Health Financing for the Developing World: Supporting Countries' Search for Viable Systems [1 ed.] 9789054877752

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Health Financing in the Developing World Supporting Countries’ Search for Viable Systems

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Guy Carrin (ed.) Professor of Health Economics, University of Antwerp, Belgium

Health Financing in the Developing World Supporting Countries’ Search for Viable Systems

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The GPRC label (Guaranteed Peer Review Content) was developed by the Flemish organization Boek.be and is assigned to publications which are in compliance with the academic standards required by the VABB (Vlaams Academisch Bibliografisch Bestand).

Uitgeven met de steun van de Universitaire Stichting van België Published with the support of the Fondation Universitaire de Belgique

Cover design: Pnuts, Gent Book design: Crius Group, Hulshout Print: Wilco, Amersfoort

© 2011 University Press Antwerp (UPA is an imprint of Academic and Scientific Publishers) Ravensteingalerij 28 B-1000 Brussel Tel. + 32 (0)2 289 26 50 Fax + 32 (0)2 289 26 59 [email protected] www.upa-editions.be ISBN 978 90 5487 775 2 NUR 882/784 Legal deposit D/2011/11.161/001 All rights reserved. No parts of this book may be reproduced or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher.

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This book is the fruit of years of hard work, passion and dedication. It is the culmination of Guy's long and illustrious career in Public Health. Sadly, he did not witness its publication. After a brave battle with cancer, Guy passed on March 28th, 2011, shortly after completing the manuscript. It was Guy's hope and wish that the book would be a practical, comprehensive and widely used guide for both students and professionals in health financing. As his loving family, it is our wish that his memory will carry on through his tireless devotion to improving public health in the developing world, and that this book will cement his legacy in the field.

The Carrin Family

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Contents

Foreword

ix

Acknowledgments

xiii

Overview and perspectives Part I

1

Empirical facts on health expenditure

1

Basic patterns in national health expenditure

29

2

An overview of health financing patterns and the way forward in the WHO African region

49

Part II Reaching universal coverage 3

Universal coverage of health services: tailoring its implementation

97

4

Determinants of achieving universal coverage of health care: an empirical analysis

111

5

Impact of risk sharing on the attainment of health system goals

133

6

Impact of social health protection on access to health care, health expenditure and impoverishment – a comparative analysis of three African countries

151

Part III Insurance-based approaches III.1 Community-based health insurance 7

8 9

Social health insurance development in low-income developing countries: new roles for government and nonprofit health insurance organizations in Africa and Asia

181

Effectiveness of community health financing in meeting the cost of illness

209

Community-based health insurance in developing countries: a study of its contribution to the performance of health financing systems

229 vii

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10

Risk-pooling: necessary but not sufficient?

255

11

Scaling up community health insurance: Japan’s experience with the 19th century Jyorei scheme

259

12

The reform of the rural cooperative medical system in the People’s Republic of China: interim experience in 14 pilot countries 275

13

A framework for best practices analysis in the Rural Cooperative Medical System of the P.R. China

295

III.2 Social health insurance 14

Social health insurance in developing countries: a continuing challenge

325

Social health insurance: key factors affecting the transition towards universal coverage

339

Key performance indicators for the implementation of social health insurance

359

17

Vietnam – The development of national health insurance

375

18

Health financing reform in Kenya – assessing the social health insurance proposal

395

15 16

Part IV Resource allocation and cost-containment concerns in health financing policy 19 20

Clarifying efficiency-equity tradeoffs through explicit criteria, with a focus on developing countries

409

Provider payments and patient charges as policy tools for cost-containment: How successful are they in high-income countries

431

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Foreword

Health financing is a major challenge in many developing countries. Limited fiscal space in resource-poor countries and lack of governments’ commitment in general to allocate a sufficient part of their budgets to the health sector obliges households to pay directly for medical care, often a large proportion of their income. It deepens poverty among the poor but also throws a number of non-poor households into poverty. The poorer households may also decide not to seek health care which results in welfare loss. Moreover, either catastrophic health spending or inability to access to care by the poor is socially unacceptable; it calls for governments’ legitimate responsibility to take immediate action rather than making rhetoric statements. Another concern is that resource-poor countries generally have limited government capacity to harmonize the mushrooming donor programmes, in particular those from Global Health Initiatives. They risk being financially nonsustainable in the long run. They also result in fragmentation and at times weaken health systems, in particular draining limited well trained professionals to “small-beautiful-well-funded” fragmented projects at the expense of poorly funded public health services. As part of a response to the above-mentioned concerns, several of these countries have introduced prepayment schemes such as social health insurance or community health financing. They are often piloted in a limited number of sites and have difficulties in scaling up to larger population. Yet, a number of countries such as China, Vietnam and the Philippines have been steadily developing their prepayment schemes, although the provider payment methods used are far from optimal and the level of financial risk protection achieved is limited. Providers have been generally paid on a fee-for-service basis, thereby failing to send signals to providers to use the limited resources for health services in an efficient and rational way. This lack of rationality maintains a high level of household payments resulting in inadequate risk protection. In addition, deficits in health insurance funds often arise, resulting in an increase of premiums affecting households’ capacity and willingness to contribute. In view of the issues raised above, effective universal coverage as a crucial health system goal makes sense more than ever. Therefore, a global movement towards universal coverage was called by a World Health Assembly Resolution in 2005. The current publication edited by Dr. Carrin significantly contributes to that call. This publication is one of the most important compendiums of ix

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scientific rigorous contributions on health financing that were published in high impact international journals in the past two decades. It provides rigorous evidence, technical guidelines and practical tools for concerned ministries in developing countries as well as development partners on how to move away from the predominant role of households’ out of pocket payments towards prepayment schemes and the achievement of universal coverage. Having read through this book and gauged against my experience on health financing and the Universal Coverage policy established in Thailand in 2002, it is extremely practical and useful. It touches the real policy and administrative issues in formulating and implementing a good health financing system, ranging from mobilizing resources, pooling risk and purchasing efficiently and equitably. Experience has shown now that strategic purchasing has been one of the most difficult dimensions of health financing reform. In my opinion, the entrenched benefits of prescribers who command health care resources and the pharmaceutical and medical device industries are among the major barriers of reform. Country experiences in shifting from fee-for-service to provider payment methods such as capitation and case-mix face severe resistance from the professionals. They argue in favour of clinical freedom or the right of patients to choose the best treatment, yet often not supported by cost effectiveness evidence. From this book, the contributions by Dr. Carrin and colleagues were based on strong understanding of health systems, health policy and health financing particularly in developing countries. They also take a pro-poor stand at various occasions, namely that resources must be used not only efficiently but also equitably. Though this book furnishes all necessary evidence in reaching universal coverage, challenges remain on how technocrats, civil society and international development partners ought to work together. Especially, the evidence presented in this book needs to be translated into a political agenda. In many countries, there is a window of opportunity every four to five years during general election campaigns. In fact, in the end the move to universal coverage turns out to be first and foremost a political decision. One cannot underestimate the importance of the design of appropriate and well thought prepayment schemes based on evidence, and effective government implementation capacity in achieving improved equity and financial risk protection. Designing a pathway towards universal coverage and adapted to a country’s context is not easy, but not impossible. Experience and evidence inform us how important it is to create domestic institutional capacity to collect

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Foreword

and generate evidence. The subsequent linking of this evidence with strong political decisions by convinced government leaders is a key determinant of success. Viroj Tangcharoensathien MD. PhD. International Health Policy Program, Ministry of Public Health, Nonthaburi, Thailand

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Acknowledgments

I wanted to acknowledge first and foremost my important debt to the University of Antwerp (UA) that in 1980 entrusted me with a research project on economic evaluation of health care in developing countries. This boiled down to my formal introduction to the field of health economics. Thanks are also due to Médecins sans Frontières and Medicus Mundi, who subsequently requested me to undertake short-term advisory work on health financing in Africa. I am also thankful to several universities where I could deepen my understanding of health economics and health financing issues in developing countries, namely the Schools of Public Health of Boston University, Harvard University and the University of Michigan. In 1990, I was invited to join the World Health Organization (WHO) in Geneva, in order to further develop capacities in health economics and answer requests by policy makers in countries in greatest need. Most of these requests were focused on the macro and micro aspects of health financing. I owe many thanks to the WHO for having given me this opportunity. In addition, important financial support for this project was granted by RT International Holding (Tienen, Belgium), which is gratefully acknowledged. Over the years, advisory work and applied studies in health financing were undertaken in many developing countries. I am very grateful for all the insights I received from many policy-makers and their advisers as well as academics in developing countries. I also had the chance to write up country experiences or to join colleagues in doing so. In fact all chapters, except for one, were written with co-authors. I am most appreciative to them for having shared their thoughts and experience. Most of this work has also been used at the UA as part of the course ‘Health economics and policy’. This course is jointly organized with Diana De Graeve whom I wish to thank sincerely for her interest and our long-standing collaboration. Thanks are also due to the University Press Antwerp (UPA) for including this book in its offer of publications. UA President Alain Verschoren’s keen interest in this book project is also gratefully acknowledged. At the UPA, chief editor Stefaan Janssens was most helpful in producing the book, and I thank him for the fine cooperation. I also wanted to highlight the priority role in the overall editing of this book by Marijs Carrin. Last but not least, I have a great debt to my family for having allowed me to spend significant time away from home to support policy-makers in various

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countries to design appropriate health financing systems. Without my family’s understanding, I could not have carried out this work.

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Overview and perspectives Introduction This volume brings together twenty articles and contributions to books that I have written over the past twelve years, all but one with co-authors, on pressing health financing issues in the developing world. It comprises analyses of the health financing situation and reforms in selected countries, quantitative analyses of the impact of health financing systems on health objectives and studies on the design of health financing policies. Four parts are presented: (i) Part I addresses empirical facts on health expenditure in the world and in the African region in particular; (ii) the main thrust of Part II is the search for universal coverage of health services; (iii) insurance-based approaches, in particular community-based health insurance and social health insurance, are analysed in Part III; (iv) resource allocation and cost-containment concerns in health financing policy are discussed in Part IV. A number of the articles and contributions to books have originated from requests of countries to the World Health Organization in the field of health financing. Thus for those articles and contributions, the link with realities at country level is preponderant. In other cases, the own initiative of colleagues and myself led to summarize experiences and/or to reflect upon ways ahead in health financing for developing countries. The leitmotiv of the present book is evaluating the appropriateness of various health financing policies, and how these can help improve the transition towards universal coverage. An important audience is therefore composed of policy-makers and their technical advisers that are exposed to or have to decide upon health financing policies, or are engaged in a debate about them. The design of policies that are adjusted to the particular economic and political context of developing countries constitutes a major direction throughout this volume. It will become clear that, for one set of countries, universal coverage policy may take time and require a step-by-step approach. In other developing countries, a swift transition to universal coverage may well be feasible.

Empirical facts on health expenditure A basic set of health expenditure data is vital for policy-makers to formulate a first diagnosis of their country’s health financing system. In chapter 1, a cross-sectional analysis of the basic patterns in national health expenditure across 191 countries is conducted. This enables an initial comparison of how countries at similar levels of economic development are performing in terms 1

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of health financing. One key criterion, which is particularly interesting and pertinent to investigate, is the extent to which a country’s population is financially protected via its health financing system rather than be subjected to sizeable out-of-pocket expenditure. One of the basic measures of financial protection at the national level is the share of general government health expenditure1 in total health expenditure. Indeed, this share evaluates the degree of pooling of health risks and sharing of the financial consequences of health care costs across the whole of the population. It is evident that the more general government health expenditure is developed, the more out-ofpocket expenditure can be avoided. Thus, it will facilitate access to health care and fend off heavy direct payments, which would otherwise have to borne by households at the point of use. One of the main conclusions of the inter-country analysis is that, with growing income,2 the share of out-ofpocket spending in total health expenditure drops rather significantly. The latter is an immediate result of the establishment or continued development of government-managed and/or government regulated health financing schemes that result in a relatively high level of general government health expenditure. In fact, one observes that the share of general government health expenditure as a percentage of total health expenditure rises with income. Shares of 60 to 80 % are quite common for the high-income3 countries in the sample. Chapter 2 focuses on WHO’s African Region with its 46 countries. At first sight, the relative role of government in the health sector in Africa in 2002 does not seem to be modest: general government health expenditure was over 50 % of total health expenditure in 24 countries. However, one needs to realize immediately that, at the same time, private health expenditure that includes out-of-pocket payments was over 40 % of total health expenditure in 31 countries. Noteworthy is also that external funding occupies a fairly marked position, 17 countries receiving over 25 % of their resources for health from external sources. Analysis of the African data also reveals that the level of total health expenditure is quite moderate, at least compared to some benchmarks4

1. 2. 3. 4.

General government health expenditure includes the outlays on health by government entities, notably ministries of health and social security agencies (WHO 2006b, p. 159). Income is measured by the level of the Gross Domestic Product per capita (GDPC). High income is defined here as GDPC (at Purchasing Power Parity) above US$ 7000. A benchmark often referred to is that of the Commission on Macroeconomics and Health (2001) that estimated that a basic set of services for prevention and treatment would cost at least US$ 34 per year at 2000 prices. In addition the Task Force on Innovative International Financing for Health Systems (2009) brought out a WHO estimate that in 2015 an additional US$ 29 would be needed for the 49 low income countries in order to accelerate the achievement of the health Millennium Development Goals.

2

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Overview and perspectives

that have been proposed: in 2002, total health expenditure per person5 was less than US$ 10 in 10 countries, between US$ 10 and US$ 30 in 20 countries and over US$ 30 in 16 countries.6 Such low amounts reflect that many people in Africa simply are not able to access health care, due to financial and other access barriers, and/or that health care is simply not available. A first major challenge for many African countries is therefore seeking to increase overall resources for health. In this respect, a continued call on external funding is surely quite reasonable. The second challenge is the design and implementation of health financing mechanisms that enhance risk pooling across the population and thus move away from health care services paid directly by households via outof-pocket payments.

Reaching universal coverage Universal coverage and prepayment systems The leitmotiv of this volume, namely elaborating health financing systems that aim at universal coverage of health services, receives ample attention in chapters 3 to 6. Especially chapter 3 focuses on the concept of universal coverage and the challenge of effective implementation. Universal coverage is concerned with population coverage as well as with the extent to which the population can benefit and experience affordable access to an appropriate package of health services. Achieving universal coverage effectively is then also expected to lead to the highest possible level of health status. The notion of universal coverage implies most importantly that countries ought to turn away from excessive out-of-pocket health expenditure, and instead shift to so-called ‘prepayment’ systems. Basically, the latter are systems whereby households, enterprises, government and other agents in society prepay and pool their contributions, and subsequently use them to finance a pre-defined health services benefit package. Since the World Health Report 2000,7 these have been referred to as the health financing sub-functions of revenue collection, pooling and purchasing. We return below to the importance of these three sub-functions. Moreover, there is a paramount task for government to exercise good stewardship or governance regarding the development of its country’ health financing system and the associated sub-functions. 5. 6.

7.

At average exchange rates. In 2003, one observes a similar pattern: 10 countries have a total health expenditure per capita of less than US$ 10, 14 countries spend less than US$ 30 per capita whereas 22 countries spend between US$ 10 and US$ 30 per capita; see WHO (2006a, p. 148). WHO (2000).

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Key policy issues in a universal coverage strategy Three policy issues are especially relevant for the pursuit of a universal coverage strategy. First, a key task in this process will be for government to oversee that the various population groups become systematically and adequately covered. This is not always a clear and simple process. Country governments will need to sense what is feasible, economically, organizationally and politically. In most cases therefore, a process of transition to universal coverage will be set in motion. Initially, there might be various groups covered differently, for example via a tax-based subsystem or a social health insurance subsystem. In addition, population coverage might remain incomplete for a while, with the admitted serious risk that the poorest groups are the least well protected. Intermittently then, various private initiatives, including community-based health insurance, enterprise-based insurance or other forms of private health insurance, may have a role to play. Eventually, with upheld efforts from government and society as a whole, a universal coverage system is likely to emerge. Typically, it can be dominated by a tax-based funded system, a social health insurance system or it may be more complex organizationally with a mix of both subsystems protecting different population groups. It can be easily understood that because of differences in countries’ characteristics, detailed standard paths and timelines for universal coverage will prove to be difficult to implement. Figure 0.1 below summarizes the anticipated paths towards universal coverage. Figure 0.1 Key health financing options at different stages of the evolution towards universal coverage

Source: Carrin, James and Evans (2005); see also chapter 3.

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Overview and perspectives

A second question that follows naturally is what can speed up the transition to universal coverage? There are no miracles, however, and one can only make progress when considering and constantly paying attention to the abovementioned economic, organizational and political constraints. As far as the economic constraints are concerned, we single out the level of growth and the structure of the economy that impact in a decisive way upon the size of the contributions to a health financing system. Organizational constraints are linked in a significant way to the size and skill distribution of the labour force, as they impact upon a country’s availability and ability of human resources to manage the building up of a universal coverage system. Furthermore, government needs to be sufficiently equipped to monitor and guide the universal coverage process, to trigger the necessary legislation and issue proper regulations and to subsequently ensure enforcement. Political constraints are related importantly to the way communities, civil society and the private sector are being involved. Their involvement from the start is crucial as it will smoothen and speed up the process towards universal coverage. For example, a major issue that all stakeholders need to come to grips with is the degree of solidarity that they can accept. Universal coverage systems characteristically involve important cross-subsidization between the rich and the poor (the former generally contributing more than the latter) and between the healthy and the sick (the former subsidizing the health care costs of the latter). This cross-subsidization is not likely to take place if solidarity should be rather weak or absent. It will thus be a logical task for government to guide the development of universal coverage systems, and to explore the advancement of solidarity together with society’s many stakeholders. The relationship between the constraints discussed above and universal coverage is further explored empirically via the cross-section empirical analysis in chapter 4. This analysis addresses the determinants of the willingness or ability of a country to fully adopt the principles of universal coverage. The determinants were specified in such a way that they incorporated the abovementioned constraints. It was possible, using a sample of 52 countries, to identify statistically significant effects for a restricted number of determinants only. Two determinants come to the forefront, namely GDPC and the GINI coefficient. First, GDPC has a statistically significant positive effect upon a country’s acceptance of universal coverage. GDPC measures a country’s overall economic strength per capita, and its positive effect reveals its impact on countries’ increased capacity and acceptance of a universal coverage system. Secondly, the GINI income inequality indicator has a statistically negative effect upon countries’ interest in universal coverage. In the analysis, it is also adopted as a proxy for the degree of solidarity. Namely, if a country tolerates a high value for the GINI, it is assumed that it also consents to a low degree of

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solidarity within the population; lower values for the GINI coefficient can then be connected to higher degrees of solidarity. Reinterpreted in this way, it can be concluded that strong solidarity appears to work in the direction of universal coverage. A third policy issue in the construction of a universal coverage system is that one should not lose sight of the need to adequately organize the health financing sub-functions of revenue collection, pooling and purchasing.8 However, chapter 3 also gives the crucial message that the sheer establishment of organizations is not enough but that they need to be confronted with a set of appropriate rules.9 These rules pertain to the three health financing subfunctions, and are usually found in legislation and regulatory frameworks. Simple examples10 of rules in the area of SHI are: 5 % contribution rate calculated on wages and to be shared equally by employers and employees (revenue collection); 1 % of SHI revenues are transferred to the Ministry of Health to subsidize health services provided to the as yet non-insured (pooling); the SHI system can purchase from both public and private facilities (purchasing). Again using North’s terminology, such rules can now be said to be part of the institutional design of health financing systems.11 In the end, it will be an adequate institutional design together with the effectiveness of organizations in putting the rules into operation that will matter most for the efficiency and equity of the health financing system. In other words, they will have a major impact on eventually reaching the fundamental health financing targets of resource generation (sufficient and sustainable), optimal resource use and financial accessibility of health services for all.12 Performance indicators can be used to see to what extent a health financing system satisfies the above-mentioned targets. In chapter 6, household survey data for Kenya, Senegal and South-Africa are used to further the analysis of the

8.

‘Purchasing’ was initially used in WHO (2000). More recently ‘purchasing and/or provision’ is being used to account for the fact that collected revenues, or a part of them, may be used by health managers at the national, regional or district level, to directly provide health services. 9. We use North’s broad framework (North, 1990; chapter 1) of the role of institutions and organizations for the analysis of health financing systems. Institutions are tantamount to the ‘rules’ that guide human and organizational action. Organizations (including political, economic, social and educational bodies) are called to implement these rules. See further chapter 3. 10. An extended list of examples can be found in Table 3.1 of chapter 3. 11. See North (1990). 12. Apart from chapter 3, the health financing targets are also addressed in chapters 16 and 18.

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performance of these countries’ health financing systems. The following performance indicators are introduced: population coverage, utilization of health care given need, the extent of catastrophic payment13 as well as impoverishment among households due to health care payments.14 All three countries have an important tax-based component in their health financing system. Various forms of compulsory and voluntary health insurance schemes have been introduced in a supplementary way. In 2002, general government spending on health was 40.6 %, 44.0 % and 45.2 % in South Africa, Kenya and Senegal, respectively. Yet, despite this relative important effort on the part of government, the poor and vulnerable population groups appear to be inadequately covered. The empirical results show now that those who have supplementary health insurance (on top of the protection provided by government) receive better access to health services and are less prone to catastrophic payment and impoverishment than those who do not. One main message from this analysis is therefore that one needs to look beyond the mere existence of a health financing scheme, whatever its source of funding and whoever is organizing it, but rather investigate thoroughly how it performs. Any observed lack of performance could then be used to prepare for an adjustment or overall reform in health financing. Again which type of adjustment or reform would be feasible depends strongly on a country’s characteristics and preferences. For example, after recognizing the weaknesses in its health financing system, Kenya started to consider the establishment of a national social health insurance scheme in 2003. Kenya’s deliberations on this matter will be returned to in chapter 18. Impact of the health financing system on health system outcomes Apart from paying extensive attention to the health financing targets, one also needs to recognize the relevance of the final health systems outcomes such as a maximum and equitably distributed health status, equitable financing, and a good and equitably distributed responsiveness to population preferences. In fact, it matters considerably how well a health financing system (whether one of the archetypical systems of tax-based financing and social health insurance, or combinations of both) can strengthen the three above-mentioned subfunctions. A mere denomination of a health financing system is therefore a failing guide in predicting performance. Hence, it is submitted that tax-funding is not better a priori than social health insurance, or vice-versa. Moreover, if 13. Defined as out-of-pocket payments for health care of one or more household members, equal to above 40 % of a household’s capacity to pay; see chapter 6 and Xu et al. (2007). 14. Impoverishment is measured by the percentage of households who were not poor before but became poor after paying for health services; see chapter 6.

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countries intend to achieve universal coverage, it will be more a matter for them to choose their own organizational method, rather than being tightened by the prototypical rules of archetypical systems. This view is underpinned by the empirical analysis in chapter 5, showing that implementation of a universal coverage system via either tax-based or social health insurance has no special additional effect on the final health system outcomes related to equity and efficiency. From this perspective, it thus appears not to matter whether universal coverage is necessarily achieved via one or the other method. Still, a prominent result from this analysis is also that systems based on prepayment – whether through social health insurance, tax-based financing or mixes of the two – is preferable to systems that rely too heavily on out-of-pocket payments. Of course, countries will need to make choices. Some countries will develop forms of tax-funded systems; others will turn to various types of social health insurance systems or to mixtures of both. Their economic and political characteristics as well as their organizational background and experience will in the end determine in which way they will transit to reach universal coverage.

Insurance-based approaches Implicit versus explicit health insurance Part III examines insurance-based approaches to financing health care. Health insurance can be understood in its ‘explicit’ or its ‘implicit’ version.15 Implicit health insurance is usually associated with tax-based funding of universal coverage. In this case, households do pay contributions as well, but in an indirect way, or implicitly, via their tax payments that are partly allocated to health care services. In this book, however, we will focus on explicit health insurance systems. The latter are systems whereby economic actors such as households and enterprises and government explicitly pay for health insurance contributions. Social health insurance is an important case of explicit health insurance: households and enterprises generally contribute as a percentage of wages or via health insurance premiums, while governments often contribute via subsidies as well. Community-based health insurance also belongs to the family of explicit health insurance schemes: individuals or households contribute in a voluntary way, most of the time via flat amounts according to a premium schedule. The emphasis in this Part on health insurance systems does by no means reflect a bias in favour of such systems. Indeed, the preceding analysis in

15. WHO (2000).

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chapter 5 showed no clear differences in the performance of explicit versus implicit health insurance systems. Rather most of the chapters presented are part of the technical follow-up of specific demands from a number of countries to the WHO. These demands generally concerned a strong wish to be better informed about health insurance, to support its implementation or to assist in a health financing reform with a major role for health insurance. Social health insurance and community-based health insurance In the policy analysis and advisory work offered to countries, an important distinction was made between ‘social’ and ‘community-based’ health insurance. Social health insurance (SHI) is a very forceful method to guarantee access to health services to all of the population, with the possibility as well of introducing equity rules such as defining contributions according to one’s economic capacity. Whereas it is a conceptually attractive, most developing countries have not introduced it on a large scale. The reasons for this are similar to those given above for the difficulty in establishing universal coverage: problems to arrive at a nation-wide consensus about the economic and solidarity implications of SHI, and lack of sufficient organizational capacity to set up a nationwide insurance scheme. In addition there might be uncertainty about the ability to effectively supply the health services that would be part of a benefit package, due to shortages in physical infrastructure, human resources and pharmaceuticals. While these factors impede SHI on a national scale, one observes the launching of community-based health insurance schemes to protect particular groups in the population. Community-based health insurance (CBHI) is used here as a common denominator for various types of mutual and non-profit health organizations in urban and rural areas, micro-insurance, cooperative health schemes and rural health insurance. Chapter 7 addresses whether they could be attributed a particular role in the development of universal coverage and be complementary, at least in the short run, to national initiatives. These CBHI schemes have several potential advantages indeed: they usually target a contained population group and a relatively small group of participating providers. Hence, because of their smaller scale, they might be organized and managed more easily. Behaviour of the stakeholders involved may also be monitored more efficiently. For instance, greater control may be exercised over non-compliance with membership contributions, moral hazard on the part of patients and supplier-induced prescribing. The vast challenge though of CBHI schemes is to reach ever greater numbers of population, including the poor that are not necessarily covered by them. One scenario might be to systematically include the CBHI schemes in a government’s 9

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universal coverage policy, with government supporting the CBHI schemes in geographical areas where it is currently too weak to insure the population. This support may also well have to include co-financing of the CBHI scheme’s operations, in particular subsidizing or paying the contributions of the poorest that would otherwise not have the opportunity to join a CBHI scheme. The practice of community-based health insurance CBHI may thus have a role in tracing the path towards universal coverage, yet at the same time a reasonable performance would be expected. To better understand CBHI’s achievements and further challenges, an important part of the international empirical evidence is analysed in chapters 8 and 9. A set of performance indicators of the three health financing sub-functions is thereby proposed. In order to evaluate revenue collection, we retain the following indicators: enrolment vis-à-vis the target population (with particular attention to the inclusion of the poor), the ratio of prepaid contributions to health care costs and the degree of financial protection against the cost of illness. While enrolment in a CBHI is basically on a voluntary basis, the percentage of the population covered is an indicator of attractiveness and therefore of the performance of the scheme. For equity reasons, the distribution of enrolment across all income categories, including the vulnerable groups and the poor, is an additional indicator to be considered. A notable result is that out of 144 schemes for which information was available, less than 10 reached an enrolment ratio of more than 50 %. It also appears difficult overall to include the poorest groups, as their low income is a sheer constraint to become a contributing member to a CBHI scheme. Apart from enrolment, collecting sufficient revenues by way of prepayment is equally important, as it is a means for households to avoid the burden of paying directly for treatment costs. The ratio of prepaid contributions to members’ health care costs is therefore a useful indicator of the degree of financial protection and access at the moment of need. The experience among documented CBHI schemes was found to be mixed, with a few selected ones reaching prepayment ratios above 75 %. Enrolment and level of prepayment need to be complemented with a further criterion, i.e. risk pooling across members of the CBHI scheme. Indeed, the voluntary character of a CBHI can easily lead to adverse selection,16 which

16. Voluntary insurance tends to attract a high proportion of ‘bad’ health risks rather than a mixture of good and bad health risks. The latter is referred to as adverse selection; see also chapters 7 and 14.

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obstructs the application of risk pooling. Another possible pitfall is that CBHI allow for different arrangements for different categories of people. Economically better-off groups, for example, might be offered a more advantageous benefit package and an attractive premium, justified by a lower health risk.17 These pitfalls run counter the principle of risk pooling that requires transfers between the lower-risk and higher-risk members, as well as between the better-off and the poorer members of the CBHI. A viable risk pool also depends on a sufficient level of membership. The latter concern is already taken into account, however, via the enrolment indicator. In any case, investigating the practice of risk pooling is helpful in order to have an additional check on the financial protection of those individuals and households who need health care. From an earlier ILO study18 47 CBHI schemes, out of 85 for which information on size of the pool was available, had less than 500 members. More than 10 000 members were observed in 14 schemes only. Expansion of the smaller schemes has thus proven to be a major challenge. Better information to the target population and building up the population’s trust in management appear to be factors facilitating such an expansion. Various methods exist to enhance risk pooling. One interesting method is that of a federation of CBHI schemes. The Rwandan health financing system is a case in point, where individual CBHI’s function at health centre level, while a federation at district level covers the costs of hospital based care.19 Related to the third sub-function of purchasing, the norm is for the CBHI scheme to search strategically for a cost-effective benefit package: namely seeking the best set of health services (the most appropriate benefit package) for its target population at the best price from selected providers. It is also imperative that the benefit package is adequate and includes those health care services that help households avoid catastrophic health spending. International experience on the practice of strategic purchasing shows that only a minority of schemes pay attention to the key elements of strategic purchasing, such as the definition of a cost-effective benefit package and the use of essential and generic drugs. Also, a few selected schemes only engage in contracting with selected providers and/or introduce gate-keeping at the health centre level before CBHI members can get access to covered hospital care. Hence it looks to be that achievements of CBHI schemes in each of the health financing sub-functions have in general been rather modest. This is not a reason, however, to turn away from CBHI as an organizational method with a possible place in the search for universal coverage. One marked proposal for 17. This is often referred to as ‘cream-skimming’; see also chapter 16. 18. Baeza et al. (2002). 19. Antunes and Saksena et al (2009, p. 84).

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improving CBHI schemes might be for the community to seek financial support from stakeholders aiming at collecting adequate revenues. These stakeholders could include central and local government, enterprises and donors who may also explore to engage in contracts20 with CBHI schemes. These contracts may define the subsidies to be transferred but also stipulate the performance criteria that would need to be satisfied. Financial support is especially helpful to CBHI schemes that decide to include the vulnerable groups and exempt them from paying the same contributions as those who are able to pay. Ghana is an example where CBHI schemes and other non-profit health insurance schemes (either privately or publicly operated), are part of the organization of its national health insurance scheme: an important characteristic is thereby that the Ghana Government steps in financially to pay for the premium of the exempted population.21 Rwanda is another notable country case where its Government transfers subsidies into its country’s nationwide established mutual health funds (MHF). Moreover the Global Fund to fight AIDS, Tuberculosis and Malaria has provided important co-funding for hospital services as well as for the enrolment of the poor and vulnerable population and of persons living with HIV/AIDS22 in the MHF. Concerning the enhancement of risk-pooling, several options are mentioned in chapter 10. Exploring the feasibility of mergers of CBHI schemes is one, as they might eventually bring about larger risk pools at the regional or even national level. A second option is reinsurance whereby essentially small CBHI, also referred to as micro-insurance schemes, insure themselves against the fluctuations in their members’ health expenditures. Reinsurance will in principle reduce or avoid financial insolvency in such small risk pools, due to sudden fluctuations or high health expenditure of some insured members. Reinsurance would thus facilitate the extension of the benefit package, especially towards high health care costs, thereby improving overall risk pooling. Thirdly, in some circumstances, however, one may have the opportunity to establish larger risk pools from the start, targeting at once tens of thousands of people in a district or region. Finally, managers of CBHI schemes should also be supported in identifying an appropriate benefit package with cost-effective health services and in possibly engaging in strategic purchasing via contracts with selected providers.

20. For a brief introduction to contracting in health systems, see Perrot et al. (2005). 21. Durairaj et al. (2009). 22. Musango et al. (2009).

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Scaling-up community-based health insurance Larger numbers might well be reached by CBHI schemes, but what about reaching universal health coverage? Eventually, mergers or federations of CBHI schemes with some form of risk sharing among the schemes23 seem to be especially favourable for the universal coverage process. However, this does not imply that one would necessarily have to converge to a single social health insurance fund. Universal coverage might well be realized in the end via a multiple health insurance fund structure, with some funds emanating from a transformation process of the original CBHI’s. One prime example is that of Japan where the CBHI schemes in the 19th century Japan (called ‘Jyorei’) grew into one of the pillars of the Japanese SHI system, called the National Citizen’s Health Insurance Fund (NCHI). This experience is analysed in detail in chapter 11. The Jyorei schemes were initially run at the village level, but in the early 1930s riskpooling increased as their management shifted to the prefectural or city level and became part of the NCHI. The NCHI was targeted at farmers, the self-employed and small companies, and membership became compulsory in 1948. In 2003, the NCHI is reported to cover 34 % of the population, the other part of the population being covered by employer-related health insurance schemes. A prominent experience is also that of the People’s Republic of China aiming at scaling up coverage of its rural population through the Rural Cooperative Medical Schemes (RCMS). At the start of these schemes, in the 1960s and 1970s, these were managed at the level of the village and basically covered all of its population. They were funded through collective ‘village welfare’ funds and farmer contributions and, while their development was heavily stimulated by the Government and the Communist Party. Hence, one can safely state they constituted a hybrid form of community health insurance. The market economic reforms in the early 1980s led to a policy switch, however, and brought the RCMS development to a halt. Indeed, the shift from a collective economy to a household production system broke down RCMS’ hitherto important collective funding. This sudden stop in the RCMS resulted in millions of rural population becoming uninsured. To address this lack of financial protection against health care expenses, the Government introduced an initiative in 1994 to ‘re-establish’ the RCMS; yet, membership would be kept on a voluntary basis. In order to regain experience, the Government also launched a pilot project in 14 counties of seven provinces, which is the subject matter of chapter 12. After more than a year of experimentation, population coverage above 80 % or even full population

23. This is frequently referred to as ‘risk equalization’; see chapter 16, section 2.2.2.

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coverage did not seem to be far off for most pilot schemes. RCMS membership was voluntary in principle, but strong stewardship and pressure of local governments contributed to this progress. However, in terms of financial protection, considerable improvement still proved to be necessary. The highest average effective reimbursement of health expenditure was at most 32 %. Logically, one major advice to increase financial protection was to increase the level of prepayment, with an increase in household contributions coming to mind immediately. Yet, this study also concluded that there is also a role for the Government to subsidize the RCMS schemes in order to increase the financial protection and thus render the health insurance benefit package more attractive to the insured. Experiences in rural health insurance continued all over China in the course of the nineties. A lot could be learned from a multitude of these experiences. In order to structure the lessons learned, a framework for the analysis of best practices in RCMS was designed. It is presented in chapter 13, and basically follows the way of thinking exposed earlier on the health financing subfunctions of revenue collection, pooling and purchasing. Within each of these sub-functions, practical performance indicators adapted to the Chinese context are proposed. A number of key policy variables decision-makers can use to enhance performance are also suggested. Finally, it is interesting to observe that after more than a decade of continued efforts on policy and implementation, the Chinese Government renewed its commitment in 2002 by declaring that the establishment of the ‘New Cooperative Medical Schemes’ (NCMS) would be the main strategy for financing rural health care. These build upon the earlier experience, but feature a contribution structure that now explicitly recognizes the financial role of Government: in 2008, a contribution per household of 20 Yuan was matched by both a local and central government subsidy of 80 Yuan each.24 Currently, this new method of multi-stakeholder funding in the NCMS has achieved a 90 % coverage in those areas where the NCMS are operating. On the feasibility of social health insurance in developing countries Being aware that CBHI is not a panacea, there is a legitimate concern about the feasibility of SHI, which is economically, organizationally and politically more complex. The issue of feasibility of SHI in developing countries is the subject of chapters 14 and 15. Several bottlenecks in implementing this principally compulsory form of health insurance may indeed arise at country level, as

24. Ke Xu et al (2009, p. 5); note that US$ 1 = 7.97 Yuan.

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already alluded to above. In the beginning of its development, it may be demanding to reach a large consensus among the population. In particular adequate pooling which implies financial solidarity between the various population groups as well as between administrative units (such as villages and districts) may not be easy to achieve rapidly. Secondly, the structure of the economy may impede a smooth development of SHI: if the so-called informal sector of the economy is dominating, there may be extra difficulties in assessing incomes and then collecting contributions. Thirdly, ensuring that the health service providers respond to the demands from the insured is indispensable. If the latter is not the case, confidence of the SHI-insured risks fading away. Last but not least, governments as regulators and stewards may not yet have the sufficient organizational capacity to fully design, manage and monitor a SHI scheme. In addition, a minimum of open political discussion in the country about universal coverage issues would be advantageous, in order to better assess the bottlenecks but also to identify the opportunities ahead. The above mentioned difficulties explain why most countries, that now have mature SHI systems, were obliged to take the necessary time to build these up. They also went for a policy that extended compulsory insurance in a step-bystep way, starting with specific groups of workers and/or employees, and then systematically enrolling the other population groups. For example Germany needed a multitude of decades to perfect its SHI system, since introducing its first social health insurance law in 1883. Belgium followed a similar path, with earlier health insurance laws in the late nineteen hundreds, to be followed by compulsory insurance for salaried workers in 1944 and extension to the remaining population groups in the period 1964-1969. The Republic of Korea had a remarkably fast development, however. This country introduced compulsory health insurance in 1977, and gradually achieved universality in merely 12 years. What would be the prospect for SHI in developing countries? If countries are interested in pursuing the SHI route, the gradual lessening of the abovementioned bottlenecks is obviously a natural way to reach a mature SHI system. A major drawback, however, is that much time may be needed. In addition, attention will have to be paid to the organization of the intermediate stages of coverage, in preparation of a fully-fledged universal coverage scheme. Thus countries may pursue various paths towards universal coverage, including through CBHI schemes that eventually could be integrated into a national SHI scheme. It is admitted that this gradual course is not very attractive to countries that want to reach universal coverage as fast as possible. Other countries may thus opt for different and faster routes. For example, after several decades of

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searching for appropriate policies, Thailand adopted its own brand of universal coverage in 2002, with three major health financing schemes: SHI-type coverage for the formal sector workers and employees and tax-based schemes for both civil servants and the remainder of the population.25 Measuring performance in the health financing sub-functions Still, no matter which organizational method is selected, monitoring the performance of the health financing sub-functions remains paramount. Indeed, any universal coverage policy needs to be checked for the status of its implementation and is real impact on the population. In chapter 16, we present a framework that features the relationships between key performance issues in revenue collection, pooling and purchasing, one the one hand, and the above mentioned health financing targets, on the other. Within this framework, the performance issues are associated with easily measurable performance indicators. Table 0.1 presents a number of selected indicators that are also used further in this ‘Overview and perspectives’. Table 0.1 Selected performance indicators relative to the health financing sub-functions Health financing sub-functions

Selected performance indicators

Revenue collection • Enrolment 폶 Overall enrolment of the population, in % 폶 Enrolment per population group, in % • Ratio of prepaid contributions to total costs of the SHI benefit package, in % Pooling

• Membership 폶 Is membership compulsory in all/some of the population groups? Yes/No • Pooling structure 폶 Is there a single pool? Yes/No 폶 In case of multiple pools, is there is a risk equalization scheme? Yes/No

Purchasing

• Is the SHI benefit package based on cost-effectiveness and equity criteria? Yes/No

Source: Based on Carrin and James (2005); see also chapter 16. 25. See Tangcharoensathien et al. (2005).

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This framework is applicable to standard as well as hybrid forms of SHI.26 It enables an evaluation of an initial SHI proposal at country level. It could also help to evaluate a country’s current state of SHI development, and therefore contribute to identify areas for improvement. Results of performance analyses will also demonstrate that all performance indicators will not necessarily improve at the same pace. A classic problem is the unequal pace of enrolling different population groups. For example, one often notices a rapid enrolment of formal sector workers and employees it being administratively easier to achieve, but which comes at the expense of important delays or uncertainty with respect to the enrolment of a possibly larger self-employed and informal sector population. Vietnam had to deal with the particular challenge of broad enrolment when it launched its Health Insurance Decree in 1992; see chapter 17. The Government chose, however, to focus first on compulsory health insurance among all salaried and retired workers in the public sector and salaried workers in the private sector. The other population groups, including the dependants of the insured, had the opportunity to enrol on a voluntary basis. Five years later, overall enrolment in the compulsory and voluntary schemes amounted to 13 % of the population. A number of targets concerning the three health financing sub-functions were formulated then: (i) continuing the coverage of the population, including the low-income and very poor in urban and rural areas; (ii) strengthening and pooling the health insurance contributions; (iii) defining the SHI benefit package so that is better adapted to meet people’s needs. There was also an appeal to the Government to further exercise solid stewardship. Namely, moving from a rather flexible ‘decree’, with its habitual weaknesses in terms of compliance and sanctions, to a universal health insurance ‘law’ proved to be essential. In response to these targets, social health insurance policy has been quite dynamic with a number of new decrees, spanning the period 19922003: these addressed voluntary health insurance, extension of compulsory health insurance to a group of dependents and health insurance for the poor. The Vietnam Social Security (VSS) agency was assigned the organization of compulsory and voluntary health insurance, whereas the Health Care Fund for the Poor (HCFP) was made responsible for the enrolment of the low-income population. Insurance coverage by both VSS and HCFP was targeted at more than 30 % of the total population in 2004-2005.27

26. A generalized framework comprising additional performance indicators has been used by Mathauer, Xu et al. (2009) in their analysis of the health financing system of the Republic of Korea. 27. Akal and Phuong (2005).

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The above mentioned performance indicator-framework was also applied to a proposed health financing reform in Kenya in 2003-2004. Government had made groundwork for the establishment of national social health insurance in Kenya, including a proposed Bill. In chapter 18, we assess the ‘expected’ performance of this earlier proposed scheme. A number of major results are the following. First, regarding revenue collection and the population coverageindicator, initial expectations were to enrol the entire population over 9-10 years. However, later on, and considering international experience, coverage levels were targeted at 60-80 % of the population instead. Still, fast inclusion of the poor population remained a priority, despite debates on the way to secure the necessary funding. Concerning the level of prepayment, the indicator ‘ratio of prepaid contributions to total costs of the SHI benefit package’, is expected to go up to 75 % compared to 44 % in 2002. Secondly, concerning pooling, a single fund (namely the National Social Health Insurance Fund-NSHIF) would be established thus avoiding fragmentation in risk pooling. Membership in the fund would be ultimately compulsory for all; in a transition period, enrolment would be voluntary for the self-employed and informal sector population, however. Thirdly, concerning purchasing, a major feature was that a comprehensive standard SHI benefit package of health services had been designed for each of the 5 health service levels in Kenya. In addition, administrative costs and reserves would not be permitted to exceed 8 % of total NSHIF expenditure. The latter rule would be a safeguard against insufficient spending on members’ health care services. Another important feature of the new NSHIF is that its financial feasibility would only be guaranteed with sizeable tax-funded government contributions. Serious issues of economic feasibility and political acceptability have turned up since the Bill was submitted to Parliament. In addition, various stakeholders, including the private Health Maintenance Organizations, have voiced serious concerns on a number of design features or the NSHIF proposal in general. However, the issue of improving health financing remains on the agenda: the Ministry of Public Health and Sanitation’s new Strategic Plan28 is reported to count on enhanced financial resources for health, while it also refers to the expected development of a national health insurance fund over the course of the Plan.

28. Ministry of Public Health and Sanitation (2008, p. 21).

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Resource allocation and cost-containment concerns in health financing policy Resource allocation and priority-setting In the final part of the book, resource allocation in the health care sector and priority setting is addressed in chapter 19. This topic is particularly related to the third health financing sub-function ‘purchasing’. Especially in developing countries that are affected by severe budget constraints for health, there is a special need to prioritize across health services and health interventions. Such priority setting will in fact be of immediate use in health financing systems. For example in a health insurance system, priority setting helps to define a benefit package of covered health services. It also assists those governments who directly fund and provide health services. Also in more privately oriented health systems, priority criteria help to guide regulation of the private health care market. Two types of criteria are advanced as strategic in priority setting: efficiency and equity. Efficiency means maximizing the health benefits of health services and health interventions to society from a given amount of resources. In practice the tool of cost-effectiveness analysis (CEA) will be used. CEA helps to search for a maximum of health benefits via the comparative analysis of, for example, life years gained associated with different sets of health services and health interventions. However, it is rare that societies would limit themselves to the efficiency criterion. There is also a usual concern for the equity aspect or for the distribution of the total health benefits across categories of patients and different population groups. It is posited that two equity criteria are especially appealing for policy-makers: (i) first, they may want to give a so-called preferential treatment to patients with severe health conditions as their need for health care is greater than for other patients; (ii) secondly, they may allocate resources in favour of the poor who generally are more in need of health services than the non-poor. By considering the equity criteria as well, policy-making will be regularly confronted with the so-called efficiency-equity trade-off. It is true that several cost-effective interventions (such as vaccinations and many child health interventions) may be equity-oriented from the start and especially benefit the vulnerable population. Yet, efficiency and equity may not always be attained at the same time. For example, certain health services or interventions for people with a deficient health status or for geographically dispersed population groups may not be very cost-effective. For equity reasons, however, resources may need to be drawn to these categories of the population as well, thereby sacrificing efficiency. Surely, policy-makers have to take decisions repeatedly 19

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concerning the efficiency-equity trade-off, but they often do so in an implicit or non-transparent way. From the point of view of enhancing democracy or giving voice to the population, it is better for society that the criteria for decisionmaking in the health care sector are made more explicit. Therefore a simple framework is proposed whereby, in a first step, the criteria of efficiency and equity are explicitly recognized via the definition of respective scores for the various health services and interventions. In a second step, these scores receive weights that are defined via a policy-decision making process ideally involving the various stakeholders in the health sector. Finally a total score will be associated with each of the health services or interventions that are considered, thus enabling an easy comparison among them. This simple and practical procedure can thus be of help in priority setting, thereby clarifying the possible trade-offs between efficiency and equity. Cost-containment in health care The critical issue of cost-containment in health care is associated especially with the purchasing and revenue collection health financing sub-functions. At the outset policy-tools for cost-containment appear to be less relevant for many low-income countries that still need to muster a minimum amount of resources, and for middle-income countries that are steadily building up the means towards a better resourced health system. Still, early on in the design of their health financing system, these countries are obliged to take decisions on policy-tools such as provider payment methods and patient charges themselves as well. Hence, it is worthwhile for these countries to be informed about the anticipated cost impact of these tools even in a high-income setting. Chapter 20 thus assesses the potential for cost-containment of: (i) a number of provider payment modes, including fee-for-service, per diem payment, case payment, capitation, salaries and budgets; (ii) tools that influence the demand behaviour of patients via patient or user charges and reference price systems for pharmaceuticals. The conclusions are useful to be borne in mind by policymakers. Related to provider payment procedures, salaries and budgets have a high potential for cost-containment, whereas case payment and capitation are associated with a medium potential. Fee-for-services and per diem payments do usually not have a cost-containment capability; on the contrary, they can even stimulate costs through supplier-induced demand for non-essential health services. Concerning measures directed at patients’ demand for health care, patient charges are most often not a successful method: patients’ sensitivity to such charges is usually modest, whereas they are associated with an adverse impact on equity in access to health care. Finally, we note the cost-containment potential of a reference price system, although its success depends on whether

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providers sufficiently adhere to prescribing of the pharmaceuticals that are part of such a system.

Final considerations The chapters that make up this book only show a part of the complexity of building up health financing systems aimed at universal coverage. Continued conceptual and empirical work will be needed on crucial issues such as exercising good stewardship, the raising of revenues to fund universal health services and spending the available resources as efficiently and equitably as possible. At least as vital is the establishment of new organizations or the adaptation of existing ones that can support the move towards universal coverage; moreover competent management of these organizations is a critical requirement. Thus, vast work remains ahead for many in the field of health financing. Yet, based on the information and evidence presented here, some selected thoughts for future discussions, debates and study on health financing policy in developing countries are offered. First, in view of each country’s specific context with its various economic, organizational and political constraints, the design and legal enactment of the universal coverage policy itself may take time. The implications of such a policy for society’s groups, citizens and other stakeholders will need to be clarified. An overall concern thereby is delineating the degree of solidarity that will be broadly acceptable to all. The implementation of universal coverage is a second important challenge. Rapid implementation, subsequent to accepting the policy, certainly belongs to the possibilities. But the latter depends on the strong willingness of society to move ahead with universal coverage, to accept the necessary solidarity, to put into place an adapted organizational and institutional framework, and to promptly allocate the necessary financial resources. When these conditions are not fulfilled right away, countries may opt for a gradual implementation, however. For organizational reasons, countries may well confront an efficiencyequity trade-off during the transition period: some population groups might be effectively covered faster than others, with the risk of leaving other groups behind. This is far from attractive, and government policy-makers would need to compensate these groups as adequately as possible, including through subsidies and special budget allocations. Within the implementation of the universal coverage plan, the overall funding is a principal concern. Identification of the various types of domestic financial resources is one critical part of health financing. There is no need to be bound by classic instruments of funding, however. For example, in the case 21

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of SHI and CBHI, one can rightfully go beyond the classic contributions calculated on wages and household contributions via premiums, respectively. Central and local government would be obvious stakeholders that can be called upon to transfer subsidies into such schemes. In turn, those levels of government can finance such subsidies out of their general revenues and/or from the revenues of earmarked taxes. Government thus needs to plan for the necessary fiscal space. However, there is also the decision of allocating part of this fiscal space to health. Clearly, the share of government revenues that is finally assigned to health care has political aspects as well; political support in government and/or stewardship by top political leaders can thereby avoid low or modest shares for the health sector. External donors are also stakeholders from whom funding will continue to be necessary to strengthen the revenue base for those developing countries that are economically most in need.29 Other developing countries that intend to implement universal coverage policies may also call on external donors for short-term to long-term financial support. Health financing schemes, irrespective of their organizational set-up, can direct this aid, for example, to co-funding the health care costs of the vulnerable groups, specific purchases such as of medicines and vaccines and temporary support for health personnel (e.g. in terms of income, housing etc.). In order to facilitate such transactions, external donors and domestic governments and/or non-government institutions can engage in contracts that stipulate the rights and obligations on the side of the parties involved. Sustainability of the planned external funding will have to occupy an important part of the contract. An equally important issue is for the contract to recognize a country’s vision on health financing as well as the intended policy changes proposed by its policy-makers. Given the available revenues for the health financing system, devising appropriate methods for paying providers will remain an important challenge. Again they would need to be adapted to what is organizationally efficient and feasible, at the same time making the choice acceptable to the various stakeholders; in this case broad agreement from providers and their associations is a vital requirement. Fairly new provider payment methods, such as ‘payment-for-performance’30 whereby providers receive monetary incentives to raise performance, are being introduced. The challenge related to such innovations is to keep such methods and the resulting financial flows integrated

29. On this issue, see also the debate between Kirigia and Diarra-Nama (2008), Ooms and Van Damme (2008) and Masiye (2008). 30. For a recent evaluation of experiences in sub-Saharan Africa, see Toonen et al. (2009).

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in the health financing system, rather than them leading to a separate funding system. It is admitted that fulfilling all prerequisites and responding promptly to all challenges may be too much of a tall order. A transition to a mature universal coverage system may therefore take time, whatever the type of prepayment system. Thorough evaluations of the current state of health financing systems are important, as they will be able to assist in developing a health financing system adapted to a country’s constraints and preferences. A first type of evaluation concerns the strengths and bottlenecks in institutional design and organizations’ effectiveness in implementation and their current impact on health financing system performance. Subsequently, changes in institutional design and organization practice can be proposed to achieve the desired level of health system performance.31 The timing of the proposed changes can also be adapted to the speed of transition that the country intends to assume.32 A second type of evaluation that proves to be useful is the building of alternative future financial scenarios. From these scenarios policy-makers can select the most feasible ones, taking account of important constraints such as the costs of the health services benefit package, households’ economic capacity and willingness to contribute, fiscal space made available for health and external donors’ budgets for health. Various techniques can be used for financial forecasting. Among these, simulation modelling, using a simple though complete structure of the health financing system, while giving an important role to policy-makers to assess the financial implications of alternative levels of crucial policy-variables, belongs to the user-friendly techniques for financial forecasting.33 The final consideration may be quite sobering but is also promising. For implementation of universal coverage of health services, it tends to be difficult for countries to rely on particular blueprints. But this should not discourage countries, however. Each country can decide what is most workable in its own environment. Many countries have built up their own home-grown paths to

31. See Mathauer and Carrin (2010) for an analytical framework of the linkages between institutional design, organizational practice and performance in the health financing system. 32. For an application of this analytical framework, see Mathauer et al. (2010). 33. Simulation modeling has proved to be very useful in analyzing the financial feasibility of SHI reforms in several developing countries. SimIns, a health insurance model (Carrin and James, 2008) has been used to analyze health financing reform in such countries as Argentina (Cavagnero et al., 2010), Lesotho (Mathauer et al., 2007) and Yemen (Carrin et al., 2005).

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universal coverage, inviting other countries to also forge a path that is best for them. And, in addition, for these countries to remember that the overall gains from universal coverage for society and its citizens will be plentiful.

Acknowledgments I wanted to recognize especially the very helpful comments and suggestions by Chris James and Inke Mathauer on previous drafts of this introductory overview and perspectives.

References Akal A. and Nguyen Kim Phuong (2005). Vietnam. In Social Health InsuranceSelected case studies from Asia and the Pacific. Manila: WPRO and New Delhi: SEARO; March 2005: 325-361. Antunes A. and P. Saksena et al. (2009). Health financing systems review of Rwanda-options for universal coverage. Geneva: WHO and Kigali Republic of Rwanda: MOH; April 8th 2009. Baeza C., Montenegro F. and Nunez M. (2002). Extending social protection in health through community based health organizations. Evidence and Challenges. Geneva: ILO. Carrin G. and James C. (2005). Key performance indicators for the implementation of social health insurance. Applied Health Economics and Health Policy, vol. 4, no. 1, pp. 15-22; see also chapter 16 in this book. Carrin G., James C. and Evans D.B. (2005). Achieving universal coverage: developing the health financing system. Technical Briefs for Policy Makers no. 1. Geneva: WHO/EIP/HSF/PB/05.01. Carrin G., Doetinchem O. and Sabri B. (2005). Macro-financial projections of the projected National Health Insurance. Chapter 4 in Schwefel D., Holst J., Gericke C. et al., Towards a National Health Insurance System in Yemen. (Sana’a: WHO, World Bank and ILO). Carrin G. and James C. (2008). SimIns-Health Insurance Simulation Model, version 2.1. Software: Synxx Solutions GmbH with Ole Doetinchem. Geneva: WHO and Eschborn, Germany: Gesellschaft für Technische Zusammenarbeit (GTZ).

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Overview and perspectives

Cavagnero E., Carrin G. and Torres R. (2010). A national social health insurance plan for Argentina: simulating its financial feasibility. Geneva: WHO/HSS/HSF/ DP.E.10.4. Commission on Macroeconomics and Health (2001). Macroeconomics and health: investing in health for economic development. Geneva: WHO. Durairaj V., D’Almeida S. and Kirigia J. (2010). Ghana’s approach to health social protection, Background paper for the World Health Report 2010 no. 2. Geneva: WHO, Department of Health Systems Financing. Kirigia J. and Diarra-Nama A. (2008). Can countries of the WHO African Region wean themselves off donor funding for health? Bulletin of the World Health Organization, vol. 86, no. 11, pp. 889-892. Masye F. (2008). The role of aid in the long term. Bulletin of the World Health Organization, vol. 86. no. 11. p. 895. Mathauer I., Doetinchem O., Kirigia J. and Carrin G. (2007). Feasibility assessment and financial projection results for a Social Health Insurance Scheme in Lesotho. Geneva: WHO, Department of Health Systems Financing. Mathauer I., Ke Xu with Carrin G. and Evans D.B. (2009). An analysis of the health financing system of the Republic of Korea and options to strengthen health financing performance. Geneva: WHO, Department of Health Systems Financing. Mathauer I., Cavagnero E., Vivas G. and Carrin G. (2010). Health financing challenges and institutional options to move towards universal coverage in Nicaragua. Geneva: WHO/HSS/HSF/DP.E.10.2. Mathauer I. and Carrin G. (2010). The role of institutional design and organizational practice for health financing performance and universal coverage. Forthcoming in Health Policy. Ministry of Public Health and Sanitation (2008). Strategic Plan 2008-2012. Nairobi, Republic of Kenya. Musango L., Doetinchem O. and Carrin G. (2009). De la mutualisation du risque maladie à l’assurance maladie universelle-Expérience du Rwanda. Discussion Paper no. 1. Geneva: WHO/HSS/HSF/DP.F.09.1. North D.C. (1990). Institutions, institutional change and economic performance. Cambridge: Cambridge University Press. Ooms G. and Van Damme W. (2008). Impossible to “wean” when more aid is needed. Bulletin of the World Health Organization, vol. 86, no. 11, pp. 893-894.

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Perrot J., Carrin G. and Evans D.B. (2005). Application of contracting in health systems: key messages. Technical Briefs for Policy Makers no. 4. Geneva: WHO/ EIP/HSF/PB/05.04.E. Task Force on Innovative International Financing for Health Systems (2009). Constraints to Scaling up and Costs. Report of Working Group 1. Toonen J., Canavan A., Vergeer P. and Elovainio R. (2009). Performance-based financing for health – Lessons from sub-Saharan Africa. (Amsterdam: KIT Publishers) Viroj Tangcharoensathien, Phusit Prakongsai, Walaiporn Patcharanarumol. Chitpranee Vasavid and Kanjana Tisayaticom (2005). Thailand. In Ron A., Bayarsaikhan D. and Sein T. (eds). Social Health Insurance. Selected case studies from Asia and the Pacific. Manila: WPRO and New Delhi: SEARO; March 2005: 299-324. World Health Organization (2000). Health Systems: Improving performance. The World Health Report 2000. Geneva: WHO. World Health Organization (2006a). The Health of the People. The African Regional Health Report. Geneva: WHO Regional Office for Africa. World Health Organization (2006b). Working together for health. The World Health Report 2006 Geneva: WHO. Xu Ke, Evans D.B., Carrin G., Aguilar-Rivera A.M., Musgrove Ph. and Evans T. (2007). Protecting Households from catastrophic health spending. Health Affairs, vol. 26, no. 4, pp. 972-983. Xu Ke, Saksena P., Xie Zhe Huang Fu, Haichao Lei, Ningshan Chen and Carrin G. (2009). Health care financing in Rural China: New Rural Cooperative Medial Scheme. Technical Briefs for Policy Makers, no. 3. Geneva: WHO/HSS/HSF/PB/ 09.03.

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Part I Empirical facts on health expenditure

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Chapter 1 Basic patterns in national health expenditure1 Scope of the analysis We describe in this paper what WHO’s 191 Member States spend on health and how it is financed from out-of-pocket spending and prepayments, including social health insurance contributions, government ‘general revenue’, and voluntary and employment-related insurance. To analyse the adequacy of spending, and the distribution of financial burden among sources of finance and households, we used simple comparisons and linear regression analyses. Most of the analyses consider all the Member States, to maximize the number of observations, and cover a wide range of incomes. Some analyses were also conducted on a regional basis, the results of which are sometimes reported, but not shown in detail. The principal source of our data is the set of national health accounts estimates prepared by WHO, with revisions up to 31 May 2001. Because of subsequent revisions, the numbers do not always match those that have been published previously.2 The estimates refer to 1997, although they may be based on data for earlier years as well. We do not discuss the primary data sources or estimation methods here, since they have been described elsewhere.3 The quality of the information varies considerably among countries, so that initial estimates for 1997 were classified as follows: ‘complete data with high reliability’, ‘incomplete data with high-to-medium reliability’, or ‘incomplete data with low reliability’. Originally, there were only 15 countries in the last category. The classification has not been modified as improved data have been obtained, so the data for a country are at least as good as the categorization shown here. We do not expect that revisions to the data used here will significantly modify the patterns found.

1.

2. 3.

Co-authored with Philip Musgrove and Riadh Zeramdini. At the time of the publication of this article, they were Lead Economist, Health Nutrition and Population at the World Bank (Washington, USA), and Research Associate at the Department of Health Financing and Stewardship at the WHO (Geneva, Switzerland), respectively. Reprinted from Bulletin of the World Health Organization, 2002, vol. 8, no. 2, pp. 134-142. The World Health Report 2000 – Health systems: improving performance. Geneva: World Health Organization; 2000. Poullier JP, Hernandez P. Estimates of national health accounts (NHA) for 1997. Geneva: World Health Organization; 2000. WHO/EIP Discussion Paper No. 27.

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The three data categories are always distinguished in the graphical presentations which follow and in the statistical analyses. Table 1.1 shows WHO estimates (see web version of this chapter at URL: www.who.int/ bulletin), and Table 1.2 classifies countries according to WHO region and per capita income level, distinguished as follows: very low income (US$ 7000). Although WHO regions are further divided into strata according to estimated adult and child mortality levels,4 as indicated in Table 1.2, we did not analyse the data according to the strata because sometimes there were very few countries in a region/mortality cell. The analysis begins with total health spending relative to gross domestic product (GDP), as a function of GDP per capita (GDPC). To visualize relations to income, we took natural logarithms of all money amounts. Fig. 1.1 shows the share of total health expenditures in GDP as a percentage of GDP (THE%GDP), as a function of Ln (GDPC), over the income range 6-11 (ca. US$ 400-60 000). Fig. 1.2-1.4 refer to the same income range. All graphical, and most statistical, analyses refer to percentage shares, relative to total health expenditure, government revenues, or total public or central government expenditure. Comparisons to the need for health spending, however, require amounts in US$, so per capita levels of total health expenditure, out-of-pocket spending, and total public spending are compared to per capita income in purchasing power parity dollars (PPP$). Table 1.1 Countries grouped by WHO region, mortality stratum, and GDP per capita 5 WHO region5

Mortality stratum (Child/Adult)

PPP income class (GDP per capita) Very low (US$ 7000)

AFRO

Both high

Benin, Burkina Faso, Chad, Guinea-Bissau, Madagascar, Mali, Niger, Nigeria, Sierra Leone

Angola, Cameroon, Cape Verde, Comoros, Equatorial Guinea, Gambia, Ghana, Guinea, Mauritania, Sao Tome and Principe, Senegal, Togo

Algeria, Gabon, Liberia

Mauritius, Seychelles

4. 5.

Lozano R. Mortality regionalization in the world. Geneva: World Health Organization, 2000 (unpublished note). AFRO=WHO Regional Office for Africa; AMRO=WHO Regional Office for the Americas; EMRO=WHO Regional Office for the Eastern Mediterranean; EURO=WHO Regional Office for Europe; SEARO=WHO Regional Office for South-East Asia.

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Basic patterns in national health expenditure

WHO region5

AMRO

EMRO

Mortality stratum (Child/Adult)

PPP income class (GDP per capita) Very low (US$ 7000)

High/very high

Burundi, Congo, Democratic Republic of the Congo, Eritrea, Ethiopia, Kenya, Malawi, Mozambique, Rwanda, United Republic of Tanzania, Zambia

Central African Republic, Côte d’Ivoire, Lesotho, Uganda

Botswana, Namibia, Swaziland, Zimbabwe

South Africa

Both very low Both low

Cuba

Belize, Brazil, Colombia, Dominica, Dominican Republic, El Salvador, Grenada, Guyana, Honduras, Jamaica, Panama, Paraguay, St Lucia, St Vincent, Venezuela

Canada, USA Antigua and Barbuda, Argentina, Bahamas, Barbados, Chile, Costa Rica, Mexico, St Kitts and Nevis, Suriname, Trinidad and Tobago, Uruguay

Both high

Haïti

Bolivia, Ecuador, Guatemala, Nicaragua, Peru

Both low

Both high EURO

Both very low

Islamic Republic of Iran, Jordan, Lebanon, Syria, Tunisia,

Afghanistan, Somalia, Yemen

Djibouti, Pakistan, Sudan

Bahrain, Cyprus, Kuwait, Lybian Arab Jamahirya, Oman, Qatar, Saudi Arabia, United Arab Emirates

Egypt, Iraq, Morocco Croatia

Andorra, Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, Malta, Monaco, Netherlands, Norway, Poland, Portugal, San Marino, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom

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WHO region5

Mortality stratum (Child/Adult)

EURO

Both very low

Both low

PPP income class (GDP per capita) Very low (US$ 7000)

Croatia

Andorra, Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, Malta, Monaco, Netherlands, Norway, Poland, Portugal, San Marino, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom

Armenia, Azerbaijan, Tajikistan, Uzbekistan

Albania, Bulgaria, Georgia, Kyrgyzstan, Macedonia, Romania, Turkey, Turkmenistan, Yugoslavia

Moldova

Belarus, Kazakhstan, Latvia, Lithuania, Ukraine

Both low Both high

Middle (US$ 2200–7000)

Indonesia, Sri Lanka, Thailand Bhutan, Myanmar

Bangladesh, Democratic People’s Republic of Korea, India, Nepal

Maldives

Both very low

Both low

32

Estonia, Hungary, Russian Federation

Australia, Brunei Darussalam, Japan, New Zealand, Singapore Cambodia, Kiribati, Lao People’s Democratic Republic, Marshall Islands, Micronesia, Mongolia, Tuvalu, Vietnam

China, Cook Islands, Fiji, Nauru, Papua New Guinea, Philippines, Samoa, Solomon Islands, Tonga, Vanuatu

Malaysia, Niue, Palau, Republic of Korea

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What do countries spend on health? The %GDP rises from 2 % to 9 % as income increases (Fig. 1.1). Regression analysis shows that health spending is (slightly) a luxury good: the regression coefficient on income for all countries together is 0.0109, and 0.0137 for the set of 72 countries with high-quality national expenditure data. The complete regression statistics for all three country groups according to data quality, and for all 191 countries together, are shown in Table 1.3. In this and all other regressions, the absolute value of the coefficient is greater for the high-quality data, but the difference between the estimated coefficients for all countries and for the high-reliability group is never significant, and both coefficients always differ from zero. The fit of the regression line, adjusted for degrees of freedom, sometimes improves substantially when only the most reliable data are used. In summary, the inclusion of lower quality data introduces additional ‘noise’, but does not appreciably change the slope of any relation. A better comparison would be to use per capita income net of subsistence, rather than income without deduction for basic needs, but there is no common estimate of the concept. Many countries are so poor (28 have incomes under US$ 1000 per year; Table 1.2) that spending even 4 % of total income on health is equivalent to a high share of non-subsistence income, comparable to that in richer countries. The share of heath spending in total income varies greatly at all income levels: the standard deviation of the share is 0.014 for the very low income group, and 0.0198-0.021 for the three higher income groups. The health share of GDP ranges from 0 DUCi = 0 otherwise This equation was estimated as a logit regression, using a maximum likelihood method. A number of key characteristics of a country’s economy and society determine its willingness or ability to adopt universal coverage. First, there is the general level of resources available to the country. This defines the effective budget constraint faced, and we posit that a greater amount of resources available will be associated with an increased capacity to contribute to the financing of universal coverage, either via general taxation or through social health insurance contributions. Resource availability is measured by Gross Domestic Product (GDP) per capita in international dollars [GDPC], with the 115

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expectation being that a higher GDPC results in a higher probability of achieving universal coverage, ceteris paribus. Secondly, it is important to note that the structure of GDP – and not simply the level of GDP per capita – also matters. What is most relevant here is the size of the formal and informal sectors, because of the administrative difficulties in assessing incomes and taxes of workers who do not receive a formal salary. This may hamper provision of health protection for the informal segment of the population, especially in a SHI framework that relies significantly on household contributions. This second characteristic is measured by the value added by agriculture as a percentage of GDP [AGRIC], a measure that is posited to be inversely related to the taxable income base, as farmers and agricultural workers often do not receive a formal salary. Hence we expect that the higher the value of AGRIC, the less likely it is that the government can achieve universal coverage. In a similar vein, it is essential that the costs of administration are not prohibitively high. Such costs (which also include time and travel costs) depend, inter alia, on the quality of infrastructure and communications, and the density of the population being administered. From this, it follows that urban areas are likely to have lower administrative costs, and so we use the percentage of the total population that live in urban areas [URBAN] as our measure of the costs of administration. The importance of this structural characteristic, along with the value added by agriculture, was highlighted by Ensor (1999, p. 875). The level of administrative capacity, however, also depends on the country’s ability to administer. Providing a system of full coverage in health care requires a sufficiently skilled labour force. The secondary net enrolment ratio [SEC] is used as a proxy for this requirement, since an economy with a higher level of enrolment is more likely to have a labour force with sufficient skills in management and administration, ceteris paribus. The fifth characteristic identified here is the level of solidarity within a society. A system of full protection requires a significant amount of crosssubsidisation, both from rich to poor and from low risks to high risks. Therefore, the amount of cross-subsidisation undertaken reflects society’s concern for improving equity. On this basis, we use the Gini index [GINI] as our measure of solidarity, with a higher Gini value reflecting less cross-subsidisation and hence a higher level of implicit acceptance of income inequality. That is, there is less concern for improving equity, and so we posit that societies with higher income inequality are associated with a lower degree of solidarity. Hence we should expect a negative relationship between GINI and our latent variable. The importance of having “a sense of social solidarity” as a factor behind the goal of universal access was earlier noted by Musgrove (1996, p. 52). 116

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Determinants of achieving universal coverage of health care: an empirical analysis

Finally, another essential determinant of achieving universal coverage is the extent to which the population has voice in social policy making. The latter is measured via a political rights index (PRI). We hypothesise that in a country with less political rights, the population will have less trust in government, ceteris paribus. Without the population’s trust, it is more difficult for a government to introduce and implement policies that aim at expanding coverage. Further, if there is only limited political rights, the government is less likely to respond to population’s needs, including those related to better protection against health risks. The general theory of the size of government by Meltzer and Richard (1981) rationalizes this point, with increased suffrage forcing the government to account for the needs of a wider segment of the population. The data The data came from a number of sources, detailed in the Annex. Wherever possible, information was gathered from the year 1997. Otherwise, data as close to this year was used. Lack of data on a number of variables meant that only 52 of the 139 countries analysed above could be used for the logit analysis. But since the countries included had wide and unsystematic characteristics, this should not reduce the wider applicability of the results. For the variable GDPC, international dollars are used. This is based on WHO estimates of purchasing power parity that were also used to calculate, amongst others, public health expenditure in international dollars.9 Methodology of the regression analysis The general strategy is similar to previous work on the determinants of health care expenditure,10 in that we start from our most general – the unrestricted – equation, and gradually restrict the model by setting the least statistically significant variable coefficients (apart from the constant) to zero. Four different models are presented: a ‘general’ model, as described above, and three restricted models. The restricted models are: a ‘generous’ model, which is based on the Akaike information criterion (AIC); a ‘moderate’ model, where we constrain the next least significant coefficient to zero; and a ‘strict’ model, where only the coefficients which are statistically significant at least at the 95 % confidence level are included. Each of the restricted models allows us to increase the 9. See WHO (2000, Table 8). 10. See Gerdtham and Jonsson (2000) for a summary of the literature on the determinants of health care expenditure.

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sample size, achieved through including countries with data on the restricted model variables but not on the general model variables. This gave a simple check of the robustness of our results. Analysis of the pairwise correlations between the explanatory variables found no correlation above the ‘high’ value of 0.8. Of course, this is not an argument which can be used to conclude that multicollinearity does not exist, but it implies that the estimates should be reasonably stable. Regression results The results are presented in Table 4.1. All four models have high explanatory power, in terms of their ability to correctly predict whether a country has universal coverage or not (the percentage correct), and in their high McFadden R 2 ‘s, although less so for the ‘strict’ model. Further, the coefficients were of the expected sign for five out of the seven explanatory variables, with the two incorrect coefficients for AGRIC and URBAN being the least statistically significant and excluded from the ‘moderate’ and ‘strict’ models. In choosing between the four models, Harvey’s criteria (Harvey 1981, pp. 5are used as a guide. That is, a good model should be parsimonious, identifiable, have a high goodness of fit, be theoretically consistent, and have a good predictive power. From this, it was decided to select the ‘moderate’ model. This is as it is the most theoretically consistent, and, given its coherence, is most likely to have the best predictive power.12 On the other three criteria, all four models seem to score similarly (and well): they are all parsimonious in that only theoretically relevant variables are included, are all identifiable, and all score well on the goodness of fit criteria (apart from, perhaps, the ‘strict’ model).

7)11

The selected ‘moderate’ model indicated that four variables best explain the willingness or ability of a country to adopt full coverage: GDPC, SEC, GINI and PRI. The implied elasticities13 associated with these variables are strong: a 1 % increase in GDPC results in a 5 928 % increase in the probability of full protection; a 1 % increase in SEC results in a 5 248 % probability increase; and 11. See further the adapted and more detailed criteria in Harvey (1990, pp. 5-7); using these criteria as a guide gave the same choice of model. See also Gujarati (1999, ch. 13). 12. The predictive power of this ‘moderate’ model is further explored. 13. Elasticities of these variables’ coefficients were calculated by taking the average value of the relevant variable (i) from the sample, and multiplying this by its coefficient times one minus the observed probability of universal coverage: (i*(j*(1-probs). Note that the value for probs is the observed relative frequency of universal coverage systems in the sample. To calculate the effect of unit changes in PRI, changes in probability were calculated, with all other variables set to their average.

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a 1 % decrease in GINI results in a 4 624 % probability increase. For PRI, an improvement in political rights on the left-to-right scale from 7 to 6 increases the probability of full protection by 6 719 %; and an improvement from 3 to 2 results in an increase of 16 128 %. Note also that re-estimation of the logit regressions with GDP per capita in market exchange rates (not reported here) gave very similar results. These results suggest that the decision to adopt universal coverage is more than just the pragmatic economic choice of having the resources available, it is also a management decision based on having sufficiently skilled labour to utilize these resources effectively. Moreover, and importantly, the decision is a deliberate political choice based on society’s willingness to redistribute and society’s trust in government to achieve this. But such a choice is only made indirectly by the populace through the government, and is therefore also dependent on the government’s responsiveness to such demands of the population. The coefficient estimates of the ‘moderate’ model appeared unstable when the sample size was increased from 52 to 64, to include countries with data on the four selected variables but not on AGRIC and URBAN: there were marked differences in the strength of the coefficients, especially GDPC. However, examination of the residuals suggested that this was caused by inclusion of the outlier, the United States, which has a very high GDP per capita but does not have full population coverage. When the United States was omitted from the sample on the grounds that it has a very unique historical and social context,14 we get very stable coefficient estimates, which adds to the robustness of our results. This was also the case for the ‘strict’ model (the sample size increased to 82). Expansion of the sample size for the ‘generous’ model was limited to only one extra observation, with the United States still excluded, and unsurprisingly the coefficient estimates were stable here too. In summary, what we can infer from these results is that the transition to universal coverage is not an unobtainable mirage for countries that cannot maintain the high economic growth rates required to increase the available resources. A more educated work-force, reduced income inequality and improved political rights should facilitate the transition to universal coverage even in slower growing countries. In the next section, we discuss how much time is needed to reach universal coverage, as predicted by the model.

14. See, for instance, Fuchs (1986) for a discussion of the unique characteristics of the United States health system.

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Table 4.1 Regression results on the willingness or ability to adopt full population coverage Variable

Model 1: ‘General’ ␤ (Standard error)

CONSTANT

-15.49678 (11.92149)

GDPC

2.247062 (1.334248)

AGRIC URBAN SEC

-1.3072

-6.088401

0.5057

64.0 % (2.541746)

38.7 %

1.6841

1.8455

2.1019

1.049086

2.7081

96.4 % (0.387395)

99.3 %

2.147599

1.306047

93.5 % (0.621372) 1.4104

(0.057368)

82.9 %

0.112132)

84.2 %

0 1.89805

-0.011481

-0.2001

(0.057368)

15.9 %

0.198694

1.4552

(1.016152)

1.285331

80.9 % (6.652437)

0.158146

-1.469251

-0.9152

Z statistic Confidence level

80.6 % (11.66457) 90.8 % (1.163709)

-0.189335

n–k

-15.24808

Model 4: ‘Strict’

Z statistic (Standard Confidence error) level

1.3677

(0.111298) PRI

-1.2999

Model 3: ‘Moderate’

Z statistic (Standard Confidence error) level

0.156860

(0.136538) GINI

Model 2: ‘Generous’

Z statistic1 (Standard Confidence error) level

85.4 % (0.126240) -1.7012

-0187830

91.1 % (0.107999) -1.3459

-1.439525

85.2 % (1.008270)

0

0

0

0

0

0

0

0

0

1.5035

0.119052

1.4591

0

0

86.7 % (0.081593)

85.6 % -0.180230

-2.2337

92.7 % (0.080686)

97.5 %

-1.7392

-0.165581

91.8 % (0.092274) -1.4277

-1.7945

-0.947207

-1.3727

84.7 % (0.690022)

83.0 %

0

52-7

52-6

52-5

52-3

Pr (DUC=1)

0.326923

0.326923

0.326923

0.326923

McFadden R2

0.786925

0

(17/52) % Correct

0.9615 (50/52)

0.786316 0.9423 (49/52)

0.9615 (50/52)

0.750717 0.9423 (49/52)

0.9615 (50/52)

0.656169 0.9038 (47152)

0.9038 (47/52)

SSE/(n-k)

0.206448

0.206604

0.221911

0.26300

AIC

0.538549

0.500856

0.507391

0.549972

0.9038 (47/52)

Notes: % Correct= % Correct predictions; SSE=sum of squared residuals; AIC=Akaike information criterion.

Transition from incomplete to universal coverage: how much time? Model forecasts We use the ‘moderate’ model in Table 4.1 to analyze the probability of introducing a universal coverage system in the future. By means of forecasting the probabilities over the period 2001-2050, we derive the number of years it will take to reach a probability of 0.6, 0.8 and 1. This analysis is undertaken for four countries. The countries selected are Tunisia, Philippines, Vietnam and Zambia. From Table 4.1, one observes that the first three belong to the three subcategories of mixed SHI/GT systems; Zambia belongs to the GT category. Three different scenarios are explored: (i) a base-line scenario whereby it is assumed that countries’ GDP per capita grows as in the period 1990-1998; (ii) a high growth scenario whereby it is assumed that the growth rate of GDP per

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capita is 5 %; (iii) a medium growth scenario, with a growth rate of GDP per capita of 2.5 %, combined with a reduction in income equality and an increase in political rights and enrolment in secondary education.15 The second scenario is coherent with a strategy of growth-mediated security as defined by Drèze and Sen (1991). In the third scenario, it is assumed that the GINI coefficient drops by 1 % yearly from 2001 on; however, as soon as the GINI reaches 0.31, it keeps this particular value. Note that a GINI of 0.31 corresponds to the median value of the GINI coefficient in the sample of countries with universal coverage systems. An increase in political rights corresponds to a reduction in the value of PRI. We assume that PRI reduces by 1 every decade; of course, when PRI becomes the unit value, it retains this value which is associated with the maximum degree of political rights. Finally, we assume that the incremental enrolment in secondary education is 0.5 % per year. The results are presented in Table 4.2. Using the strictest criterion of a probability of 1, and assuming a high growth scenario, only Tunisia and the Philippines would adopt universal coverage in less than forty years time. The other countries would need close to or more than half a century to do so, irrespective of the scenario envisaged. The use of a probability of 0.8 as an acceptable threshold instead, reduces the number of years needed to reach universal coverage. The speed to universal coverage in Tunisia and the Philippines is again generally higher than in the other countries. Table 4.2 Number of years before adopting a universal coverage system, at different probability levels Countries Tunisia Philippines Probabilities and scenarios Probability = 0.6 • Baseline scenario 22 >50 • High growth scenario 11 11 • Medium growth with less 11 10 income inequality and more political rights

Vietnam

Zambia

33 41 45

>50 48 49

15. For the data used for the base year 2000, see the Simulation statistics in the Annex.

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Countries Tunisia Philippines Probabilities and scenarios Probability = 0.8 • Baseline scenario 26 >50 • High growth scenario 13 14 • Medium growth with less 14 11 income inequality and more political rights Probability = 1 • Baseline scenario >50 >50 • High growth scenario 29 35 • Medium growth with less 41 >50 income inequality and more political rights

Vietnam

Zambia

35 42 50

>50 49 >50

46 >50 >50

>50 >50 >50

In Tunisia it would take between 14 and 26 years to achieve universal coverage. The range in the Philippines is between 11 and more than 50 years. It is interesting to see that in the third scenario, the Philippines would reach universal coverage earlier than Tunisia. The latter is the result of differences in the forecasted value of the PR index, the Philippines achieving the maximum political freedom far more rapidly than Tunisia. When comparing Vietnam with Zambia, the former country would be somewhat quicker to move to universal coverage; one of the main reasons is that it performs better in the area of secondary education and has less income inequality. Finally, observe that lowering the threshold probability to 0.6 shows similar differences across countries, whereas transition periods to universal coverage become shorter. How plausible are the projections? From a strict historical perspective, the projections may be quite plausible. They may even look optimistic, when recalling the experiences in countries like Germany, where SHI was implemented, and Britain, where GT was implemented. Germany took basically a century to develop its system.16 Its first sickness-law was passed in 1883,17 covering about 10 % of the population from the start. The coverage rate proceeded to 35 % in 1914 and further to 88 %

16. For an insightful study on the emergence of health insurance programmes in selected European countries, amongst which is Germany, see Carroll and Johansson (2000). 17. For a detailed analysis of the social and economic history of the German health insurance system, from its start until the early eighties, see Zollner (1982).

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currently.18 Britain introduced a National Insurance Act addressing health care coverage in 1911, which was almost exclusively limited to outpatient care (Ogus 1982). However, extension of the scheme to dependants never materialised in the period 1911-1946. Thereafter, the National Health Service was established, introducing universal coverage financed via general taxation. Another historical reference point is the experience in the Republic of South Korea (ROK). In this country, the compulsory health insurance programme was introduced in 1977, and universal coverage was achieved after a mere 12 years, namely in 1989 (Moon 1998). A major explanatory factor for this very fast transition is that during the period 1977-1989, the ROK had benefited from an average annual exponential growth rate of GNP per capita of 13.3 %.19 Such a rapid growth led to job creation and increased household, enterprise and government revenues, which greatly facilitated the drive towards universal coverage. However, one should also not forget that the years 1977-1989 were preceded by a voluntary programme period between 1965 and 1977, subsequent to enacting the statutory Health Insurance Act in 1963. Hence, one may say it took 26 years to achieve universal population coverage since the inception of the 1963 Act. The results for Tunisia and the Philippines in the high and medium growth scenarios are close to the time that was needed in the ROK to achieve universal coverage. This is not that surprising, however, as Tunisia and the Philippines already have stronger health financing systems now than the ROK had in 1977. Already in 1995, 38 % of the total population in the Philippines was covered by the Medicare programme (Galvez Tan 1998, p. 243). And in 1997, 80.7 % of the qualified Tunisian working population and their dependants were eligible for health insurance (Ben Malek, 1999, Table 4.2, p. 10). In addition, nearly 10 % of the population benefits from tax-financed medical care. Note that in the ROK, only 8.6 % of the population were insured at the start of its compulsory health insurance programme, and 5.7 % were covered by a Medical Aid Programme for the poor. Our results also convey that Tunisia and the Philippines have stronger economies, which would help to achieve acceptable transition periods. Further, the projections for the Philippines also do not seem to be at odds with the declared objective of the country’s National Health Insurance Programme to seek full population coverage in a period of 15 years (Galvez Tan 1998, p. 246). 18. European Observatory on Health Care Systems (2000, p. 10 and p. 39). Note that 9 % of the population is insured privately, 2 % receive free governmental health care and 0.1 % are not insured. 19. GNP per capita in US$ (at market exchange rates) was 1 012 and 4 994 in 1977 and 1989, respectively.

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The projections for Vietnam and Zambia imply much longer periods of transiting to universal coverage, although the former country has a faster pace. The latter is also understandable, in view of the fact that Vietnam passed a national Health Insurance Decree in 1992, and that by 1997, 13 % of the population had come to be covered by health insurance (Ron et al. 1998). In Zambia, equity of access to health care is a major health reform goal. Efforts are undertaken to translate this goal into practical policies (Soeters 1997, p. 53), including a proposal to extend prepayment to the informal sector (Lambo 1999, p. 14). A thorny issue, however, is how to prevent the transition from taking several decades before reaching full population coverage. The question whether there are ways to speed up the process of expanding coverage was already posed by Mills (1998). From the results of our econometric model, it is our hypothesis now that low-income countries such as Vietnam and Zambia do not necessarily have to repeat the long histories of quite a number of countries that are currently developed. While the study of the major options merits a separate treatment, we already submit that the major challenge in many developing countries will be to include the rural and informal sector population in a universal coverage plan. Provided governments are determined, and build upon the population’s trust, achieving universal coverage could well take less time than what is predicted by historical experience. While our econometric results showed that the level of economic resources is one of the determinants, it should not be seen as the supreme requirement. Other factors play an equally important role. Such a view is related to what Drèze and Sen (1991) call a strategy of support-led security. Concluding remarks In this chapter, we focused on the probability of countries to opt for universal coverage. What is equally important, however, is to gain more knowledge about the determinants of transiting from one phase in the drive to universal coverage to another. While this could be researched via latent variable techniques as well, a complementary method may include the analysis of the determinants of the number of years between different phases. In addition, a pooled crosssection time series analysis may be envisaged. While more insight at the macro level is needed about the speed between transition phases, much applied action-research is also needed at the country level to design and test suitable policies. In this respect, Sanguan (1998) suggests that there may be models for achieving universal coverage which are more appropriate for developing countries. In any case, an important question is to what extent governments can initiate the establishment of the necessary mechanisms to secure (co-) financing of covered health services and to extend 124

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population coverage. Some of the mechanisms may well have to involve new partners. For instance, international donors might be solicited to co-finance the health insurance premiums of the most vulnerable population groups. Governments may also want to stimulate and regulate the establishment of non-profit health insurance schemes as a vehicle for reaching as yet noncovered population groups (Carrin, Desmet and Basaza, 2001). Still, as our analysis has shown, such policies involving additional financial resources and new institutions will be all the more effective in an environment that allows the population to express its preferences, that shows a sufficient degree of solidarity and that develops administrative capacity.

References Barr, N. 1992. “Economic Theory and the Welfare State: A Survey and Interpretation”, Journal o f Economic Lite rature 30, no. 2, 0, 741-803. Ben Malek, M. 1999. “Social security medical care: the national experience of Tunisia”, Thirteenth African Regional Conference, Accra, Ghana, 6-9 July. Burgess, R. and Stern, N. 1991. “Social Security in Developing Countries: What, Why, Who and How?”, chapter 2 in Social Security in Developing Countries, Ahmad, E., Drèze, J., Hills, J. and Sen, A. (eds.), Oxford, Clarendon Press. Carrin, G., Desmet, M. and Basaza, R. 2001. “Social health insurance development in low-income developing countries: new roles for Government and Non-profit health insurance organisations”, chapter 10 in Building social security: the challenge of privatisation, Scheil-Adlung X. (ed.), New Brunswick and London, Transaction Publishers; see also chapter 7 in this book. Carroll, E. and Johansson, P. 2000. Mutual benefit societies, institutional design, and the emergence of public sickness insurance program: A comparative analysis, Stockholm: Swedish Institute for Social Research. Drèze, J. and Sen, A. 1991. “Public Action for Social Security: Foundations and Strategy”, chapter 1 in Social Security in Developing Countries, Ahmad E., Drèze J., Hills J. and Sen A. (eds.), Oxford, Clarendon Press. Ensor, T. 1999. “Developing health insurance in transitional Asia”, Social Science and Medicine 48: 7, 871-79. European Observatory on Health Care Systems 2000, Health Care Systems in Transition: Germany, Copenhagen, WHO Regional Office for Europe. Freedom House, Country Ratings, 2000. http://w ww.freed omh ouse. org/ rati ngs/ Fuchs, V. 1986, The Health Economy, Cambridge Mass., Harvard University Press. 125

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Galvez Tan, J. 1998. “Achieving universal coverage for health care: Experiences from the Philippines”, chapter 9 in Achieving Universal Coverage of Health Care, Sanguan Nityarumphong and Anne Mills (eds.), Nontaburi, Thailand: Office of Health Care Reform, Ministry of Public Health. Gerdtham, U.-G. and Jönsson, B. 2000. “International comparisons of health expenditure: theory, data and econometric analysis”, chapter 1 in Handbook of Health Economics, Culyer and Newhouse (eds.) Gujarati, D. 1999. Essentials of Econometrics (2nd edition), Boston: Irwin/ McGraw-Hill, United States. Harvey, A.C. 1981. The Econometric Analysis of Time Series, Oxford: Philip Allan. Harvey, A.C. 1990, The Econometric Analysis of Time Series (2nd ed.), Oxford: Philip Allan. ILO 1997. Social Health Insurance, Geneva: ILO and ISSA. Lambo, E. 1999. “Social security medical care: overview of current status of implementation and options for extending coverage”, Thirteenth African Regional Conference, Accra, Ghana, 6-9 July 1999. Maddala G.S. 1988. Introduction to Econometrics, MacMillan Publishing Company. Meltzer A. and Richard S. 1981. “A rational theory of the size of government”, Journal of Political Economy 89:5, 914-27 Mills A. 1998. “The Route to Universal Coverage”, chapter 11 in Achieving Universal Coverage of Health Care, Sanguan Nityarumphong and Anne Mills (eds.), Nontaburi, Thailand, Office of Health Care Reform, Ministry of Public Health. Musgrove, P. 1996. “Public and Private Roles in Health-Theory and Financing Patterns”, Washington D.C., World Bank Discussion Paper no. 339. Moon Ok Ruyen 1998. “The Korean Health Insurance Progamme”, chapter 8 in Achieving Universal Coverage of Health Care, Sanguan Nityarumphong and Anne Mills (eds.), Nontaburi, Thailand, Office of Health Care Reform, Ministry of Public Health. Nichols, L.M., Prescott, N. and Kai Hong Phua 1997. “Medical Savings Accounts for Developing Countries”, in Innovations in Health Care Financing, G.J.Schieber (ed.), Washington D.C. World Bank Discussion Paper no. 365.

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Normand, C. and Weber, A. 1994. Social Health Insurance. A Guidebook for Planning, Geneva: WHO and ILO. Ogus, A.I. 1982. “Great Britain”, in The evolution of social insurance 1981-1991, Köhler P.A. and Zacher H.F. (eds.), NY: St.Martins’s Press. Preker, A.S. 1998. “The introduction of universal access to health care in the OECD: Lessons for Developing Countries”, chapter 3 in Achieving U niversal Coverage of Health Care, Sanguan Nityarumphong and Anne Mills (eds.), Nontaburi, Thailand, Office of Health Care Reform, Ministry of Public Health. Ron, A., Carrin, G. and Tran Van Tien 1998. “Vietnam. The development of national health insurance”, International Social Security Review 51: 3, 89-103; see also chapter 17 in this book. Sanguan Nitayarumphong 1998. “Universal coverage of health care: Challenges for the Developing Countries”, chapter 1 in Achieving Universal Coverage of Health Care, Sanguan Nityarumphong and Anne Mills (eds.), Nontaburi, Thailand, Office of Health Care Reform, Ministry of Public Health. Social Security Administration 1999. Social Security Programs Throughout the World-1999, Washington: U.S. Government Printing Office. Soeters, R. 1997. Rapid Assessment of Health Reforms in Africa. The case of Zambia, Amsterdam: Faculty of Medicine, University of Amsterdam. Ph.D thesis. UNDP 1999. Human Development Report, NY: Oxford University Press. WHO 2000. “Health Systems: Improving Performance”, The World Health Report 2000, Geneva: WHO. World Bank 1999, World Development Report, NY, Oxford University Press. World Bank 2000. World Development Report, NY: Oxford University Press. Zöllner, H. 1982. “Germany”, in Köhler P.A. and Zacher H.F. (eds.), The evolution of social insurance 1981-1991, NY: St. Martins’s Press.

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Annex Data Sources Variable DUC GDPC AGRIC URBAN SEC GINI PRI

Description Dummy variable equal to one when a country has universal coverage, zero otherwise; 1999 Gross Domestic Product (GDP) per capita in international dollars; 1997 Value added by agriculture as a % of GDP; 1997 Urban population as % of total; 1997 Secondary net enrolment ratio; 1997

Source Social Security Administration 1999 WHO (2000)

World Bank (1999), Table 12 UNDP (1999), Table 16 UNDP (1999), Table 10 World Bank (2000), Gini index; 1985-1996 Table 5 Political rights index, on a left to right, Freedom House one-to-seven scale; 1997 (2000)

Simulation Statistics

GDPC SEC GINI PRI Baseline growth estimate

128

Tunisia 4.601 74.3 40.2 6

Philippines 2.893 77.8 42.9 2

VietNam 1.355 55.1 35.7 7

Zambia 1.089 42.2 49.8 5

2.4

0.7

6.1

0.5

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Table A4.1 Classification of countries by health financing system20 21 Universal Coverage Social Health Insurance

Universal Coverage General Taxation

SHI – GT All employees and self-employed (with some exclusions) covered by SHI

SHI – GT All employees covered by SHI

SHI – GT Specific groups only covered by SHI

GT

Australia Austria Belgium Bulgaria Chile Colombia Costa Rica Croatia Czech Republic Estonia France Germany Greece Hungary Israel Japan Latvia Luxembourg Monaco Netherlands Norway Poland Rep of Korea Romania San Marino Slovakia Slovenia Spain Switzerland Yugoslavia

Albania Canada Cuba Denmark Finland Iceland Ireland Italy Kuwait Lithuania Malaysia New Zealand Oman Portugal Singapore Sweden United Kingdom Uzbekistan

Argentina Brazil Ecuador Equatorial Guinea Lebanon Nicaragua Peru Russian Tunisia Turkey Uruguay Venezuela

Algeria Andorra Bolivia Cape Verde Egypt El Salvador Gabon Guinea Honduras Libya21 Mali21 Malta Mexico Pakistan Panama Paraguay Philippines Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Senegal United States of America

Burkina Faso21 Cameroon China Congo Côte d’Ivoire21 Dominican Republic Guatemala India Indonesia Iran Iraq Kenya Madagascar21 Mauritania21 Myanmar Niger21 Thailand Trinidad and Tobago Vietnam Yemen

Afghanistan Antigua-Barbuda Armenia Bahamas Barbados Belarus Beliu Benin21 Botswana21 Burundi21 Central African Republic21 Chad21 Cyprus Democratic Republic of Congo21 Dominica Georgia Ghana21 Guyana Jamacia Kyrgyzstan Malawi Mauritius Morocco Nigeria Papua New Guinea Qatar Rwanda Sao Tome and Principe Sierra Leone21 Sri Lanka Syrian Arab Republic Togo21 Turkmenistan Uganda Ukraine Zambia Zimbabwe

20. SHI and GT refer to “social health insurance” and “general taxation”, respectively. 21. Refers to high reliance on employer based schemes.

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Table A4.2 Classification of countries by health financing system23 and by income24 group 2223 24 Universal Coverage Social Health Insurance

Universal Coverage Universal System

Low Income

SHI – GT All employees and selfemployed (with some exclusions) covered SHI

SHI – GT All employees covered by SHI

SHI – GT specific groups only covered by SHI

GT

Nicaragua

Guinea Honduras Mali Pakistan Senegal

Burkina Faso22 Cameroon China Congo Côte d’Ivoire22 India Indonesia Kenya Madagascar Mauritania22 Myanmar Niger22 Vietnam Yemen

Afghanistan Armenia Benin22 Burundi22 Central African Republic22 Chad22 Democratic Republic of Congo Ghana22 Kyrgyzystan Malawi Nigeria Rwanda Sao Tome and Principe Sierra Leone22 Togo22 Turkmenistan Uganda Zambia Zimbabwe

$ 760 or less

Lower-middle income $ 761 to $ 3030

Upper-middle income $ 3031 to $ 9630

Bulgaria Colombia Costa Rica Romania Yugoslavia

Albania Cuba Lithuania Uzbekistan

Ecuador Equatorial Guinea Peru Russia Tunisia

Algeria Cape Verde Egypt E Salvador Paraguay Philippines Saint Vincent and the Grenadines

Dominican Republic Guatemala Iran Iraq Thailand

Belarus Belize Dominica Georgia Guyana Jamaica Morocco Papua New Guinea Sri Lanka Syrian Arab Republic Ukraine

Chile Croatia Czech Republic Estonia Hungary Poland Republic of Korea Slovakia

Malaysia Oman

Argentina Brazil Lebanon Turkey Uruguay Venezuela

Gabon Libya Mexico Panama Saint Kitts and Nevis Saint Lucia

Trinidad and Tobago

AntiguaBarbuda Barbados Botswana22 Mauritius

22. Refers to high reliance on employer based schemes. 23. SHI and GT refer to “social health insurance” and “general taxation”, respectively. 24. Income groups are defined according to 1998 GNP per capita in US dollars; see World Bank (2000, pp. 290-291)

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Table A4.2 Classification of countries by health financing system23 and by income24 group (cont.)

High income $ 9361 or more

Universal Coverage Social Health Insurance

Universal Coverage Universal System

Australia Austria Belgium France Germany Greece Israel Japan Latvia Luxembourg Monaco Netherlands Norway San Marino Slovenia Spain Switzerland

Canada Denmark Finland Iceland Ireland Italy Kuwait New-Zealand Portugal Singapore Sweden United Kingdom

SHI-GT All employees and self-employed (with some exclusions) covered SHI

SHI-GT All employees covered by SHI

Andorra Malta Unites States of America

SHI-GT Specific groups only covered by SHI

GT

Bahamas Cyprus Qatar

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Table A4.3 Classification of countries by type of health financing system25 and by the share of public health expenditure in total health expenditure26 27

% of public expend iture in total health expenditure: 75 % to 100 %

% of public expenditure in total health expenditure: 50 % to < 75 %

% of public expenditure in total health expenditure: