This study was based on more than 12,000 in-person interviews conducted approximately 2 years after a lottery that randomly assigned access to Medicaid for low-income, able-bodied, uninsured adults — a group that comprises the majority of persons who are newly eligible for Medicaid under the 2014 expansion.12 The results confirm that Medicaid coverage increased overall health care utilization, improved self-reported health, and reduced financial strain; these findings are consistent with previously published results based on mail surveys conducted approximately 1 year after the lottery.4 With these new data, we found that increased health care utilization observed at 1 year persisted, and we present new results on the effects of Medicaid coverage on objectively measured physical health, depression, condition-specific treatments, and other outcomes of interest.

Medicaid coverage had no significant effect on the prevalence or diagnosis of hypertension or high cholesterol levels or on the use of medication for these conditions. It increased the probability of a diagnosis of diabetes and the use of medication for diabetes, but it had no significant effect on the prevalence of measured glycated hemoglobin levels of 6.5% or higher. Medicaid coverage led to a substantial reduction in the risk of a positive screening result for depression. This pattern of findings with respect to clinically measured health — an improvement in mental health but not in physical health (Table 2) — was mirrored in the self-reported health measures, with improvements concentrated in mental rather than physical health (Table 3). The improvements appear to be specific to depression and mental health measures; Medicaid coverage did not appear to lead to an increase in self-reported happiness, which is arguably a more general measure of overall subjective well-being.

Hypertension, high cholesterol levels, diabetes, and depression are only a subgroup of the set of health outcomes potentially affected by Medicaid coverage. We chose these conditions because they are important contributors to morbidity and mortality, feasible to measure, prevalent in the low-income population in our study, and plausibly modifiable by effective treatment within a 2-year time frame.13-16 Nonetheless, our power to detect changes in health was limited by the relatively small numbers of patients with these conditions; indeed, the only condition in which we detected improvements was depression, which was by far the most prevalent of the four conditions examined. The 95% confidence intervals for many of the estimates of effects on individual physical health measures were wide enough to include changes that would be considered clinically significant — such as a 7.16-percentage-point reduction in the prevalence of hypertension. Moreover, although we did not find a significant change in glycated hemoglobin levels, the point estimate of the decrease we observed is consistent with that which would be expected on the basis of our estimated increase in the use of medication for diabetes. The clinical-trial literature indicates that the use of oral medication for diabetes reduces the glycated hemoglobin level by an average of 1 percentage point within as short a time as 6 months.15 This estimate from the clinical literature suggests that the 5.4-percentage-point increase in the use of medication for diabetes in our cohort would decrease the average glycated hemoglobin level in the study population by 0.05 percentage points, which is well within our 95% confidence interval. Beyond issues of power, the effects of Medicaid coverage may be limited by the multiple sources of slippage in the connection between insurance coverage and observable improvements in our health metrics; these potential sources of slippage include access to care, diagnosis of underlying conditions, prescription of appropriate medications, compliance with recommendations, and effectiveness of treatment in improving health.17

Anticipating limitations in statistical power, we prespecified analyses of subgroups in which effects might be stronger, including the near-elderly and persons who reported having received a diagnosis of diabetes, hypertension, a high cholesterol level, a heart attack, or congestive heart failure before the lottery. We did not find significant changes in any of these subgroups. To try to improve statistical power, we used the Framingham risk score as a summary measure. This allowed us to reject a decrease of more than 20% in the predicted 10-year cardiovascular risk or a decrease of more than 10% in predicted risk among the participants with high-risk diagnoses before the lottery. Our results were thus consistent with at best limited improvements in these particular dimensions of physical health over this time period, in contrast with the substantial improvement in mental health.

Although changes in health status are of great interest, they are not the only important potential benefit of expanded health insurance coverage. Health insurance is a financial product that is aimed at providing financial security by protecting people from catastrophic health care expenses if they become injured or sick (and ensuring that the providers who see them are paid). In our study, Medicaid coverage almost completely eliminated catastrophic out-of-pocket medical expenditures.

Our estimates of the effect of Medicaid coverage on health, health care utilization, and financial strain apply to able-bodied, uninsured adults with incomes below 100% of the federal poverty level who express interest in insurance coverage — a population of considerable interest for health care policy, given the planned expansion of Medicaid. The Patient Protection and Affordable Care Act of 2010 allows states to extend Medicaid eligibility to all adults with incomes of up to 138% of the federal poverty level. However, there are several important limits to the generalizability of our findings. First, the low-income uninsured population in Oregon differs from the overall population in the United States in some respects, such as the proportions of persons who are members of racial and ethnic minority groups. Second, our estimates speak to the effect of Medicaid coverage on the subgroup of people who signed up for the lottery and for whom winning the lottery affected their coverage status; in the Supplementary Appendix we provide some additional details on the characteristics of this group. Medicaid coverage may have different effects for persons who seek insurance through the lottery than for the general population affected by coverage mandates. For example, persons who signed up for the lottery may have expected a greater health benefit from insurance coverage than those who did not sign up. Of course, most estimates suggest imperfect (and selective) Medicaid take-up rates even under mandates.18 Third, the newly insured participants in our study constituted a small share of all uninsured Oregon residents, limiting the system-level effects that insuring them might generate, such as strains on provider capacity or investment in infrastructure. Fourth, we examined outcomes in people who gained an average of 17 months of coverage (those insured through the lottery were not necessarily covered for the entire study period); the effects of insurance in the longer run may differ.

Despite these limitations, our study provides evidence of the effects of expanding Medicaid to low-income adults on the basis of a randomized design, which is rarely available in the evaluation of social insurance programs. We found that insurance led to increased access to and utilization of health care, substantial improvements in mental health, and reductions in financial strain, but we did not observe reductions in measured blood-pressure, cholesterol, or glycated hemoglobin levels.