Population Health: Behavioral and Social Science Insights


Innovations in Population Health Research: The Challenge

By Robert M. Kaplan, Daryn H. David, and Michael L. Spittel

The National Institutes of Health (NIH) is the largest supporter of biomedical research in the world. The NIH provides stewardship for medical and behavioral research for the United States and for populations throughout the world. The well-known basic science mission of the NIH is to pursue fundamental knowledge about the nature and behavior of living systems. Perhaps less appreciated is the second clause of the mission statement which emphasizes the application of knowledge to enhance health, lengthen life, and reduce illness and disability.

The goal of enhancing health, lengthening life, and reducing illness and disability is consistent with the health objectives for the United States and for the overall mission of the U.S. Department of Health and Human Services (HHS). Specifically, since the introduction of the Healthy People reports in 1990,1 the overarching national goal has been to extend life expectancy and improve the quality of life for people living in America. The HHS mission is to enhance the health and well-being of Americans by providing effective health and social services and by fostering sound, sustained advances in the sciences underlying medicine, public health, and social services.

It is easy to resonate with the aim of lengthening human life and improving quality of life during the years in which people survive. In order to work toward these objectives, it is necessary to understand the factors associated with longer life and reduced illness and disability. It is typically assumed that the best mechanism for increasing life expectancy and reducing the burden of illness and disability is investment in medical care. However, a variety of different analyses using several different research methodologies have shown that medical care accounts for only a small portion of the variation in health outcomes.2,3 The traditional biomedical model is thus limited in its ability to foster health and well-being, in part because the biomedical model often focuses exclusively on measures of biological process rather than the more global goal of helping people live longer and higher quality lives. Understanding how to improve overall health and well-being thus requires explorations beyond the health care system.

NIH's mission is to seek fundamental knowledge about the nature and behavior of living systems and to apply that knowledge to enhance health, lengthen life, and reduce illness and disability.

The purpose of this book is to gain a better understanding of the multitude of factors that determine longer life and improved quality of life in the years a person is alive. While the emphasis is primarily on the social and behavioral determinants that largely impact the health and well-being of individuals, this publication also addresses quality of life factors and determinants more broadly. This book originated with the Office of Behavioral and Social Sciences Research (OBSSR) in NIH's Office of the Director. As part of long-term strategic priority setting, we sought to identify the most important factors that influence the length and quality of human life. We were given the opportunity to invite a group of the most distinguished scholars to contribute chapters summarizing current research. The authors were asked to summarize what is currently known and to suggest directions for future scientific research. Each chapter in this book considers an area of investigation and ends with suggestions for future research and implications of current research for policy and practice. To contextualize these chapters and to highlight the pressing nature of the questions this book tackles, we first provide a summary of the state of Americans' health and well-being in comparison to our international peers and present more background concerning the limitations of current approaches to improving health and well-being.

U.S. Life Expectancy in International Perspective

Extending life and improving the quality of life may require greater attention to factors beyond of the current health care system. In Figure 1, data from the Organization for Economic Co-operation and Development (OECD) are used to graph total health care expenditures for various countries (x-axis) against life expectancy (y-axis). The United States is an extreme outlier in terms of expenditures. We spend more than 17 percent of our gross domestic product on health care, while most of our economic competitors spend about 10 percent. If the United States reduced its health care expenditures to the level of most European countries, we would save over $1 trillion per year—or about half the amount as the total public debt held by the U.S. Federal Reserve. Given our very high expenditures on health care, it is appropriate to explore how well the United States is doing in achieving its goal of longer life and improved quality of life.

Figure 1. Relationship between percent of GDP spent on health care and female life expectancy in OECD countries

Chart depicts the relationship between percent of gross domestic product (GDP) spent on health care and the corresponding life expectancy for females in more than 30 countries participating in the Organization for Economic Co-Operation and Development in 2011. For example, female life expectancy in the United States in 2011 was about 81 years of age, while the country spent nearly 18 percent of its GDP on health care that year. Japan spent just under 10 percent of its GDP on health care in 2011 and had a corresponding female life expectancy of nearly 86 years. Turkey, at the bottom the scale, spent just under 6 percent of its GPD for health care in 2011 to achieve a female life expectancy of 77 years.

Source: Created using data from the Organization for Economic Co-operation and Development (OECD), 2011. When missing, life expectancy data estimates were imputed from prior year.

International studies of life expectancy have gained particular attention in the last few years. These studies tend to show that the life expectancy advantage experienced by American citizens in comparison to other countries has been on the decline. One study from the National Research Council considered current life expectancy for 50-year-old women between the years 1955 and 2010.4 Current life expectancy is the number of years of life on average remaining once a milestone age has been reached. So, current life expectancy for 50-year-old women is the median number of years of life remaining following the 50th birthday. In 1955 women in America ranked about 12th in the world on this indicator. By 2006, they had slipped to about the 26th position, just below Korea and Malta. In a life expectancy comparison of 10 wealthy countries, women in America were 3rd out of 10 in 1955, but they were 9th out 10 in 2006. Among the many countries with more rapid increases in life expectancy in the 50 years following 1955 were Japan, France, and Spain. Japan, for example, was considerably below the United States in 1955 and now is many years ahead.4

In response to these findings, the Office of Behavioral and Social Sciences Research, along with the National Institute on Aging, sponsored another study that compared life expectancy in the United States against 17 peer countries.5 These comparison countries were primarily in Western Europe, but they also included Australia, Japan, and Canada. The results of the comparison are quite disturbing. Among the 17 countries studied, the United States had the second highest mortality rate from non-communicable diseases. Mortality from communicable diseases was fourth from the bottom for the United States. The United States had the third highest AIDS rates, exceeded only by Brazil and South Africa. And, the AIDS incidence in the United States was 122 per million, which is about nine times the average of countries in the OECD.

We have known for some time that U.S. life expectancy at birth is not keeping pace with other developed countries. Although our life expectancies are increasing, the rate of increase is much slower than it has been for our economic competitors. This trend has been developing over the course of several decades. Perhaps the most surprising finding from the Institute of Medicine (IOM) study5 concerned years of life lost prior to age 50. The IOM committee considered international differences in the probability of celebrating a 50th birthday. On this indicator, the United States was last among the 17 comparison countries for both men and women. U.S. losses in life expectancy prior to age 50 are about double the rate observed in Sweden. Perhaps most disturbing is that this problem profoundly affects women. Figure 2 shows the trend in years of life lost in 21 high-income countries between the years 1980 and 2006. For men, the United States started at the low end of the distribution and worked its way to the bottom. For women, the United States started near the bottom and now has gone off the scale in relation to the comparison countries.4

Figure 2. Probability of survival to age 50 in 21 high-income countries, 1980-2006

Chart, which is based on 2010 data, shows the likelihood of survival to age 50 for males and females in 21 high-income countries who were born between 1980 and 2006. For men, the United States started at the low end of the distribution and worked its way to the bottom. For women, the United States started near the bottom and now has gone off the scale in relation to the comparison countries.

Source: National Research Council: Explaining divergent levels of longevity in high income countries, 2010. Used with permission.

Advances in Medical Care

It is often argued that the United States has the very best medical care in the world. So, we would expect advances in medical therapies to address many of our health care problems. The reality is that recent clinical trials often have not demonstrated the level of benefit that the public expects from medical therapies. In fact, most recent large randomized clinical trials have failed to show the expected benefit of medical and surgical therapies.6

To more accurately assess the benefits and limitations of current medical interventions for health maintenance and prevention, it is crucial to understand the "Patient Centered Outcomes Research (PCOR)" perspective.7 The PCOR perspective argues that physiological measures are only important if they relate to life duration or life quality. Blood pressure, for example, is a meaningful biological measure because it is highly predictive of early death or disability associated with myocardial infarction or stroke. In contrast, other measures and outcome variables less clearly relate to the twin objectives of improved life quality or lengthened life expectancy. One example is catecholamine variations in response to acute stress, which are less directly linked to the objectives and outcomes that may concern health science researchers.

A second key perspective arising from PCOR is the focus on all-cause mortality as opposed to disease-specific mortality.8 A variety of large clinical trials in medicine have demonstrated reductions in one cause of death but compensatory increases in other causes of death.9 One example that helps justify the PCOR perspective is illustrated by the Physicians Health Trial. In this study, approximately 22,000 physicians were randomly assigned to take either 325 mg of aspirin every other day or a placebo. When the data were first analyzed, significantly fewer physicians in the aspirin component had died of myocardial infarction. However, considering all causes of cardiovascular death, the number of physicians who had died was exactly the same in the aspirin and placebo groups (Figure 3). All of these deaths were in the study period, and all were considered premature deaths.10 In essence, aspirin had altered the course of one possible cause of death (myocardial infarction), but the medication ultimately did not extend participants' life expectancy11; it merely changed what was recorded on the death certificate. From a patient's perspective, we would argue that people and their families are most concerned with an individual's vital status and less concerned with a specific cause of death.

With these PCOR concepts of longer life and higher quality of life in mind, it is helpful to consider the ACCORD trial of aggressive therapy for the treatment of non-insulin-dependent diabetes mellitus.12 Patients were randomly assigned to standard therapy or intensive therapy. The intensive therapy significantly changed biological outcomes in the expected direction. Specifically, those assigned to intensive therapy had significantly lower levels of glycosylated hemoglobin. From a traditional perspective, the treatment achieved its goal. However, long-term followup considered total mortality and deaths from cardiovascular disease. Considering all-cause mortality, those assigned to intensive therapy had a higher probability of cardiovascular death in comparison to the standard therapy condition.

Figure 3. Total mortality in the aspirin component of the Physician's Health Study

Chart shows total mortality in the aspirin component of the Physicians’ Health Study which enrolled 22,000 physicians who were randomized into two groups: those who took aspirin and those who took placebo. Deaths were grouped into five categories: acute myocardial infarction (5 percent aspirin, 18 percent placebo); sudden death (13 percent aspirin, 9 percent placebo); ischemic (9 percent aspirin, 8 percent placebo); stroke (6 percent aspirin, 2 percent placebo); and other cardiovascular disease (10 percent aspirin, 6 percent placebo).

Source: Adapted from the Final Report on the Aspirin Component of the Physicians' Health Study. N Engl J Med 1989;321:129-35.

The ACCORD trial is just one of many randomized clinical trials with similar results. Trials considering intensive therapy for anemia suggest that agents that increase red blood cells do their job and bring hemoglobin counts toward normal. Yet patients in these conditions have a higher probability of renal failure requiring dialysis and other adverse outcomes.13 Large studies on hormone replacement therapy usually show that estrogen levels are raised toward normal premenopausal levels. Yet the consequences for patients, from a PCOR perspective, are often poorer rather than better.14

Certainly, we need to continually evaluate promising new therapies; however, we also need to devote more attention to nontraditional determinants of health outcome. For instance, we now know that medicines and surgery, despite their value and importance, do not explain most of the variation in human health outcomes. For example, Schroeder15 estimated the contribution of a variety of different factors to premature death. His analysis suggested that health care contributes about 10 percent of the variation in outcomes, while environmental exposures contribute about 5 percent. Genetic predisposition may account for about 30 percent, but behavioral patterns contribute about 40 percent, and another 15 percent is contributed by social circumstances. Other methodologies lead to similar conclusions. For example, epidemiologists typically look for the relationship between risk factors and specific diseases. But, when they look at the relationship between behavioral risk factors and death from all causes, estimates typically suggest that behavioral factors, including tobacco smoking, physical activity, and diet are by far the largest contributors to poorer outcomes. These factors usually are much stronger than biochemical measures, including cholesterol, inflammation, and even early detection of breast cancer.16

These findings highlight the crucial importance of considering behavioral factors and social context when attempting to understand the elements impacting patient-centered outcomes, such as quality of life, health maintenance and improvement, and life expectancy.

Behavioral and Social Factors Underlying Disease

Although the exact number varies across different methods, most analyses suggest that behavioral and social factors account for at least half of the variation in health outcomes. One interesting example is provided by the Marmot report from the United Kingdom.17 An expert panel reviewed a variety of factors associated with life expectancy. For example, Marmot and colleagues in the United Kingdom studied the relationship between neighborhood income deprivation and life expectancy and disability-free life expectancy. They found that there is a systematic relationship between these variables, with individuals from the most deprived neighborhoods having the shortest life expectancies.17

A growing body of evidence reinforces the observation that social factors have a profound impact on life expectancy. One example comes through the Los Angeles County Public Health Department, which reports on life expectancy by sex and racial/ethnic background.18 The difference between Asian women with an average life expectancy of nearly 89 years and African-American men with a life expectancy of about 70 years is a full 19 years! There have been significant improvements in the life expectancy of black males in recent years, but on average, white women still live about 9 years longer than black men.19 We do not fully understand the mechanisms underlying these enormous differences in longevity, but we do recognize that the effects are profound.

While people with infections benefit from rapid diagnosis and treatment, we now live in an age in which most of the burden of illness and disability is associated with multiple chronic conditions and non-communicable diseases, including coronary heart disease, cancer, and diabetes. These conditions are expensive to treat, and their outcomes vary greatly by socioeconomic status and lifestyle. Health behaviors are the biggest risk factor for these conditions.20 Modification of risky health behaviors is central to the successful management of chronic conditions, and social factors—including income, social support, and access to information—play a crucial role in health outcomes.

Another very interesting finding that has emerged from recent research is the impact of behavioral and social factors in relation to diagnoses. Wennberg and colleagues studied the wide variations in health care costs across 306 hospital service areas in the United States.21 The investigators used Medicare claims data to estimate what factors account for the variations in cost and in mortality. They used three different factors to explain this variation: one, a medical hierarchical conditions categories (HCC) index that adjusts for medical diagnoses; two, a poverty index; and three, a behavioral health index based simply on the number of people with hip fractures or strokes and behavioral responses including self-rated health, obesity, and smoking. The analysis was adjusted for demographic factors including age, sex, and race. Figure 4 summarizes the results of the analysis. The adjustment factor based on clusters of diagnoses does the best job in explaining health care costs. However, it does the very worst job in explaining mortality. Conversely, the behavioral index does a much better job than the medical index of explaining variation in mortality. The most striking finding, consistent with several other lines of evidence, is that medical care explains only a small amount of variation in health mortality rates.

Figure 4. Proportion of variance in spending and mortality using models that adjust for medical conditions (HCC), poverty, or health and behavioral indexes

Chart shows the proportion of variance in spending and mortality using models that adjust for medical conditions, poverty, or health and behavioral indexes and shows that only a small proportion (less than 5 percent) of the variation in mortality can be attributed to medical care, while health and behavior together explain more than 60 percent of mortality.

Source: Data adapted from Wennberg DE, Sharp SM, Bevan G, et al. A population health approach to reducing observational intensity bias in health risk adjustment: cross sectional analysis of insurance claims. Br Med J 2014;348:g2392.

The United States now spends more on health care than any other country in the world. In fact, on a per capita basis, no other country comes close. Brusha estimates that the total expenditure on health services in the United States is approximately $2.9 trillion/year. Given this huge expenditure, we need to ask the question, "What is the rate of return on our investment?" With only about 10 percent of the variability in life expectancy associated with health care,3 and with about half of health outcomes associated with health behaviors and another other 40 percent as explained by a variety of other nonmedical factors, we must consider the cost side of the equation.

Of the $2.9 trillion in annual health-related expenditures, about 97 percent is devoted to health care, while only 3 percent is devoted to factors outside of the health care system. In other words, 97 percent of the investment is chasing the potential for 10 percent of the benefit, while as little as 3 percent of the expenditure is devoted to factors that may explain 50 to 90 percent of the potential benefit.b We believe that it is time to re-examine what is known about the relationship between factors outside of the health care system and health outcomes. Further, we need to develop a new research agenda directed toward maximizing the use of our resources to produce health. Although behavioral and social factors are likely to explain up to five times as much of the variation in health outcomes in comparison to medical care, basic biological mechanisms remain the almost exclusive focus of biomedical research. While we strongly recognize the value of basic biomedical research and of advances in biomedical technology, there remains a pressing need to take a broader view in our approach to research and medical care.

Scope and Importance of Current Effort

Clearly we have an inadequate understanding of the non-medical care influences that help to explain health outcomes. Many of the chapters in this book focus on specific aspects of quality of life and mortality that are associated with behavioral and social variables. Explored in detail are factors that contribute to premature mortality and/or lower quality of life, including cigarette smoking (Abrams), unintentional injuries (Sleet and Gielen), insufficient physical activity (Sallis and Carlson), low educational attainment (Zimmerman and Woolf), challenging and/or unequal social circumstances (Pickett and Wilkinson; Williams and Purdie-Vaughn), ongoing HIV infection and prevalence (Holtgrave), and workplace policies (Berkman). In addition to highlighting the impact that these factors and behaviors can have on quality of life, each of the above chapters also provides recommendations for relevant policies, practices, and interventions that could help improve overall life expectancy and well-being.

Other chapters explore the rich public health dimensions to quality of life and life expectancy. for instance, Preston's piece provides an elegant overview of various social, behavioral, and public health factors that have contributed to improvements in health since the mid-19th century in different areas of the world. Stewart and Cutler highlight six distinct behavioral factors (smoking, obesity, heavy alcohol use, and unsafe use of motor vehicles, firearms, and poisonous substances) and illustrate how each has influenced health-related quality of life in the U.S. context. Baldwin's chapter asserts the grave impact that the emerging use of tobacco by youth in middle and lower income countries could have on the incidence of non-communicable illnesses in the developing world.

As noted earlier, the intersection of biological and social factors must also be considered when studying the determinants of quality of life and life expectancy. McEwen provides a nuanced overview of how neurological and endocrinological responses to varying degrees of social, environmental, and physical stress can affect lifelong well-being, while Boyce highlights the long-term impact that early exposure to adversity or trauma can have on health and quality of life. Adler and Prather pull the various contributions together with a strong case for integrating biological, biomedical, behavioral, and social science research perspectives to most effectively promote and advance individual and population-level health and longevity.

Several chapters in this book raise questions about the most promising systemic and methodological approaches to achieving longer life and to reducing the burden of illness and disability for populations. Some argue that our established methods of medical training (Satterfield and Carney) and mental health service delivery (Kazdin) are not sufficient for addressing current and emerging population health needs. Instead, cost-effectiveness and cost-benefit analyses can help reveal those modes of intervention and health care delivery that can have a strong impact on public health without depleting resources that could be used for other valuable expenditures (Russell), and health impact assessments can provide prospective insight into the potential health effects of public health and social service interventions (Teutsch, Butler, Simon, and Fielding). Still other chapters shine light on the importance of cross-disciplinary approaches and advanced behavioral and social science methodologies for improving behavioral health policy and practice (Frank and Glied), containing the spread of emerging infectious diseases (Orr and colleagues), and developing novel population health interventions (Marteau). Finally, we conclude the volume with a summary of some of the lessons learned and suggestions for future research investigation.

Our major institutions have set the goal of improving human health by extending life expectancy and improving health-related quality of life. Usually the preferred tool for improving health is greater investment in medical care. Yet, despite the accomplishments of modern medicine, health care is limited in its potential to achieve the goals of longer and higher quality life. Many of the factors that determine health outcome are beyond the reach of the traditional biomedical model. Our ultimate goal in producing this book is to encourage the development of better evidence to inform medical and public health practice and to develop public policies that will result in better health for our populations. We hope this book initiates these important new directions.


Portions of this chapter are similar to an article by Robert M. Kaplan, Behavior change and reducing health disparities. Prev Med 2014;68:5-10. The opinions presented herein are those of the authors and do not necessarily represent the official position of the Agency for Healthcare Research and Quality, the National Institutes of Health, or the U.S. Department of Health and Human Services.

Authors' Affiliations

Robert M. Kaplan, PhD, Agency for Healthcare Research and Quality; formerly Office of Behavioral and Social Sciences Research, National Institutes of Health, Office of the Director. Daryn H. David, PhD, and Michael L. Spittel, PhD, Office of Behavioral and Social Sciences Research, National Institutes of Health, Office of the Director.

Address correspondence to: Robert M. Kaplan, PhD, Office of the Director, Agency for Healthcare Research and Quality, 5600 Fishers Lane, Mailstop 06E37A , Rockville, MD 20857; email Robert.Kaplan@ahrq.hhs.gov.


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a. Unpublished data courtesy of Rick Brush, Collective Health, 2014.

b. We recognize that this analogy is oversimplified. For example, we have substantial investments in water safety, pollution control, and public safety. Even though these expenditures are designed to protect health, they typically are not included in estimates of health resources.

Robert M. Kaplan Robert M. Kaplan, PhD, is Chief Science Officer, Agency for Healthcare Research and Quality (AHRQ). Formerly, he was Associate Director for Behavioral and Social Sciences and Director of the Office of Behavioral and Social Sciences Research in the Office of the Director, National Institutes of Health; Distinguished Professor of Health Services at the University of California Los Angeles (UCLA); and Distinguished Professor of Medicine at the UCLA David Geffen School of Medicine. In 2005, Dr. Kaplan was elected to the Institute of Medicine of the National Academy of Sciences.
Daryn H. David Daryn H. David, PhD, is a researcher and clinical psychologist who has expertise in developing and evaluating innovative social science, psychological, and educational programs. Currently, she is a Clinical Instructor in the Department of Psychiatry at the Yale School of Medicine. She gained national science policy experience during a 2-year American Association for the Advancement of Science (AAAS) Science & Technology Policy Fellowship in the Office of Behavioral and Social Sciences Research at the National Institutes of Health.
Michael L. Spittel Michael L. Spittel, PhD, is a health scientist administrator in the Office of Behavioral and Social Sciences Research, Office of the Director, National Institutes of Health. Previously, he was a program officer at the Demographic and Behavior Sciences Branch (DBSB), Eunice Kennedy Shriver National Institute of Child Health and Human Development. He also served as the program officer/scientist for the National Longitudinal Study of Adolescent to Adult Health (Add Health), Data Sharing for Demographic Research, Community Child Health Network, and co-managed the DBSB training program which supports pre- and postdoctoral researchers in demography.
Page last reviewed July 2015
Page originally created August 2015
Internet Citation: Introduction. Content last reviewed July 2015. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/professionals/education/curriculum-tools/population-health/intro.html
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