Do 68,000 People Die Every Year From Lack of Health Insurance? Probably Not
Last week the figure “68,000” was being promoted online as the estimated number of excess deaths in the United States caused by not having health insurance. One commentator noted, “According to a recent study from Yale, there are 68,000 excess deaths per year that can be attributed to people who delay or avoid care because they lack health insurance.” But the empirical evidence isn’t as strong as that statement suggests.
The Yale study cited here was published in 2020 in The Lancet. If we overlook the questionably biased section headings throughout the paper, such as “The life-saving potential of Medicare for All,” we can start to decipher where the 68,000 figure comes from.
The authors claim that uninsured individuals experience a 40% elevation in age-specific mortality risk. Using this estimate, they calculate that universal coverage would save 68,531 American lives on an annual basis. They also note that these lives are mostly relatively young people, as individuals older than 64 are already covered by Medicare.
So where did the authors get the 40% figure? It comes from a 2009 study based on a survey of 9,004 individuals, 2,350 of whom were uninsured. Over the 1988-2000 observation period, uninsured individuals were found to be 40% more likely to die than the insured survey participants.
Contrast this finding with that of a larger survey taken over a slightly longer period. This 2009 study published in Health Service Research surveyed 672,526 individuals between 1986 and 2002. The author found that “the risk of subsequent mortality is no different for uninsured respondents than for those covered by employer-sponsored group insurance at baseline (hazard ratio 1.03, 95 percent confidence interval [CI], 0.95–1.12).”
Notice that the confidence interval crosses below 1, suggesting no statistically significant relationship between insurance coverage and mortality. The author concludes:
The Institute of Medicine’s estimate that lack of insurance leads to 18,000 excess deaths each year is almost certainly incorrect. It is not possible to draw firm causal inferences from the results of observational analyses. … The answer suggested by the evidence presented here is that there would not be much change in the number of deaths in the United States as a result of universal coverage.
Moving on from survey-based studies, a 2014 nonrandomized study comparing health outcomes in Massachusetts (which had near-universal health coverage) with control-group counties found that insurance coverage reduced all-cause mortality by 2.9 percentage points. The authors were careful to note, however, that the study design was subject to unmeasured confounding variables.
Even if we take that 2.9 percentage points at face value, the reduction would imply about 2,189 fewer deaths with full-population health coverage, not quite the 68,531 annual deaths claimed in the Yale study.
Observational survey data and nonrandomized studies are not well-suited for measuring the complex relationship between insurance coverage and mortality, which is subject to confounding variables and other factors. Perhaps the best methodological approach is to review randomized control trials (RCTs).
One such RCT was published in the Quarterly Journal of Economics in 2021. Around 2017, the Internal Revenue Service (IRS) started a pilot program in which it sent letters to uninsured taxpayers under the individual mandate of the Affordable Care Act. Because the pilot led to increased coverage, this group of taxpayers was used to explore the causal relationship between health insurance and mortality.
Two years after receiving the IRS letters, the rate of mortality among 45- to 64-year-olds declined by 0.06 percentage points compared to the control group, or one fewer death for every 1,648 individuals. Interestingly, the RCT’s authors found no evidence of changes in mortality for younger individuals. This finding is noteworthy because the Yale study said that excess deaths were “predominantly the lives of young people.”
Another RCT study published in 2013 observed the Oregon state Medicaid lottery that allocated a limited number of Medicaid slots to poor, able-bodied, uninsured adults age 19 to 64. The lottery winners showed a lower death rate of 0.8% compared to lottery losers, representing a “dose-adjusted” reduction of 0.13 percentage points. But the finding was not statistically significant.
The claim that tens of thousands of Americans die annually due to lack of insurance rests on fragile empirical ground. Estimates like the Yale study’s 68,000 figure hinge on small observational studies that struggle to isolate causality, while larger samples and randomized evidence point to far smaller, and often statistically insignificant, effects.
None of this implies insurance is unimportant, but it does mean the magnitude of its effect on mortality is frequently overstated. Policymaking should be guided by careful evidence, not attention-grabbing figures that exceed what the data can credibly support.