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Article Excerpt
In a widely cited report, the Institute of Medicine (IoM)'s Committee on the Consequences of Uninsurance estimated that the mortality rate of the uninsured is 25 percent higher than for otherwise similar people with health insurance (IoM 2002). The IoM estimated that 18,000 excess deaths occurred annually because 40 million Americans lacked insurance. Relying on the methods used by the IoM, Stan Dorn at the Urban Institute updated this work to reflect the growth in the uninsured and estimated that there were 22,000 excess deaths as a result of lack of insurance in 2006 (Dorn 2008).
At the time that the IoM published its report, only two studies had been conducted that analyzed the relationship between lack of insurance and mortality in a random sample of adults (Franks, Clancy, and Gold 1993; Sorlie et al. 1994), and the IoM relied heavily on these two studies to estimate that lack of insurance increased the mortality rate by 25 percent. The studies were similar to each other in design. Each started with a random sample of the U.S. population, and linked the survey participants to death certificate information to calculate the probabilities of survival over a follow-up period. Each study used Cox proportional hazards regression to estimate whether lack of insurance at baseline, controlling for other characteristics, was associated with elevated risk of subsequent mortality. Both studies estimated that lack of insurance was associated with approximately a 25 percent increased risk of mortality during the follow-up period. The apparent similarity of the results emboldened the IoM committee to conclude that lack of insurance was associated with a 25 percent increase in risk.
There are three reasons to be skeptical of the IoM conclusion that universal coverage would result in an annual reduction of 18,000 deaths. First, the 95 percent confidence intervals (CI) in each of the two studies were large, stretching from approximately no effect to increased mortality of 50 percent. Thus, even ignoring the next two concerns, we could be pretty sure that the right answer is somewhere between and 36,000 excess deaths.
Second, although the two studies were similar in general design, they were different in a crucial detail: the analysis using National Health and Nutrition Examination Survey (NHANES) data controlled for self-reported health status and smoking behavior, while these two variables were not available in the Current Population Survey (CPS) data. As I show below, if the study using CPS data had been able to control for self-reported health status and smoking behavior, the authors would almost certainly have concluded that their best estimate was that there was no difference in the survival probabilities of otherwise similar insured and uninsured persons. Given the wide CIs in both studies, the estimated null effect in the CPS study would not have been inconsistent with the estimate of a 25 percent effect in the NHANES study, but undoubtedly would have given the IoM Committee members pause in deciding whether or 25 percent or some other number was the best estimate of the excess mortality due to lack of insurance.
Third, as has been discussed by Levy and Meltzer (2004), it is difficult to draw inferences about the causal relationship between lack of insurance and mortality from the kinds of observational analyses relied upon by the IoM. The inferential difficulties inherent in observational studies lead us to prefer evidence from quasi-experimental or experimental designs. However, as discussed below, there is no experimental evidence and virtually no quasi-experimental evidence about the relationship between health insurance and mortality.
In this paper, I replicate the earlier observational studies, making three major improvements. First, I use a dataset that is substantially larger than that in the previous work, providing a much more precise estimate of the association between lack of insurance and mortality. Second, I explicitly consider the implications of alternative choices for the variables that are included on the right-hand side of the model. Third, I probe more deeply into understanding the associations between lack of insurance and mortality with a variety of subsidiary analyses.
The next section reviews the results of previous work that attempts to estimate the relationship between lack of insurance and mortality and discusses the difficulties in drawing inferences about causality from the results of observational analyses. Subsequent sections present data and methods, results, and discussion.
PREVIOUS WORK
A large body of research analyzes the relationship between lack of insurance and morbidity (Hadley 2003). Almost all of this work shows that the uninsured have worse health outcomes than the insured, although methodological difficulties plague many of the studies, and the magnitude of the estimated effects varies greatly with the outcome, the population, and the study methods.
At the time of the IoM report in 2002, only two published studies analyzed the relationship between lack of insurance and mortality in a random sample of adults. Key features of the two studies relied upon by the IoM in reaching the conclusion that lack of insurance led to a 25 percent increase in mortality risk are summarized in Table 1. As described above, the two studies were similar in design, differing primarily in the variables included on the right-hand side of the Cox proportional hazard regression--in particular, the inclusion of self-reported health status and smoking behavior in the NHANES study, and the exclusion of these variables from the CPS study.
Subsequent to the publication of the IoM report, two studies analyzed data on 51-61-year-olds interviewed in the 1992 Health and Retirement Survey (HRS) (McWilliams et al. 2004; Baker et al. 2006). These studies used a similar design to the previous work, and controlled for self-reported health status and health behaviors in addition to basic demographic and socioeconomic factors. Results from the HRS-based studies are consistent with results from the Franks study, but different from the results that Sorlie would have produced if the CPS data had included information on self-reported health and smoking status.
Helen Levy and David Meltzer assert that observational studies are "not likely to provide much insight into the causal effects of health insurance on health" (Levy and Meltzer 2004), and this review of the Franks, Sorlie, McWilliams, and Baker studies illustrates some of the reasons for their assertion.
One vexing problem is the question of which covariates should be included on the right-hand side of the analysis. The Franks, McWilliams, and Baker studies each included health and smoking status on the fight-hand side of the model, while the Sorlie...
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