Approximately 20 percent of the sample reported not having health insurance at baseline (). On almost every characteristic measured, the uninsured are in higher risk groups. The uninsured are more likely to be low income, living without a telephone or in a mobile home, not in the labor force, poorly educated, in poorer health, and current smokers.
Relationships among Health Insurance Status, Baseline Characteristics, and Unadjusted Mortality Rate
By the end of the follow-up period, 23,667 (3.5 percent of the weighted sample) had died. Controlling for age, the mortality rate is greater for uninsured than for insured respondents ().1
Mortality Rate, by Insurance Status and Attained Age, 18–64
However, after adjustment for the characteristics shown in , lacking health insurance at baseline is not independently associated with an increased risk of mortality (hazard ratio 1.03, 95 percent CI, 0.95–1.12) ().
Proportional Hazards for Survival Time, Adjusted for Baseline Characteristics
Other baseline characteristics are associated with probability of survival in expected directions: respondents who are male, older, African American, not married, with less education, not in the labor force, in poorer baseline health, and who smoke have higher mortality risk than others.
It is difficult to believe that lack of insurance is not associated with a greater risk of mortality, and I conduct a variety of subsidiary analyses in an attempt to understand whether the finding is robust to alternative analytic approaches.
The null effect observed in may result, in part, from changes in insurance status during the follow-up period—some of those who are uninsured gain coverage, while some of the insured lose coverage. Two analyses explore whether changes in insurance status might account for the lack of effect. First, the follow-up period is shortened to increase the likelihood of comparing people who were continuously insured and uninsured. There is no indication that the relationship between lack of insurance and mortality is greater when the follow-up period is shorter (Table S1
). Second, in 1993 and subsequent surveys, the NHIS asked uninsured respondents how long they had been uninsured. Respondents who were uninsured for longer periods of time before the interview are more likely than others to remain uninsured subsequent to the interview. If changes in insurance status after the interview attenuate the estimated effect of lack of coverage, the estimated effect should be larger among respondents reporting longer periods of uninsurance. However, there is no indication of a dose–response relationship between time uninsured and probability of survival (Table S2
Another possible explanation for the null effect is that many causes of death cannot be prevented by better health care. I reestimate the basic model, but limit the causes of death to causes thought to be amenable to better health care (Nolte and McKee 2003
). These “amenable” causes, including pneumonia, influenza, hypertension, diabetes, and cancers of the breast, cervix, and colon, account for approximately 15 percent of the deaths in the sample. There is no indication that lack of insurance has any effect on this subset of deaths (Table S3
). Nolte and McKee suggest that ischemic heart disease, which accounts for approximately 8 percent of the deaths in the sample, might potentially be included in the list of “amenable” causes. Replicating the analysis after classifying ischemic heart disease as “amenable” produces virtually identical results (data not shown).
Lack of insurance may have no effect on survival probabilities for the entire population, but might matter for those who are most vulnerable. I estimate the basic model in on subsets of the population—respondents with low income, low levels of education, those not in the labor force, those in poor health, those who are smokers, those who are 50–64 years at baseline—and find no significant effect of insurance among any disadvantaged subset (Table S4
I also investigate the relationship between lack of insurance and mortality after previously uninsured respondents turn 65. We might expect that the difference in mortality rates between the uninsured and insured in would narrow after people reach age 65, when Medicare coverage is virtually complete. However, the relationship between lack of insurance at baseline and mortality does not change after respondents turn 65 (Figure S1
), although relatively small numbers of under-65 NHIS respondents who are 65 and over during the follow-up period limit the power to observe a significant effect of Medicare on mortality. To test whether including deaths after age 65 affects the estimated relationship between lack of insurance and mortality, I censor the follow-up period when respondents reach age 65 and reestimate the model in . The results are no different from the uncensored model (Table S5
As anticipated by the discussion above, the estimated magnitude of the relationship between lack of insurance and mortality does depend on the variables that are controlled for in the analysis. When baseline health status is removed from the model, the hazard ratio for lack of insurance increases from the 1.03 estimated in to 1.10 (95 percent CI, 1.03–1.19) (). Omitting smoking status and body mass index from the model increases the hazard ratio to 1.20 (95 percent CI, 1.15–1.24). Omitting labor force participation increases the hazard ratio to 1.25, and omitting income increases it further to 1.37. Controlling only for age and gender, the hazard ratio is 1.71 (95 percent CI, 1.65–1.76). In the discussion below I consider the implications of these results.
Proportional Hazards Estimates of the Lack of Insurance on Survival Time, Controlling for Baseline Characteristics
Comparison to Previous Work
The results presented here are largely consistent with results from Sorlie and colleagues. Sorlie estimated a hazard ratio of 1.3 (95 percent CI, 1.0–1.6) for white males in a model controlling for age and income; in a similar model using NHIS data, I estimate a hazard ratio of 1.43 (95 percent CI, 1.34–1.53) (data not shown).
The results here appear to be different from the point estimate in the Franks study, but the 95 percent CIs overlap. The Franks study included privately insured and uninsured respondents who were 65 and older at the time of the interview, while my work excludes all respondents who were over 65 when interviewed, because almost all of them are covered by Medicare, and the few who are not are clearly different from those who are. Although there were relatively few over-65 non-Medicare respondents included in the Franks' study, approximately one third of them were uninsured, and their mortality rate was very high, possibly accounting for some of the difference between the results of the Franks study and the results reported here (P. Franks, personal communication, September 2, 2006).
Similarly, the point estimate in this study appears to be different than the point estimates in the studies using data on 51–61-year-olds from the HRS studies. Without adjustment for covariates, the crude relative risk in the NHIS data is 1.5 (n
=46,999; 95 percent CI, 1.38–1.53), compared with a crude relative risk of 1.77 (n
=8,789; 95 percent CI, 1.50–2.07) in the HRS data (Table S6
). Adjustment for covariates has virtually identical effects in both datasets, decreasing the point estimate of relative risk to 1.13 in the NHIS data and 1.35 in the HRS data.
The difference between the unadjusted mortality rates of the insured and uninsured is smaller in the NHIS data than in either the NHANES or the HRS data. I am unaware of any systematic reason why results would be different in the NHIS data.