Population Health: Behavioral and Social Science Insights

Determinants of Health and Longevity

By Nancy E. Adler and Aric A. Prather

Abstract

The chapters in this volume document the increasing evidence over the past few decades regarding the contribution of social and behavioral sciences to understanding the etiology and progression of disease and the patterning of disparities in health across groups. During this same period, scientific advances in understanding the genetic substrates of human biology and disease processes have increased the focus of scientific research on genetic determinants and the development of individually targeted treatments. The biological sciences operate largely in isolation from the social and behavioral sciences. In this chapter we consider current approaches to health and argue for greater integration of approaches to discovery and treatment in order to achieve our national goals of improving health and reducing disparities.

Poor Population Health

Despite spending far more on health care than any other nation, the United States ranks near the bottom on key health indicators. This paradox has been attributed to underinvestment in addressing social and behavioral determinants of health. A recent Institute of Medicine (IOM) report1 linked the shorter overall life expectancy in the United States to problems that are either caused by behavioral risks (e.g., injuries and homicides, adolescent pregnancy and sexually transmitted infections (STIs), HIV/AIDS, drug-related deaths, lung diseases, obesity, and diabetes) or affected by social conditions (e.g., birth outcomes, heart disease, and disability).

While spending more than other countries per capita on health care services, the United States, spends less on average than do other nations on social services impacting social and behavioral determinants of health. Bradley et al., found that Organization for Economic Co-operation and Development (OECD) nations with a higher ratio of spending on social services relative to health care services have better health and longer life expectancies than do those like the United States that have a lower ratio.2

Health Determinants

A series of analyses have examined the factors accounting for overall health and longevity. In a landmark 1993 paper, McGinnis and Foege observed that although deaths are attributed to a specific disease (e.g., heart disease or cancer), the actual causes of death reside in the factors that determine whether and when an individual develops and succumbs to disease. These factors include tobacco, diet, activity patterns, alcohol use, microbial and toxic agents, firearms, risky sexual behaviors, motor vehicle accidents, and illicit drug use.3

Genetic vulnerabilities, health care, and exposures in the physical environment also contribute to health and mortality, but Centers for Disease Control and Prevention (CDC) analyses suggested a relatively greater impact of social and behavioral factors: 10 percent of premature mortality was attributed to inadequacies of health care, 20-30 percent to genetics, 5 percent to the physical environment, 15 percent to the social environment, and 40-50 percent to health-damaging behaviors.4-6 Taken together, over half of all deaths in the United States can be "attributed to a limited number of largely preventable behaviors and exposures" (p. 1242).6

While "actual causes" referred to social and behavioral determinants of individual health, Link and Phelan7 drew attention to the "fundamental causes" of these more proximal determinants. Fundamental causes reflect upstream social and economic policies that drive the behavioral and biological risk factors. Galea et al. estimated that social determinants (e.g., poverty, income inequalities, racial segregation) accounted for more than 800,000 deaths in 2000, which is "comparable to the number attributed to pathophysiological and behavioral causes (p. 1456)."8

The above estimates relied on available data. Although imprecise and failing to account for overlapping effects, they provide a rough order of magnitude of the health impact of various levels of determinants. The greatest uncertainty involved the contribution of genetics, since direct epidemiological evidence is lacking, and its estimated effect was not based on direct calculation. Rather, after calculating deaths attributable to other factors, remaining deaths were attributed to genetic causes. This likely overestimates the effect, since the residual variation that was attributed fully to genetics includes the interaction of genes with social and physical environments, as well as error variance across all determinants.

Social and behavioral determinants affect a wide range of health problems, which substantially increase the burden of disease (depression, diabetes, cardiovascular disease) and shorten life expectancy. For example, smoking increases risk for specific cancers, respiratory diseases (asthma, chronic obstructive pulmonary disease [COPD]), diabetes, heart disease, and stroke.9 Eating behaviors (e.g., consuming excess fat and insufficient fruits and vegetables) and inadequate levels of physical activity contribute to coronary heart disease, type 2 diabetes, and some neurodegenerative diseases.

Genetic Determinants

The largest health research initiative in recent years was the mapping of the human genome. It has generated a great deal of research and some important advances in diagnosis and treatment. However, the impact on overall population health has been modest. Most of the advances have occurred in relation to cancer, where it has enabled development of chemotherapeutic drugs based on the genetic composition of an individual's tumor. In contrast, there have been relatively few findings on genetic determinants of disorders such as diabetes that create the largest burden of disease and greatest percent of overall mortality.

Obesity is a major determinant of diabetes and represents a threat to population health. Genetic factors appear to play a minor role; only 7 percent of severe obesity in young children can be traced to monogenetic causes, and affected children represent less than .01 percent of the population.10 The impact of genetic variation on degree of obesity is also limited. On average, adults homozygous for a risk allele for obesity (found in 16 percent of individuals) weigh only 3 kilograms more than those lacking the risk allele; such adults have only modestly elevated odds of being classified as obese and en smaller elevations in their odds of diabetes.10,11 A genome-wide association (GWA) study of 2.8 million single-nucleotide polymorphisms (SNPs) identified 32 loci for body mass index (BMI), which together accounted for only 1.45 percent of variance in BMI. Despite the attention given to the FTO allele as the "fat gene," the variation directly accounted for by FTO SNP amounted to only 0.34 percent.12

Importantly, genetic factors could not have changed rapidly enough in a few decades to account for the marked increase in the prevalence of obesity and diabetes in this time period. Rather, changes in behaviors linked to diet and exercise, fostered by environmental changes such as "super-sizing" of food portions, an increasing proportion of meals eaten outside the home, the availability of "fast food," and agricultural policies that have increased the availability of cheap, low-nutrient food are more likely explanations.13,14

Social and Behavioral Determinants

A vast literature documents direct associations between sociodemographic characteristics (e.g., income, education, race/ethnicity), personality characteristics and affective states (e.g., hostility, anxiety, hopelessness, optimism, conscientiousness), and health behaviors (e.g. smoking, exercise, diet) with bio-markers that reflect dysregulation of cardiovascular, metabolic, and immune function (e.g., high blood pressure, excess inflammation, insulin resistance).15,16,17 Perturbations in these biological processes contribute to the premature onset and progression of diseases, such as coronary heart disease, diabetes, and acute infectious illness, and to accelerated biological aging and mortality risk.

Many of the social and behavioral variables linked to disease risk are involved in the stress response. Stress experiences—especially those that are severe and chronic—produce changes in brain and body that promote disease onset and progression.18 Acute fluctuations in cortisol, sympathetic nervous system activity, and metabolic hormones are adaptive for meeting short-term demands. However, they lay the foundation for chronic illness if persistent activation occurs. Neuroscience research is establishing the neural mechanisms through which stress shapes threat perception, the brain areas where emotion regulation resides, and how external stimuli are transduced from brain to body.19

Social conditions determine the acuity and frequency of stress exposures including major life events (e.g., loss of a loved one) and conflicts of daily living and their impact on mental and physical health. Physiological stress responses occur when individuals encounter situations in which threats or demands exceed their capacity to overcome or ameliorate them.20 The chronic stress experienced by socially disadvantaged individuals who are subjected to more adverse situations and have fewer resources with which to address them help account for the pervasive health disparities associated with socioeconomic status and race/ethnicity.17,21

Getting into the Body

Research on the ways in which social conditions "get under the skin" to produce the social patterning of morbidity and mortality has identified several important pathways and mechanisms by which this may occur.

Cellular Aging

Accelerated cellular senescence is indexed by telomere length in immune cells. Telomeres are DNA-protein complexes that cap the ends of the chromosome, conferring chromosomal stability. In mitotic human cells, telomeres shorten with each successive cell division. When critically short, they can send cells into replicative senescence, causing cell cycle arrest and malfunction. Short telomere length in immune cells may serve as a marker, and possibly a mechanism, of earlier onset of diseases of aging.

In a break-through discovery in 2004, shorter telomere length was found to be associated with greater exposure to stress and psychological distress.22 Subsequently, the stress-telomere link has been replicated in numerous samples with varied ages and demographic distributions. Shorter telomere length has also been observed among individuals characterized by elevated levels of hostility, depression, and/or low social support.23-25 Accelerated telomere attrition has been associated with social determinants, including various indicators of socioeconomic status (e.g., income, education, employment status) and neighborhood social environment.26-28

Gene-Environment Interaction

Social conditions and the environment interact with one's genetic endowment to affect health. For instance, a history of childhood maltreatment predicts development of antisocial characteristics among individuals with the monoamine-oxidase risk allele29 but not in those lacking it. Reliance on a candidate gene approach has been criticized since many failed to replicate in independent samples,30 and the polymorphisms under investigation are rarely identified in GWA studies. The FTO gene is an exception; its effect on obesity has been shown to be modified by activity. A meta-analysis covering over 218,000 adults and nearly 20,000 children, showed that the risk of obesity among those with the FTO risk allele was reduced by more than 25 percent in individuals who were physically active but not among the sedentary.31

Epigenetics represents a biological mechanism through which social determinants can modulate the genome. Epigenetic processes, such as DNA methylation and chromatin modification, regulate developmental programming and cellular identity and serve as conduits through which the social environment can interact with the genome. It provides a mechanism though which the environment can regulate the transcriptional control of a gene that can persist for prolonged periods, even across generations. Seminal animal research demonstrated effects of early rearing on an animal's lifelong response to stress via epigenetic modifications of their hypothalamic-pituitary-adrenal (HPA) axis.32 In humans, early evidence is showing variation in methylation patterns across levels of psychological stress, early life socioeconomic status, and sociodemographic factors.33 Demethylation of the DNA near the glucocorticoid response element in FKBP5 gene may reflect an epigenetic process that underpins a previously demonstrated gene by environmental interaction between a polymorphism in the FKBP5 gene and prior exposure to childhood trauma in predicting rates of posttraumatic stress disorder (PTSD).34,35

Efforts to identify methylation status across the entire genome are underway, as are investigations to determine whether epigenetic modifications vary by tissue type. Although still a nascent area of work, it has great potential for linking socially-patterned exposures with the "omics" inside the body. It underlines the need for a more systematic mapping of the "exposome," including social and behavioral exposures along with physical and chemical exposures.

Overcoming Obstacles to Integration

Despite the promising examples given above, obstacles remain to creating a full, integrated model of health determinants to inform care for individuals and achieve optimal health of populations. Spanning the full range of levels requires harmonizing different methods, languages, and values. While true in any cross-disciplinary collaboration, specific issues arise in collaborations between researchers studying social and behavioral determinants and those working on biological processes.

Different Perspectives, Valued Differently

An iconic cover of the New Yorker magazine depicts the New Yorker's view of the United States. It has fine detail of streets east of the Hudson River, with a largely undifferentiated flat plane west of the Hudson to the Pacific. A subsequent variant depicts the reciprocal Californian's view.

Figure 1 shows the scientific equivalent of those two maps. On the top is the central figure from a New England Journal of Medicine (NEJM) article linking biomedical advances and deaths from cardiovascular disease.36 Below it, from a letter to the editor, the same graph depicts public health advances.37 Yet another letter, not shown here, shows the similarity of the graph to changing rates of cigarette consumption in the United States over the same time period.38

Figure 1a. Original depiction of biomedical contributions to drops in cardiovascular mortality

Figure shows a timeline running from 1950 to 2020 along with the major biomedical contributions that have led to reductions in cardiovascular mortality. It shows a fairly steep decline in cardiovascular deaths that began about 1972, from about 400 deaths per 100,000 population in 1972 to about 100 deaths per 100,000 population in 2010.

Source: Nabel EG, Braunwald E. A tale of coronary artery disease and myocardial infarction. New Engl J Med 2012;366:54-63. Used with permission.

Figure 1b. The public health perspective

Figure charts public health advances—including legislation, research, and important reports—over the same time period (1950-2020) that have contributed to declining cardiovascular mortality.

Source: Laing BY, Katz MH. Coronary arteries, myocardial infarction, and history. N Engl J Med 2012;366:1258-9. Used with permission.

The above examples show that the same data may be construed and understood in different ways. Under the right conditions, this divergence can spark new ideas and formulations. Discoveries often result from encounters across the bounds of disciplines where new ways of understanding findings can foster paradigm shifts. Ongoing interaction among researchers from different fields fosters such insights. The value of "water cooler" interactions was demonstrated in a study that found greater proximity among research collaborators, even within the same building, to be associated with a higher impact of their research.39 Unfortunately, the organization of most universities and academic health centers does not facilitate interactions between social and behavioral scientists and biomedical researchers. "Desktop" research is generally done in different places than is "wet lab" research.

Proximity alone will not guarantee meaningful interaction. Unless there is mutual respect and valuing of the perspective of other disciplines, interactions are less likely to result in meaningful engagement. Although rarely explicit, the view that social and behavioral sciences are less valuable for understanding disease than are biological sciences may inhibit productive encounters. The omission of behavioral and public health landmarks associated with drops in cardiovascular disease noted above is, regrettably, not an isolated example of social and behavioral data going unnoticed. Such data may be ignored because they are assumed to be less rigorous or valuable than evidence emanating from the bench. The scientific community implicitly construes a hierarchy in the value and prestige of various types of science. As Jared Diamond noted, even the terms describing hard versus soft science reflect this valuation; in the extreme, the former are the only ones qualifying as real sciences.40

Social and behavioral sciences may be viewed as less valuable because of the relatively greater challenge in operationalizing and controlling the variables they study. The gold standard for making causal inferences is the randomized experiment. However, the challenges of gaining sufficient control over social and behavioral factors to enable random assignment have fostered development of alternative methods to allow rigorous tests of predictions about causal associations. If the defining characteristic of good science is rigor in testing theoretical predictions against empirical findings, Diamond argued it would be more appropriate to view the sciences whose phenomena lend themselves easily to manipulation and control as the "easy" sciences, in contrast to those where the difficulty of doing so makes them "hard" sciences.40 Thus, rather than our current classification of "hard" versus "soft" sciences, biological sciences might be characterized as the "hard" sciences, with social and behavioral sciences characterized as the "harder" sciences.

The consequences of placing a lower valuation on social and behavioral sciences are difficult to quantify since they mostly represent lost opportunities. For example, social scientists were not eligible to become members of the National Academy of Sciences (NAS) until the mid-1970s. As a result, the perspectives and methods of these disciplines were lacking, or at the very least, underrepresented, in the deliberations, reports, and culture of the major institution providing science advice to the Nation. Within the Institute of Medicine of the NAS, the membership sections appropriate for social and behavioral scientists are substantially smaller than are most other sections. Funding for social and behavioral research constitutes a small fraction of the National Institutes of Health (NIH) budget, and the vast majority of intramural research is in the biological sciences. Taken together, these result in a lack of visibility of social and behavioral scientists in positions that would allow them to shape the culture of research and encourage inclusion of social and behavioral measures and analyses into interdisciplinary investigations.

Changing Views

Recent developments within and outside of the research world may enhance the perceived value of social and behavioral data and encourage greater collaboration among the range of disciplines. The Clinical & Translational Science Awards (CTSAs) established by NIH have helped initiate interdisciplinary programs in over 60 institutions that aim to advance the translation of research findings from "bench" to "bedside" to "community." Social and behavioral issues are inherent aspects of the translation of findings at the bench into better care and better health. Acceptability, adherence, adoption of innovation, and diffusion of knowledge all involve cognitive, affective, and social factors that need to be understood and addressed. Insofar as Clinical and Translational Science Institutes (CTSIs) will be evaluated for renewal not only on the basis of their bench science discoveries, but also by their ability to move these discoveries into practice and improve individual and population health, the CTSIs should be motivated to include social and behavioral scientists in their work.

A similar "pull" for social and behavioral data is coming from the health care system as a result of the co-occurrence of high health care cost and relatively poor population health outcomes. Just as NIH leaders enacted policies establishing CTSIs to increase the yield on NIH investments in basic research, Congress—following a recommendation from its policy advisory group, the Medicare Payment Advisory Commission (MEDPAC)—mandated the establishment of "Accountable Care Organizations" (ACOs), which hold health systems and providers financially at risk for poor health outcomes of the patients they serve. ACOs provide incentives for health providers and systems to address modifiable determinants of health. Given the powerful contribution of social and behavioral factors to health, health systems are motivated to address these factors in order to reduce utilization and cost. Solutions will not only require social and behavioral knowledge regarding effective translation of findings from the bench to the population, but also basic research on social and behavioral phenomena that are linked to onset or progression of disease.

Explicit examples of how social and behavioral understanding can improve diagnosis and treatment should accelerate this demand. Currently, for example, misclassifications regarding coronary heart disease (CHD) based on the Framingham Risk Score (FRS) results in both under- and over-treatment. The latter is especially likely in low socioeconomic status (SES) populations. Adding SES information to a patient's FRS has been shown to result in a better match of predicted cases of CHD with observed cases. The improved prediction exceeds that of adding information on genetic factors.41,42

The Diabetes Prevention Program provides another compelling example of the value of addressing behavioral determinants directly. This randomized trial assigned pre-diabetic patients to one of four arms, three of which involved drug interventions with standard lifestyle recommendations (Metformin in one and Rezulin in another, and placebo), and an intensive lifestyle intervention targeting exercise and diet. The Rezulin arm was ended early based on evidence of liver damage. Significantly fewer patients in the lifestyle intervention (4.8 percent) subsequently developed diabetes than did those assigned to Metformin (7.8 percent) or to placebo (11 percent).43

Finally, there is increasing interest in mining "big data" and using new analytic methods to test associations. By linking genomic information to biobanks, electronic health records, biosensor data, and environmental data, researchers are hoping to make new discoveries about the determinants of disease and the effectiveness of various treatments. One of the largest projects to date, the Research Program on Genes, Environment and Health (RPGEH) links data from electronic health records to biological specimens (i.e., saliva and blood samples) to enable genetic screening and to survey and geocode information to assess environmental and behavioral factors. Analyses may reveal direct effects of social and behavioral factors. However, even more important may be the interactions of these factors with environmental exposures and genetic vulnerabilities in determining disease risk. Such findings would fuel interest in gathering data on social and behavioral determinants and including these variables in analyses.

Increasing Visibility of Social and Behavioral Determinants

Social and behavioral determinants of disease may be underestimated in part because they involve factors that are more complex and less easily observed than are medical interventions. In addition, addressing these determinants often takes more personal effort; they are rarely modified once and for all by a given action. Understandably, people prefer simple, high-leverage solutions that involve a single action with long-lasting effects over those requiring ongoing action. Apart from considerations like cost, difficulty and side-effects, a one-time vaccination is likely to be preferred to a drug that has to be repeatedly taken. In turn, taking a drug is likely to be preferred to having to modify a behavior.

"Miracle cures" like the polio vaccine and the new drug, sofosbuvir (Sovaldi), for hepatitis C are examples of high-leverage discoveries. These fuel expectations that bench discoveries will provide solutions to health problems. Such solutions do not force us to change habitual patterns or gratifying behaviors. The quest for the "fat gene" to enable a pharmacologic solution to obesity is appealing; it promises a pill that would keep one's weight in check despite a poor diet or lack of exercise.

The occasional discovery of high-leverage cures can foster unrealistic expectations that similar cures will be found for a wide range of ailments. Research on the "availability heuristic" shows that people tend to overestimate the probability that a given event will occur if it is dramatic and concrete and can easily be brought to mind.44 The vividness and appeal of a new vaccine or drug encourages people to anticipate other such break-throughs.

The limitations of "break-through" treatments are harder to grasp and get less attention. For example, crizotinib (Xalkori) is a drug for lung cancer patients whose tumors have the right genetic match. One media account described its use as having "commuted" the "death sentence" of a diagnosis for lung cancer in a 64-year-old woman.45 This dramatic account failed to mention that only about 4 percent of lung cancer patients have tumors with the appropriate genetic composition to benefit from the drug, or that "progression-free added survival" is limited among those whose tumors have the right genetic match (7.7 months versus 3.0 months for traditional chemotherapy). While gaining even a few additional months is highly meaningful to a person with cancer, on a population basis, an addition of a few months for 4 percent of patients does not translate into a major overall advance in longevity.

In addition to overestimating the probability and value of future pharmacologic "cures," people may underestimate the obstacles to their effective use. In addition to cost (e.g., $84,000 for a typical course of Solvadi), obstacles include side-effects, behavioral demands for adherence, and possible interactions with other drugs. Currently, a quarter of newly prescribed drugs are never obtained by the patient,46 and half of those obtained are not taken as prescribed.47 The more prescriptions one has, the greater the chances for non-adherence, and this overload is likely to get worse as more drugs are made available for a variety of ailments.

The biases noted above not only affect the lay public, but researchers and funders as well. The preference for dramatic, easily visualized results may skew resources towards work on approaches that have a chance, no matter how slim, of resulting in a high-leverage cure. Such approaches may be favored, even if the likelihood of success and the population-wide impact are small, over research on more distal factors that may not eliminate a given problem but reduce risk across a much larger number of people. Explicit public debate is needed about the appropriate mix of research that varies on the probability and value of the potential results. In the meantime, social and behavioral researchers may want to more vividly describe the problems their work addresses and convey more compellingly to their scientific colleagues and the public alike how their findings are improving outcomes.

Market Forces

The rewards and demands of the market may affect what research questions are asked. Research that yields marketable products is likely to be favored over equally impactful research that does not produce a profitable commodity. The success of some genetically targeted drugs, along with substantial drops in the cost of gene sequencing, is likely to generate even greater interest in the genetic underpinnings of disease. Such interest is not problematic, but greater focus on genetic risk could shift attention and funding away from social and behavioral research.

Market forces associated with health care financing create demands for specific types of research. The predominant fee-for-service system provides little financial incentive for discovering or implementing social and behavioral interventions, and it has been difficult to make the business case for taking social and behavioral discoveries to scale. However, as noted earlier, changes in health care financing associated with the Affordable Care Act (ACA) and Accountable Care Organizations (ACOs) are pressuring health providers to seek effective ways to address the major drivers of disease and disability. They will, in brief, need to incorporate social and behavioral determinants of health into more traditional health care.

Finally, two trends will enable new types of research on social and behavioral determinants and create demand for the findings. One is the rapidly increasing development of sensors, mobile monitors, and digital communication. Their use will expand the reach of research on health determinants, and findings can inform improved design. Finally, increasing inclusion of social and behavioral data in electronic health records, as recommended in a recent IOM report,48 will not only support analyses linking these factors to disease risk and treatment efficacy, but also will enable providers to address these factors in their patients.

Conclusion

Advancing both individual and population health will require health systems to address the whole range of the determinants of health from genetic inheritance to the society in which we live. To accomplish this, knowledge generation needs to be supported across the entire spectrum. NIH represents the major force shaping the health research agenda of our Nation. Its mission is to "seek fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to enhance health, lengthen life, and reduce illness and disability."49 Each aspect of this mission requires an understanding of the role of social and behavioral factors.

While NIH supports this range of research, it has allocated far fewer resources to the social and behavioral determinants of health than to biological substrates of disease. The establishment of CTSAs should direct greater attention to the whole range of determinants and how they play out in all aspects of translation from bench to application in the real world. However, if these efforts are going to be successful, specific policies and plans for incorporating social and behavioral sciences as an integral part of the spectrum will be required. Achieving optimal health for the population requires not just the biological knowledge, but also its union with social and behavioral knowledge. Our biological bodies develop in a physical and social context. So, too, must our science.

Acknowledgments

We are grateful to Stephanie Chernitskiy for her able assistance in preparation of this manuscript. The opinions presented herein are those of the authors and do not necessarily represent the 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

Nancy E. Adler, PhD, Center for Health and Community, Department of Psychiatry, University of California San Francisco. Aric A. Prather, PhD, Center for Health and Community, Department of Psychiatry, University of California San Francisco.

Address correspondence to: Nancy E. Adler, PhD, University of California, San Francisco, 3333 California St., Suite 465, San Francisco, CA 94118; email Nancy.Adler@ucsf.edu.

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Nancy E. Adler Nancy E. Adler, PhD, is the Lisa and John Pritzker Professor of Psychology, in the Departments of Psychiatry and Pediatrics at the University of California, San Francisco, where she directs the Center for Health and Community. She directed the MacArthur Foundation Research Network on SES and Health, where she developed a widely used measure of subjective social status. Dr. Adler is a member of the American Academy of Arts and Sciences and the Institute of Medicine (IOM). She has received the James McKeen Cattell Award from the American Psychological Society, the Distinguished Scientific Award for the Application of Psychology from the American Psychological Association, the Marion Spencer Fay Award from the Institute for Women's Health and Leadership, the David Rall medal from the Institute of Medicine, and the Lloyd Holly Smith award and the Chancellor's Award for Advancement of Women from UCSF.
Aric A. Prather Aric A. Prather, PhD, is an Assistant Professor in the Department of Psychiatry, and Associate Director of the Center for Health and Community at the University of California, San Francisco. Prior to joining the faculty at UCSF, he completed a 2-year postdoctoral fellowship in the Robert Wood Johnson Foundation Health and Society Scholars program (RWJF-HSS). Dr. Prather is an affiliated faculty member at the UCSF Osher Center for Integrative Medicine, faculty member in the National Institute of Mental Health-sponsored postdoctoral program in Psychology and Medicine, program faculty in the RWJF-HSS program, and is on the executive board of the UCSF Center for Obesity Assessment, Study, and Treatment.

 

Page last reviewed July 2015
Page originally created September 2015
Internet Citation: Determinants of Health and Longevity. Content last reviewed July 2015. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/professionals/education/curriculum-tools/population-health/adler.html
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