Martin Karlsson, Tor Iversen, and Henning Øien
An open issue in the economics literature is whether healthcare expenditure (HCE) is so concentrated in the last years before death that the age profiles in spending will change when longevity increases. The seminal article “Ageing of Population and HealthCare Expenditure: A Red Herring?” by Zweifel and colleagues argued that that age is a distraction in explaining growth in HCE. The argument was based on the observation that age did not predict HCE after controlling for time to death (TTD). The authors were soon criticized for the use of a Heckman selection model in this context. Most of the recent literature makes use of variants of a two-part model and seems to give some role to age as well in the explanation. Age seems to matter more for long-term care expenditures (LTCE) than for acute hospital care. When disability is accounted for, the effects of age and TTD diminish. Not many articles validate their approach by comparing properties of different estimation models. In order to evaluate popular models used in the literature and to gain an understanding of the divergent results of previous studies, an empirical analysis based on a claims data set from Germany is conducted. This analysis generates a number of useful insights. There is a significant age gradient in HCE, most for LTCE, and costs of dying are substantial. These “costs of dying” have, however, a limited impact on the age gradient in HCE. These findings are interpreted as evidence against the “red herring” hypothesis as initially stated. The results indicate that the choice of estimation method makes little difference and if they differ, ordinary least squares regression tends to perform better than the alternatives. When validating the methods out of sample and out of period, there is no evidence that including TTD leads to better predictions of aggregate future HCE. It appears that the literature might benefit from focusing on the predictive power of the estimators instead of their actual fit to the data within the sample.
Andrea Gabrio, Gianluca Baio, and Andrea Manca
The evidence produced by healthcare economic evaluation studies is a key component of any Health Technology Assessment (HTA) process designed to inform resource allocation decisions in a budget-limited context. To improve the quality (and harmonize the generation process) of such evidence, many HTA agencies have established methodological guidelines describing the normative framework inspiring their decision-making process. The information requirements that economic evaluation analyses for HTA must satisfy typically involve the use of complex quantitative syntheses of multiple available datasets, handling mixtures of aggregate and patient-level information, and the use of sophisticated statistical models for the analysis of non-Normal data (e.g., time-to-event, quality of life and costs). Much of the recent methodological research in economic evaluation for healthcare has developed in response to these needs, in terms of sound statistical decision-theoretic foundations, and is increasingly being formulated within a Bayesian paradigm. The rationale for this preference lies in the fact that by taking a probabilistic approach, based on decision rules and available information, a Bayesian economic evaluation study can explicitly account for relevant sources of uncertainty in the decision process and produce information to identify an “optimal” course of actions. Moreover, the Bayesian approach naturally allows the incorporation of an element of judgment or evidence from different sources (e.g., expert opinion or multiple studies) into the analysis. This is particularly important when, as often occurs in economic evaluation for HTA, the evidence base is sparse and requires some inevitable mathematical modeling to bridge the gaps in the available data. The availability of free and open source software in the last two decades has greatly reduced the computational costs and facilitated the application of Bayesian methods and has the potential to improve the work of modelers and regulators alike, thus advancing the fields of economic evaluation of healthcare interventions. This chapter provides an overview of the areas where Bayesian methods have contributed to the address the methodological needs that stem from the normative framework adopted by a number of HTA agencies.
Matteo M. Galizzi and Daniel Wiesen
The state-of-the-art literature at the interface between experimental and behavioral economics and health economics is reviewed by identifying and discussing 10 areas of potential debate about behavioral experiments in health. By doing so, the different streams and areas of applications of the growing field of behavioral experiments in health are reviewed, by discussing which significant questions remain to be discussed, and by highlighting the rationale and the scope for the further development of behavioral experiments in health in the years to come.
Cristina Bellés-Obrero and Judit Vall Castelló
The impact of macroeconomic fluctuations on health and mortality rates has been a highly studied topic in the field of economics. Many studies, using fixed-effects models, find that mortality is procyclical in many countries, such as the United States, Germany, Spain, France, Pacific-Asian nations, Mexico, and Canada. On the other hand, a small number of studies find that mortality decreases during economic expansion. Differences in the social insurance systems and labor market institutions across countries may explain some of the disparities found in the literature. Studies examining the effects of more recent recessions are less conclusive, finding mortality to be less procyclical, or even countercyclical. This new finding could be explained by changes over time in the mechanisms behind the association between business cycle conditions and mortality.
A related strand of the literature has focused on understanding the effect of economic fluctuations on infant health at birth and/or child mortality. While infant mortality is found to be procyclical in countries like the United States and Spain, the opposite is found in developing countries.
Even though the association between business cycle conditions and mortality has been extensively documented, a much stronger effort is needed to understand the mechanisms behind the relationship between business cycle conditions and health. Many studies have examined the association between macroeconomic fluctuations and smoking, drinking, weight disorders, eating habits, and physical activity, although results are rather mixed. The only well-established finding is that mental health deteriorates during economic slowdowns.
An important challenge is the fact that the comparison of the main results across studies proves to be complicated due to the variety of empirical methods and time spans used. Furthermore, estimates have been found to be sensitive to the use of different levels of geographic aggregation, model specifications, and proxies of macroeconomic fluctuations.
Diane McIntyre, Amarech G. Obse, Edwine W. Barasa, and John E. Ataguba
Within the context of the Sustainable Development Goals, it is important to critically review research on healthcare financing in sub-Saharan Africa (SSA) from the perspective of the universal health coverage (UHC) goals of financial protection and access to quality health services for all. There is a concerning reliance on direct out-of-pocket payments in many SSA countries, accounting for an average of 36% of current health expenditure compared to only 22% in the rest of the world. Contributions to health insurance schemes, whether voluntary or mandatory, contribute a small share of current health expenditure. While domestic mandatory prepayment mechanisms (tax and mandatory insurance) is the next largest category of healthcare financing in SSA (35%), a relatively large share of funding in SSA (14% compared to <1% in the rest of the world) is attributable to, sometimes unstable, external funding sources. There is a growing recognition of the need to reduce out-of-pocket payments and increase domestic mandatory prepayment financing to move towards UHC. Many SSA countries have declared a preference for achieving this through contributory health insurance schemes, particularly for formal sector workers, with service entitlements tied to contributions. Policy debates about whether a contributory approach is the most efficient, equitable and sustainable means of financing progress to UHC are emotive and infused with “conventional wisdom.” A range of research questions must be addressed to provide a more comprehensive empirical evidence base for these debates and to support progress to UHC.
Since the 1980s policymakers have identified a wide range of policy interventions to improve hospital performance. Some of these have been initiated at the level of government, whereas others have taken the form of decisions made by individual hospitals but have been guided by regulatory or financial incentives. Studies investigating the impact that some of the most important of these interventions have had on hospital performance can be grouped into four different research streams. Among the research streams, the strongest evidence exists for the effects of privatization. Studies on this topic use longitudinal designs with control groups and have found robust increases in efficiency and financial performance. Evidence on the entry of hospitals into health systems and the effects of this on efficiency is similarly strong. Although the other three streams of research also contain well-conducted studies with valuable findings, they are predominantly cross-sectional in design and therefore cannot establish causation. While the effects of introducing DRG-based hospital payments and of specialization are largely unclear, vertical and horizontal cooperation probably have a positive effect on efficiency and financial performance. Lastly, the drivers of improved efficiency or financial performance are very different depending on the reform or intervention being investigated; however, reductions in the number of staff and improved bargaining power in purchasing stand out as being of particular importance.
Several promising avenues for future investigation are identified. One of these is situated within a new area of research examining the link between changes in the prices of treatments and hospitals’ responses. As there is evidence of unintended effects, future studies should attempt to distinguish between changes in hospitals’ responses at the intensive margin (e.g., upcoding) versus the extensive margin (e.g., increase in admissions). When looking at the effects of entering into a health system and of privatizations, there is still considerable need for research. With privatizations, in particular, the underlying processes are not yet fully understood, and the potential trade-offs between increases in performance and changes in the quality of care have not been sufficiently examined. Lastly, there is substantial need for further papers in the areas of multi-institutional arrangements and cooperation, as well as specialization. In both research streams, natural experiments carried out using program evaluation design are lacking. One of the main challenges here, however, is that cooperation and specialization cannot be directly observed but rather must be constructed based on survey or administrative data.
In many countries of the world, consumers choose their health insurance coverage from a large menu of often complex options supplied by private insurance companies. Economic benefits of the wide choice of health insurance options depend on the extent to which the consumers are active, well informed, and sophisticated decision makers capable of choosing plans that are well-suited to their individual circumstances.
There are many possible ways how consumers’ actual decision making in the health insurance domain can depart from the standard model of health insurance demand of a rational risk-averse consumer. For example, consumers can have inaccurate subjective beliefs about characteristics of alternative plans in their choice set or about the distribution of health expenditure risk because of cognitive or informational constraints; or they can prefer to rely on heuristics when the plan choice problem features a large number of options with complex cost-sharing design.
The second decade of the 21st century has seen a burgeoning number of studies assessing the quality of consumer choices of health insurance, both in the lab and in the field, and financial and welfare consequences of poor choices in this context. These studies demonstrate that consumers often find it difficult to make efficient choices of private health insurance due to reasons such as inertia, misinformation, and the lack of basic insurance literacy. These findings challenge the conventional rationality assumptions of the standard economic model of insurance choice and call for policies that can enhance the quality of consumer choices in the health insurance domain.
Peter Sivey and Yijuan Chen
Quality competition between alternative providers is an increasingly important topic in the health economics literature. This literature includes theoretical and empirical studies that have been developed in parallel to 21st-century policies to increase competition between doctors or hospitals. Theoretical studies have clarified how competitive markets can give healthcare providers the incentive to improve quality. Broadly speaking, if providers have an incentive to attract more patients and patients value quality, providers will raise quality until the costs of raising quality are equal to the additional revenue from patients attracted by the rise in quality. The theoretical literature has also investigated how institutional and policy parameters determine quality levels in equilibrium. Important parameters in models of quality competition include the degree of horizontal differentiation, the level of information about provider quality, the costs of switching between providers, and the time-horizon of quality investment decisions.
Empirical studies have focused on the prerequisites of quality competition (e.g., do patients choose higher quality providers?) and the impact of pro-competition policies on quality levels. The most influential studies have used modern econometric approaches, including difference-in differences and instrumental variables, to identify plausibly causal effects. The evidence suggests that in most contexts, quality is a determinant of patient choice of provider, especially after greater patient choice is made available or information is published about provider quality.
The evidence that increases in competition improve quality in healthcare is less clear cut. Perhaps reflecting the economic theory of quality competition, showing that different parameter combinations or assumptions can produce different outcomes, empirical results are also mixed. While a series of high-quality studies in the United Kingdom appear to show strong improvements in quality in more competitive areas following pro-competition reforms introducing more choice and competition, other studies showed that these quality improvements do not extend to all types of healthcare or alternative measures of quality.
The most promising areas for future research include investigating the “black box” of quality improvement under competition, and behavioral studies investigating financial and nonfinancial motivations for quality improvements in competitive markets.
Anna Vassall, Fiammetta Bozzani, and Kara Hanson
In order to secure effective service access, coverage, and impact, it is increasingly recognized that the introduction of novel health technologies such as diagnostics, drugs, and vaccines may require additional investment to address the constraints under which many health systems operate. Health-system constraints include a shortage of health workers, ineffective supply chains, or inadequate information systems, or organizational constraints such as weak incentives and poor service integration. Decision makers may be faced with the question of whether to invest in a new technology, including the specific health system strengthening needed to ensure effective implementation; or they may be seeking to optimize resource allocation across a range of interventions including investment in broad health system functions or platforms. Investment in measures to address health-system constraints therefore increasingly need to undergo economic evaluation, but this poses several methodological challenges for health economists, particularly in the context of low- and middle-income countries.
Designing the appropriate analysis to inform investment decisions concerning new technologies incorporating health systems investment can be broken down into several steps. First, the analysis needs to comprehensively outline the interface between the new intervention and the system through which it is to be delivered, in order to identify the relevant constraints and the measures needed to relax them. Second, the analysis needs to be rooted in a theoretical approach to appropriately characterize constraints and consider joint investment in the health system and technology. Third, the analysis needs to consider how the overarching priority- setting process influences the scope and output of the analysis informing the way in which complex evidence is used to support the decision, including how to represent and manage system wide trade-offs. Finally, there are several ways in which decision analytical models can be structured, and parameterized, in a context of data scarcity around constraints. This article draws together current approaches to health system thinking with the emerging literature on analytical approaches to integrating health-system constraints into economic evaluation to guide economists through these four issues. It aims to contribute to a more health-system-informed approach to both appraising the cost-effectiveness of new technologies and setting priorities across a range of program activities.
Helen Hayes and Matt Sutton
Contracts and working conditions are important influences on the medical workforce that must be carefully constructed and considered by policymakers. Contracts involve an enforceable agreement of the rights and responsibilities of both employer and employee. The principal-agent relationship and presence of asymmetric information in healthcare means that contracts must be incentive compatible and create sufficient incentive for doctors to act in the payer’s best interests. Within medicine, there are special characteristics that are believed to be particularly pertinent to doctors, who act as agents to both the patient and the payer. These include intrinsic motivation, professionalism, altruism, and multitasking, and they influence the success of these contracts. The three most popular methods of payment are fee-for-service, capitation, and salaries. In most contexts a blend of each of these three payment methods is used; however, guidance on the most appropriate blend is unclear and the evidence on the special nature of doctors is insubstantial. The role of skill mix and teamwork in a healthcare setting is an important consideration as it impacts the success of incentives and payment systems and the efficiency of workers. Additionally, with increasing demand for healthcare, changing skill mix is one response to problems with recruitment and retention in health services. Health systems in many settings depend on a large proportion of foreign-born workers and so migration is a key consideration in retention and recruitment of health workers. Finally, forms of external regulation such as accreditation, inspection, and revalidation are widely used in healthcare systems; however, robust evidence of their effectiveness is lacking.
Deborah J. Street and Rosalie Viney
Discrete choice experiments are a popular stated preference tool in health economics and have been used to address policy questions, establish consumer preferences for health and healthcare, and value health states, among other applications. They are particularly useful when revealed preference data are not available. Most commonly in choice experiments respondents are presented with a situation in which a choice must be made and with a a set of possible options. The options are described by a number of attributes, each of which takes a particular level for each option. The set of possible options is called a “choice set,” and a set of choice sets comprises the choice experiment. The attributes and levels are chosen by the analyst to allow modeling of the underlying preferences of respondents. Respondents are assumed to make utility-maximizing decisions, and the goal of the choice experiment is to estimate how the attribute levels affect the utility of the individual. Utility is assumed to have a systematic component (related to the attributes and levels) and a random component (which may relate to unobserved determinants of utility, individual characteristics or random variation in choices), and an assumption must be made about the distribution of the random component. The structure of the set of choice sets, from the universe of possible choice sets represented by the attributes and levels, that is shown to respondents determines which models can be fitted to the observed choice data and how accurately the effect of the attribute levels can be estimated. Important structural issues include the number of options in each choice set and whether or not options in the same choice set have common attribute levels. Two broad approaches to constructing the set of choice sets that make up a DCE exist—theoretical and algorithmic—and no consensus exists about which approach consistently delivers better designs, although simulation studies and in-field comparisons of designs constructed by both approaches exist.
Gabriella Conti, Giacomo Mason, and Stavros Poupakis
Building on early animal studies, 20th-century researchers increasingly explored the fact that early events—ranging from conception to childhood—affect a child’s health trajectory in the long-term. By the 21st century, a wide body of research had emerged, incorporating the original fetal origins hypothesis into the developmental origins of health and disease. Evidence from Organization for Economic Cooperation and Development (OECD) countries suggests that health inequalities are strongly correlated with many dimensions of socioeconomic status, such as educational attainment, and that they tend to increase with age and carry stark intergenerational implications. Different economic theories have been developed to rationalize this evidence, with an overarching comprehensive framework still lacking. Existing models widely rely on human capital theory, which has given rise to separate dynamic models of adult and child health capital within a production function framework. A large body of empirical evidence has also found support for the developmental origins of inequalities in health. On the one hand, studies exploiting quasi-random exposure to adverse events have shown long-term physical and mental health impacts of exposure to early shocks, including pandemics or maternal illness, famine, malnutrition, stress, vitamin deficiencies, maltreatment, pollution, and economic recessions. On the other hand, studies from the 20th century have shown that early interventions of various content and delivery formats improve life course health. Further, given that the most socioeconomically disadvantaged groups show the greatest gains, such measures can potentially reduce health inequalities. However, studies of long-term impacts as well as the mechanisms via which shocks or policies affect health, and the dynamic interaction among them, are still lacking. Mapping the complexities of those early event dynamics is an important avenue for future research.
While definitional and measurement problems pose a challenge, there is no doubt that disability affects a noticeable share of the population, the vast majority of whom live in low- and middle-income countries (LMICs). The still comparatively scarce empirical data and evidence suggests that disability is closely associated with poverty and other indicators of economic deprivation at both the country and—if with slightly greater nuance—at the individual/household level. There is also a growing body of evidence documenting the sizeable additional costs incurred by persons with disabilities (PwDs) as a direct or indirect consequence of their disability, underlining the increased risk of PwDs (and the households they are part of) falling under the absolute poverty line in any given LMIC.
Looking ahead, there remains considerable scope for more evidence on the causal nature of the link between disability and poverty, as well as on the (cost-)effectiveness of interventions and policies attempting to improve the well-being of PwDs.
Denzil G. Fiebig and Hong Il Yoo
Stated preference methods are used to collect individual level data on what respondents say they would do when faced with a hypothetical but realistic situation. The hypothetical nature of the data has long been a source of concern among researchers as such data stand in contrast to revealed preference data, which record the choices made by individuals in actual market situations. But there is considerable support for stated preference methods as they are a cost-effective means of generating data that can be specifically tailored to a research question and, in some cases, such as gauging preferences for a new product or non-market good, there may be no practical alternative source of data. While stated preference data come in many forms, the primary focus in this article will be data generated by discrete choice experiments, and thus the econometric methods will be those associated with modeling binary and multinomial choices with panel data.
Michael Drummond, Rosanna Tarricone, and Aleksandra Torbica
There are a number of challenges in the economic evaluation of medical devices (MDs). They are typically less regulated than pharmaceuticals, and the clinical evidence requirements for market authorization are generally lower. There are also specific characteristics of MDs, such as the device–user interaction (learning curve), the incremental nature of innovation, the dynamic nature of pricing, and the broader organizational impact. Therefore, a number of initiatives need to be taken in order to facilitate the economic evaluation of MDs. First, the regulatory processes for MDs need to be strengthened and more closely aligned to the needs of economic evaluation. Second, the methods of economic evaluation need to be enhanced by improving the analysis of the available clinical data, establishing high-quality clinical registries, and better recognizing MDs’ specific characteristics. Third, the market entry and diffusion of MDs need to be better managed by understanding the key influences on MD diffusion and linking diffusion with cost-effectiveness evidence through the use of performance-based risk-sharing arrangements.
Eline Aas, Emily Burger, and Kine Pedersen
The objective of medical screening is to prevent future disease (secondary prevention) or to improve prognosis by detecting the disease at an earlier stage (early detection). This involves examination of individuals with no symptoms of disease. Introducing a screening program is resource demanding, therefore stakeholders emphasize the need for comprehensive evaluation, where costs and health outcomes are reasonably balanced, prior to population-based implementation.
Economic evaluation of population-based screening programs involves quantifying health benefits (e.g., life-years gained) and monetary costs of all relevant screening strategies. The alternative strategies can vary by starting- and stopping-age, frequency of the screening and follow-up regimens after a positive test result. Following evaluation of all strategies, the efficiency frontier displays the efficient strategies and the country-specific cost-effectiveness threshold is used to determine the optimal, i.e., most cost-effective, screening strategy.
Similar to other preventive interventions, the costs of screening are immediate, while the health benefits accumulate after several years. Hence, the effect of discounting can be substantial when estimating the net present value (NPV) of each strategy. Reporting both discounting and undiscounted results is recommended. In addition, intermediate outcome measures, such as number of positive tests, cases detected, and events prevented, can be valuable supplemental outcomes to report.
Estimating the cost-effectiveness of alternative screening strategies is often based on decision-analytic models, synthesizing evidence from clinical trials, literature, guidelines, and registries. Decision-analytic modeling can include evidence from trials with intermediate or surrogate endpoints and extrapolate to long-term endpoints, such as incidence and mortality, by means of sophisticated calibration methods. Furthermore, decision-analytic models are unique, as a large number of screening alternatives can be evaluated simultaneously, which is not feasible in a randomized controlled trial (RCT). Still, evaluation of screening based on RCT data are valuable as both costs and health benefits are measured for the same individual, enabling more advanced analysis of the interaction of costs and health benefits.
Evaluation of screening involves multiple stakeholders and other considerations besides cost-effectiveness, such as distributional concerns, severity of the disease, and capacity influence decision-making. Analysis of harm-benefit trade-offs is a useful tool to supplement cost-effectiveness analyses. Decision-analytic models are often based on 100% participation, which is rarely the case in practice. If those participating are different from those not choosing to participate, with regard to, for instance, risk of the disease or condition, this would result in selection bias, and the result in practice could deviate from the results based on 100% participation. The development of new diagnostics or preventive interventions requires re-evaluation of the cost-effectiveness of screening. For example, if treatment of a disease becomes more efficient, screening becomes less cost-effective. Similarly, the introduction of vaccines (e.g., HPV-vaccination for cervical cancer) may influence the cost-effectiveness of screening. With access to individual level data from registries, there is an opportunity to better represent heterogeneity and long-term consequences of screening on health behavior in the analysis.
Sandra G. Sosa-Rubí and Omar Galárraga
Conditional economic incentives are a theoretically grounded approach for eliciting behavior change. The rationale stems from present-biased preferences, by which individuals attach greater value to benefits in the present and heavily discount long-term health. A growing literature documents the use of economic incentives in the HIV field. Small and frequent conditional economic incentives offered to vulnerable populations can contribute to behavior change. Economic incentives accompanied with other strategies can help overcome obstacles to access health services and in general seem to improve linkage to HIV care, prevention interventions, and adherence to HIV treatment. Future identification of promising combinations of intervention components, modalities, and strategies may yield maximum impact.
Jason M. Fletcher
Two interrelated advances in genetics have occurred which have ushered in the growing field of genoeconomics. The first is a rapid expansion of so-called big data featuring genetic information collected from large population–based samples. The second is enhancements to computational and predictive power to aggregate small genetic effects across the genome into single summary measures called polygenic scores (PGSs). Together, these advances will be incorporated broadly with economic research, with strong possibilities for new insights and methodological techniques.
Ya-Chen Tina Shih
The goal of cancer prevention and control is to reduce cancer risk, morbidity, and mortality through transdisciplinary collaborations across biomedical, behavioral, and social sciences. Risk reduction, early detection, and timely treatment are the rationales behind policy efforts to promote cancer prevention. Economics makes three important contributions to cancer prevention and control research. Firstly, research built upon the human capital model by Grossman and the insurance model by Ehrlich and Becker offers solid theoretical foundations to study human behaviors related to preventive care. Secondly, economic evaluation provides useful analytical tools to assess the “cancer premium” (through the stated preference research approach) and to identify the optimal screening strategy (through cost-effectiveness analysis). Lastly, the rich set of quantitative methods in applied economics contributes to the estimation of the relative contribution of prevention versus treatment in the reduction of cancer mortality and the evaluation of the impact of guidelines to regulate screening practices or policy initiatives to promote cancer screening.
Obesity is widely recognized as a chronic disease characterized by an elevated risk of adverse health conditions in association with excess body fat accumulation. Obesity prevalence reached epidemic proportions among adults in the developed world during the second half of the 20th century, and it has since become a major public health concern around the world, particularly among children and adolescents. The economics of childhood and adolescent obesity is a multi-faceted field of study that considers the numerous determinants, consequences, and interventions related to obesity in those populations.
The central economic framework for studying obesity is a life-cycle decision-making model of health investment. Health-promoting investments, such as nutritional food, healthcare, and physical activity, interact with genetic structure and risky health behaviors, such as unhealthy food consumption, to generate an accumulation or decumulation of excess body fat over time. Childhood and adolescence are the primary phases of physical and cognitive growth, so researchers study how obesity contributes to, and is affected by, the growth processes. The subdiscipline of behavioral economics offers an important complementary perspective on health investment decision processes, particularly for children and adolescents, because health investments and participation in risky health behaviors are not always undertaken rationally or consistently over time.
In addition to examining the proximate causes of obesity over the life cycle, economists study obesity’s economic context and resulting economic burden. For example, economists study how educational attainment, income, and labor market features, such as wage and work hours, affect childhood and adolescent obesity in a household. Once obesity has developed, its economic burden is typically measured in terms of excess healthcare costs associated with increased health risks due to higher obesity prevalence, such as earlier onset of, and more severe, diabetes. Obesity among children and adolescents can lead to even higher healthcare costs because of its early influence on the lifetime trajectory of health and its potential disruption of healthy development.
The formulation of effective policy responses to the obesity epidemic is informed by economic research. Economists evaluate whether steps to address childhood and adolescent obesity represent investments in health and well-being that yield private and social benefits, and they study whether existing market structures fail to appropriately motivate such investments. Potential policy interventions include taxation of, or restricting access to, obesogenic foods and other products, subsidization of educational programs about healthy foods and physical activity inside and outside of schools, ensuring health insurance coverage for obesity-related preventive and curative healthcare services, and investment in the development of new treatments and medical technologies.