Outside of the few states where it is illegal to deny coverage based on medical history, I am probably uninsurable. Though I’m in pretty good health, I have several latent conditions, including an autoimmune disease. If I lost the generous insurance that I have through The Atlantic, even the most charitable insurer might hesitate to take me on.
So I took a keen interest when, at the fervid climax of the health-care debate in mid-December, a Washington Post blogger, Ezra Klein, declared that Senator Joseph Lieberman, by refusing to vote for a bill with a public option, was apparently “willing to cause the deaths of hundreds of thousands” of uninsured people in order to punish the progressives who had opposed his reelection in 2006. In the ensuing blogstorm, conservatives condemned Klein’s “venomous smear,” while liberals solemnly debated the circumstances under which one may properly accuse one’s opponents of mass murder.
But aside from an exchange between Matthew Yglesias of the Center for American Progress and Michael Cannon of the Cato Institute, few people addressed the question that mattered most to those of us who cannot buy an individual insurance policy at any price—the question that was arguably the health-care debate’s most important: Was Klein (not to mention other like-minded editorialists who cited similar numbers) right? If we lost our insurance, would this gargantuan new entitlement really be the only thing standing between us and an early grave?
Perhaps few people were asking, because the question sounds so stupid. Health insurance buys you health care. Health care is supposed to save your life. So if you don’t have someone buying you health care well, you can complete the syllogism.
Last year’s national debate on health-care legislation tended to dwell on either heart-wrenching anecdotes about costly, unattainable medical treatments, or arcane battles over how many people in the United States lacked insurance. Republicans rarely plumbed the connection between insurance and mortality, presumably because they would look foolish and heartless if they expressed any doubt about health insurance’s benefits. It was politically safer to harp on the potential problems of government interventions—or, in extremis, to point out that more than half the uninsured were either affluent, lacking citizenship, or already eligible for government programs in which they hadn’t bothered to enroll.
Even Democratic politicians made curiously little of the plight of the uninsured. Instead, they focused on cost control, so much so that you might have thought that covering the uninsured was a happy side effect of really throttling back the rate of growth in Medicare spending. When progressive politicians or journalists did address the disadvantages of being uninsured, they often fell back on the same data Klein had used: a 2008 report from the Urban Institute that estimated that about 20,000 people were dying every year for lack of health insurance.
But when you probe that claim, its accuracy is open to question. Even a rough approximation of how many people die because of lack of health insurance is hard to reach. Quite possibly, lack of health insurance has no more impact on your health than lack of flood insurance.
Part of the trouble with reports like the one from the Urban Institute is that they cannot do the kind of thing we do to test drugs or medical procedures: divide people randomly into groups that do and don’t have health insurance, and see which group fares better. Experimental studies like this would be tremendously expensive, and it’s hard to imagine that they’d attract sufficient volunteers. Moreover, they might well violate the ethical standards of doctors who believed they were condemning the uninsured patients to a life nasty, brutish, and short.
So instead, researchers usually do what are called “observational studies”: they take data sets that include both insured and uninsured people, and compare their health outcomes—usually mortality rates, because these are unequivocal and easy to measure. For a long time, two of the best studies were Sorlie et al. (1994), which used a large sample of census data from 1982 to 1985; and Franks, Clancy, and Gold (1993), which examined a smaller but richer data set from the National Health and Nutrition Examination Survey, and its follow-up studies, between 1971 and 1987. The Institute of Medicine used the math behind these two studies to produce a 2002 report on an increase in illness and death from lack of insurance; the Urban Institute, in turn, updated those numbers to produce the figure that became the gold standard during the debate over health-care reform.
The first thing one notices is that the original studies are a trifle elderly. Medicine has changed since 1987; presumably, so has the riskiness of going without health insurance. Moreover, the question of who had insurance is particularly dodgy: the studies counted as “uninsured” anyone who lacked insurance in the initial interview. But of course, not all of those people would have stayed uninsured—a separate study suggests that only about a third of those who reported being uninsured over a two-year time frame lacked coverage for the entire period. Most of the “uninsured” people probably got insurance relatively quickly, while some of the “insured” probably lost theirs. The effect of this churn could bias your results either way; the inability to control for it makes the statistics less accurate.