Observational Studies Health Insurance

Literally hundreds of studies have examined the association between health insurance status and health status, and these studies have been reviewed in several comprehensive review articles, including several within the past few years (OTA, 1992; Brown et al., 1998).

These reviews have typically focused on important methodological issues such as how the sample of individuals with and without insurance is identified (e.g. identification at a site of care, in the community, etc.) and how health utilization and/or health outcomes are measured.

While the health utilization studies clearly suggest increases in utilization among those with health insurance, these reviews also emphasize that increases in utilization need not necessarily translate into improvements in health.

As a result, the reviews place less weight on results concerning effects on health care utilization than they place on results concerning effects on health. Nevertheless, the reviews are able to cite many studies that show a direct association between health insurance and health status.

The heath outcomes that are demonstrated to correlate with health insurance range from death, to objective physiologic measures of health such as hypertension, to subjective measures such as self-reported health status, to name few (See Brown et al., 1998).

In reviewing these studies, Brown et al. state “[b]ecause there were no randomized trials, none of the articles reviewed fulfills criteria for the highest quality evidence.”

This statement is not, strictly speaking, true; the RAND Health Insurance Experiment, discussed in more detail below, meets these criteria.

But it is mostly true, since there are hundreds of papers that attempt to study the effect of insurance on health using non-experimental data that do not account for the potential consequences of the non-random nature of insurance status.

Brown et al. present the results of these papers with very little comment on the implications of this non-experimental nature of insurance status other than that it prevents inference of “causal relationships”. We agree with this assessment but perhaps place more emphasis on it than do Brown et al.

Therefore, unlike Brown et al., we focus our analysis on studies that attempt to address this concern. We place this emphasis on the experimental and quasi-experimental studies because we are concerned that studies that do not exploit some random or quasi-random variation in insurance status are not able to provide clear evidence of the actual causal connection between insurance status and health.

This problem is most easily illustrated by considering simple comparisons of health status among persons with and without insurance.

Depending on the population studied, these uninsured persons may be young healthy people in entry-level jobs that lack health insurance, or older persons not yet eligible for Medicare but with health conditions that prevent them from purchasing insurance.

Thus the uninsured may be more or less healthy than others. This makes it difficult to determine by simple comparisons of the health status of the insured and uninsured whether any correlation between health insurance and health status reflects an effect of health insurance on health, an effect of health on health insurance status, or the effects of some third variable (such as age) on both health and health insurance status.

The vast majority of studies suggest a positive correlation between health insurance status and health. This suggests either a true positive effect of health insurance on health or a dominant tendency for some other factors such as income or education to be positively correlated with both health and health insurance.

However, there may also be important factors such as underlying illness that produce a downward (negative) bias on the observed relationship between having health insurance and health status. These effects are just some of the many complicating the relationship between health insurance and health that are illustrated by the many arrows in figure 1.

In an attempt to address such issues, some studies attempt to use multivariate analysis to control for observable differences between persons with and without insurance.

There is good evidence from a variety of sources that observable aspects of socioeconomic status such as education, income, and social integration are associated with improved health outcomes (Pincus, 1998; Ross and Mirowsky, 2000).

These same variables are also often associated with health insurance status. In studying the effects of health insurance on health, controlling for these factors may be useful if variation in insurance status is determined solely by such observable variables.

However, to the extent that observable differences are controlled for, the variation in insurance status that remains will be more heavily driven by unobservable differences between insured and uninsured people such as those illustrated above, and there is no guarantee that those unobservable attributes will be any less correlated with health outcomes than the observable attributes that have already been controlled for through multivariate analysis.

The result may be that analyses that control for observable covariates need not be less biased than analyses that do control for such differences. An interesting example is to consider the relationship between health insurance status and health around age 65.

As the study by Lichtenberg cited below describes, one sees a marked improvement in health status at age 65 when people become eligible for Medicare. This seems to suggest a positive effect of health insurance on health.

However, when one controls for the observable characteristic (age) that drives this variation and focuses in on persons with or without health insurance just below age 65, the relationship between health insurance status and health may now be complicated by such factors as the effects of preexisting illnesses that may decrease health and make it less likely someone can obtain or afford health insurance and thus create a negative association between having health insurance and health.

This helps illustrate the more general point that controlling for covariates need not improve our ability to accurately estimate the effects of health insurance on health.

Other largely unobservable factors that may also complicate understanding the relationship between health insurance status and health include underlying belief in the efficacy of health care or valuation of health, and similar factors that could affect care-seeking behaviors.

Some of the very best observational studies have attempted to address such concerns by considering plausibly exogenous health shocks such as motor vehicle accidents. For example, Doyle (2000) analyzes data on serious car crashes on Wisconsin.

Using data from police accident reports linked to hospital discharge records, he finds that the uninsured are significantly more likely than either the publicly or privately insured to die following a car accident in which they were initially incapacitated at the scene of the accident.

Although this study cleverly surmounts the problem of selection into initial treatment – both insured and uninsured accident victims are all taken to the hospital and, being incapacitated, have no say in the matter – such studies can never ensure that unobservable differences may not remain and affect outcomes.

For example, in Doyle’s study, it is possible that the even though the observable attributes of the auto accidents he observes occurring among insured and uninsured individuals are similar, that the accidents differ in some unobservable ways.

Even if the accidents are truly identical, a positive bias in the relationship between health insurance and health might be created if insured persons are more compliant patients or have better underlying baseline health status.

Alternatively, a negative bias might be created if insured persons have better access to home care so that those insured people who are hospitalized are likely to have more severe injuries on average.

There may be certain observational studies in which the such biases can be clearly signed or perhaps bounded in magnitude, but the complexity of the determinants of health status suggests that this will generally be a very difficult task, and we are not aware of any observational study that has been able to comprehensively address such concerns.

It is on this basis that we focus instead on quasiexperimental and experimental studies in what follows below.

Location: Indonesia