We repeated the analysis of the relationship of insurance to mortality after forcing all covariates in the model. In this Cox proportional hazards analysis, we controlled for gender, age, race/ ethnicity (4 categories), income (poverty income ratio), education, current unemployment, smoking status (3 categories), regular alcohol use, self-rated health (4 categories), physicianrated health (4 categories), and BMI (4 categories). We tested for significant interactions between these variables and health insurance status (i.e., P<.05). We handled tied failure times by using the Efron method. We performed multiple sensitivity analyses to analyze the robustness of our results.
We developed a propensity score model and controlled for the variables in our previous models (with the exception of health insurance status), as well as marital status; household size; census region; number of overnight visits in hospital in past 12 months; number of visits to a physician in past 12 months; limitations in work or activities; job or housework changes or job cessation because of a disability or health problem; and number of self-reported chronic diseases, including emphysema, prior nonskin malignancy, stroke, congestive heart failure, hypertension, diabetes, or hypercholesterolemia. Next, we included the propensity score in the multivariable model with the indicator for insurance status. In addition, we tested for the effect of including those covered by Medicaid by using our original Cox model and the propensity score adjusted analysis.
In a subsidiary analysis, we excluded employment and self- and physician-rated health, as these covariates may be a result of limited access to health care because of uninsurance. To facilitate interpretation of our hazard ratio, we first replicated the calculation in the IOM report to estimate the number of US adults who die annually because of lack of health insurance. This approach applies the overall hazard ratio to 9-year age strata and sums these figures to arrive at an annual number of deaths attributable to lack of health insurance. We then recalculated this figure by using the slightly different approach utilized by the Urban Institute, which does not age stratify when calculating total mortality. We believe this approach to be more accurate than that used to produce the IOM estimate, as it calculates mortality from the entire age range that the hazard ratio was calculated from, as opposed to calculating mortality over 10-year age strata.23
RESULTS
We display baseline characteristics of the sample in Table 1; 9004 individuals contributed 80657 person-years of follow-up time between 1988 and 2000. Of these, 16.2% (95% confidence interval [CI]=14.1%, 18.2%) were uninsured at the time of interview.
Uninsurance was associated with younger age, minority race/ethnicity, unemployment, smoking, exercise (less than 100 METs per month), self-rated health, and lower levels of education and income (P<.001 for all comparisons). Regular alcohol use and physicianrated health were also associated with higher rates of uninsurance (P<.05 for both comparisons). By the end of follow-up in 2000, 351 individuals, or 3.1% (95% CI=2.5%, 3.7%) of the sample, had died (Table 1). Significant bivariate predictors of mortality included male gender (P=.04), age (P<.001), minority race/ ethnicity (P<.001), less than 12 years of education (P=.008), unemployment (P=.02), smoking (P<.001), regular alcohol use (P=.04), worse self-rated health status (P<.001), and worse physician-rated health status (P<.001).
In the model adjusted only for age and gender, lack of health insurance was significantly associated with mortality (hazard ratio [HR]=1.80; 95% CI=1.44, 2.26). In subsequent models adjusted for gender, age, race/ ethnicity, poverty income ratio, education, unemployment, smoking, regular alcohol use, self-rated health, physician-rated health, and BMI, lack of health insurance significantly increased the risk of mortality (HR=1.40; 95%CI=1.06,1.84; Table 2). We detected no significant interactions between lack of health insurance and any other variables. Our sensitivity analyses yielded substantially similar estimates.
Replicating the methods of the IOM panel with updated census data24,25 and this hazard ratio, we calculated 27424 deaths among Americans aged 25 to 64 years in 2000 associated with lack of health insurance. Applying this hazard ratio to census data from 200526 and including all persons aged 18 to 64 years yields an estimated 35327 deaths annually among the nonelderly associated with lack of health insurance. When we repeated this approach without age stratification, (thought by investigators at the Urban Institute to be an overly conservative approach)23 we calculated approximately 44789 deaths among Americans aged 18 to 64 years in 2005 associated with lack of health insurance.
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