Martin Karlsson, Tor Iversen, and Henning Øien
An open issue in the economics literature is whether health care 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 Health Care 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.
Dominic Hodgkin and Hilary S. Connery
Drug and alcohol use disorders, also called substance use disorders (SUD), are among the major health problems facing many countries, contributing a substantial burden in terms of mortality, morbidity, and economic impact. A considerable body of research is dedicated to reducing the social and individual burden of SUD.
One major focus of research has been the effectiveness of treatment for SUD, with studies examining both medication and behavioral treatments using randomized, controlled clinical trials. For opioid use disorder, there is a strong evidence base for medication treatment, particularly using agonist therapies (i.e., methadone and buprenorphine), but mixed evidence regarding the use of psychosocial interventions. For alcohol use disorder, there is evidence of modest effectiveness for two medications (acamprosate and naltrexone) and for various psychosocial treatments, especially for less severe alcohol use disorder syndromes. An important area for future research is how to make treatment more appealing to clients, given that client reluctance is an important contributor to the low utilization of effective treatments.
A second major focus of research has been the availability of medication treatments, building on existing theories of how innovations diffuse, and on the field of dissemination and implementation research. In the United States, this research identifies serious gaps in both the availability of SUD treatment programs and the availability of effective treatment within those programs. Key barriers include lack of on-site medical staff at many SUD treatment programs; restrictive policies of private insurers, states, and federal authorities; and widespread skepticism toward medication treatment among counseling staff and some administrators. Emerging research is promising for providing medication treatment in settings other than SUD treatment programs, such as community mental health centers, prisons, emergency departments, and homeless shelters.
There is still considerable room to make SUD treatment approaches more effective, more available, and—most importantly—more acceptable to clients.
One of the most fundamental results in health economics is that a greater socio-economic status is associated with better health outcomes. However, the experience of financial pressure and lack of resources transcends the notion of low income and poverty. Families of all income categories can experience financial pressure and lack of resources. This article reviews the literature examining the relationship between financial strain and various health outcomes. There are three main approaches to the measurement of financial strain found in the research literature, each one capturing a slightly different aspect: the family’s debt position, the availability of emergency funds, and inability to meet current financial obligations.
There are two main hypotheses explaining how financial strain may affect health. First, financial strain indicates a lower amount of financial resources available to individuals and families. This may have a dual impact on health. On the one hand, lower financial resources may lead to a decrease in consumption of substances such as tobacco that are harmful to health. On the other hand, lower financial resources may also negatively affect healthcare access, healthcare utilization, and adherence to treatment, with each contributing to a decline in health. Second, financial strain may produce greater uncertainty with regard to the availability of financial resources at present as well as in the future, thereby resulting in elevated stress, which may, in turn, result in poorer health outcomes. Examining the relationship between financial strain and health is complicated because it appears to be bidirectional. It is not only the case that financial strain may impact health but that health may impact financial strain.
The research literature consistently finds that financial strain has a detrimental impact on a variety of mental health outcomes. This relationship has been documented for a variety of financial strain indicators, including non-collateralized (unsecure) debt, mortgage debt, and the inability to meet current financial obligations. The research on the association between financial strain and health behavior outcomes is more ambiguous. As one example, there are mixed results concerning whether financial strain results in a higher likelihood of obesity. This research has considered various indicators of financial strain, including credit card debt and the inability to meet current financial obligations. It appears that both among adults and children there is no consistent evidence on the impact of financial strain on body weight. Similarly, the results on the impact of financial strain on alcohol use and substance abuse are mixed.
A number of significant questions regarding the relationship between financial strain and health remain unresolved. The majority of the existing studies focus on health outcomes among adults. There is a lack of understanding regarding how family exposure to financial strain can affect children. Additionally, very little is known about the implications of long-term exposure to financial strain. There are also some very important methodological challenges in this area of research related to establishing causality. Establishing causality and learning more about the implications of the exposure to financial strain could have important policy implications for a variety of safety net programs.
Audrey Laporte and Brian S. Ferguson
One of the implications of the human capital literature of the 1960s was that a great many decisions individuals make that have consequences not just for the point in time when the decision is being made but also for the future can be thought of as involving investments in certain types of capital. In health economics, this led Michael Grossman to propose the concept of health capital, which refers not just to the individual’s illness status at any point in time, but to the more fundamental factors that affect the likelihood that she will be ill at any point in her life and also affect her life expectancy at each age. In Grossman’s model, an individual purchased health-related commodities that act through a health production function to improve her health. These commodities could be medical care, which could be seen as repair expenditures, or factors such as diet and exercise, which could be seen as ongoing additions to her health—the counterparts of adding savings to her financial capital on a regular basis. The individual was assumed to make decisions about her level of consumption of these commodities as part of an intertemporal utility-maximizing process that incorporated, through a budget constraint, the need to make tradeoffs between health-related goods and goods that had no health consequences. Pauline Ippolito showed that the same analytical techniques could be used to consider goods that were bad for health in the long run—bad diet and smoking, for example—still within the context of lifetime utility maximization. This raised the possibility that an individual might rationally take actions that were bad for her health in the long run. The logical extension of considering smoking as bad was adding recognition that smoking and other bad health habits were addictive. The notion of addictive commodities was already present in the literature on consumer behavior, but the consensus in that literature was that it was extremely difficult, if not impossible, to distinguish between a rational addict and a completely myopic consumer of addictive goods. Gary Becker and Kevin Murphy proposed an alternative approach to modeling a forward-looking, utility-maximizing consumer’s consumption of addictive commodities, based on the argument that an individual’s degree of addiction could be modeled as addiction capital, and which could be used to tackle the empirical problems that the consumer expenditure literature had experienced. That model has become the most widely used framework for empirical research by economists into the consumption of addictive goods, and, while the concept of rationality in addiction remains controversial, the Becker-Murphy framework also provides a basis for testing various alternative models of the consumption of addictive commodities, most notably those based on versions of time-inconsistent intertemporal decision making.
Payment systems based on fixed prices have become the dominant model to finance hospitals across OECD countries. In the early 1980s, Medicare in the United States introduced the Diagnosis Related Groups (DRG) system. The idea was that hospitals should be paid a fixed price for treating a patient within a given diagnosis or treatment. The system then spread to other European countries (e.g., France, Germany, Italy, Norway, Spain, the United Kingdom) and high-income countries (e.g., Canada, Australia). The change in payment system was motivated by concerns over rapid health expenditure growth, and replaced financing arrangements based on reimbursing costs (e.g., in the United States) or fixed annual budgets (e.g., in the United Kingdom).
A more recent policy development is the introduction of pay for performance (P4P) schemes, which, in most cases, pay directly for higher quality. This is also a form of regulated price payment but the unit of payment is a (process or outcome) measure of quality, as opposed to activity, that is admitting a patient with a given diagnosis or a treatment.
Fixed price payment systems, either of the DRG type or the P4P type, affect hospital incentives to provide quality, contain costs, and treat the right patients (allocative efficiency). Quality and efficiency are ubiquitous policy goals across a range of countries.
Fixed price regulation induces providers to contain costs and, under certain conditions (e.g., excess demand), offer some incentives to sustain quality. But payment systems in the health sector are complex. Since its inception, DRG systems have been continuously refined. From their initial (around) 500 tariffs, many DRG codes have been split in two or more finer ones to reflect heterogeneity in costs within each subgroup. In turn, this may give incentives to provide excessive intensive treatments or to code patients in more remunerative tariffs, a practice known as upcoding. Fixed prices also make it financially unprofitable to treat high cost patients. This is particularly problematic when patients with the highest costs have the largest benefits from treatment. Hospitals also differ systematically in costs and other dimensions, and some of these external differences are beyond their control (e.g., higher cost of living, land, or capital). Price regulation can be put in place to address such differences.
The development of information technology has allowed constructing a plethora of quality indicators, mostly process measures of quality and in some cases health outcomes. These have been used both for public reporting, to help patients choose providers, but also for incentive schemes that directly pay for quality. P4P schemes are attractive but raise new issues, such as they might divert provider attention and unincentivized dimensions of quality might suffer as a result.
Marjon van der Pol and Alastair Irvine
The interest in eliciting time preferences for health has increased rapidly since the early 1990s. It has two main sources: a concern over the appropriate methods for taking timing into account in economics evaluations, and a desire to obtain a better understanding of individual health and healthcare behaviors. The literature on empirical time preferences for health has developed innovative elicitation methods in response to specific challenges that are due to the special nature of health. The health domain has also shown a willingness to explore a wider range of underlying models compared to the monetary domain. Consideration of time preferences for health raises a number of questions. Are time preferences for health similar to those for money? What are the additional challenges when measuring time preferences for health? How do individuals in time preference for health experiments make decisions? Is it possible or necessary to incentivize time preference for health experiments?
Hans Olav Melberg
End-of-life spending is commonly defined as all health costs in the 12 months before death. Typically, the costs represent about 10% of all health expenses in many countries, and there is a large debate about the effectiveness of the spending and whether it should be increased or decreased. Assuming that health spending is effective in improving health, and using a wide definition of benefits from end-of-life spending, several economists have argued for increased spending in the last years of life. Others remain skeptical about the effectiveness of such spending based on both experimental evidence and the observation that geographic within-country variations in spending are not correlated with variations in mortality.
Alexandrina Stoyanova and David Cantarero-Prieto
Long-term care (LTC) systems entitle frail and disabled people, who experience declines in physical and mental capacities, to quality care and support from an appropriately trained workforce and aim to preserve individual health and promote personal well-being for people of all ages. Myriad social factors pose significant challenges to LTC services and systems worldwide. Leading among these factors is the aging population—that is, the growing proportion of older people, the main recipients of LTC, in the population—and the implications not only for the health and social protection sectors, but almost all other segments of society. The number of elderly citizens has increased significantly in recent years in most countries and regions, and the pace of that growth is expected to accelerate in the forthcoming decades. The rapid demographic evolution has been accompanied by substantial social changes that have modified the traditional pattern of delivery LTC. Although families (and friends) still provide most of the help and care to relatives with functional limitations, changes in the population structure, such as weakened family ties, increased participation of women in the labor market, and withdrawal of early retirement policies, have resulted in a decrease in the provision of informal care. Thus, the growing demands for care, together with a lower potential supply of informal care, is likely to put pressure on the provision of formal care services in terms of both quantity and quality. Other related concerns include the sustainable financing of LTC services, which has declined significantly in recent years, and the pursuit of equity.
The current institutional background regarding LTC differs substantially across countries, but they all face similar challenges. Addressing these challenges requires a comprehensive approach that allows for the adoption of the “right” mix of policies between those aiming at informal care and those focusing on the provision and financing of formal LTC services.
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.
Economics can make immensely valuable contributions to our understanding of infectious disease transmission and the design of effective policy responses. The one unique characteristic of infectious diseases makes it also particularly complicated to analyze: the fact that it is transmitted from person to person. It explains why individuals’ behavior and externalities are a central topic for the economics of infectious diseases. Many public health interventions are built on the assumption that individuals are altruistic and consider the benefits and costs of their actions to others. This would imply that even infected individuals demand prevention, which stands in conflict with the economic theory of rational behavior. Empirical evidence is conflicting for infected individuals. For healthy individuals, evidence suggests that the demand for prevention is affected by real or perceived risk of infection. However, studies are plagued by underreporting of preventive behavior and non-random selection into testing. Some empirical studies have shown that the impact of prevention interventions could be far greater than one case prevented, resulting in significant externalities. Therefore, economic evaluations need to build on dynamic transmission models in order to correctly estimate these externalities. Future research needs are significant. Economic research needs to improve our understanding of the role of human behavior in disease transmission; support the better integration of economic and epidemiological modeling, evaluation of large-scale public health interventions with quasi-experimental methods, design of optimal subsidies for tackling the global threat of antimicrobial resistance, refocusing the research agenda toward underresearched diseases; and most importantly to assure that progress translates into saved lives on the ground by advising on effective health system strengthening.