrated approach would improve information collection on patients’ healthcare information and medicine utilization through pharmacy. Furthermore, the pharmacy is a useful stakeholder in reaching community people in implementing disease prevention and mitigation. Role of Pharmacists in Public Health Surveillance Pharmacists working in both hospital and community settings are continuously accessible and closely connected to healthcare personnel to people even at healthcare pandemic or emergency. Therefore, pharmacists significantly play a valuable role in the surveillance of public health issues in the society. Some of them are as follows (Herrera 2016): • Identifying and reporting a health-related event. • Reporting of factors associated with the causation of diseases. • Monitoring and reporting of the medicines taken by the patient in curing their illness. • Detecting the people’s status of medication and vaccination to diseases. • Monitoring of disease progression of people. • Monitoring, evaluating, and reporting adverse drug events, medication errors, and therapeutic failure associated with disease. • Awaking and counselling patients on etiology, signs and symptoms, transmission, prevention, treatment, and reporting on rising disease. Role of Pharmacists in Disease Surveillance Role of pharmacists in pandemic (e.g., COVID19)-induced drug shortages can be enumerated as follows (Badreldin and Atallah 2021): • Design evidence-based treatment guidelines. • Conduct demand forecasting to explore medications of importance during pandemics. • Design practice and consumption alert electronically. • Advocate for rational prescribing and dispensing of pharmaceuticals. • Develop ad hoc drug approval procedures for certain medications at the time of pandemics.

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• Design alternative medications or therapeutic options if the main medications are unavailable due to the supply chain disruption. • Liaise with other healthcare providers and collaborate globally to share experiences on drug shortage minimization or avoidance during pandemics. Community Pharmacists

The first step in disease surveillance is to diagnose cases at HCFs. For example, the rural health facilities in sub-Saharan Africa depend on clinical signs and symptoms to diagnose diseases as these lack laboratory confirmations (Toda et al. 2018). Clinical Pharmacists

A case in point on the potential roles that the clinical pharmacists can play to curb the HIV epidemics can be enumerated as follows (McCree et al. 2020): • Serve as HIV testing personnel with the collaboration of health departments and practitioners. • Improve adherence to HIV therapy via effective counselling, reminder tools, and synchronized prescription refills. • Detect people with HIV who need adherence support by regularly reviewing the prescription refill histories. • Conduct medication therapy management (MTM) to improve adherence to HIV therapy. • Educate on the safe disposal of injections during HIV therapy. • Make antiretroviral therapy (ART) readily accessible and available to the needy with effective and efficient inventory management. • Provide post-exposure prophylaxis (PEP) services in case there is any mistake in medications. Impact of Pharmacists on Disease Surveillance Pharmacist-maintained pharmaceutical databases have been useful in monitoring and determining the incidence, prevalence, and pattern at different time intervals of both drug-induced and non-druginduced diseases. As medicine, specialized

Disease Surveillance in Low- and Middle-Income Countries

healthcare professional, pharmacists have a crucial impact in specialized surveillance of druginduced disease. The appropriate monitoring and reporting of pharmacovigilance and pharmacoepidemiological data is essential to determine the potential drug-induced ailments (Fattinger et al. 2000). Along with that, the pharmaceutical database has contributed significantly to determining the disease status. Maio et al. (2005) reported the proportion of various chronic diseases in Italy by analyzing the pharmaceutical data of pharmacies (Maio et al. 2005). Similar advantages have been demonstrated with pharmacy databases in Canadian studies (Widdifield et al. 2013). The surveillance of disease is comparatively easy and convenient with pharmacists and pharmacy databases. Similarly, pharmacist monitoring and appropriate interventions on patients with chronic disease medication have been demonstrated to have beneficial clinical and economic outcomes (Newmana et al. 2020). Evidence and Applications of Pharmacists as a Stakeholder in Disease Surveillance Pharmaceutical utilization databases have been shown to provide an essential basis in disease surveillance (Maio et al. 2005; Widdifield et al. 2013). Additionally, the documented information on medicine utilization can also be utilized for estimating the status of a particular disease. Statistical measure is supposed to determine the early notification of the outbreak. Therefore, pharmacists should be actively promoted in monitoring and reporting medication utilization at their place. Additionally, as pharmacists are the most accessible healthcare personnel (Tsuyuki et al. 2018), they can collect patients’ information on medicine use practice, any medication problem they faced, signs and symptoms, disease severity, and its prevalence. The proper collection of a patient’s medication-related information can significantly contribute to generating evidence on finding drug-induced disease and preventing the possible harm. Therefore, pharmacists should be facilitated and trained on appropriate evaluation, monitoring, and, subsequently, screening, documenting, and reporting.

Disease Surveillance in Low- and Middle-Income Countries

Future Implications of Pharmacists on Disease Surveillance Firstly, as the pharmacist is the closely connected and widely dispersed healthcare personnel in the community, the proper equipment and active enrollment are necessary to utilize them for surveillance effectively. Furthermore, the proper utilization of available pharmacists in community pharmacies can help to reduce healthcare and the economic burden on disease surveillance. Pharmacists can monitor, evaluate, and report patients’ disease and medicine consumption information of different time intervals. An appropriately skilled clinical pharmacist capable of diagnosing and managing the medicine errors associated with the disease can contribute to specialized documentation of drug-induced diseases. Similarly, pharmacists can be mobilized in the prevention and management of disease at the local level.

Health Outcomes Surveillance Emergence of the severe acute respiratory syndrome (SARS) in February 2003 necessitated the development and institutionalization of medical and health outcome surveillance (Litaker et al. 2003).

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infrastructures and service delivery, adoption of obsolete concepts of surveillance systems, poor resources (i.e., human, technical and financial), lack of national and local policies, and guidelines for the surveillance system (Phalkey et al. 2015). However, SS technique has been adopted to detect malaria outbreaks in Uganda, Eritrea, and Jamaica and STIs in Burkina Faso and Ivory Coast (May et al. 2009).

Conclusions Surveillance system has been successfully implemented for diabetes, hypertension and mental illness, chronic respiratory diseases, cardiovascular conditions, osteoporosis and fracture, Parkinson’s disease, multiple sclerosis, stroke, epilepsy, dementia, schizophrenia, osteoarthritis, gout, rheumatoid arthritis, and many other. Pharmacists can monitor, evaluate, and report both disease and medicine consumption information of patients of different time intervals. Hence, pharmaceutical utilization databases have shown that they can effectively contribute to the disease surveillance system in society and clinical settings.

References Disease Surveillance in LMICs Lack of involvement of experts and resources in PHS at local levels mainly contributed to the expansion and prolonged spread of Ebola outbreak (André et al. 2017). Majority of disease surveillance activities carried out in low- and middle-income countries (LMICs) are confined to humans, despite widespread detection and transmission of more than 60% of the emerging diseases by zoonotic pathogens since 1940. Moreover, such countries also adopt passive surveillance system rather than active ones due to limited resources. Lack of coordination between national, regional, and local levels usually retards the potential detection of outbreaks in LMICs. Other unresolved issues in LMICs are poor health

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Drug Safety in Children: Research Studies and Evidence Synthesis Shamala Balan1, Rabia Hussain2, Siew Chin Ong2 and Zaheer-Ud-Din Babar3 1 Pharmacy Department, Hospital Tengku Ampuan Rahimah, Klang, Malaysia 2 Discipline of Social and Administrative pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia 3 Medicines and Healthcare, Department of Pharmacy, University of Huddersfield, Huddersfield, UK

Abstract

Pharmacoepidemiology is the study of drug use and its effects in a diverse population in real-world settings. The pharmacoepidemiology approach has been proven to provide evidence on major drug safety issues in pediatrics including adverse drug reaction, offlabel drug use, medication error, and inappropriate and irrational drug use. Knowledge on and applicability of pharmacoepidemiology approach among healthcare professionals providing care for children needs to be enhanced to guarantee optimal, continuous, and updated implementation and interpretation of pharmacoepidemiology data in order to reap

the full benefits of pharmacoepidemiology in healthcare provision for children. Keywords

Pharmacoepidemiology · Drug safety · Children · Off-label

Introduction Drug safety is considered as the major concern within the broader prospect of patient safety (Hussain 2021). The core principles of pharmacoepidemiology combines the knowledge of clinical pharmacology and clinical epidemiology (WestStrum 2011). Pharmacoepidemiology studies are observational in nature, meaning the choice of drug given to patients follows the usual clinical practice and is not influenced by stringent preset criteria. Several types of pharmacoepidemiology study designs, such as case reports, case series, cross-sectional studies, cohort studies, case control studies, and nested case control studies are commonly used to generate evidence use and the effects of drugs (Verhamme and Sturkenboom 2011). Data on the effectiveness and safety of a drug are typically obtained from controlled experiments known as randomized controlled trials (RCTs). In these trials, participants are carefully selected and randomly assigned to receive the drug or a comparator treatment in order to gather information on the drug’s efficacy and safety. While RCTs are essential in providing reliable data on drug use, they may not always be the most suitable method for understanding the effects of a drug in real-world conditions. This is because RCTs often involve carefully selected participants, which may not reflect the diversity of patients who would take the drug in real life. To study drug use and its effects in a diverse population in real-world settings, researchers use pharmacoepidemiology (Montastruc et al. 2019). These studies are observational in nature, meaning that the choice of drug given to patients is based on clinical practice and not predetermined stringent criteria. However, due to ethical concerns about obtaining assent and consent, as well as difficulties in recruiting and retaining

Drug Safety in Children: Research Studies and Evidence Synthesis

participants, certain populations such as women, elderly, and children are infrequently included in RCTs (Van Spall et al. 2007). The pediatric population is a group with distinct and varied characteristics that require unique consideration in healthcare provision. The thalidomide (Kim and Scialli 2011) and sulfanilamide elixir (Woolf 2022) tragedies have highlighted the consequences of negligence in pediatric drug safety. Drug safety in pediatrics is unique due to its multifactorial attributes, including physical characteristics, developmental variability, and legal issues related to vulnerability and minor status (Woods et al. 2005; Hussain et al. 2020). With well-designed and carefully curated methods, the pharmacoepidemiology approach has been proven to provide evidence on major drug safety issues in pediatrics. This chapter contains a discussion of salient drug safety issues in children (Fig. 1) and provides evidence (Babar 2020) around the ways in which pharmacoepidemiology approaches have been used to describe and substantiate drug safety in children.

Drug Safety Issues in Children Adverse Drug Reaction An adverse drug reaction (ADR) is any undesired toxic effect of a drug that occurs during the clinical use of a drug and those effects can be classed based on predictability of the observed reactions. The type of ADRs which are related to overdosing, drug-drug interactions, and organ toxicity

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are known as Type A reactions, while Type B reactions are recognized as dose-independent, unpredicted, and rare effects of the drugs. These are usually classed into both allergic and nonallergic reactions (Kidon and See 2004). Pharmacoepidemiology have been used to provide evidence on prevalence and identification of risk factors for developing an ADR. Prevalence and Nature of Adverse Drug Reaction

Most ADRs related to pediatric population involve the gastrointestinal system (GIT) and skin causing rashes and urticaria. Besides systematic reactions and those involving central nervous system are also observed in the pediatric population. The drugs involved in causing ADRs are vaccines, antibiotics, GIT drugs, nonsteroidal anti-inflammatory drugs, and antipyretics (Napoleone 2010). The incidence of ADRs is found to have ADRs in pediatric outpatient and hospitalized patients as up to 11% and 10.3%, respectively (Sugioka et al. 2020). A collaborative study from Australia, Germany, Hong Kong, Malaysia, and the UK has stated 1340 admissions over a period of 3 months. The ADRs identified in 211 patients were 380 with an incidence rate of 16.5% with a total of 24% serious ADRs, including one fatal incidence of ADR. Moreover, Malaysia and the UK were noted as countries with the highest incidence of ADRs in the study (Rashed et al. 2012a). Overall, the incidence of ADRs were as high as compared to those in adult population and older pediatric patients were more likely to had ADRs irrespective of the gender (Rashed et al. 2012b).

Off-label drug use

Adverse drug reaction

Drug safety in paediatrics

Medication error

Inappropriate and irrational drug use

Drug Safety in Children: Research Studies and Evidence Synthesis, Fig. 1 Overview of major issues related to drug safety in pediatric patients

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A study from Saudi Arabia has indicated an incidence rate of ADRs as 4.5% in retrospective study while 8.2% in prospective study. ADRs were more in patients who received 5–6 drugs, with 15.5% in retrospective study and 22.1% in prospective study. The drugs found with increased incidence of ADRs were anti-infectives with 48.2% and 40.8% in retrospective and prospective study, respectively (Khan et al. 2013). The study also demonstrated that skin reactions were the most obvious effects of ADR; however, none of the ADR proved to be fatal. Due to the limitation of clinical trials and less evidence about clinical safety and efficacy of drugs among pediatric population, many drugs are being prescribed and used as off label. This coupled with lack of pharmacokinetic and pharmacodynamics data, susceptibility to druginduced growth issues, and development disorders cause challenges to the provision of drug safety in children (Leitzen et al. 2021). Besides, the market size for pediatric medicines is relatively small and less profitable. This makes the pharmaceutical manufacturers less inclined towards the pediatric population (Tomasi et al. 2017). Moreover, the childhood diseases may differ from the adult population and requires special attention during the process of drug usage (Napoleone 2010). During the pediatric premarketing studies, the chances of having serious adverse reactions are rare especially if there is latent period before the onset of any ADR. This is because of inclusion of limited population into clinical trials. This makes it difficult to interpret and extrapolate the results to a wider population. Furthermore, many regularly used drugs among pediatric patients have been in the market for a long period of time (Dittrich et al. 2020). Polypharmacy in pediatric is referred to as the use of two or more prescribed medicines and the risk of ADR may increase in the cases where polypharmacy is involved (Sugioka et al. 2020). A Saudi Arabian study has revealed that prevalence of ADRs was less in those children who were prescribed with less than five drugs comparing to those who were prescribed with five or

more drugs during a univariate analysis (Khan et al. 2013). A study from Japan has found that polypharmacy increased the prevalence of ADR. This resulted in increased hospital visits among children. Moreover, Grade 1 ADR, antibiotics induced ADRs and ADRs related to gastrointestinal system were the most common among 1330 patients, who visited Gifu Municipal Hospital in Japan (Sugioka et al. 2020). Another reason that contributes to the prevalence of ADR is the unlicensed and off-label drug use (OLDU). The need to use drug as a treatment option is commonplace in almost all types of diseases. The supply and use of drug are determined by the drug licensing process. However, the drug licensing process does not limit the use of a drug, resulting in drugs used for indications, age groups, dosages, formulations, or routes of administration outside the terms of its license or marketing authorization. The use of a drug outside the marketing authorization is known as off-label drug use (OLDU) (Rusz et al. 2021). The benefit risk drug profile in pediatric patients is entirely different from the adult population in terms of pharmacokinetic, pharmacodynamic, and pathophysiological changes, resulting in decreased therapeutic effectiveness and greater risk of ADRs (Bernardini et al. 2022). The highest number of off-label prescriptions was found to link with treatment of diseases such as cancer, liver, and kidney. Moreover, 36–67% pediatric patients in intensive care received off-label medicines (Horen et al. 2002). Off-Label Drug Use (OLDU) and Identification of Risk Factors for Developing an ADR

The off-label use of a drug is a therapeutic necessity and is recommended, where there is no availability of therapeutic alternative (Neville et al. 2014). There are several studies that have indicated the relationship between the off-label drug use and ADRs. A review about the safety of off-label and unlicensed drug use in pediatrics indicated the percentage ADR related to unlicensed and/or off-label prescriptions were found to be 23% and 60% (Cuzzolin et al. 2006;

Drug Safety in Children: Research Studies and Evidence Synthesis

Dittrich et al. 2020). Moreover, ADRs were associated to 25 organ system including 25 classes of drugs. Thiesen and colleagues performed a prospective observational analysis of 6601 admissions from a secondary and tertiary care referral center in England. They have found that 17.7% of pediatric inpatients experienced at least one ADR in 48 h, while a total of 58% of the ADRs occurred in patients undergoing general anesthesia. Other factors contributing to the incidence of ADR were increasing number of drugs, age, and oncological treatment (Thiesen et al. 2013). A study by Dittrich et al. (2020) reviewed the medical records of 301 patients and found 132 suspected ADRs among those cases. Eighty-one patients were suffering from one or more ADRs and 55% were not explicitly noted by the treating physicians, while none was reported to the national pharmacovigilance system (Dittrich et al. 2020). In a German study, all patients were intensively monitored for ADRs by a pharmacoepidemiologic team. A total of 178 patients were included in the study and a total of 46 ADRs were observed in 31 patients, receiving at least one unlicensed or off-label drug prescription during hospitalization (n ¼ 92). They also experienced an ADR significantly more frequently than patients receiving only licensed drugs. Thus, the study has concluded that patients treated with unlicensed, or off-label drugs were more prone to develop ADRs than those who did not receive any unlicensed of off-label drug (Neubert et al. 2004). A balance of benefit and risk assessment is crucial to establish the evidence for the OLDU in children to avoid the situations, whereby pediatric population may expose to harm, toxicity, and lack of efficacy. A Dutch study has established a decision framework namely Benefit and Risk Assessment for Off-label use (BRAvO) to support structured assessment of benefits and risks of offlabel drug use, including dose selection to ultimately optimizing drug efficacy and safety (van der Zanden et al. 2021). A study by Nguyen et al. in 2021 has evaluated the relationship between OLDU and ADRs among hospitalized children (Nguyen et al. 2021).

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Off-Label Drug Use and Pharmacoepidemiological Approaches The need to use drugs as a treatment option is commonplace in almost all types of diseases. The supply and use of drugs are determined by the drug licensing process. However, the drug licensing process does not limit the use of a drug, resulting in drugs used for indications, age groups, dosages, formulations, or routes of administration outside the terms of its license or marketing authorization. The use of a drug outside the marketing authorization is known as off-label drug use (OLDU) (Rusz et al. 2021). Off-label drug use among pediatrics is highly prevalent (Balan et al. 2018) mainly due to unmet medical needs in the pediatric population (Rusz et al. 2021; van der Zanden et al. 2021). Research on OLDU has increased in the past two decades, mainly focusing on OLDU in hospitalized pediatric patients (Sweileh 2022). In particular, pharmacoepidemiology approaches have been used to identify the extent of OLDU and ways to manage the ambiguities associated with OLDU in children. Nature and Extent of OLDU

More than 100 studies have been conducted and reviewed in the past (Balan et al. 2018; Meng et al. 2022b) to identify the extent and nature of OLDU in children. It has been reported that OLDU was persistently highest in the NICU settings and involved OLDU due to age and dose discrepancies (Balan et al. 2018). Nevertheless, newer pharmacoepidemiological studies are still being conducted and have been recently reported from previously unmapped settings such as the palliative care unit (García-López et al. 2020) and patient characteristics such as pediatric burn (Saputro et al. 2021), obesity (Czepiel et al. 2021), and rare disease patients (Fung et al. 2021). Despite the existence of a vast amount of evidence from the past, the generation of evidence on the extent and nature of OLDU in children remains important as evidence from standard reference sources are lacking (Sachs et al. 2012), and pediatric guidelines still contain recommendations involving OLDU (Chen et al. 2022).

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Newer knowledge and exploration of newer scope on OLDU in children, for example, in pediatric dentistry, will uncover the trend in these settings and subsequently guide evidence synthesis for better management of diseases in children. Harmonization of Dosage Recommendations Database for OLDU

Harmonization of dosage recommendations for drugs used in an off-label manner has been proven beneficial in recognizing and overcoming the dosing variation in pediatric OLDU. A Swiss study (Tilen et al. 2022) reported on the development of a database to provide healthcare professionals with national harmonized dosage recommendations. A pharmacoepidemiological study approach was used in the collection of drug consumption data to decide on the priority list of drugs to be harmonized. This was followed by the compilation of dosage recommendations used in the hospitals. A similar effort was done in Germany (Zahn et al. 2021) where dosing recommendations for off-label use in a pediatric dosing database were recorded. This was based on systematic searches of primary literature on clinical studies and case series. As OLDU does not equate to off-evidence use, the Dutch Paediatric Formulary is an existing example of the benefits reaped from the pharmacoepidemiology approach in the development of a knowledge-based formulary (van der Zanden et al. 2017). Guidance on OLDU

Healthcare professionals are uncertain about how to handle OLDU, particularly regarding the clinical, safety, and ethical concerns associated with it. Various guidelines, including algorithms (Gazarian et al. 2006; Verhagen et al. 2008), frameworks (Largent et al. 2009; van der Zanden et al. 2021), policies (Ansani et al. 2006; Schrier et al. 2020), (Meng et al. 2022a), have been suggested in the past to address these issues. These guidelines primarily focused on assessing the appropriateness of OLDU through a riskbenefit evaluation using available evidence, including pharmacoepidemiology studies. Additionally, they advocate for the collection of

prospective data on the effectiveness and safety of OLDU. To cater the needs of Chinese population regarding OLDU in children, Meng et al. (2021) has developed guidelines to assist primary care physicians, pediatricians, pharmacists, and policy makers. The guidelines are comprised of 21 recommendations, which are grouped into 8 themes and a list of common type off-label drugs for children (Meng et al. 2021). Similarly, to solve the issue of the safety of OLDU in children, a joint policy statement was released by the European society for Developmental Perinatal and Paediatric Pharmacology and European Academy of Paediatrics to provide practical guidance on the issue (Schrier et al. 2020). Exposure to Excipients of OLDU

When a drug is used outside its marketing authorization, the possibilities of overdosing and being exposed to incorrect doses is high, notably in children. Additionally, OLDU requires modification to drug formulation by splitting, grounding up, crushing, cutting, or diluting existing formulations as there is a lack of age-appropriate dosage form for children (Duncan et al. 2022). Modification of existing formulations to suit the dosing needs in OLDU may potentially cause excipients toxicity, a potential risk that may be overlooked in the quest to provide treatment options for children. Pharmacoepidemiological studies have shown that many children have been exposed to excipient toxicity as a result of OLDU (Belayneh et al. 2020). Evidence on Effectiveness and Safety of OLDU

When a manufacturer develops a drug for a particular indication, its purpose, efficacy, and safety would have been thoroughly evaluated by the drug regulatory bodies before making the drug available in the market. This ensures that evidence on the licensed drug for that particular indication is systematically verified and made readily available for users. However, this is not the case for drugs used in an off-label manner, so healthcare professionals often resort to evidence generated through professional practice and judgement.

Drug Safety in Children: Research Studies and Evidence Synthesis

A previous study has reported that the majority of evidence for off-label drug use (OLDU) is derived from consensus (van der Zanden et al. 2022). Pharmacoepidemiology approaches have been used to generate evidence on the effectiveness and safety of OLDU (Lee et al. 2014, 2018). These studies (Lee et al. 2014, 2018) not only provided evidence on the effectiveness and safety of OLDU but also highlighted on the additional monitoring parameters that could enhance the evaluation of OLDU in children. Medication Error The foundation of medical practice has been long depended on the age-old principle of “first do no harm.” However, pediatric patients have been subjected to harm in the form of medication errors. The National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) defines a medication error as “any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the health care professional, patient, or consumer” (The National Coordinating Council for Medication Error Reporting and Prevention n.d.). Medication errors (MEs) can occur during drug prescribing, manufacturing, dispensing, administering, or monitoring (Aronson 2009). Pharmacoepidemiology has been used to discern the different aspects of medication errors in children. In this section, the use of pharmacoepidemiology to study the frequency, risk factors, and possible solutions for medication errors in children has been discussed. Prevalence and Nature of Medication Errors

Children are vulnerable to medication errors (Ferranti et al. 2008), with higher rates among critically ill younger patients (Alghamdi et al. 2019). However, underreporting of ME in children is a major concern. Determining the prevalence of ME among a subset of pediatric patients is challenging due to the diversity of patient characteristics and treatment options. The use of pharmacoepidemiology approaches can help identify the prevalence of ME in children by capturing data from large datasets (Conn et al. 2020)

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over a long period of time. This could result in clinically meaningful findings. Previous studies have extracted ME data for up to 10 years in general (Azar et al. 2021; Tansuwannarat et al. 2022) among subsets of pediatric patients (Lobaugh et al. 2017). Data collected through self-reporting was further enhanced with retrospective chart review (Bekes 2021), as well as through the use of direct observation as a complementary method (Palmero et al. 2019). This was helpful in detecting ME at different stages during the treatment process. Evaluation of Medication Errors Monitoring System

Monitoring of medication errors involves reporting, documenting, coding, and analyzing. This is in order to identify trends and take actions to minimize or eliminate preventable risk factors Quasi-experimental study designs have been used to report an increase in the medication errors reporting using web-based, anonymous reporting systems (Unal and Intepeler 2020). Cross-sectional study designs coupled with expert panel reviews have also been used to evaluate ME monitoring systems. This has shown that the discrepancy between manual and online reported errors is minimal in terms of overall error type categories (Miller et al. 2006). Pharmacoepidemiology approaches embedded in quality improvement initiatives have demonstrated that simple paper-based error-reporting tools can increase reporting of medication errors in critically ill pediatric patients (Kolovos et al. 2008). To monitor the medication errors by physicians and pharmacists, a medication error monitoring system was developed by Xiamen Maternity and Child Care Hospital, China. The study has shown the effectiveness of ME monitoring systems in reducing medication errors in children (Chen et al. 2019). Prediction of Contributing Factors

Using the data on prevalence and nature of medication errors, inferential data analysis has been used to predict the contributing factors of ME in children (Baraki et al. 2018; Feyissa et al. 2020). As medication errors in children pose serious threat to the life (Ghaleb et al. 2010), it is utmost

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important to identify the contributing factors in order to devise strategies to reduce these errors. The discussion on contributing factors of ME included error-producing factors and errorpredisposing factors. Prospective and retrospective observational studies have outlined that the main contributing factors of medication errors were individualized dosing and calculations; off-licence prescribing; medication formulations; communication with children; and experience working with children (Conn et al. 2019). A live audit of medication error data from neonatal unit has revealed that on an average, 5.7% medication errors happen per fortnight. Moreover, 56% of medication errors were due to the prescribing errors and 19% were contributed by the administration error, while 24% ME were due to both the prescribing and administration error (Sharma et al. 2022). The factors contributing to medication administration errors are communication failure, nurses workload, failure to adhere to policy and guidelines, interruptions, and insufficient continuous education (Alomari et al. 2015). In comparison to prescribing and administration error, studies analyzing the contributing factors of dispensing error per se in children were lacking. However, pharmacoepidemiological approaches were useful to analyze the characteristics of dispensing errors (Costa et al. 2008), as the dispensing errors also occur with high-alert medications (Silva et al. 2011) and pediatric chemotherapy (Watts and Parsons 2013). Reducing Medication Errors

The identification of the prevalence, nature, and contributing factors of medication errors (ME) in children has led to the uncovering of sources of recurring errors and the implementation of strategies to reduce medication errors. Systematic reviews have outlined the strategies implemented and their effectiveness in reducing medication errors in children (Rinke et al. 2014; Santesteban et al. 2015). Given the established benefits of medication errors reducing strategies, depriving pediatric

patients of these benefits in the name of randomization to comparator groups is not considered ethical. Therefore, the pharmacoepidemiology approach remains the gold standard in assessing the effectiveness of strategies to reduce ME in children. Multiple strategies implemented over time have been found to be effective to reduce errors (Leahy et al. 2018). Longitudinal research methods (Simpson et al. 2004; Campino et al. 2009), failure mode effects analysis (Martin et al. 2017), and pre-intervention and post-intervention cross-sectional studies (Otero et al. 2008) have been used to assess the effectiveness in reducing medication errors. Computerized physician order entry (CPOE) and the inclusion of pediatric clinical pharmacy services have been found to be highly beneficial (Fernandez and Gillis-Ring 2003; Wong et al. 2009) and preferred by prescribers (Fortescue et al. 2003). These are used as a strategy to reduce errors. The Institute for Safe Medication Practices (ISMP) published guidelines for preventing medication errors in pediatric patients. These included unit dose dispensing systems, computerized provider order entry (CPOE), barcoding technology (Hardmeier et al. 2014), and education for healthcare professionals (Levine et al. 2001, Westbrook et al. 2021). A study from Spain has indicated that the medication errors reduced from 20.7% to 3% after the implementation of a multidisciplinary educational intervention in a Spanish regional neonatal intensive care unit (Campino et al. 2009). Another study has highlighted decrease in medication error over a period of 3 months after the intervention (Simpson et al. 2004). Inappropriate and Irrational Drug Use The World Health Organization (WHO) defines rational use of drugs as ensuring that “patients receive medications appropriate to their clinical needs, in doses that meet their own individual requirements, for an adequate period of time, and at the lowest cost to them and their community” (World Health Organisation 1987). However, in children, two major challenges arise in

Drug Safety in Children: Research Studies and Evidence Synthesis

achieving this goal: ensuring appropriate and individualized clinical needs and dose requirements. Additionally, certain drugs or drug classes may be deemed “potentially inappropriate” for use in children due to the high risk they pose, with safer alternatives available (Li et al. 2022a). Irrational and inappropriate drug use in children can lead to adverse drug reactions (ADR), prolonged hospital stays, and higher medical costs. To address these issues, pharmacoepidemiology approach has been used to develop detection tools and to determine the prevalence of irrational and inappropriate drug use among children. Irrational Drug Use

The World Health Organization (WHO) has created indicators to evaluate rational drug use in healthcare facilities (World Health Organization 1993). The WHO prescribing indicator evaluates rationale drug use in terms of average number of drugs per prescription, proportion of drugs prescribed using generic names, proportion of prescription containing at least one antibiotic and injections, and proportion of drugs prescribed from the essential medicine list. Cross-sectional studies were used to collect data on these indicators in outpatient pediatric settings to assess rational prescribing for children. Most of the studies (Table 1) among children showed that at least one antibiotic deviated from the standard recommended by WHO. In line with that, newer cross-sectional studies were focused on rationale antibiotic prescribing in children. However, a limitation of the WHO prescribing indicators is that they only measure the number of medications prescribed and not their appropriateness related to diagnosis (Aldabagh et al. 2022). Despite this, the WHO indicators remain a crucial tool for providing feedback to practitioners and raising awareness of rational drug use (Aldabagh et al. 2022; Chandika et al. 2019). Development of Tools for Detecting Potentially Inappropriate Prescriptions in Children

Tools to detect potentially inappropriate prescriptions in children have been first developed in

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France (Prot-Labarthe et al. 2014) and was further modified to suit country-specific (Corrick et al. 2019) and international (Sadozai et al. 2020) needs. Other tools were developed for primary care setting (Barry et al. 2016) in China (Li et al. 2022b) and Côte d’Ivoire (Doffou et al. 2021). Several institution-based tool were also found to be in use (Mirza et al. 2009; Cui et al. 2021). A tool mimicking the concepts of potentially inappropriate medicine use in elderly was developed in the United States for pediatric use (Meyers et al. 2020). Although a major component in the tool development process involved obtaining consensus from relevant experts, pharmacoepidemiological approach was profoundly used to narrow down the health problems frequently encountered in pediatrics and gather evidence on its corresponding recommended pharmacological treatments and associated risks and issues (ProtLabarthe et al. 2014; Barry et al. 2016; Corrick et al. 2019; Meyers et al. 2020; Sadozai et al. 2020; Doffou et al. 2021; Li et al. 2022a). In some studies, pharmacoepidemiology approach was used for measuring the appropriateness of medicine use using a self-developed tool (Mirza et al. 2009; Cui et al. 2021). However, these tools have not reached full maturity when it comes to being suitable for use across all pediatric age groups (van den Anker and Allegaert 2019) (Choonara 2021). It is important to note that these tools are therapeutic guidance and should always be used in conjunction with professional clinical judgement. Prevalence of Inappropriate Drug Use

The tools developed to determine the prevalence of irrational and inappropriate drug use in children have not been extensively utilized. A review of recent studies (Table 2) have shown that the pediatrics omission of prescriptions and inappropriate prescriptions (POPI) tool (Prot-Labarthe et al. 2014) is frequently used to evaluate potentially inappropriate medication (PIM) and prescription omissions among pediatric patients. The majority of the studies found that the prevalence of PIM was higher than prescription omissions. PIM was

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