In most countries, non-communicable diseases have taken over infectious diseases as the most important causes of death. Many non-communicable diseases that were previously lethal diseases have become chronic, and this has changed the healthcare landscape in terms of treatment and prevention options. Currently, a large part of healthcare spending is targeted at curing and caring for the elderly, who have multiple chronic diseases. In this context prevention plays an important role, as there are many risk factors amenable to prevention policies that are related to multiple chronic diseases. This article discusses the use of simulation modeling to better understand the relations between chronic diseases and their risk factors with the aim to inform health policy. Simulation modeling sheds light on important policy questions related to population aging and priority setting. The focus is on the modeling of multiple chronic diseases in the general population and how to consistently model the relations between chronic diseases and their risk factors by combining various data sources. Methodological issues in chronic disease modeling and how these relate to the availability of data are discussed. Here, a distinction is made between (a) issues related to the construction of the epidemiological simulation model and (b) issues related to linking outcomes of the epidemiological simulation model to economic relevant outcomes such as quality of life, healthcare spending and labor market participation. Based on this distinction, several simulation models are discussed that link risk factors to multiple chronic diseases in order to explore how these issues are handled in practice. Recommendations for future research are provided.
Pieter van Baal and Hendriek Boshuizen
Philip DeCicca, Donald S. Kenkel, Michael F. Lovenheim, and Erik Nesson
Smoking prevention has been a key component of health policy in developed nations for over half a century. Public policies to reduce the physical harm attributed to cigarette smoking, both externally and to the smoker, include cigarette taxation, smoking bans, and anti-smoking campaigns, among other publicly conceived strategies to reduce smoking initiation among the young and increase smoking cessation among current smokers. Despite the policy intensity of the past two decades, there remains debate regarding whether, and to what extent, the observed reductions in smoking are due to such policies. Indeed, while smoking rates in developed countries have fallen substantially over the past half century, it is difficult to separate secular trends toward greater investment in health from actual policy impacts. In other words, smoking rates might have declined in the absence of these anti-smoking policies, consistent with trends toward other healthy behaviors. These trends also may reflect longer-run responses to policies enacted many years ago, which also poses challenges for identification of causal policy effects. While smoking rates fell dramatically over this period, the gradient in smoking prevalence has become tilted toward lower socioeconomic status (SES) individuals. That is, cigarette smoking exhibited a relatively flat SES gradient 50 years ago, but today that gradient is much steeper: relatively less-educated and lower-income individuals are many times more likely to be cigarette smokers than their more highly educated and higher-income counterparts. Over time, consumers also have become less price-responsive, which has rendered cigarette taxation a less effective policy tool with which to reduce smoking. The emergence of tax avoidance strategies such as casual cigarette smuggling (e.g., cross-tax border purchasing) and purchasing from tax-free outlets (e.g., Native reservations in Canada and the United States) have likely contributed to reduced price sensitivity. Such behaviors have been of particular interest in the last decade as cigarette taxation has roughly doubled cigarette prices in many developed nations, creating often large incentives to avoid taxation for those who continue to smoke. Perhaps due to the perception that traditional policy has been ineffective, recent anti-smoking policy has focused more on the direct regulation of cigarettes and smoking behavior. The main non-price-based policy has been the rise of smoke-free air laws, which restrict smoking behavior in workplaces, restaurants, and bars. These regulations can reduce smoking prevalence and exposure to secondhand smoke among nonsmokers. However, they may also shift the location of smoking in ways that increase secondhand smoke exposure, particularly among children. Other non-tax regulations focus on the packaging (e.g., the movement towards plain packaging), advertising, and product attributes of cigarettes (e.g., nicotine content, cigarette flavor, etc.), and most are attempts to reduce smoking by making it less desirable to the actual or potential smoker. Perhaps not surprisingly, research in the economics of smoking prevention has followed these policy developments, though strong interest remains in both the evaluation of price- and non-price policies as well as any offsetting responses among smokers that may undermine the effectiveness of these regulations. While the past two decades have provided fertile ground for research in the economics of smoking, we expect this to continue, as governments search for more innovative and effective ways to reduce smoking.
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.
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.