Population Health: Behavioral and Social Science Insights

By Steven M. Teutsch, Katherine M. Butler, Paul A. Simon, and Jonathan E. Fielding

Abstract

Despite paying more for medical care than any other nation, health in the United States lags other developed nations. This is not altogether surprising given that the largest contributors to overall health are the social and physical environments and health behaviors, areas in need of greater investment. To understand the health consequences of policy and program interventions in these areas requires a very different approach than the reductionist, biomedical model of disease. Complex studies are required to understand how education and income, family and other social structures, community resources, and the natural and built environments interact to shape health and well-being. Indeed, by their very nature these factors are contextual and change over time. Outcomes often occur long into the future, making longitudinal studies difficult and costly. However, we can harness and synthesize existing information to identify and understand the consequences of interventions that naturally occur. Health impact assessments (HIAs) are a practical way to identify and understand the key health benefits and harms that may result from an intervention or be of concern to stakeholders, estimate the importance of each, recommend steps to enhance benefits or reduce harms, communicate findings to key stakeholders, and track the impact of the assessment on the decisionmaking process. We present three case studies to show how HIAs can influence policy choices.

Introduction

It is now widely recognized that health in the United States lags other developed nations and, worse, is slipping further and further behind.¹ The single measure where the United States far exceeds competitor nations is the amount we spend on clinical care. The concomitant toll these factors take on the Nation's productivity is enormous. The excessive costs are functionally a tax on our international competitiveness, as ill health increases health care costs to business as well as lost productivity from absenteeism and presenteeism.² Our investment in basic and applied clinical research as well as clinical care has been second to none,³ yet that investment fails to adequately address the primary drivers of health which lie outside the clinical care system. Health is commonly attributed 20 percent to clinical care, 30 percent to health behaviors, 40 percent to the social environment, and 10 percent to the built and natural environments.⁴ Failure to invest in the underlying drivers of health contributes to excessive medical care costs. In embracing this phenomenon, the Triple Aim (better health, better care, lower cost)⁵ recognizes that health will only be substantially improved if we attend to the underlying determinants of health. The larger problem will not be solved by more and better medical care alone.

To address behaviors, the social and physical environments require a very different approach than the reductionist, biomedical model of disease. Complex studies are required to understand how education and income, family structures and community resources, and the natural and built environments interact to shape health and well-being. Indeed, by their very nature these factors are contextual and change over time. Outcomes often occur long into the future making longitudinal studies difficult and costly. Most importantly, though, we can harness and synthesize existing information to identify and understand the consequences of interventions that naturally occur. Models are particularly important to assess long-term effects, since interventions to address them are challenging to conduct and generally not amenable to the most rigorous randomized study designs.⁶

The "Health in All Policies" (HIAP) framework recognizes the importance of policies and activities in other sectors in shaping health.⁷ A transportation project can influence physical activity patterns or contribute to pollution and asthma. Living wage laws can reduce stress and free-up time for parenting. Yet decisions in other sectors have traditionally not considered the health consequences of those decisions or what can be done to ameliorate them. Growing recognition that these issues are critical to health as well as the salience of health in decisionmaking has fostered the growth of HIAP internationally and in the United States.⁸

Good decisions require good information. However, definitive studies about the consequences of many decisions are seldom available. This is due in part to the complexity of policies and the context in which they will be implemented, but it is also because the policies affect upstream social and environmental determinants which have many ramifications that affect health. To understand even the most important health aspects requires the synthesis of information from many sources. To make the task even more challenging, decisions are often made over relatively short periods of time that limit the opportunity to conduct new studies.

Assessing Interventions

While there is growing recognition of the impact of the social environment on health, the evidence on the effectiveness of interventions remains scant. However important the issues may be for society, there is no equivalent to a private biomedical industry ready to capitalize on the findings of basic research and develop profitable products; and few resources for research are available in the not-for-profit or government services sectors. But additionally, demonstrating the effectiveness of social sector policies and programs on health has intrinsic challenges. While social factors have broad and profound impacts on health, they generally are less specific than outcomes of typical clinical interventions. The complex interrelationships differ mightily from the more tidy pathophysiological pathways of specific diseases. Hence the effectiveness of social and environmental interventions on health is inherently more difficult to measure.

The problem is compounded by the many social, personal, and environmental factors that contribute to health outcomes but vary over time. These confounders are rarely fully understood or measured, despite the fact that their interaction effects can be large. Short-term intervention effects are usually about processes—was the intervention implemented with high fidelity? What was the participation rate? Intermediate and long term health outcomes are scarce. To overcome these challenges, investigators pull information from multiple sources and synthesize it using techniques of systematic literature reviews, meta-analyses, and simulation modeling, which can be used to project longer-term health and economic outcomes.

From Application to Real-Time Decisionmaking

Programmatic and policy decisions need to be informed by the best available information. In fact, decisions are commonly made in the face of great uncertainty even when few data are available. In non-health sectors, health outcomes frequently are not considered at all, largely because they have neither appeared central to the decision process nor has anyone solicited or provided such information. Health impact assessments (HIAs) can fill this gap.

Health Impact Assessment

HIA is an important bridge between research and practice. It is a tool to identify and understand the key health benefits and harms that are of concern to stakeholders and those known to result from an intervention, estimate the importance of each, recommend steps to enhance benefits or reduce harms, communicate findings to key stakeholders, and track the impact of the assessment on the decisionmaking process. The National Research Council has described the process.⁹ Figure 1 shows the steps in conducting an HIA. We present three case studies of how HIAs can influence policy choices. Each HIA is described more fully elsewhere.

Figure 1. Framework for health impact assessment (HIA), summarizing steps and outputs

Source: Adapted from improving health in the United States: the role of health impact assessment. Washington, DC: National Academies Press; 2011. Used with permission.

Case Study 1: Menu Labeling

Laws that require menu labeling^a at large chain food restaurants as a means of reducing the obesity epidemic began generating interest and legislative support as early as 2006 with the passage of a local ordinance in New York City. The rationale for this strategy is at least threefold. Studies suggest that the dramatic growth in per capita consumption of restaurant food contributes heavily to the U.S. obesity epidemic.¹⁰ Restaurant super-sizing of food and beverage portions has become widespread and, unlike mandated calorie and nutrition information on packaged foods, such information is not readily apparent to customers at the point of purchase. In addition, studies show that most people, including nutritionists, greatly underestimate the caloric content of restaurant menu items.¹¹

In California, a menu labeling bill (Senate Bill 120)^b was introduced in 2007 to require posting calorie information on menus and menu boards at all chain restaurants with 15 or more in-State outlets. The public broadly supported the bill, but it was actively opposed by the California Restaurant Association and other trade organizations. After bruising political debate, it was passed by the State Assembly and Senate but vetoed by Governor Schwarzenegger.

A similar bill (Senate Bill 1420)^c was introduced in 2008 but was limited to chain restaurants with 20 or more in-State venues. To inform the decisionmaking process on the bill, the Los Angeles County (LAC) Department of Public Health staff reviewed related research literature to assess the strategy's potential impact on obesity. Finding very limited direct information, the department initiated an HIA using small studies and assumptions to project the potential impact of a menu labeling law on obesity in LAC, all else being equal. In the base case, it assumed that in response to calorie postings, 10 percent of restaurant patrons ordered reduced-calorie meals, resulting in an average reduction of 100 calories per meal. This is not a large reduction; changing from a large to a medium soft drink would save 95 calories, from a large to medium order of French fries saves 163 calories, and from a double meat to a single meat burger would save 244 calories. The analysis (Table 1) found that menu labeling would avert 41 percent of the 6.75 million pound average annual weight gain in the LAC population aged 5 years and older.¹² A sensitivity analysis (Table 2) suggested that substantially larger impacts could be realized if more patrons ordered reduced-calorie meals or if average per-meal calorie reductions increased. On the other hand, fewer individuals may actually reduce their consumption, and those who do may not achieve 100 calorie reductions in the meals they actually order. More perversely, some might actually increase their purchase of high-calorie offerings believing they provide better value! In addition, the study does not consider that reductions may be attenuated over time.

Table 1. Projected impact of menu labeling

Source: Adapted from Simon P, Jarosz CJ, Kuo T, et al. Menu labeling as a potential strategy for combating the obesity epidemic: a health impact assessment. Los Angeles County Department of Public Health, 2008. Available at http://www.publichealthadvocacy.org/printable/CCPHA_LAPHmlaspotentialstrategy.pdf. Used with permission.

*Cited in the U.S. District Court Declaration of Thomas R. Frieden, Commissioner of the New York City Department of Health and Mental Hygiene, July 5, 2007 (pg. 31).
**Los Angeles County Department of Public Health, Office of Health Assessment and Epidemiology, Health Assessment Unit, 2005 Los Angeles County Health Survey. Estimates are based on self-reported data by a random sample of 8,648 Los Angeles County adults and 6,032 parents/guardians of children 0-17 years, representative of the population of Los Angeles.
***Burton S, Cryer EH, Kees J, et al. Attacking the obesity epidemic: the potential health benefits of providing nutrition information in restaurants. Am J Publ Health 2006;96(9):1669-75.
†Bassett MT, Dumanovsky T, Huang C, et al. Purchasing behavior and calorie information at fast-food chains in New York City, 2007. Am J Publ Health 2008;98(8):1457-9.
‡Duyff RL. Complete Food and Nutrition Guide, 2nd ed. American Dietetic Association. Hoboken, NJ: John Wiley & Sons; 2002.
§California Department of Education. Physical Fitness Testing (PFT) program Web page. Available at: http:// www.cde.ca.gov/ta/tg/pf/. Accessed February 1, 2008.

Table 2. Sensitivity analysis of menu labeling impact on percentage of population weight gain averted

*Base Case

Source: Adapted from Simon P, Jarosz CJ, Kuo T, et al. Menu labeling as a potential strategy for combating the obesity epidemic: a health impact assessment. Los Angeles County Department of Public Health, 2008. Available at http://www.publichealthadvocacy.org/printable/CCPHA_LAPHmlaspotentialstrategy.pdf.

Despite the limitations noted above, the report influenced policy. The May 2008 study report came at the height of the public debate on the menu labeling bill, and it garnered extensive media coverage. Several department staff testified before State legislative committees on the HIA findings and on the toll of the obesity epidemic. One sponsor of the bill reported that the HIA was instrumental in negotiations with legislators and the governor. In response to the report, the County Board of Supervisors voted to implement a county menu labeling ordinance if the State bill was not enacted. In turn, the California Restaurant Association lowered its opposition, recognizing the advantages of a uniform statewide measure compared to the threat of this and similar action in other counties, enacting a patchwork of varying county ordinances. The bill was passed and signed into law by the Governor. Two years later, a similar measure was approved at the national level as part of Federal health care reform, pre-empting the California law.

The Department of Public Health's Environmental Health Division staff has begun to assess compliance with posted calorie counts on menus at large chain restaurants as part of their routine restaurant inspections. The Environmental Health staff intends to collect food specimens from randomly sampled chain restaurants to check the accuracy of posted calorie information. Grants also will be sought to conduct pre- and post-implementation surveys of customers at chain restaurants to assess the law's impact on menu selections and calories consumed. The calorie content of restaurant offerings will be monitored before and after implementation of the law to determine if restaurant operators have modified recipes to reduce the calorie content of menu items. Finally, the department will track the trajectory of the obesity epidemic, though the independent effects of the menu labeling law may be difficult to ascertain in the context of many complementary obesity prevention efforts.

Studies of the impact of menu labeling in New York City and Seattle have produced mixed results, some showing modest reductions in the caloric content of food purchases, others showing no effect.^13-15 However, these short-term studies reflect consumer response relatively soon after menu labeling began, and it will be important to replicate them to see whether the impacts grow or diminish as customers become more familiar with the calorie information. Results may depend on the degree to which the intervention is accompanied by community education that promotes the use and interpretation of the calorie information. Lastly, menu labeling is but one component of a comprehensive healthy diet strategy.

Perhaps the most interesting early impact of menu labeling is the restaurant industry's response. Newspaper articles and industry trade publications and Web sites report that many chains are reformulating their menus, reducing the caloric content of standard items, or adding new low-calorie options. We are unaware of any formal assessment or quantification of this phenomenon, but the anecdotal reports suggest that the menu labeling policy may have additional benefits not measured in the department's HIA.

Case Study 2: Equal Employment Opportunity and Mental Health

In 2004, major U.S. cities began revising employment policies to prevent discrimination against people with criminal records, which has a disproportionate effect on minority communities.¹⁶ This spurred a larger trend of looking into legislation at the State and Federal levels—an initiative widely known as "ban the box" to remove the box on employment applications that ask about criminal records. The ban-the-box movement and other efforts to revisit hiring laws coincided with an increased number of U.S. adults with arrest or conviction records and employers conducting background checks.¹⁷

In response to a proposed revision to employment policy in Illinois, the Adler School of Professional Psychology Institute on Social Exclusion initiated a Mental Health Impact Assessment (MHIA) in 2011 through work with Chicago's Englewood community to examine the impacts of using arrest information on mental health and well-being. The MHIA process introduced a "mental health lens" to an area of law typically evaluated in the context of civil rights or economic analyses. Peer-reviewed literature was available to describe the relationships between arrest, employment, and mental health; however, evidence was lacking to describe the interplay of important social determinants of health, such as social exclusion, income, and neighborhood conditions (Figure 2). To fill these data gaps, the MHIA employed a wide range of research activities such as community surveys, focus groups, and employer interviews in Englewood. Focus groups of job-seekers with arrest or other criminal records reported feeling "depressed," "hopeless," and "discouraged."

Based on the synthesis of evidence from the literature review, surveys, and interviews, the MHIA predicted that the updated employment policy prohibiting blanket exclusions of people with criminal records would likely decrease the severity of depression and psychological distress.¹⁸

Figure 2. Assessing mental health impacts of EEOC policy revisions with multiple data sources

Source: Todman L, Taylor JS, McDowell T, et al; Adler School of Professional Psychology, Institute on Social Exclusion. U.S. Equal Employment Opportunity Commission policy guidance: a mental health impact assessment; 2013. Used with permission.
Note: EEOC = Equal Employment Opportunity Commission; MHIA = mental health impact assessment.

The efforts in Illinois to revise the employment policy were thwarted. However, a similar initiative to enforce anti-discrimination laws at the Federal level was developing. The Equal Employment Opportunity Commission (EEOC) was seeking to provide updated research to strengthen its existing employment policy and soliciting input from stakeholders through a series of public meetings in 2011 and 2012. Over the course of the public comment periods, the Adler School of Professional Psychology submitted recommendations to the EEOC to emphasize the importance of mental health considerations in the guidance. The EEOC Commissioner recognized the importance of the MHIA recommendations and commented that the guidance was updated to reflect additional health research and analysis provided by the Adler School, with examples of how arrest and criminal records can have disparate impacts on minority populations.¹⁹ The 2012 final revision of the EEOC policy guidance states that arrest records cannot be routinely used as a basis for exclusion, and employers must justify exclusion with job-related reasons.

Aside from informing decisionmakers of mental health considerations, possibly one of the most significant impacts of the MHIA was the process itself. The MHIA team involved community members in every step of the process, from creating the research objectives to developing policy recommendations. Local youth helped to design survey questions, and community members were trained to carry out research tasks. Throughout this process, the Englewood residents became more aware of employment and civil rights law and developed a better understanding of their community health conditions.

By evaluating the proposed revision to EEOC's policy and contributing to social science research content in the policy, HIA proved to be an important tool to understand mental health impacts of an issue in unchartered territory for public health practice—the intersection of employment, criminal justice and civil rights.

Case Study 3: Living Wage Ordinance

In 2006, San Francisco's legislative board was in the midst of considering adoption of a living wage of $11 per hour for municipal workers and commissioned an economic analysis to study the potential effects. Since poverty is one of the strongest determinants of poor health,²⁰ the San Francisco Department of Public Health (SFDPH) recognized the value of conducting an HIA on the impending living wage decision.

SFDPH was invited to join the city legislators during policy discussions, and staff provided testimony on the potential health benefits of increasing the minimum wage. The SFDPH pointed out five key considerations regarding the relationship between income and health.²¹ Aside from low income wage earners not receiving health care benefits, low income is linked to poor health and disease. Low income neighborhoods lack access to basic needs, such as adequate food and housing. Difficult financial circumstances make it challenging to socialize with friends and family, even calling out of town relatives or inviting friends over to enjoy a meal together; this financial constraint may contribute to depression and social isolation. Low income neighborhoods tend to have fewer options for safe access to physical activity and public services, and therefore health-promoting activities are less common. Obesity, for example, is associated with socioeconomic conditions. And lastly, parents who are stressed financially may have to work more than one job, juggle irregular hours, and have less time to spend reading and conversing with their children. This may result in lower literacy rates and slower development of verbal skills among children.

In a quantitative approach to predict potential health benefits of adopting a living wage, SFDPH provided the San Francisco Board of Supervisors Budget and Finance Committee with estimates of decreased premature mortality risk, decreased sick days, and increased chances of children of workers completing high school (Table 3).²² Overall, the analysis concluded that adoption of an $11 per hour living wage may have long-term positive benefits on individual and community health. The HIA contributed significant health research findings to the policy debate, and in 2003, San Francisco increased the minimum wage from $6.75 to $8.50 per hour for over 50,000 city workers. As of 2014, the San Francisco minimum wage was increased to $10.74, approaching the $11 per hour examined in the HIA. An increase to $15 per hour is currently under consideration for the November 2014 ballot.

Table 3. Estimated health and educational effects on workers and their children resulting from adoption of a living wage for families with incomes of $20,000: San Francisco Bay Region, California, 1997-1999

Source: Bhatia R, Katz M. Estimates of health benefits from a local living wage ordinance. Am J Pub Health 2001;91(9):1398-1402. Used with permission.
Note: CI = confidence internval; ADL = activities of daily living; CES = Center for Epidemiologic Studies; OLS—ordinary least squares.

a = Effect measures for the 24- to 44-year age groups were used.
b = The probit models required specifying the values of all the model covariates; the values given above were calculated for a married 30-year White female with 2 children living in a metropolitan area.
c = The 2-part model used least squares regression on a log transformation of the dependent variable, with a conditional sample of subjects with positive values used for the outcome. The effect measure, elasticity, did not enable us to calculate confidence intervals.
*Backlund E, Sorlie P, Johnson N. The shape of the relationship between income and mortality in the United States. Evidence from the National Longitudinal Mortality Study. Ann Epidemiol 1996;6(1):12–20.
†Ettner SL. New evidence on the relationship between income and health. J Health Econ 1996;15 (1):67–85.
‡Duncan GJ, Yeung W, Brooks-Gunn J, et al. How much does childhood poverty affect the life chances of children? Am Sociol Rev 1998;63(3):406–24.

Discussion

The most important determinants of health and the underlying causes of health disparities lie outside the traditional health sector, are woven into the very fabric of our daily lives, and shape our health behaviors. Social science research undergirds our understanding of how the social environment affects health. That knowledge compels the population health system to work with other sectors to assure that health consequences are regularly considered in programmatic and policy decisions. HIAs provide a systematic way to assess the potential health effects of interventions and communicate the results to decisionmakers, community organizations, and other stakeholders. By engaging stakeholders and identifying health consequences of concern, HIAs can assess the size and strength of those outcomes and provide guidance on how benefits can be enhanced or harms mitigated.

Implications for Research and Practice

Although decisions often are made before all the desired evidence is available, decisionmakers need the best available scientific information. HIAs provide baseline characteristics and health projections, then estimate the changes that would occur if policies or programs were implemented, using systematic searches of the scientific literature as well as additional studies conducted to fill in important gaps. Existing meta-analyses may be used or new ones conducted. Focus groups and interviews of key opinion leaders use well-established qualitative social science methods to shed light on local issues and context. These inquiries often elicit perspectives that need to be included in HIAs—in other words, they are a mechanism for empowering communities and assuring that their concerns are captured, substantiated, and communicated.

Surveys and analyses of existing data provide quantitative information that can be incorporated into models to predict health outcomes. Nevertheless, significant information gaps are commonly encountered, highlighting gaps that need to be informed by carefully performed research. In the absence of high-quality studies, expert opinion complemented by sensitivity analyses is commonly used. Although economic and financial analyses are not regularly included in HIAs, they may be of keen interest to decisionmakers. Understanding the financial costs and benefits of an action, and to whom they accrue, can help identify strategies for gain sharing or mechanisms to make decisions more palatable to stakeholders.

HIAs can help engage decisionmakers and broaden their understanding of the health consequences of their choices. Since decisionmakers and communities acknowledge that health is one of the most important considerations in many policies, HIAs can robustly meet that need. They provide an important addition to the policy-analytic arsenal and can facilitate improvements in the social and physical environments.

Acknowledgements

We acknowledge the assistance provided by Dr. Lynn Todman and Dr. Tiffany McDowell, who are primary authors of the MHIA on Equal Employment Opportunity and Mental Health. The opinions presented herein are those of the authors and may not necessarily represent the position of the Agency for Healthcare Research and Quality, the National Institutes of Health, the U.S. Department of Health and Human Services, or the Los Angeles County Department of Public Health.

Authors' Affiliations

Los Angeles County Department of Public Health (ST, KB, PS, JEF); University of California Los Angeles (UCLA) Fielding School of Public Health (ST, PS, JEF); and UCLA Geffen School of Medicine (JEF), Los Angeles, CA.

Address correspondence to: Steven Teutsch, 841 Moon Ave., Los Angeles, CA 90065. email steventeutsch@gmail.com.

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^a. Adapted from Paul Simon and Suzanne Bogert, Tackling toxic food environments: A response to the obesity epidemic. In Fielding JE, Teutsch SM, eds. Public health practice: what works. Oxford University Press; 2013.

^b. See California Legislative Information, SB-120, Food Facilities: Nutritional Information. Available at http://eginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=200720080SB120.

^c. See California Nutrition Law: Information and Enforcement (SB-1420). Available at http://nutrition.levitas.com/.

	Steven M. Teutsch, MD, is an independent consultant, Adjunct Professor at the Fielding School of Public Health, UCLA, and Senior Fellow, Schaeffer Center, University of Southern California. Until 2014, he was the Chief Science Officer, Los Angeles County Public Health Department, where he continued his work on evidence-based public health and policy. Prior to that, he had been in the Outcomes Research and Management program at Merck. Before joining Merck, he was Director of the Division of Prevention Research and Analytic Methods at the Centers for Disease Control and Prevention. He has served as a member of the U.S. Preventive Services Task Force and chaired the U.S. Department of Health and Human Services Secretary's Advisory Committee on Genetics, Health, and Society. Dr. Teutsch is certified by the American Board of Internal Medicine and the American Board of Preventive Medicine and is a Fellow of the American College of Physicians and American College of Preventive Medicine.
	Katherine M. Butler, MPH, is a health impact assessment analyst with the Health Impact Evaluation Center at the Los Angeles County Department of Public Health, where she designs and leads health assessment projects to examine the benefits and risks of policy and program decisions. Her experience includes collaborating with private and public organizations, including criminal justice, education, natural resources and energy, and housing. Ms. Butler previously worked for a private environmental health consulting firm, where she managed a team of health professionals for a variety of health assessment and epidemiology projects in the United States and abroad. In 2014, she served as President of the Southern California Society for Risk Analysis.
	Paul Simon, MD, MPH, is the Director of the Division of Chronic Disease and Injury Prevention at the Los Angeles County Department of Public Health and an Adjunct Professor in the Department of Epidemiology at the UCLA School of Public Health. He oversees the Tobacco Control and Prevention Program, Nutrition and Physical Activity Program, Cardiovascular and School Health Program, Policies for Livable Active Communities and Environments (PLACE) Program, Injury and Violence Prevention Program, and Office of Senior Health. Dr. Simon also oversees a First 5 LA-funded early childhood obesity prevention project and a Centers for Disease Control and Prevention-funded Community Transformation Grant addressing obesity, tobacco use, and chronic disease prevention. Dr. Simon is board certified in pediatrics and preventive medicine.
	Jonathan E. Fielding, MD, MPH, MBA, MA, has contributed to the field of public health for more than 40 years and has served in a variety of leadership positions. He led the public health activities for Los Angeles County as Director of Public Health and Health Officer for 16 years. He also served as Commissioner of the First 5 LA Commission, which provides over $100 million in funding annually for programs to improve the health and development of children aged 5 and younger. In September 2014, Dr. Fielding retired from county government service and is currently a Distinguished Professor of Public Health and Pediatrics at the University of California, Los Angeles, where he has been a tenured faculty member since 1979.