The Biological Consequences of Socioeconomic Inequalities 9781610447935, 9780871548924

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The Biological Consequences of Socioeconomic Inequalities

The Biological Consequences of Socioeconomic Inequalities

Barbara Wolfe, William Evans, and Teresa E. Seeman, editors

Russell Sage Foundation New York

The Russell Sage Foundation The Russell Sage Foundation, one of the oldest of America’s general purpose foundations, was established in 1907 by Mrs. Margaret Olivia Sage for “the improvement of social and living conditions in the United States.” The Foundation seeks to fulfill this mandate by fostering the development and dissemination of knowledge about the country’s political, social, and economic problems. While the Foundation endeavors to assure the accuracy and objectivity of each book it publishes, the conclusions and interpretations in Russell Sage Foundation publications are those of the authors and not of the Foundation, its Trustees, or its staff. Publication by Russell Sage, therefore, does not imply Foundation endorsement. BOARD OF TRUSTEES Robert E. Denham, Esq., Chair Kenneth D. Brody W. Bowman Cutter III John A. Ferejohn Larry V. Hedges Lawrence F. Katz

Nicholas Lemann Sara S. McLanahan Nancy L. Rosenblum Claude M. Steele Shelley E. Taylor

Richard H. Thaler Eric Wanner Mary C. Waters

Library of Congress Cataloging-in-Publication Data The biological consequences of socioeconomic inequalities / Barbara Wolfe, William Evans, Teresa E. Seeman, editors.   pages cm   Includes bibliographical references and index.   ISBN 978-0-87154-892-4 (pbk. : alk. paper)  1.  Poverty—Social aspects.  I.  Wolfe, Barbara.   HC79.P6B5396 2012   362.1´042—dc23      2012032504 Copyright © 2012 by the Russell Sage Foundation. All rights reserved. Printed in the United States of America. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Reproduction by the United States Government in whole or in part is permitted for any purpose. The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences-Permanence of Paper for Printed Library Materials. ANSI Z39.48-1992. Text design by Genna Patacsil. RUSSELL SAGE FOUNDATION 112 East 64th Street, New York, New York 10065 10  9  8  7  6  5  4  3  2  1

Contents

List of Tables and Figures

vii

Contributors

xi

Preface Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

xiii

T he SES and Health Gradient: A Brief Review of the Literature William Evans, Barbara Wolfe, and Nancy Adler P romise of Biomarkers in Assessing and Predicting Health Arun S. Karlamangla, Tara L. Gruenewald, and Teresa E. Seeman  iological Imprints of Social Status: B Socioeconomic Gradients in Biological Markers of Disease Risk Tara L. Gruenewald, Teresa E. Seeman, Arun S. Karlamangla, Elliot Friedman, and William Evans  issecting Pathways for Socioeconomic D Gradients in Childhood Asthma Edith Chen, Hannah M.C. Schreier, and Meanne Chan Cardiovascular Consequences of Income Change David H. Rehkopf, William H. Dow, Tara L. Gruenewald, Arun S. Karlamangla, Catarina Kiefe, and Teresa E. Seeman

v

1

38

63

103

126

vi      Contents Chapter 6

Cognitive Neuroscience and Disparities in Socioeconomic Status Jamie Hanson and Daniel A. Hackman

Chapter 7

Brain Development and Poverty: A First Look Jamie Hanson, Nicole Hair, Amitabh Chandra, Ed Moss, Jay Bhattacharya, Seth D. Pollak, and Barbara Wolfe

Chapter 8

 eversing the Impact of Disparities in R Socioeconomic Status over the Life Course on Cognitive and Brain Aging Michelle C. Carlson, Christopher L. Seplaki, and Teresa E. Seeman

Chapter 9

158 187

215

Conclusions William Evans, Teresa E. Seeman, and Barbara Wolfe

248

Index

263

List of Tables and Figures

Table 1.1 Table 2.1 Table 2.2 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Figure P.1 Figure 1.1 Figure 1.2 Figure 1.3 Figure 1.4

Disparities in Health by Socioeconomic Status Major Physiological Systems and Corresponding Biomarkers Clinical High-Risk Criteria for Commonly Used Biomarkers Characteristics of CARDIA Participants Socioeconomic Indicators, 1992 to 1993 Log Income, 1992 to 2006 Random-Effect, Fixed-Effect, and Long-Difference Models Models Run with Alternative Categorizations of Outcome Variables, Odds Ratios Models Run with Alternative Categorizations of Income Exposure Variables Demographic Summary Attrition by Income Summary Statistics for Brain Regions of Interest Model Estimates for Association Between SES Measures and Brain Regions of Interest Interdisciplinary Schematic The Income-Health Relationship Marginal Effects on Income Dummy Variables, Children, Fair or Poor Health Marginal Effects on Income Dummy Variables, Children, School Absence Ten Days or Longer Marginal Effects on Income Dummy Variables, Children, Limitation on Activity

vii

16 41 51 136 138 144 148 151 152 193 194 194 207 xv 2 5 5 6

viii      List of Tables and Figures Figure 1.5 Figure 1.6 Figure 1.7 Figure 1.8 Figure 1.9 Figure 1.10 Figure 1.11 Figure 1.12 Figure 1.13 Figure 1.14 Figure 1.15 Figure 1.16 Figure 1.17 Figure 1.18 Figure 1.19 Figure 1.20 Figure 1.21 Figure 1.22 Figure 2.1 Figure 2.2 Figure 2.3 Figure 3.1 Figure 3.2

Marginal Effects on Income Dummy Variables, Children, Hospital Stay Marginal Effects on Income Dummy Variables, Children, Emergency Room Visit Marginal Effects on Income Dummy Variables, Children, Injury or Poisoning Marginal Effects on Income Dummy Variables, Children, Asthma Marginal Effects on Income Dummy Variables, Adults, Fair or Poor Health Marginal Effects on Income Dummy Variables, Adults, Mental Health Days Marginal Effects on Income Dummy Variables, Adults, Bad Physical Health Days Marginal Effects on Income Dummy Variables, Adults, Current Smoker Marginal Effects on Income Dummy Variables, Adults, Obese Marginal Effects on Income Dummy Variables, Adults, Overweight Marginal Effects on Income Dummy Variables, Adults, No Exercise Marginal Effects on Income Dummy Variables, Adults, Ages Eighteen to Seventy-Four, Limited Fruits and Vegetables Odds Ratio for Income Variables, Adults Marginal Effects of Household Income, Australian Adults, Fair or Poor Health Marginal Effects of Household Income, Australian Adults, Psychological Distress Risk Marginal Effects of Household Income, Australian Adults, Long-Term Health Condition Odds Ratio of General Physical Health Measures, Europe Odds Ratio of Self-Perceived Health, Europe Adjusted Seven-Year All-Cause Mortality Odds All-Cause Mortality Rate in Those Younger than Sixty-Five Years Old All-Cause Mortality Rate in Those Sixty-Five Years Old or Older Conceptual Model of SES and Health Links AUC Cortisol Area as Function of Quintile of SEP

6 7 7 8 10 10 11 11 12 12 13 13 15 17 17 18 18 19 50 52 53 65 70

List of Tables and Figures       ix Figure 3.3 Figure 3.4 Figure 3.5 Figure 3.6 Figure 3.7 Figure 3.8 Figure 3.9 Figure 3.10 Figure 3.11 Figure 3.12 Figure 4.1 Figure 4.2 Figure 5.1 Figure 5.2 Figure 5.3 Figure 5.4 Figure 5.5 Figure 5.6 Figure 6.1 Figure 6.2 Figure 7.1 Figure 7.2 Figure 7.3 Figure 7.4 Figure 7.5 Figure 7.6 Figure 7.7 Figure 7.8 Figure 7.9 Figure 7.10

Mean Overnight Norepinephrine Levels Marginal Effects, C-Reactive Protein Marginal Effects, Glycated Hemoglobin Levels Marginal Effects, Total Cholesterol Marginal Effects, High-Density Lipoprotein Marginal Effects, Waist-to-Hip Ratio Marginal Effects, Systolic Blood Pressure Marginal Effects, Diastolic Blood Pressure Marginal Effects, Resting Pulse Regression Coefficients, Allostatic Load SES and Inflammatory Responses Model: SES Effects and Clinical Health Outcomes Metabolic Syndrome Score versus BMI Income Change Between Exams, Men Income Change Between Exams, Women Income Change Between Exams, Men Without Marital Change Income Change Between Exams, Women Without Marital Change First Difference Nonlinear Models, 1992 to 2005 Four Basic Lobes of the Brain Five Neurocognitive Systems of Interest Axial Brain Slice Sagittal Brain Slice Association Between Hippocampal Volume and Family Income Association Between Superior Prefrontal Cortex Volume and Family Income Association Between Ventral Medial Prefrontal Volume and Family Income Association Between Cerebellar Gray Matter Volume and Family Income Association Between Total Cerebellar Volume and Family Income Association Between Occipital Gray Matter Volume and Family Income Ventral Medial Prefrontal Cortex Superior Prefrontal Cortex

73 84 84 85 85 86 86 87 87 88 111 117 137 140 141 142 143 146 160 162 195 196 197 198 199 201 202 203 205 206

Contributors

Barbara Wolfe  is professor of economics, population health sciences, and public affairs and faculty affiliate at the Institute for Research on Poverty at the University of Wisconsin–Madison. William Evans is Keough-Hesburgh Professor of Economics at the University of Notre Dame. Teresa E. Seeman is professor of medicine and epidemiology at the University of California, Los Angeles.

Nancy Adler  is Lisa and John Pritzker Professor of Psychology in the Departments of Psychiatry and Pediatrics at the University of California, San Francisco. Jay Bhattacharya is associate professor of medicine at the Stanford University School of Medicine. Michelle C. Carlson is associate professor of mental health and associate director of the Center on Aging and Health at Johns Hopkins Bloomberg School of Public Health. Meanne Chan  is Ph.D. candidate in psychology at Northwestern University. Amitabh Chandra is professor of public policy at the Harvard Kennedy School of Government.

xi

xii            Contributors Edith Chen is professor of psychology at the Institute for Policy Research at Northwestern University. William H. Dow is Henry J. Kaiser Professor of Health Economics at the University of California, Berkeley School of Public Health, and associate director of the Berkeley Population Center. Elliot Friedman is assistant professor of human development and family studies at Purdue University. Tara L. Gruenewald  is assistant professor of gerontology in the Davis School of Gerontology at the University of Southern California. Daniel A. Hackman is Ph.D. candidate in psychology at the University of Pennsylvania. Nicole Hair is Ph.D. candidate in economics at the University of Wisconsin–Madison. Jamie Hanson is Ph.D. candidate in psychology at the University of Wisconsin–Madison. Arun S. Karlamangla  is professor of medicine in the David Geffen School of Medicine at the University of California, Los Angeles. Catarina Kiefe is professor of quantitative health sciences at the University of Massachusetts Medical School. Ed Moss is director of the Developmental Neuropsychology Service in the Department of Pediatric Psychology of the Children’s Seashore House (Department of Pediatrics, University of Pennsylvania School of Medicine). Seth D. Pollak is College of Letters and Science Distinguished Professor of Psychology at the University of Wisconsin–Madison. David H. Rehkopf is assistant professor of medicine at the Stanford University School of Medicine. Hannah M. C. Schreier is Ph.D. candidate in psychology at the University of British Columbia. Christopher L. Seplaki is assistant professor of community and preventative medicine at the University of Rochester Medical Center.

Preface

In 2001, the Russell Sage Foundation and the Carnegie Corporation of New York began a research program designed to examine the implications of rising economic inequality in the United States. The initial program funded interdisciplinary working groups at a number of universities, representing the fields of economics, sociology, political science, education, public health, and public policy. In the early phases of the program, scholars were asked to consider three broad questions about social inequality. First, are those groups that have increasingly been left behind economically also those that have lost ground in other ways that limit their full participation in society? This activity produced detailed descriptive research documenting changes in social inequality across a wide spectrum of outcomes including the financial well-being of families, the quality and quantity of time parents spend with children, participation in the democratic process, the quality of education from preschool to college, interactions with the criminal justice system, and the quality of health care and health outcomes. A second group of papers examined the consequences of rising inequality within a particular domain and sought to consider whether rising social inequality has impacts that tend to work alongside economic inequality. The third question asked why social and economic disparities exist, why they have changed so dramatically over the past forty years, and whether any policy levers are available to potentially reduce these disparities. After this initial phase of research, the Russell Sage Foundation continued its work on social inequality by turning to in-depth examinations of key institutions in the United States. It is out of this second phase of the foundation’s initiative that the project discussed here emerged. The third question, about why inequality exists and may have grown in scope, may be the most difficult to answer. The question we explore here is one aspect of the question: the nature of the links between socioeconomic status (SES) and health. The literature is vast and the results to date are rather stark.

xiii

xiv       Preface Thousands of studies across a variety of disciplines have documented a gradient between greater SES and better health. This pattern holds across all ages and all countries for which such studies have been conducted; for virtually all measures of health such as mortality, morbidity, measures of general health, health habits, and functional limitations; and for a variety of measures of SES such as income, wealth, occupation, and education. Despite this extensive literature, scholars have only begun to scratch the surface on some of the important questions concerning mechanisms, causal relationships, and possible policy responses. Why do we observe a relationship between SES and health? What does higher income or greater wealth do for families that allows them to produce better health outcomes? Does greater income allow one to purchase health insurance, better health care, or better neighborhoods? Does low income or having a low-skill job produce more stress and hence the associated poor health consequences? Alternatively, do other explanations for this relationship exist? For example, might it be that low income is caused by poor health and not the other way around? Without answers to these important questions, it is hard to fashion appropriate policy responses to the question of whether income transfers reduce health disparities. The purpose of this book is to suggest a possible research path that can potentially provide answers to these questions. Specifically, we outline a research program surrounding the biology of disadvantage that attempts to get under the skin and quantify if and how material deprivation affects basic physiological processes. To date, much of the work that has attempted to answer these questions has been produced by scholars working within their field of study. Physicians, epidemiologists, psychologists, and public health officials have attempted to identify the antecedents of the gradient by examining possible links between factors such as stress and body physiology. By contrast, economists and other social scientists have studied the links between SES and a variety of outcomes such as health and how health influences labor market activity and SES. Although one of the primary lessons of economics is the gains from specialization, it was clear to us from the start that for research to begin to formulate answers to the difficult why questions, a truly interdisciplinary group open to the ideas of other disciplines would have to coalesce behind the question. In that spirit, to better understand the set of links between SES and health, we felt that it was imperative to unite the focus of biological studies that characterize stress and the environment to those of social scientists studying income health and wealth; in the process, we have brought together an interdisciplinary team that has truly worked together on joint projects. Our ability to pursue this interdisciplinary question was enhanced by a critical series of meetings in which we could come to better

Preface       xv Figure P.1 Interdisciplinary Schematic

Biological processes

Socioeconomic status

Health

Source: Authors’ compilation.

understand the various perspectives and approaches and begin to build relationships and mutual respect that could be the basis of the research as reported in the chapters that follow. The basic set of links we try to understand in this volume and a broad approximation of what different disciplines have examined is captured in figure P.1. Work from a variety of authors has established a clear link between SES and health. Unfortunately, as we outline in chapter 1, assigning a clear causal pathway is difficult. Envisioning how SES alters health is easy, but poor health alters earnings capacity, so the direction of causation can go either way. Likewise, the intervening variable we explore in detail in this book is how SES affects basic biological processes. Poor social standing may influence health indirectly by heightening stress, impairing brain growth, triggering asthma, and slowing cognitive processes. From a research standpoint, that causal pathways can potentially move in both directions complicates the exercise. SES may affect health, but poor health also reduces wealth by requiring out-of-pocket spending and reducing work. Likewise, poor health in one area may directly influence biological processes, which in turn reduces earnings capacity and affects SES status. This book has three main purposes. First, we hope to provide a resource for scholars interested in questions relating to SES and health who may not have expertise in either more socioeconomic dimensions or more biological dimensions. To that end, the first three chapters provide a guide to the current state of the literatures in the respective fields.

xvi       Preface The current literature in social sciences surrounding the SES-health gradient is outlined in chapter 1 by Barbara Wolfe, William Evans, and Nancy Adler. This literature is vast, and reviewing the entire field would require hundreds of pages. As a result, the authors focus on one particular relationship—between income and health—as a conical example. This focus is strategic. First, income helps illustrate the basic statistical relation between the two variables that is indicative of other measures of SES. Second, all of the possible basic correlations between SES and health signal both reverse causation and omitted variables bias, and it is quite easy to outline how these problems arise in the case of the income-health relationship. For example, poor health reduces work and hence income, so some of the relationship may simply signal reverse causation. Third, it is arguably easier to outline the pathways through which income influences health compared to other measures of SES such as occupation or education. Fourth, among the various measures of SES, if income causally affects health, it is easy to imagine various policy levers that can be employed to increase the income levels for those most at risk. Policies such as lowering tax rates, raising the minimum wage, increasing Earned Income Tax Credit payments, raising welfare payments, and providing subsidized day care are but a few legislative remedies that can be employed, with varying degrees of success, to directly raise the incomes of citizens. In contrast, it is less obvious how one can alter the assignment of workers to occupations or, unfortunately, how governments can raise the education levels of recipients. Complementing this chapter by social scientists, chapters 2 and 3 provide data from the medical sciences and outline how biomarkers are collected, what they measure, and what future health events they predict; outline some potential pathways through which SES status can potentially affect the physiological operation of the body; and provide some evidence of the SES gradients for these biomarkers. In chapter 2, Arun Karlamangla, Tara Gruenewald, and Teresa Seeman outline the promise of biomarkers in assessing and predicting health. They describe the major physiological systems, how they are measured by biomarkers, and the predictive capacity of particular tests for health endpoints such as disease incidence or death. They discuss the variation in quality of various biomarkers and the limitations associated with using particular values. The chapter ends with a discussion noting that more information can be obtained about the underlying health of an individual by aggregating results from multiple tests. In chapter 3, Tara Gruenewald, Teresa Seeman, Arun Karlamangla, Elliot Friedman, and William Evans outline the biological imprint of deprivation on health by examining the SES gradients in biomarkers of disease

Preface       xvii risk. They note that the premise underlying such efforts is that everyday experiences such as behaviors, stress, and cognitive-emotional processes lead to variation in biological functioning and subsequent disease risk. This chapter therefore serves as our transition from the measurement of biomarkers to how they can be used in the analysis of the SES gradient in health. The chapter begins by “getting under the skin” and outlining how variation in SES can potentially impact the physiology of body operation. Particular focus is paid to two regulatory systems: the hypothalamic-pituitary-adrenal (HPA) axis, which regulates many body processes such as energy consumption, and the sympathetic nervous system (SNS), which controls the body’s fight-or-flight response to stress. The authors outline how stress induced by low SES may alter biomarkers associated with these two systems and how these metrics are measured. The authors also outline the SES gradient in metabolic and immune system disorders as measured by biomarkers, and like the previous chapter, the authors outline how combining information about multiple systems provides more information than would any single marker. To close the chapter, the authors provide data on the income gradient for eight of the most frequently used biomarkers (C-reactive protein, glycated hemoglobin, diastolic and systolic blood pressure, total cholesterol and high-density lipoproteins [HDL], hip-to-waist ratio, and resting pulse rate), plus a composite measure of health referred to as allostatic load that is derived from these eight measures. The second purpose of this book is to illustrate how teams from the social and biological sciences can combine the benefits of biomarkers with the methods used by social scientists to isolate causal relationships in nonexperimental settings. These strategies are illustrated in chapters 4 through 8 of this book. This section begins with a chapter by Edith Chen, Hanna M. C. Schreier, and Meanne Chan on the link between SES and asthma. Asthma is the most common chronic condition in childhood, affecting 12.5 percent of children during their lifetimes. Asthma is also steeply correlated with SES. When deciphering why this relationship exists, the authors advocate the importance of understanding the pathophysiology of the disease. Using this approach, the authors illustrate how SES can be linked to the asthma inflammatory process at the cell level. Initially, the authors describe in detail the process that generates the inflammation necessary to produce an asthma attack. Next, they explore at the cell level whether the biological markers necessary for asthma attacks are more prevalent among low-SES families. For example, as the chapter outlines, one of the steps in the biology of asthma attacks is the activation of cells called eosinophils, which bring on edema, muscle constriction, and mucus production in the airways. The authors illustrate that these cells are more

xviii       Preface prevalent in low-SES than in higher-SES children. Work by the authors is some of the first to establish a link between SES and disease-specific biological markers in patient populations. The authors then report on a series of studies that illustrate how stress at the individual, family, and neighborhood levels can affect biological markers at the cell level, increasing the incidence of asthma. In chapter 5, using data from a variety of sources, David Rehkopf, William Dow, Tara Gruenewald, Arun Karlamangla, Catarina Kiefe, and Teresa Seeman examine the impact of income changes on metabolic syndrome. A primary concern among public health officials is the rising obesity epidemic, and, as many authors have established, obesity rates are much higher in lower-income groups. A key question is whether low income is a contributing factor to the crisis or whether there is something endemic about certain groups that manifests as both low income and greater rates of obesity. To get closer to identifying whether this represents a causal relationship, the authors exploit longitudinal data from CARDIA—the Coronary Artery Risk Development in Young Adults study—a dataset of roughly 5,100 young adults as they aged over a twenty-year period. The key outcome is a composite measure that identifies whether a respondent had three or more risky levels of biomarkers associated with metabolic syndrome: hip-to-waist ratios, body mass index, triglycerides, HDL, blood pressure, or blood glucose levels. The authors begin the chapter by reproducing the basic cross-sectional results showing higher than average rates of metabolic syndrome among the low-income respondents. The panel nature of the data allows one to hold constant the time-invariant characteristics that would typically contaminate a cross-sectional model and instead examine whether metabolic syndrome changes over time for an individual as his or her income varies. Chapter 4 showed an important role for income in childhood asthma. By contrast, the authors in chapter 5 find little if any role for time-series changes in individual-level income explaining the changes in metabolic syndrome over time. Chapter 6 expands the use of biomarkers in a new and exciting way by considering cognitive neuroscience and SES disparities. The chapter, written by Jamie Hanson and Daniel Hackman, begins by outlining the basics of brain organization. The brain comprises two hemispheres, four lobes, and three substances. The chapter outlines the purposes and function of all these divisions. The authors then review how advances in neuroimaging have greatly facilitated the measurement of the volume of various components of the brain. Next, the authors outline how the brain develops during childhood and describe some ways that material deprivation during these years might manifest itself in brain development and what current evidence suggests about these hypotheses. This chapter thus serves

Preface       xix as a primer on the use of a new biomarker as well as a literature review to help future researchers understand the current landscape. In chapter 7, armed with these new tools, Jamie Hanson, Nicole Hair, Amitabh Chandra, Ed Moss, Jay Bhattacharya, Seth Pollak, and Barbara Wolfe use an exciting new longitudinal dataset to provide some preliminary evidence on the impact of poverty on brain development. The data for this project are taken from the National Institutes of Health MRI (magnetic resonance imaging) study of normal brain development. This public access database contains a large sample of MRI brain scans for a sample of children age four to eighteen. The data are longitudinal in nature in that children are scanned every two years for up to seven years. Exploiting the longitudinal nature of the data, the authors find evidence that material deprivation is associated with smaller sizes of the hippocampus, prefrontal cortex, and cerebellum, areas of the brain that have previously been associated with the quality of environmental inputs and stress. Chapter 8, by Michelle Carlson, Christopher Seplaki, and Teresa Seeman, extends the focus from chapter 7 into later life by examining the role of SES on risks for cognitive decline among older adults. The chapter begins by summarizing the literature about how early-life and environmental conditions potentially affect cognitive decline and brain functioning later in life. The authors note the potential role of cognitive reserve in explaining the role of SES in later life brain function. According to this hypothesis, the greater the store of cognitive ability generated in reserve by environment and life experiences, the greater the insult necessary to negatively impact cognitive ability later in life. Despite large disparities in cognitive function among the elderly based on early-life experiences, the authors note that the areas affected most by early-life events, the pre-­ frontal-limbic circuits, remain plastic and responsive to the environment in late-life development. The chapter moves past the question of whether SES alters health outcomes but considers whether disparities can be altered by tailored interventions. The authors report some exciting positive results from interventions designed to assist low-SES elderly in improving cognitive function through basic interventions such as increased physical activity and greater social engagement. Overall, this volume seeks both to provide necessary background for those who may be less familiar with socioeconomic and biological brain development and function and to illustrate, through selected examples, the ways in which incorporating these biological and neurocognitive processes allows for enhanced interdisciplinary work to elucidate the processes through which SES gets under the skin and leads to the consistent and widespread SES health disparities seen worldwide. The remaining goal of this project is to encourage joint research projects that will bring

xx       Preface the theory and methods of economists together with complementary knowledge and methods of biological scientists, along with available data and measures, to greatly improve research and understanding of the tie between income and income inequality and changes in health. Only time will tell whether we have been successful. This work could not have been done without the willingness of the Russell Sage Foundation to support a series of meetings where a mix of social and biological scientists could get to know one another’s work and perspectives. And it is unlikely to have taken place without the foundation’s earlier activities in the area of inequality. The earlier Russell Sage project on inequality was the direct stimulus to this project, and we thank both the president of the foundation, Eric Wanner, and the board for their initiatives in this area. We also thank the early participants in our project, whose encouragement and enthusiasm contributed to the interest of participants in this work. These include David Cutler, Stephanie Robert, and Thomas Boyce.

Chapter 1 The SES and Health Gradient: A Brief Review of the Literature William Evans, Barbara Wolfe, and Nancy Adler

Numerous studies have documented a positive gradient between socioeconomic status (SES) and health—the better off individuals are, the better their health. The positive relationship between good health and higher SES is generally accepted, but until we understand both the nature of the relationship and what explains the link, policy may be ineffective in substantially reducing disparities across groups. The graded association between various indicators of SES and health holds across all ages and for all countries in which it has been studied. The gradient emerges in relation to a range of health indicators, including mortality, morbidity, measures of general health, health habits, and functional limitations. These health indicators are associated with a range of alternative measures of SES, such as income, wealth, occupation, and education. These indicators of SES are in turn related to one another, but each has unique aspects. Each provides different material and social resources. In addition, they differ in terms of their potential role in serving as a cause of health and as an outcome of health status. For example, income may fluctuate as a result of poor health, while simultaneously poor health may be the result of financial constraints. In contrast, education is generally established relatively early in life and is less likely to be subject to changes in health status. Figure 1.1 illustrates the general shape of the relationship between income and health when compared across individuals or groups or countries. The horizontal axis measures income, the vertical axis measures a

1

2     Biological Consequences of Socioeconomic Inequalities Figure 1.1 The Income-Health Relationship Health (H) Hb* H = f(Y) Hb Ha* Ha

Ya

Ya + 100

Yb

Yb + 100

Income (Y)

Source: Authors’ figure.

positive health outcome such as life expectancy, and the curve represents the empirical relationship between the two variables. Although higher income is associated with better health at all levels, the steepest association is at the bottom of the income distribution. As a result, and as shown in the figure, the relative gain in a given health outcome as the result of adding $100 to a person’s income (Ya to Ya + 100 versus Yb to Yb + 100) is greater for those whose incomes are lowest. This graph clearly portrays that the marginal benefit of additional income declines as income rises. Adding an extra $100 to income at Ya improves outcomes Ha to Ha*, but that same $100 increment at Yb improves outcomes only marginally from Hb to Hb*. The income-health gradient portrayed in figure 1.1 is widely interpreted to indicate that income causally influences health. At the same time, poor health can reduce a person’s productivity and hence income and wealth. These two scenarios lead to the question of whether low income leads to poor health or whether poor health leads to low income. Given that both may be true, the more appropriate question is the extent to which income affects health and the extent to which health affects income.

The SES and Health Gradient    3 A third scenario is also possible: a correlation between SES and health may not simply represent the impact of a given aspect of SES on health or the impact of health on SES but also reflect an underlying common determinant of both health and SES. For example, factors such as motivation or genetics could account for the presence of both low income and poor health. To date, these alternatives remain as active hypotheses of what lies behind the income-health gradient. In this chapter, we attempt to set the groundwork for the volume by reviewing the existing evidence on the SES-health relationship. This includes discussions of the basic descriptive models that may enable us to better test the nature of the gradient, two of the more influential streams of empirical literature attempting to understand the gradient, and finally some assessment of which alternative approaches may allow us to make progress in increasing our understanding of the SES gradient in health.

Descriptive Evidence Literally thousands of papers document the SES-health gradient. These studies use different samples, outcomes, measures of SES, and statistical methods and cover very different periods. Rather than try to summarize this vast literature, we present a number of samples and similar models to document the persistence of the SES-health link and its changing nature over time. Although the gradient occurs in relation to health, illness, and mortality at every stage of life, the strength of the gradient varies at different ages. The gaps in health are greatest in mid- to late adulthood, when rates of disease begin to rise and more variation is linked to socioeconomic factors. The gap narrows after age sixty-five, possibly because of differential survival and the buffering effects of safety net programs, including Medicare, that are available starting at age sixty-five. Despite the somewhat weaker gradient in childhood, this period is important to examine for two reasons. First, the SES-health gradient for children is less susceptible to reverse causation concerns because it is less likely that poor health is “causing” low income.1 Second, although the magnitude of SES differences is greater in adulthood, previous work has provided evidence that the origin of the SES-health gradient among adults has its roots in childhood (Case and Paxson 2008; Singh-Manoux et al. 2004). To illustrate the breadth of the income gradient for children, we use data from the 2001 through 2003 National Health Interview Surveys (NHIS), an annual survey designed to measure the health status of the U.S. noninstitutionalized population. From the NHIS, we select a popula-

4     Biological Consequences of Socioeconomic Inequalities tion of school-age children, age six through seventeen, giving us 39,357 observations.2 We focus on seven measures of child health. All of the measures are characterized as dummy variables, in which the variable equals one if the child has the condition and zero otherwise, and all are constructed such that the realization of the outcome is a measure of poor health. These outcomes are whether the child has fair or poor health (on a 5-point scale) as reported by the adult in the house; has missed ten days or more of school in the past year due to injury or illness; has a physical, mental, or emotional condition that limits activity; had a hospital stay in the previous twelve months; had an emergency room visit in the previous twelve months; had an injury or poisoning in the past year; and has ever been diagnosed with asthma. For each outcome, we run a simple probit model controlling for a variety of characteristics.3 The key covariate in these models is a measure of family income, which is reported by an adult within the household. The variable is categorical, and we break it into six broad income categories ( 3, ≤ 4

> 4, ≤ 5

Poverty-to-Income Ratio versus Reference Group (>5) Source: Authors’ calculations based the Third National Health and Nutrition Examination Survey (NHANES III; Centers for Disease Control and Prevention n.d.). Note: Risky level of biomarker outcomes, NHANES III, adults age twenty to seventy-four. Error bars represent 95 percent confidence intervals.

Figure 3.7 Marginal Effects, High-Density Lipoprotein

Marginal Effect from Probit

0.2

Fraction Answering Yes = 0.237

0.1

0.0

≤1 −0.1

> 1, ≤ 2

> 2, ≤ 3

> 3, ≤ 4

> 4, ≤ 5

Poverty-to-Income Ratio versus Reference Group (>5)

Source: Authors’ calculations based on the Third National Health and Nutrition Examination Survey (NHANES III; Centers for Disease Control and Prevention n.d.). Note: Risky level of biomarker outcomes, NHANES III, adults age twenty to seventy-four. Error bars represent 95 percent confidence intervals.

Figure 3.8 Marginal Effects, Waist-to-Hip Ratio

Marginal Effect from Probit

0.25

Fraction Answering Yes = 0.634

0.20 0.15 0.10 0.05 0.00

≤1

> 1, ≤ 2

> 2, ≤ 3

> 3, ≤ 4

> 4, ≤ 5

Poverty-to-Income Ratio versus Reference Group (>5) Source: Authors’ calculations based on the Third National Health and Nutrition Examination Survey (NHANES III; Centers for Disease Control and Prevention n.d.). Note: Risky level of biomarker outcomes, NHANES III, adults age twenty to seventy-four. Error bars represent 95 percent confidence intervals.

Figure 3.9 Marginal Effects, Systolic Blood Pressure

Marginal Effect from Probit

0.10

Fraction Answering Yes = 0.125

0.05

0.00 ≤1

> 1, ≤ 2

> 2, ≤ 3

> 3, ≤ 4

> 4, ≤ 5

Poverty-to-Income Ratio versus Reference Group (>5) −0.05 Source: Authors’ calculations based on the Third National Health and Nutrition Examination Survey (NHANES III; Centers for Disease Control and Prevention n.d.). Note: Risky level of biomarker outcomes, NHANES III, adults age twenty to seventy-four. Error bars represent 95 percent confidence intervals.

Figure 3.10 Marginal Effects, Diastolic Blood Pressure 0.10

Fraction Answering Yes = 0.064

Marginal Effect from Probit

0.08 0.06 0.04 0.02 0.00 −0.02 −0.04

≤1

> 1, ≤ 2

> 2, ≤ 3

> 3, ≤ 4

> 4, ≤ 5

Poverty-to-Income Ratio versus Reference Group (>5) Source: Authors’ calculations based on the Third National Health and Nutrition Examination Survey (NHANES III; Centers for Disease Control and Prevention n.d.). Note: Risky level of biomarker outcomes, NHANES III, adults age twenty to seventy-four. Error bars represent 95 percent confidence intervals.

Figure 3.11 Marginal Effects, Resting Pulse

Marginal Effect from Probit

0.10

Fraction Answering Yes = 0.066

0.05

0.00 ≤1

> 1, ≤ 2

> 2, ≤ 3

> 3, ≤ 4

> 4, ≤ 5

Poverty-to-Income Ratio versus Reference Group (>5) Source: Authors’ calculations based on the Third National Health and Nutrition Examination Survey (NHANES III; Centers for Disease Control and Prevention n.d.). Note: Risky level of biomarker outcomes, NHANES III, adults age twenty to seventy-four. Error bars represent 95 percent confidence intervals.

88     Biological Consequences of Socioeconomic Inequalities Figure 3.12 Regression Coefficients, Allostatic Load

Ordinary Least Squares Coefficient

1.00

Mean Count = 1.62

0.80 0.60 0.40 0.20 0.00 ?1 −0.20

> 1, ≤ 2

> 2, ≤ 3

> 3, ≤ 4

> 4, ≤ 5

Poverty-to-Income Ratio versus Reference Group

Source: Authors’ calculations based on the Third National Health and Nutrition Examination Survey (NHANES III; Centers for Disease Control and Prevention n.d.). Note: Allostatic load regressions, NHANES III, adults age twenty to seventy-four. Error bars represent 95 percent confidence intervals.

A similar regression for an overall allostatic load score constructed by summing high-­risk scores on the eight individual biomarkers also demonstrates a pronounced income gradient. These results are graphically illustrated in figure 3.12. The impact of the income-to-­poverty ratio on counts of high-­risk biomarker levels declines monotonically as income rises, with the four lowest income groups generating coefficients statistically different from zero. The results are qualitatively large. Those in the lowest three income groups have AL scores 0.58, 0.35, and 0.27 points higher, respectively, than those in the highest income group, representing values 36, 25, and 16 percent of the sample mean, respectively. The results in figures 3.4 through 3.12 are by construction rather limited. A much deeper analysis is possible. The data could be analyzed for separate subgroups (such as by age, race, ethnicity), or we could use different measures of SES (education, for example). Such a detailed analysis is beyond the scope of this review. Despite these limitations, the results in the figures are broadly consistent with the outlined literature and provide three key results. First, the income gradient in biomarkers is rather broad

Biological Imprints of Social Status    89 based, encompassing many different measures of biological risk. Second, these measures of health show some heterogeneity, the most noticeable being the lack of any income gradient for diastolic blood pressure. Finally, aggregating biomarkers and generating a cumulative measure of insults appears to be advantageous. The fraction of the population with risky biomarkers is large. Although most risky biomarkers show some income gradient, the results are sometimes of weak precision and the results are not always monotonic. However, when the multiple risks are accumulated, the gradient is more pronounced and is estimated with a great deal more precision.

Do Observed Biomarker Gradients Mediate SES Disparities in Morbidity and Mortality? As this review makes clear, SES disparities in biomarkers levels and physiological functioning are evident across a wide range of physiological systems. These disparities are present early in life and persist across the life course. Although the frequency and persistence of these observations are striking, the relevance of such disparities is borne out only in the ability of SES variations in biomarker level and function to predict SES variations in clinical health outcomes. Although available data sources—which include information on SES characteristics, biomarkers, and clinical outcomes— are sparse, information is slowly accumulating that suggests that social status variations in biomarker level and function may translate into SES variations in health outcomes. Most investigations examining the role of biomarkers in explaining clinical health outcomes have examined the role of individual or clusters of biomarkers in accounting for SES variations in cardiovascular events or mortality. Individual inflammatory biomarkers (IL-­6, CRP, fibrinogen) have been found to account for small to moderate proportions (8 percent to 22 percent) of SES gradients in the incidence of cardiovascular disease and events (Marmot et al. 2008; Rosvall et al. 2008) and mortality in older adults (Ramsay et al. 2009). Interestingly, support is less consistent for a mediating role of more traditional cardiovascular risk factors, such as blood pressure and metabolic biomarkers, with some studies finding no or little mediating role (Loucks et al. 2009; Ramsay et al. 2009) and others finding that they account for a small proportion of SES gradients in cardiovascular outcomes (Marmot et al. 2008). Biomarkers have been found to account for a greater share of SES gradients in disease when examined simultaneously in analytic models or when composite indices of biomarker risk are examined. For example, a set of inflammatory biomarkers

90     Biological Consequences of Socioeconomic Inequalities explained 23 percent and another of cardiovascular-­metabolic biomarkers explained 27 percent of the variance in coronary heart disease incidence in the Whitehall cohort (Marmot et al. 2008). Including both cardiovascular-­ metabolic and inflammatory biomarkers together in an analysis indicated that these biomarkers accounted for 42 percent of the social class gradient in heart disease incidence. As noted, similar greater explanatory power of aggregated biologic risk was observed in a study examining mortality in older adults from the MacArthur Studies of Successful Aging (Seeman et al. 2004); a composite allostatic load index of biological risk explained more than one ­third of the education gradient in late-­life mortality, but the mediating role of individual biomarkers was much lower when examined separately in analyses.

Conclusions This overview documents SES gradients in biomarkers of a wide array of physiological regulatory systems in the body, including systems involved in organizing biological responses to stressful experiences and our cognitive and affective responses to our social world. These include neuroendocrine systems, such as the HPA axis and SNS hormones, as well the cardiovascular, metabolic, and immune systems. The sensitivity of biomarkers of each of these systems to SES-­related exposures, experiences, and behaviors represents potential routes through which social status may get under the skin to affect health and well-­being. Although most research has examined cross-­sectional associations between SES and biomarkers, longitudinal research is accumulating that suggests that the imprints of SES adversity can be observed in the level and activity of biomarkers of physiological regulatory systems across the life course. However, longitudinal research designs with comprehensive assessments of SES, biomarkers, and the psychosocial and behavioral pathways through which socioeconomic status may be linked to physiological functioning remain few in number. A related challenge is the need to document that observed SES gradients in biomarkers actually translate into SES variations in disease risk and thus could serve as early warning signals of greater risk for poor health in later life. Despite these limitations, the large number of studies and the wide array of biomarker targets for which SES gradients in biomarker levels and function have been found do suggest that our status in socioeconomic structures is intimately linked with the functioning of the internal regulatory systems in our body. The continued inclusion of biomarker measures in studies of social status and health, hopefully facilitated by advancements in the ease and reliability of biomarker measurement, may prove central to understanding the mechanisms through which SES may be

Biological Imprints of Social Status    91 linked to health as well as aiding in the identification of those most at risk or those in greatest need of intervention. Indeed, using childhood asthma as an example, chapter 4 is designed to illustrate how a more targeted program of research can further delineate the mechanisms through which socioeconomic status may affect health.

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Chapter 4 Dissecting Pathways for Socioeconomic Gradients in Childhood Asthma Edith Chen, Hannah M.C. Schreier, and Meanne Chan

The goal of this chapter is to describe a program of research on socioeconomic status (SES) and childhood asthma as a specific, in-­depth illustration of an integrated biological and psychosocial approach to establishing the mechanisms underlying SES and health relationships. Beginning with an established clinical phenomenon—that is, the link between low SES and asthma morbidity—we focus on the importance of understanding the basic pathophysiology of a disease to determine which steps in the disease process are plausibly altered by social factors. Researchers will be able to develop a more accurate understanding of why health disparities are so pervasive in our society and what types of interventions may hold the most promise for reducing these disparities. Socioeconomic status has profound effects on physical health outcomes throughout the lifespan (Adler et al. 1994; Braveman et al. 2010; Chen, Matthews, and Boyce 2002). For years, researchers have sought to understand why these relationships exist, but compelling explanations have proven elusive. For example, lack of insurance and access to health care is clearly one reason why low-­SES individuals suffer worse health. And yet countries that have universal health-­care systems show the same gradient relationship of SES with health as countries that do not, indicating that differential access to care is not the primary explanation for SES disparities (Adler et al. 1993). Similarly, low-­SES individuals are known to engage in poorer health behaviors, and yet the impact of SES on health per-

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104     Biological Consequences of Socioeconomic Inequalities sists after health behaviors are controlled for, suggesting that health behaviors also do not provide a complete explanation for the gradient (Lantz et al. 1998). Hence a need exists for more sophisticated models that convincingly explain how SES has such pervasive effects on health. One approach is to simultaneously consider factors at multiple levels, spanning broad neighborhood and family influences to basic genomic processes within the individual, all in an effort to more thoroughly understand the contributors to health disparities. This type of approach is important because rarely have studies that conducted in-­depth assessments of basic, biological processes also probed social contexts comprehensively. Similarly, research on neighborhood-­level effects often does not incorporate individual-­level factors into models, and vice versa. By considering factors across multiple levels of influence, researchers will be able to better answer the challenging question of why disparities by SES exist. Thus the overall goal is to be able to explain how broad, distal social environment characteristics such as SES get manifest within an individual in a way that affects clinical disease outcomes. In this chapter, we focus on two primary research questions aimed at addressing this overall goal— using childhood asthma as our illustrative health outcome. First, what are the plausible biological mechanisms by which low SES can exert effects on physical health? To make convincing arguments about the social environment affecting disease, we need credible biological explanations for how social factors could plausibly influence disease processes. Second, what are the more proximal social factors that can explain the effects that distal variables such as SES have on individuals? To understand why SES has effects on individual health, we need models that articulate the neighborhood, family, and individual characteristics shaped by SES that have implications for biological processes linked to disease.

An Approach to Conducting Mechanistic Research We have previously articulated an approach to conceptualizing a search for biological mechanisms to explain links between social factors and disease (Miller, Chen, and Cole 2009). First, a robust association needs to be documented between a social factor such as SES and a disease outcome; at that point, mechanisms can be investigated on both the biological and social fronts. Biologically, it can be helpful to understand the basic processes that drive the progression of the specific disease linked to the social factor of

Socioeconomic Gradients in Childhood Asthma    105 interest. This can allow researchers to draw on basic biomedical research regarding the pathophysiology of a disease and to systematically test which steps within the pathophysiological processes leading to disease are patterned by the social factor. In this way, researchers can begin to build a systematic and convincing argument about the causal chain of biological mechanisms that underlie the links between a social factor and a clinical outcome. Complementing the biological approach, one also needs to understand the processes on the social end that operate to bring a social environment variable such as SES to the level of the individual. Thus one needs to move across different levels of social factors, such as neighborhood influences, family factors, individual characteristics, and test whether these are associated with biological disease processes. In this way, one can start broadly on the social end with a construct such as SES and broadly on the clinical end with an outcome like mortality and systematically establish the links in between that bring the social and the clinical health worlds closer together. The ultimate goal is to lay out a step-­by-­step mechanistic model of the linear progression from broader social environment to physical health outcome. In the remainder of this chapter, we illustrate this approach by describing research on childhood asthma disparities.

Asthma Asthma is the most common chronic illness in childhood. Approximately 9 million children (12.5 percent) in the United States have had asthma during their lifetime (Dey and Bloom 2005). Asthma is the third-­ranking cause of hospitalizations among children (Kozak, Owings, and Hall 2004), resulting in close to 200,000 hospitalizations a year (Akinbami 2006). The economic impact of asthma, in terms of the annual estimated cost of asthma care for children, lies around $3.2 billion (Weiss, Sullivan, and Lyttle 2000). Asthma is also one of the leading causes of school absenteeism, resulting in 12.8 million missed school days a year (Akinbami 2006).

SES Disparities in Childhood Asthma Importantly, asthma morbidity is not equally distributed across the population. Children with lower-SES backgrounds are twice as likely to be hospitalized for asthma, and to suffer activity limitations due to asthma, than their counterparts (Miller 2000; Simon et al. 2003). More specifically, poor children with asthma are significantly more likely to have greater asthma symptoms, to have more severe asthma episodes, and to be hospi-

106     Biological Consequences of Socioeconomic Inequalities talized for asthma than more affluent children with asthma (Miller 2000; Simon et al. 2003; Wood et al. 2002). Fewer years of parent education also are associated with greater risk of asthma hospitalizations and emergency department visits in children with asthma (Dales et al. 2002; Maziak et al. 2004). At the neighborhood level, similar relationships exist. Neighborhoods with lower income levels and higher unemployment rates have been found to have higher rates of pediatric asthma hospitalizations (Castro et al. 2001; Claudio, Stingone, and Godbold 2006; Goodman, Stukel, and Chang 1998). These relationships are stronger for predicting outcomes among those already diagnosed with disease (morbidity) than for predicting risk of getting the disease among those who are initially healthy (prevalence; Chen, Matthews, and Boyce 2002). Overall, however, there is a pressing public health need to better understand the reasons why disparities by SES in asthma exist: to identify promising targets for interventions aimed at alleviating asthma disparities in our society.

Why Is Low SES Associated with Greater Asthma Morbidity? Increasing health disparities research is being conducted on general biological markers that can be linked to low SES. This work has implicated hormonal pathways such as cortisol, markers of systemic inflammation such as C-­reactive protein, and the accumulation of long-­term pathogenic mechanisms such as allostatic load (Cohen, Doyle, and Baum 2006; Cohen, Schwartz, et al. 2006; Evans et al. 2007; Evans 2003; McDade, Hawkley, and Cacioppo 2006; Seeman et al. 2004, 2008). However, these studies have largely been conducted in healthy individuals. Hence, though this work is able to shed some light on general biological mechanisms, it may not be as useful for explaining specific disease outcomes, given that different diseases will have different underlying mechanisms. Hence to understand associations of SES with asthma morbidity, we turn to models of the biology of asthma. Biological Models of Asthma  Asthma is a disease involving inflammation of the airways. Certain cytokines (chemical messengers of the immune system) are important for orchestrating cellular events related to airway inflammation (Busse and Lemanske 2001; Chung and Barnes 1999). These cytokines are produced by T helper (Th) cells, often in response to an external stimulus such as allergen exposure. Th cells are now recognized to have multiple phenotypes, but the best characterized are Th-­1 and Th-­2 cells. Th-­1 cells generally coordinate cellular immune responses by de-

Socioeconomic Gradients in Childhood Asthma    107 ploying cytokines such as IL-­2 and IFN-­γ. In contrast, Th-­2 cells coordinate what are called humoral responses—that is, those that involve the production of antibodies. These cells do this by inducing B cells to proliferate, and it is the B cells that then secrete antibodies. Th-­2 cells release specific cytokines such as IL-­4, IL-­5, and IL-­13, and these Th-­2 cytokines have been implicated in asthma. For example, secretion of IL-­4 and IL-­13 induces B cells to produce IgE antibodies, which initiates an inflammatory cascade leading to airway constriction and mucus production (Bacharier and Geha 2000). IL-­5 also recruits eosinophils to the airways and activates them, leading to a late-­phase asthma response including airway inflammation and obstruction (Kamfar, Koshak, and Milaat 1999; Ying et al. 1997). What Biological Pathways Relevant to Asthma Are Patterned by SES?  To establish the biological mechanisms linking SES and asthma, we tested whether SES could be linked to the various types of inflammatory processes implicated in asthma. As we alluded to earlier, the literature does link SES to systemic inflammatory markers (Hemingway et al. 2003; McDade, Hawkley, and Cacioppo 2006; Owen et al. 2003; Panagiotakos et al. 2005; Phillips et al. 2009). However, these studies typically use healthy populations and measure risk markers, such as C-­reactive protein. As far as we are aware, our studies are among the first to establish associations of SES with disease-­specific biological markers in patient populations. In a sample of thirty-­seven children, age nine through eighteen, recruited from the community, with a physician-­diagnosis of asthma, and a comparison sample of thirty-­nine healthy children in the same age range with no chronic illnesses, blood samples were collected, and parents were interviewed about family SES in terms of family savings and home ownership. As mentioned, one of the last steps in the biology of asthma exacerbations is the recruitment and activation of cells called eosinophils, which bring about edema, smooth muscle constriction, and mucus production in the airways, resulting in clinical symptoms of asthma like wheezing, chest tightness, and shortness of breath. We found that children with asthma who had lower-SES backgrounds had significantly greater eosinophil counts than those with higher-SES backgrounds, even after controlling for a variety of medical and demographic characteristics (Chen et al. 2006). In contrast, no associations were found among healthy children. We next investigated the immune processes that foster the production and activation of eosinophils—that is, Th-­2 cytokines in this same sample. Because cytokines are released only when immune cells are activated, we set up a laboratory model for activating immune cells in vitro and tested

108     Biological Consequences of Socioeconomic Inequalities whether low SES would be associated with the production of IL-­5 (which specifically activates eosinophils) after participants’ mononuclear cells were stimulated with a mitogen cocktail. This approach provides a laboratory approximation of what the immune cells might do if they encountered allergens in real life and also allows researchers greater control by equalizing the dose of exposure (to the mitogen cocktail) across all participants’ cells. We found that lower SES was associated with significantly greater stimulated production of IL-­5 in children with asthma (Chen et al. 2006), suggesting that even if the exposure is equivalent, children with asthma and lower-SES backgrounds will exhibit heightened inflammatory responses to a stimulus compared to children with asthma but higher-SES backgrounds. Can SES Effects Be Seen at the Level of the Genome?  Thus far, we have highlighted research that focuses on what cells do, such as produce cytokines, and whether this function can be potentially shaped by SES. Another question is what regulates the function of these immune cells and whether this process may also be patterned by SES. To better understand these mechanisms, we focused on the genomic processes that control the production of proteins secreted by immune cells. Proteins such as cytokines are made by immune cells when the genes coding them get switched on by transcription factors. Transcription factors are molecules that serve as conduits between a cell’s environment and the genes that govern its behavior. For example, when a receptor on an immune cell registers the presence of external stimuli (such as bacteria), it initiates a signaling cascade that results in a specific transcription factor migrating to the cell’s nucleus. There it binds to a specific segment of DNA and switches on a gene that codes for a certain protein, such as a cytokine. Dysregulation of a number of transcription factors has been implicated in asthma inflammation (Barnes and Adcock 1998; Busse and Lemanske 2001). Bringing this biological understanding back to the social world, we asked the question of whether we could document associations between SES and the activity of transcription factors that regulate inflammation. Other aspects of social adversity have been linked in recent studies to gene expression profiles (Cole et al. 2007; Lutgendorf et al. 2009; Miller et al. 2008). However, never has this understanding of genomic pathways been used as a way to understand SES and health relationships. We conducted genome-­wide transcriptional profiling on the immune cells of a sample of thirty-­one children ages nine through eighteen recruited from the community with a physician diagnosis of asthma and either a low-SES or a high-SES family (bottom or top 15 percent of the

Socioeconomic Gradients in Childhood Asthma    109 distribution in terms of both family income and parent education). Bioinformatic analyses suggested that across overexpressed genes, low-­SES children with asthma showed a pattern consistent with significantly increased activity of NF-­κB and decreased activity of CREB transcriptional signaling compared to high-­SES children with asthma (Chen et al. 2009). NF-­κB is a transcription factor that mediates induction of pro-­inflammatory cytokines, whereas CREB is a transcription factor that relays messages between catecholamines and immune cells via beta 2 adrenergic receptors. This was the first study to provide evidence that even molecular pathways involved in the regulation of inflammation can be shown to be patterned by SES in children with asthma. Note that in all of these studies, we find more robust associations of SES with asthma-­relevant biological markers when using resource-­based SES measures, such as family income or savings. This suggests that material resources—more so than status or prestige (such as parental occupation)— may play an important role in affecting the physiological processes underlying diseases such as asthma. Overall, these findings demonstrate that if one takes an approach of systematically investigating the steps in the pathophysiology of a disease, one can develop a working model of the specific biological processes that may be susceptible to alterations based on changes in the broader social environment. Here we document this specifically with respect to asthma and show that SES is linked to peripheral markers related to asthma inflammation such as eosinophil counts, cellular processes like the production of Th-­2 cytokines that govern inflammation, and genomic pathways that coordinate these cellular responses such as the activation of transcription control pathways that regulate inflammation such as NF-­κB. Importantly, all of these relationships are in a direction consistent with the clinical phenomenon of lower-­SES children experiencing greater asthma morbidity.

What Social Processes Govern SES Effects on Asthma Inflammation? As mentioned, in developing plausible models linking the social environment and physical health outcomes, it is important to identify the intervening social processes that explain how distal variables such as SES register at the level of the individual. To do this, one needs to understand the various neighborhood, family, and individual levels of social factors linked to SES. We present several examples of such an approach in the context of asthma.

110     Biological Consequences of Socioeconomic Inequalities What Individual Factors Link SES to Asthma Inflammatory Processes?  One of the most promising psychosocial explanations at the individual level for the SES and health relationship is the notion that lower-­SES individuals experience greater stress in their daily lives, which in turn takes a physiological toll, affecting physical health outcomes (Adler et al. 1994). Low SES has been associated with more frequent exposure to stressful life events (Attar, Guerra, and Tolan 1994; Brady and Matthews 2002; Garbarino, Kostelny, and Dubrow 1991; Hatch and Dohrenwend 2007), and children who live in low-­SES environments report greater subjective stress (Goodman et al. 2005). Moreover, stress has been found to form one pathway between SES and health (Cohen, Kaplan, and Salonen 1999; Khang and Kim 2005; Lantz et al. 2005). Asthma itself has been linked to psychological stress (for a review, see Chen and Miller 2007). Clinically, experiencing stressful life events is associated with an increased risk for a subsequent asthma attack in children with asthma (Sandberg et al. 2000). Daily diary studies have shown that daily stress is associated with poorer lung functioning as well as increased reports of asthma symptoms (Smyth et al. 1999). In young children, perceived stress in parents has been linked prospectively to children’s risk of developing wheezing in the first two years of life (Wright et al. 2002). In turn, research has linked experiences of stress to immune responses relevant to asthma. For example, college students with asthma showed a greater immune response to allergen challenge during high-­stress periods (final exams) than in low-­stress (no exam) periods (Liu et al. 2002). High school students with asthma showed greater IL-­5 production after high stress (post-­exam) than students who were healthy and experiencing stress (Kang et al. 1997). Similarly, atopic individuals show a decreased Th-­1/Th-­2 ratio in response to exam stress relative to healthy control participants (Hoglund et al. 2006). However, missing from this earlier research is an integration of stress into the broader social environment. That is, it remains unclear whether stress can provide one explanation for why low SES comes to affect asthma inflammatory pathways. We tested this idea with respect to how children with different SES backgrounds perceive the events in their social world. In previous work, we hypothesized that children who grow up in low-­SES environments develop a pattern of thinking about the world as a threatening place that requires constant vigilance based on previous negative life experiences (Chen and Matthews 2003). We hypothesized that these children would develop a tendency toward interpreting even ambiguous events as threatening. That is, when outcomes are ambiguous, such as a teacher asking to speak with you after class, low-­SES children will be more

Socioeconomic Gradients in Childhood Asthma    111 Figure 4.1 SES and Inflammatory Responses

SES

β = −.40**

Threat appraisal

βs= .28† − .37*

IL-5 Eosinophils

Source: Authors’ compilation based on Chen et al. (2006). Note: Indirect pathway from SES to asthma inflammatory pathways signficant at p 100 mg/dL). Categories of BMI used are both obese class I and above (BMI >30 kg/m2) and obese class II and above (BMI >35 kg/m2; see table 5.5).

Statistical Models The first type of models we fit were standard cross-­sectional regressions controlling for potential confounding factors:

yi = b0 + b1log incomei + b2xi + εi,

(5.1)

where yi is the outcome of interest (BMI or metabolic syndrome score) for individual i, b0 is the intercept, b1 is an estimate of the association between the primary exposure of interest and the dependent variable, b2 is a vector

Cardiovascular Consequences of Income Change    133 of estimated effect of observed control variables xi, and εi is the error term that we are concerned may contain omitted confounders as discussed earlier. Models of the association between BMI and the metabolic syndrome in years 1992, 1995, 2000, and 2005 were examined separately in a series of gender stratified regression models for both BMI and the metabolic syndrome (we only show results from 1992, as associations did not differ substantially across time). The independent variables included in five different models are as follows. Model 1a is log income, age, age squared, married indicator, divorced indicator, number living in the household, study site indicator, and race indicator. Model 1b is identical to model 1a except that it replaces the variable log income with a covariate for education level in 1992. Model 1c is identical to model 1a except that it replaces the variable log income with a covariate for parental education. Model 1d adds to model 1a a covariate for education level in 1992. Model 1e adds to model 1d a covariate for parental education. The next set of models exploit the longitudinal nature of the data to estimate individual-­level fixed-effect models using years 1992, 1995, 2000, and 2005 to examine the association between changing BMI or the metabolic syndrome and changing log income.

yit = b1log incomeit + b2xit + b3yeart + μi + νit,

(5.2)

where yit is the outcome of interest (BMI or metabolic syndrome score) for individual i at time t, and b1 is again the main coefficient of interest. The key difference between this model and the equation 5.1 models (aside from now including multiple years of data in the analysis) is that the error term is decomposed into two parts: εit = μi + νit. The former part μi represents time-­invariant (fixed) unobserved characteristics of individuals such as genetics and preferences formed early in life. To the extent that these are correlated with socioeconomic indicators such as income, their omission from the vector of observed control variables x may cause bias in b1. For example, higher time discount rates (focused more on present gratification than the future) may lead to both lower income and higher obesity, resulting in overly negative estimates of the obesity effects of income. To address this concern, we estimate equation 5.2 using an individual-­level fixed-­effects estimator that removes from the error term those unobservables μi that are fixed across survey years (this is equivalent to adding to the model a dummy variable for each person). This fixed-­effects model can roughly be interpreted as capturing the effects of changes over time in income on changes over time in cardiovascular risk. Essential to the model is the assumption that time-­varying confounders (such as changes in mar-

134     Biological Consequences of Socioeconomic Inequalities ital status) are included in the xit vector, and thus the remaining error component νit is unrelated to income. Although these fixed-­effects models will be more robust to omitted variables bias, a drawback is their lower efficiency and hence wider confidence intervals if omitted variables bias was not indeed a problem. To examine this trade-­off, we use Hausman tests to compare these fixed-­ effects models against a null specification in which the μi were instead modeled as random effects. If the models yield statistically similar estimates, then we would prefer the random-effects models to the less efficient fixed-­effects approach; if the models yield different estimates, then we reject the random-effects models as biased (and implicitly also reject the cross-­sectional models in equation 5.1). For all but one model using the CARDIA data, we rejected (p  .05) for the household income and BMI model in women, but for consistency of presentation we report only the fixed-­effects specifications. Similarly, for all but one model using the National Longitudinal Survey of Youth (NLSY) data we rejected (p  .05) for the household income and BMI model in men, but, again for consistency of presentation, the NLSY tables include only fixed-effects results. We also examined the fixed-­effect models with interaction terms between income and indicators for having become married, having become divorced, or having had an income increase rather than decrease since the last wave of observation. We found none of these interaction terms to be statistically significant at p