Population Health: Behavioral and Social Science Insights

By Richard Frank and Sherry Glied

Abstract

Economic research on behavioral health policy has informed the design of public policies that directly prevent the development of health burdens; contributed to improvements in the efficiency and effectiveness of care delivery; and led to improved outcomes for people with behavioral health problems through an understanding of the relationships between behavioral health and other services. In this chapter, we review the history of economic research on behavioral health and particularly its relationship to Arrow's analysis of market failures in the health care system and to Becker and Grossman's models of human and health capital. The theory of health capital formed the basis of a strand of economic research focused on unhealthy behavior, including an influential set of analyses showing that taxes and regulations on alcohol could reduce crime, accidents, and child abuse. Research building from Arrow's analysis of market failures has contributed to significant improvements in insurance design and provider payment and in enabling a shift in the locus of care from institutions to the community. Finally, the health production model provided a theoretical basis for research analyzing programs that focus on the interaction between mental health and other services, including research on supported housing and supported employment and research on spillovers from behavioral health to general health. The significant impact of economic research on policy stems from the discipline's emphasis on providing empirical estimates of the likely impacts of policy on both people and budgets.

Introduction

Economic research on behavioral health policy has contributed to well-being in three broad ways. First, this research has informed the design of behavioral health-oriented public policies that directly prevent the development of behavioral and physical health burdens. Second, research has informed the design of policies that affect the efficiency and effectiveness of care delivery and thus help more people gain the benefits of effective treatments. Third, economic research has improved outcomes by explicitly recognizing the interactions between behavioral health and other services and institutions that contribute to well-being.

Research has documented how social programs improve behavioral health and augment mental health care. It has also illustrated how behavioral health interventions improve social outcomes, leading to improvements in well-being that depend on access to both social programs and health interventions. In this chapter we review how the field of health economics has contributed to the development of behavioral health policy and how those policies have made a difference in the outcomes, efficiency, and fairness of behavioral health care in the United States. The chapter is organized into five sections. We begin with a brief overview of the intellectual traditions in health economics that serve as a foundation for applications to the behavioral health area. The subsequent three sections address the connection of research to policymaking in the areas of prevention of behavioral health problems, efficiency and effectiveness of care delivery, and interactions between behavioral health care and social policy. In the final section, we offer some concluding observations, discuss implementation issues, and consider implications for future research.

How Health Economics Has Approached Behavioral Health Issues

Economists have long been interested in problems of health and health policy. Economic research on behavioral health policy is a subset of this more general work. The earliest work in applying economics to health care took one of two approaches. The first, spearheaded by scholars such as Reuben Kessel¹ and Milton Friedman,² examined how the institutions of the health sector compared to those that would be expected in a perfectly competitive market for health services. These analyses often pointed to guilds and excessive government involvement as departures from this theoretical competitive market and from (ostensibly) efficient outcomes. A second line of analysis consisted of careful studies of how resources were used in the health sector.³ This line of research focused on providing rich, statistically-informed descriptions of the demand and supply of health care services and the financing of those services. Several researchers in this tradition examined issues of behavioral health policy. Rashi Fein provided the first systematic assessment of how money was spent on mental health care and a description of how mental health services were financed in the 1950s. These analyses were used to establish a foundation for efforts to shift behavioral health funding from institutional to community care.⁴

Beginning in the 1960s, two new lines of analysis were pioneered in economics that had profound effects for expanding the understanding of the health sector. The first flowed from an analysis of health care markets by Kenneth Arrow.⁵ That work, an application of the new economics of information, led to a philosophical shift away from a normative approach that viewed departures from a perfectly competitive market as inefficient policy failures. Instead, Arrow tried to understand the basis for institutions in the health care system that differed from those seen in perfectly competitive markets and to consider whether these impeded or promoted social well-being.

The second line of work stemmed from the research of Gary Becker on the concept of human capital. That area of research sought to understand how "human capital"—the productivity attributes of workers (education, health, experience)—contributed to the overall growth in economic productivity and national output.⁶ Human capital research was extended to include the analysis of how households produced and accumulated human capital, including health.

In the 1970s, researchers building on the framework set out by Arrow, began a systematic program of research into the health sector, including behavioral health. They developed theoretical models describing—among many other issues—how insurance affects the use of services (moral hazard); how private information can affect the functioning of insurance markets (adverse selection); how payment incentives affect physician and hospital behavior; how regulation of markets can improve efficiency when information on quality of care is difficult and costly to obtain; and the interplay among financing mechanisms. More recent theoretical research has incorporated new concepts, such as behavioral economics and economic sociology to the tool set of health economists. The insights of these various theoretical models suggested new directions for policy development.

The research flowing from the human capital tradition considered how individuals make trade-offs among leisure, consumption, and health in allocating their time and resources, a process labeled health production. The health production model was initially developed by Michael Grossman in 1970. His ideas led to a sea change in thinking about risky and unhealthy behaviors.⁷ Grossman argued that people made trade-offs among products and services that gave them enjoyment, subject to constraints that arose from their income, time, and knowledge. Good health could be understood as one among these enjoyment-producing products and services—but, products and services that were bad for health, such as tobacco, alcohol, and laziness, might also be among these enjoyment-producers. In this rational utility maximizing framework, raising the price of such "bads" could induce individuals to change the tradeoffs they made, without requiring that they alter their underlying preferences about what did or did not give them enjoyment. Recently, this approach has been extended to take account of addictive behavior, consumer short-sightedness, and other forms of irrational behavior. The applications have focused largely on cigarettes and addictive drugs.^8,9 This line of work is becoming increasingly influential with respect to policy addressing addictive behaviors.

In health economics, the application of theoretical models was almost immediately tied to empirical analyses that could directly inform public policy through estimation of the magnitude of theoretically-posited effects. The best known example of these efforts was the 1979 Health Insurance Experiment, which randomly assigned households to different levels of insurance coverage and measured spending and health outcomes.¹⁰

Improvements in data collection and in computing capacity further enabled this type of research. As we discuss in detail below, economists, using a variety of experimental and quasi-experimental methods, have assessed the magnitude of many of these effects, and these findings have better equipped private and public policymakers to design institutions and health policies. In some cases, empirical analyses of existing behaviors have been used to forecast the effects of future policy changes. In other cases, evaluations of policy changes that had been implemented have led to improvements and modifications. Empirical estimates have also been incorporated into cost-benefit and cost-effectiveness analyses that directly assess the net impact of a new technology or a policy change.

Health economics research has not produced new pills or procedures. But in similarly tangible ways, the theoretical and empirical findings of health economics research in behavioral health have given consumers, providers, insurers, and local, State, and Federal policymakers insights and a medicine chest of tools and techniques to improve behavioral health outcomes.

Preventing the Development of Behavioral Health Problems

Grossman's theory of health production⁷ is among the most influential conceptual approaches to prevention and early intervention for behavioral health problems. It led to a rich body of research on how changes in the price of "bads," induced by changing taxes or penalties, might affect individual behavior. In the behavioral health arena, much of this research focused on alcohol use. Grossman's model implied that, whatever the psychological and physiological antecedents of excessive alcohol use might be, changes in the price of alcohol would change patterns of utilization. Changes in alcohol prices could be induced by increases in alcohol taxes. Changes in use could be generated through legal restrictions that raised the cost of obtaining alcohol for at-risk populations (teenagers), and they could also be produced through increases in penalties associated with excessive alcohol use (sanctions on driving, criminal penalties).

One strand of this research took advantage of existing variation in the level of alcohol and beer taxes across States and localities within the United States. Quasi-experimental analyses exploiting this variation found that raising alcohol taxes led to reductions in alcohol consumption, as might be expected. More unexpected—but consistent with Grossman's model⁷—were a series of findings indicating that significant reductions in alcohol use had occurred among heavy drinkers, including those at greatest risk of experiencing (or inflicting) substantial harm through alcohol use. For example, zero tolerance laws, adopted by a number of States, reduced heavy episodic drinking by under-age males by 13 percent.¹¹

Studies of the impact of raising alcohol taxes consistently show that tax increases contribute to meaningful reductions in motor vehicle fatalities.¹² For example, the consensus estimates suggest that a roughly 6 percent increase in the beer tax reduces highway fatalities by about 2 percent.¹³ Raising alcohol taxes also reduces other pathological behaviors associated with heavy drinking. States that raise alcohol taxes see reductions in teenage pregnancies and abortions.¹⁴ Higher alcohol taxes are also associated with reduced arrests for child abuse.¹⁵ A 10 percent increase in alcohol taxes is estimated to reduce violence towards children by women by about 2.1 percent. Higher alcohol taxes even reduce the rates of child homicide.¹⁶ A State with 10 percent higher alcohol taxes can expect 1.9 percent fewer deaths of children by homicide than one with lower taxes.

Similar results were found from studies of other interventions that raise the effective price of alcohol to particular groups. Raising the minimum drinking age, which increases the price at which adolescents and young adults can gain access to alcohol, is associated with reductions in traffic fatalities and in crime.^17,18 In States with higher minimum drinking ages, there were significant reductions in non-violent crimes like vandalism. The implementation of zero-tolerance laws reduced property crime arrests by 3.4 percent and saved tens of billions of dollars over time.¹⁸

Rational actor models, such as those described above, generate the implication that policies designed to curb the use of "bads" reduce consumer well-being and, by extension, overall economic welfare, unless the policies have benefits for non-consumers (as is the case when alcohol taxes reduce drunk driving, for example). More recent research building on this framework has relaxed the assumption that actors are always fully rational.²⁰ In this newer literature, policies that lead to reductions in the consumption of "bads" also improve the well-being of the consumers themselves (and economic welfare). In these analyses, policies enable myopic and otherwise rationally constrained consumers to improve their own self-interested behavior.⁹ Empirical analyses of the health effects of policies to reduce "bads" support policy aimed at raising or equalizing alcohol taxes.

Improving the Efficiency and Effectiveness of Care Delivery

The analysis of markets for health insurance and health care services derives from the work of Arrow.⁵ From an economic standpoint, health care is plagued by problems of uncertain and asymmetric information. People know more about their own health than their insurers or providers do, and providers know more about the care they are providing than consumers do. The problems that Arrow identified in the general health sector, however, are compounded in the arena of behavioral health. Because the extent of behavioral health problems can rarely be measured with a physical diagnostic test and because behavioral health treatments often cannot be readily quantified either, information asymmetries are generally more complex and serious in behavioral health than in general health.

The particular challenges of behavioral health make it more difficult to design optimal financing and delivery incentives in this arena. As a consequence, considerable economic research has focused on addressing problems of moral hazard in the design of behavioral health insurance benefits; adverse selection in competitive behavioral health insurance markets; and agency in the payment of providers and programs that deliver behavioral health services.

Insurance coverage for behavioral health problems has long been far less complete than the coverage for other types of health care.²¹ The principal economic forces behind this result were two: moral hazard and adverse selection. Moral hazard is the term economists use for the increased utilization that occurs in the presence of insurance because insured consumers obtain care at a discount and do not face the full cost of services. The result of the insurance discount is a tendency to use "too much" of those services. The amount of the "overuse" depends on how responsive consumer demand for services is to their price—the elasticity of demand. An intensive program of research on the demand for mental health services, including both observational and experimental studies, found that under fee-for-service indemnity insurance arrangements, the elasticity of demand for ambulatory mental health services was about twice that for general medical care.^22,23 This implies that the cost of increasing the generosity of insurance coverage was considerably greater for mental health than for general medical care. This argument provided an efficiency rationale for the unequal mental health coverage that prevailed in the 1970s and 1980s.

Adverse selection also had significant effects on the form of coverage of mental health services in private insurance. Adverse selection occurs when consumers who expect that they are more likely than average to use a particular type of service buy the most comprehensive insurance coverage for these services.²⁴ This means that insurers with the most generous coverage will attract the least healthy segments of a population. This problem is particularly prevalent in behavioral health because there is strong predictability in mental health use.²⁵ The presence of adverse selection creates an incentive for insurers to adopt measures that will discourage unhealthy (and costly) enrollees from choosing their plans. They compete to avoid the highest risk clients. In behavioral health this pattern is consistent with the health insurance adage that the last thing one wants to be is the best mental health plan in a market. This competition leads to reduced coverage of behavioral health services, which provides an economic rationale for mandating plans to include behavioral health benefits and for parity statutes that require plans to cover behavioral health benefits at parity with general health benefits. Thus, health economics research in the 1980s contributed to understanding the efficiency rationales for (a) differential cost sharing for ambulatory mental health care under fee-for-service arrangements and (b) mandated behavioral health benefit statutes.

The fundamental economics of insurance coverage for behavioral health services were dramatically altered by the advent of managed behavioral health care in the 1990s. Managed behavioral health arrangements offered insurers and employers a method of controlling utilization and costs without resorting to reduced coverage. Managed care introduced a set of administrative and technological tools that permitted health plans to control utilization, negotiate lower prices, and apply clinical algorithms to the delivery of behavioral health care. These processes reduced moral hazard in the presence of more generous insurance coverage. Many managed behavioral health care contracts were structured so that a single managed behavioral health care vendor was selected to manage the care for all behavioral health services regardless of which general health plan among several was chosen by an enrollee, which eliminated competition to avoid high-cost cases. This addressed the problem of adverse selection.²² The results were impressive.²⁶ Spending growth in behavioral health was sharply reduced, and for some services (inpatient care), absolute spending was also reduced. The ability to control service use without changing cost sharing arrangements under insurance meant that the demand response to coverage was altered, changing the efficiency rationale for differential coverage for behavioral health. This was confirmed by empirical studies of demand.^27,28 Quasi-experimental studies of parity statutes further buttressed this logic, showing that coverage for behavioral health services could be expanded without increasing the level of total spending on behavioral health care over what it otherwise would have been.^29,30 These studies proved to be highly influential in Congressional consideration of legislation that expanded and mandated behavioral health coverage (Mental Health Parity and Addictions Equity Act^a; mental health provisions of the Affordable Care Act^b).

The policy impact of this line of research was that over a 10-year period, the Congressional Budget Office reduced its estimates of the expected costs of parity legislation by an order of magnitude, and the Congress became receptive enough to the parity idea that it enacted the Mental Health Parity and Addictions Equity Act in 2008.^c The Act did not constitute a mandate for coverage, but it did require that coverage for mental health and substance use disorder benefits, if offered, be equal to that for general benefits. The Affordable Care Act and its Essential Health Benefits regulations mandated mental health and substance use disorder coverage as an essential benefit. As the economics literature suggested would happen, the managed behavioral health care carve-out, a separate insurance sub-contract for mental health services, became a dominant approach to organizing insurance for behavioral health services.

Economic research also played a central role in the evolution of payment arrangements for insurers and providers. Economic analyses suggest that the incentives in fee-for service and other cost-based reimbursement arrangements can contribute to rapid cost increases and "overuse" of hospital care. Public policy began to depart from these reimbursement arrangements, shifting towards toward greater use of prospective payment for paying health care providers, including hospital prospective payments (the diagnostic related group, or DRG, system) in the 1980s and physician prospective payment (e.g., capitation) plans in the 1990s. Behavioral health services were largely insulated from this shift toward prospective payment because of several concerns.³¹ First, it is more challenging to develop relatively homogeneous diagnostic categories for behavioral health services than it is for other types of health care services. This makes it more difficult to adjust prospective payments for the riskiness of patients within the category, leaving more incentives for providers to avoid people with mental and addictive disorders.³² Second, empirical evidence based on provider behavior and the behavior of capitated health plans suggested that behavioral health care providers are more responsive to these new incentives than are general medical care providers, so that reductions in service use are much greater, and possibly excessive.²⁵ This may be due to the greater variability in clinical practice, to less developed quality assurance and measurement systems, or to weaker consumer knowledge. These findings created concern among policymakers that greater caution would be needed in applying prospective payment systems to behavioral health care. The Federal Government exempted specialty hospital and general hospital psychiatric units from Medicare's DRG case-based prospective payment system. Most managed behavioral health care carve-out contracts used a less intensive form of prospective payment—risk corridors—rather than purely capitated payment arrangements in their initial years. Improvements in case mix adjustment methodologies and monitoring practices have led to a greater use of prospective payment mechanisms over time. Capitation is used more frequently today to pay for managed behavioral health care, and Medicare now uses a modified prospective per diem payment system for inpatient psychiatric care.

More recently, these same economic considerations have prompted the use of pay-for-performance systems in general medical care. Pay-for-performance programs reward providers for meeting specific quality targets. As was the case for prospective payment, however, pay-for-performance has been introduced more slowly into the behavioral health arena.³³ The challenges of case-mix adjustments and of measuring relevant outcomes are more difficult in behavioral health than in general health care, and this area requires further research and policy development.

Another area where economic research on provider behavior has been important is in the balance between inpatient and community programs. Policymakers have many reasons to disfavor inpatient hospitalization, including issues of individual rights, concerns over coercive treatment, and the desire to integrate people with severe mental illnesses into their home communities. Publicly owned and operated inpatient psychiatric hospitals and specialty units constitute a significant source of inpatient service supply. These publicly-owned providers have historically been funded directly from State budgets and were frequently decoupled from community programs that were supposed to be providing community-based treatment for people with severe mental disorders.²¹ This created perverse incentives: since the community programs could rely on the hospital—an off-budget resource—to care for the sickest patients, community programs were often oriented to serving less severely ill segments of the population. Economic research on this pattern led some States to begin experimenting with new ways of transferring funds to local treatment programs so that there were stronger incentives to treat even the sickest people in community settings. Building on these insights, policy designers began to write contracts in which they charged community facilities for the costs they imposed on publicly-funded services.³⁴ For example, in Ohio, the State transferred funds to local programs based on the total expected cost of serving the population and then charged those programs the average per diem cost of publicly provided inpatient care for each day of care used. In addition, bonus payments were awarded to programs based on the number of clients with a severe and persistent mental illness they served. The result was a clear decline in inpatient care use attributable to the policy change. Other States used related approaches to restructure the intergovernmental transfer of mental health funds.³⁵

Together, shifting payment policy and the use of managed behavioral health care in insurance-like financing systems, alongside changes in intergovernmental transfer methods, were important contributors to the shift of treatment resources from institutional to community-based behavioral health care that mostly was realized in the period 1985 to 2000. Of course, economic policy was not the only factor leading to these changes. Other important factors included judicial rulings on the rights of patients, new treatments, and the expansion of disability programs.

As noted earlier, new financing approaches coupled with new approaches to treatment of the most severely mentally ill people in community programs allowed for major changes in mental health care delivery. Social sciences and economic research have been influential in understanding the value of the new approaches. Cost-benefit and cost-effectiveness studies of innovative treatment approaches provided evidence about the efficiency of community treatments.³⁶ That research documented which population segments were most efficient to target with the new programs.³⁷ It also showed some important limitations to the application of high intensity community-based treatment programs.³⁸ This research has contributed to widespread adoption of Assertive Community Treatment programs and efforts aimed at targeting these treatments to the populations that generate the greatest cost-effectiveness.

The net result of the confluence of these forces and the policy responses to the expanded understanding has been a mental health system that offers individuals with mental disorders greater autonomy in pursuing treatment, much more complete insurance coverage than at any time in U.S. history, a set of payment arrangements that promote community-based treatment of the most severely ill people, and a set of clinical and financing arrangements that offer more rewards for efficient delivery of care. It is striking to note that there has been a dramatic increase in the share of people with a mental disorder treated since the 1980s (over a 65 percent increase) while the share of GDP devoted to mental health care has hardly changed.³⁹

Interactions Between Behavioral Health and Social Policy

Several economic theories suggest that there are connections between behavioral health policy and social policy. As in the case of inpatient facilities and community treatment centers described above, the existence of several programs with different funding streams serving the same populations provides incentives to shift costs. Several analyses have examined the cost-shifting impact of cuts to one type of program on the delivery of services in others. Grossman's health production model,⁷ which provided the theoretical basis for empirical examinations of policies to reduce "bads," also offers two additional components of a framework for understanding the interactions between behavioral health and social welfare policy. First, the corollary of the effects of "bads" on health is that many goods, including but not limited to medical care, are inputs into the production of health. in general health, the model captures the idea that diet and exercise can substitute for medical care in the production of health. In behavioral health, the range of non-medical factors that can contribute to health is broader still, encompassing housing, employment, and security and stress or insecurity. Picking up on these ideas, health economists have examined the health and well-being benefits, as well as spillover effects, of several aspects of non-clinical care.

Inpatient facilities historically provided some people with serious and persistent mental illness with some treatment. They also provided this population with housing. In the era of deinstitutionalization, it quickly became evident that identifying and subsidizing housing options for people with serious mental illness would be critical to keeping them in the community. Housing was a key input into well-being; the cost of housing might offset the cost of keeping a patient in a psychiatric institution. Following this insight, a range of studies have documented that housing, especially supported housing, both enables people to live in the community and reduces hospitalization and other social costs.^40,41 At least seven controlled studies of permanent supported housing have been conducted, encompassing different populations and a range of settings across the United States, including residents of urban and rural locations, veterans, people with severe mental disorders, severe alcohol abusers, and those with a range of disabilities.^41-49 Only one out of the eight studies showed no significant savings. The other seven estimated savings that ranged from $1,514 per person to as much as $32,302 (converted to 2015 dollars).^46,49 Overall, these studies suggest that clinical and other service savings offset 10 percent to 100 percent of the costs of permanent supportive housing. Savings on the cost of homelessness shelters account for between 18 percent and 45 percent (local funding); Medicaid health services account for between 20 percent and 34 percent of savings; and mental health services (mostly inpatient) account for 20 percent to 40 percent of savings (mix of Federal and State funds), depending on the study. Only one or two studies have measured savings from detoxification (mostly State and local funds) and nursing home use (mix of Federal and State funds).^44,47 However, when they have been measured, these savings tend to be large.

In a similar vein, mental health policy advocates have argued that the experience of work is a vital component to well-being. Paid work may also have spillover effects, defraying the cost of maintaining a person in a hospital or in the community. Analyses of supported employment programs suggest that these programs can generate modest improvements in work.^50,51 In general, however, these increases in paid employment are not sufficient to reduce reliance on other income supports.

The availability of publicly-funded mental health services provides people with implicit insurance coverage for mental health needs. Expansions of insurance coverage can offer an alternative source of financing for this care. Recent economic research on an expansion of Medicaid coverage in Oregon suggests that providing people with symptoms of depression with health insurance has substantial effects on their well-being. Those who gained coverage through the expansion of Medicaid had a much higher rate of being diagnosed with depression (an increase from 4.8 percent to 8.6 percent, p<0.04) and were more likely to be receiving antidepressants (16.8 percent to 22.3 percent, p<0.07). In addition, the increased security that people experienced by having insurance coverage also appears to have had a direct effect on depression and mental health well-being.⁵²

The natural complement of spillover effects from social programs to mental health programs is that mental health may have spillover benefits on other dimensions of well-being. Likewise, a second aspect of Grossman's model7 is that it formalizes the idea that health is both a good that is desirable in itself (a consumption good) and also one that acts as an investment, enabling people to produce other valuable goods. The perspective of mental health as an investment good lies behind economic analyses of the role of better behavioral health in producing healthier children, more education, and better employment outcomes.

A growing body of evidence suggests that parental depression can harm young children. Postpartum depression has been related to poor parenting, childhood developmental delays, weaker cognitive skill development, attention disorders, and a greatly elevated rate of behavioral problems in children.⁵³ Research by economists has bolstered psychological studies showing these effects.⁵⁴ Economic analyses have strengthened the case that interventions that improve the circumstances of young children can be a very cost-effective strategy for improving adult outcomes.

Another dimension of spillovers from behavioral health to other outcomes relates to the labor market. Several studies have shown that improving depression care can reduce absenteeism and improve the productivity of employees at work. These improvements in productivity are positive spillovers from mental health treatment, and they offset a portion of the increased cost of that treatment.^55,56

Economic analyses of spillovers from mental health to other aspects of child and adult well-being have contributed to interest in the development and dissemination of evidence-based programs that address behavioral health in these contexts. Recent expansion of Home Visiting,^d and changes in Head Start standards, note the importance of parental mental well-being on child health.^e

Economic analyses of the spillover benefits of behavioral health intervention programs have been most explicitly incorporated in decisionmaking in the State of Washington, where the University of Washington Evidence-Based Practice Institute and the Washington State Institute for Public Policy consider the broad fiscal impacts of diverse behavioral health programs and make recommendations to the State legislature based on these analyses.⁵⁷ In that context, research documenting the benefits to the criminal justice system of programs to reduce conduct disorder in adolescents has led the State to fund such programs.

Conclusions

Economic analysis has contributed to behavioral health policy in several broad ways. Economists have provided a framework that has broadened and enhanced our thinking about policies to reduce dangerous behaviors, improve the design of coverage and provider payment, and link social programs and behavioral health treatments. Economic theory has pushed empirical research in the direction of studying causal mechanisms. The resulting empirical work has been useful and influential in informing public policy design. Economic evaluations of programs have led to improvements in policy design and implementation. Finally, an economic analysis of the magnitude of potential policy and budgetary effects has often been a key ingredient in the design and enactment of policies related to behavioral health. These policies, in turn, have had striking, large, and tangible effects on the well-being of people with mental illness.

As the discussion above suggests, many of the insights of economic research on behavioral health have been disseminated broadly and incorporated into policy and practice. Two features of economic research have contributed to its policy success. First, the discipline's emphasis on providing empirical estimates of the likely impacts of policy has made economic research valuable to assessments of specific potential policy interventions. Second, economic research often describes outcomes in budgetary and monetary terms (or can be easily adapted to do so), making the research readily comprehensible to policymakers.

Nonetheless, the path from economic research to policy has not always been smooth. Economic research results often challenge large and established interests. For example, while economic analysis has provided a very strong argument for raising alcohol taxes, these taxes remain well below the levels that research suggests would be optimal. Second, economic analysis often challenges conventional wisdom. It took some 15 years until the idea that parity coverage could be accomplished at little to no additional cost was accepted by policymakers. Third, research showing that one or another action improves the efficiency of the provider or insurance markets does not always trump distributional considerations. Changing incentives and funding streams generates both winners and losers, and this can stall even efficient policy changes.

Despite the significant contributions of economic research to behavioral health policy to date, many gaps remain in our understanding of how to design incentives and institutions to best serve people with behavioral health problems. The relatively new field of behavioral economics offers considerable potential for analyses that will improve the design of policies for preventing the development of mental health and substance use problems. The expansion of choice in health insurance markets through the development of the Affordable Care Act Marketplaces and private insurer counterparts, and new forms of prospective payment such as accountable care organizations, make further improvements in case mix and risk adjustment design even more vital. More research on assessing and paying for outcomes in behavioral health ought to accompany the increased attention to the use of incentives to generate improvements in quality through pay-for-performance throughout health care. Finally, much more research is needed to understand how best to address the incentives generated through the overlaps in services provided in the social service and behavioral health systems, including overlaps between behavioral health and disability income, housing, employment, and education.

Acknowledgments

We thank Stephanie Ma for excellent research assistance. 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, or the U.S. Department of Health and Human Services.

Authors' Affiliations

Richard Frank, PhD, is Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. Sherry Glied is Dean and Professor of Public Service, Robert F. Wagner Graduate School of Public Service, New York University.

Address correspondence to: Sherry Glied, PhD, 295 Lafayette Street, 2nd Floor, New York, NY 10012; email Sherry.Glied@nyu.edu.

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^a. Refer to Employee Benefits Security Administration, Department of Labor, at http://www.dol.gov/ebsa/mentalhealthparity.

^b. Refer to U.S. Department of Health and Human Services, Affordable Care Act, About the Law, at http://www.hhs.gov/healthcare/rights/.

^c. Refer to the Mental Health Parity and Addiction Equity Act of 2008 at https://www.federalregister.gov/articles/2013/11/13/2013-27086/final-rules-under-the-paul-wellstone-and-pete-domenici-mental-health-parity-and-addiction-equity-act.

^d. Refer to Maternal, Infant, and Early Childhood Home Visiting at http://mchb.hrsa.gov/programs/homevisiting/.

^e. Go to https://www.childwelfare.gov/topics/preventing/programs/homevisit/maternal-mental-health/ and http://eclkc.ohs.acf.hhs.gov/hslc/tta-system/family/docs/depression-pfce-rtp.pdf.

Richard G. Frank, PhD, is the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services, where he advises the Secretary on development of health, disability, human services, data, and science policy and provides advice and analysis on economic policy. He is on leave from his position as the Margaret T. Morris Professor of Health Economics in the Department of Health Care Policy at Harvard Medical School. Previously, Dr. Frank has served as the Deputy Assistant Secretary for Planning and Evaluation, directing the Office on Disability, Aging, and Long-Term Care Policy; research associate with the National Bureau of Economic Research; editor for the Journal of Health Economics; and Professor of Health Economics in the Department of Health Policy, Harvard Medical School. He was elected to the Institute of Medicine in 1997.

Sherry Glied, PhD, is the Dean of New York University's Robert F. Wagner Graduate School of Public Service. Her previous positions include Professor of Health Policy and Management at Columbia University's Mailman School of Public Health; Chair of the Department of Health Policy and Management at Mailman School of Public Health; Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services; and Senior Economist for Health Care and Labor Market Policy on the President's Council of Economic Advisors. Dr. Glied has been elected to the Institute of Medicine and the National Academy of Sciences.