The Impact of Health Insurance on Health
Institute for Social Research, University of Michigan, Ann Arbor, MI 48106-1248, USA. Annual Review of Public Health
(Impact Factor: 6.47).
02/2008; 29(1):399-409. DOI: 10.1146/annurev.publhealth.28.021406.144042
How does health insurance affect health? After reviewing the evidence on this question, we reach three conclusions. First, many of the studies claiming to show a causal effect of health insurance on health do not do so convincingly because the observed correlation between insurance and good health may be driven by other, unobservable factors. Second, convincing evidence demonstrates that health insurance can improve health measures of some population subgroups, some of which, although not all, are the same subgroups that would be the likely targets of coverage expansion policies. Third, for policy purposes we need to know whether the results of these studies generalize. Solid answers to the multitude of important questions about how specific health insurance policy options may affect health seem likely to be forthcoming only with investment of substantial resources in social experiments.
Available from: Charles J Courtemanche
- "An important part of the argument for universal coverage is the idea that health insurance improves health. As quoted by Yelowitz and Cannon (2010), Levy and Meltzer (2008) write, The central question of how health insurance affects health, for whom it matters, and how much, remains largely unanswered at the level of detail needed to inform policy decisions. . . . Understanding the magnitude of health benefits associated with insurance is not just an academic exercise[,] . . . it is crucial to ensuring that the benefits of a given amount of public spending on health are maximized (p. "
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ABSTRACT: In 2006, Massachusetts passed health care reform legislation designed to achieve nearly universal coverage through a combination of insurance market reforms, mandates, and subsidies that later served as the model for national reform. Using data from the Behavioral Risk Factor Surveillance System, we provide evidence that health care reform in Massachusetts led to better overall self-assessed health. Various robustness checks and placebo tests support a causal interpretation of the results. We also document improvements in several determinants of overall health: physical health, mental health, functional limitations, joint disorders, and body mass index. Next, we show that the effects on overall health were strongest among those with low incomes, nonwhites, near-elderly adults, and women. Finally, we use the reform to instrument for health insurance and estimate a sizeable impact of coverage on health.
Available from: Lawrence C. Pellegrini
- "Regression models may also be subject to omitted variable bias; both the labor market and health insurance expenditures' models may be correlated with other time varying confounders that influence the mortality rate, health status, healthcare spending, and healthcare professionals' employment. Nevertheless, causality among these relationships is ambiguous (Levy and Meltzer 2008). Instrumental variables approach is used to deal with endogeneity so that consistent estimates for the labor market effect are obtained. "
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ABSTRACT: The healthcare sector was one of the few sectors of the US economy that created new positions in spite of the recent economic downturn. Economic contractions are associated with worsening morbidity and mortality, declining private health insurance coverage, and budgetary pressure on public health programs. This study examines the causes of healthcare employment growth and workforce composition in the US and evaluates the labor market's impact on healthcare spending and health outcomes.
Data are collected for 50 states and the District of Columbia from 1999-2009. Labor market and healthcare workforce data are obtained from the Bureau of Labor Statistics. Mortality and health status data are collected from the Centers for Disease Control and Prevention's Vital Statistics program and Behavioral Risk Factor Surveillance System. Healthcare spending data are derived from the Centers for Medicare and Medicaid Services. Dynamic panel data regression models, with instrumental variables, are used to examine the effect of the labor market on healthcare spending, morbidity, and mortality. Regression analysis is also performed to model the effects of healthcare spending on the healthcare workforce composition. All statistical tests are based on a two-sided alpha significance of p<.05. Analyses are performed with STATA and SAS.
The labor force participation rate shows a more robust effect on healthcare spending, morbidity, and mortality than the unemployment rate. Study results also show that declining labor force participation negatively impacts overall health status (p<.01), and mortality for males (p<.05) and females (p<.001), aged 16-64. Further, the Medicaid and Medicare spending share increases as labor force participation declines (p<.001); whereas, the private healthcare spending share decreases (p<.001). Public and private healthcare spending also has a differing effect on healthcare occupational employment per 100,000 people. Private healthcare spending positively impacts primary care physician employment (p<.001); whereas, Medicare spending drives up employment of physician assistants, registered nurses, and personal care attendants (p<.001). Medicaid and Medicare spending has a negative effect on surgeon employment (p<.05); the effect of private healthcare spending is positive but not statistically significant.
Labor force participation, as opposed to unemployment, is a better proxy for measuring the effect of the economic environment on healthcare spending and health outcomes. Further, during economic contractions, Medicaid and Medicare's share of overall healthcare spending increases with meaningful effects on the configuration of state healthcare workforces and subsequently, provision of care for populations at-risk for worsening morbidity and mortality.
- "Mpembeni et al. (2007), for instance, studied the determinants of skilled delivery in Southern Tanzania where socio-economic status, distance to health facilities, education and knowledge of risk factors associated with delivery featured as key significant predictors. There is also extant literature on the determinants of health insurance, health care coverage and the effect of health insurance on health in general (Chatterjee and Gilliam 2009; Jütting 2005; Levy and Meltzer, 2008; Nketiah-Amponsah 2009). "
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