\nThe most recent available study, which also had the largest sample and
\ncontrolled for the most variables, found no effect at all–a result
\nwhich surprised the hell out of its author, a former Clinton advisor.
\nOther studies say the number is in the tens of thousands. <\/p>\n
The
\nleft is predictably fond of the study which got the largest number,
\n45,000 a year. Unfortunately, its authors are political advocates for
\na single-payer system, who also helped author the notorious studies on
\nmedical bankruptcies. Those studies are very shoddily done, with
\nparameters that somehow always conspire to produce the maximum possible
\nnumber. In the first study, they set an absurdly low threshhold for
\nwhat constituted a “medical bankruptcy”. In the second, they chose
\n2006, the year after the 2005 bankruptcy reform act had driven an
\nunprecedented spike in filings. It seems pretty likely that medical
\nbankruptcies were bound to be overrepresented in 2006, since most
\nfinancial events are easier to see coming than illnesses. But even if
\nyou disagree–and the authors offered an incredibly wan explanation of
\nwhy they did–it’s very clear that the people who filed in 2006 were
\nnot going to be a representative sample of bankruptcies in a normal
\nyear. I can’t imagine why you would choose to study 2006 unless you
\nwere looking for biased results. I have to conclude that their
\npolitical beliefs are affecting their work, which means I wouldn’t
\ntouch that 45,000 number with a bargepole–I wouldn’t cite anything
\nthey authored even if it offered to prove beyond a shadow of a doubt
\nthat I was right about everything.<\/p>\n
The right, meanwhile, shuns
\nthe subject like the plague. It will not do anyone’s career any good
\nto be attached to an argument that sounds like the health care
\nequivalent of “let them eat cake”.<\/p>\n
So allow me, maybe, to be the
\nfirst. I’m afraid I’m not confident about any number. All of these
\nstudies suffer from unobserved variable bias, which is to say, the
\nuninsured are not like the rest of us. (The long term uninsured, I
\nmean; the short term uninsured are not a large problem for society).
\nThere are all sorts of reasons that people end up uninsured, but most
\nof them are correlated with much poorer health outcomes, and only some
\nof them end up recorded in our surveys.<\/p>\n
To give you an example
\nof what I mean, one of the two studies that went into the most commonly
\ncited number–the roughly 20,000 a year figure from the Institute of
\nMedicine and the Urban Institute–found that the highest mortality was
\nnot associated with being uninsured, but being on a government health
\ncare program. (the other excluded those patients). This was true even
\nafter they’d run all their controls. Given that the bulk of the
\ncoverage expansion in both the Senate and the House plans comes from
\nMedicaid expansion, this is a little disturbing.<\/p>\n
But how likely is it that Medicaid is killing people? Possible, I suppose, but not really all that likely<\/i>.
\nMedicaid and Medicare patients, too, are not like the broader
\npopulation. The authors in fact recognized this fact in their paper,
\npointing out that these patients have higher rates of disability–but
\nthen failed to address the obvious question this raised about their
\ndata on the uninsured.<\/p>\n
This problem plagues almost all of the
\nstudies on mortality and the uninsured. Probably the best one looked
\nat patients who had been taken to the ER, which still showed higher
\nmortality for the uninsured. But it’s not clear that this indicates
\nthat lacking insurance is dangerous; it may be telling us that people
\nwho lack insurance have a lot of factors that lead to poorer health
\noutcomes.<\/p>\n
To my mind probably the single most solid piece of evidence is this:
\nturning 65–i.e., going on Medicare–doesn’t reduce your risk of
\ndying. If lack of insurance leads to death, then that should show up
\nas a discontinuity in the mortality rate around the age of 65. It
\ndoesn’t. There are some caveats–if the effects are sufficiently long
\nterm, then it’s hard to measure, because of course as elderly people
\nage, their mortality rate starts rising dramatically. But still, there
\nshould be some kink in the curve, and in the best data we have, it just
\nisn’t there.<\/p>\n
That doesn’t mean I’m prepared to say It’s a contentious question, but curiously, one that doesn’t get debated nearly as fiercely as things like “how many uninsured people are there?” I find that surprising, because after all, we don’t necessarily care whether people are marked by some survey as “insured” or “uninsured”; we care whether there is preventable suffering in the world. […]<\/p>\n","protected":false},"author":80,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-308635","post","type-post","status-publish","format-standard","hentry","category-news"],"_links":{"self":[{"href":"https:\/\/mereja.media\/index\/wp-json\/wp\/v2\/posts\/308635","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mereja.media\/index\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mereja.media\/index\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mereja.media\/index\/wp-json\/wp\/v2\/users\/80"}],"replies":[{"embeddable":true,"href":"https:\/\/mereja.media\/index\/wp-json\/wp\/v2\/comments?post=308635"}],"version-history":[{"count":0,"href":"https:\/\/mereja.media\/index\/wp-json\/wp\/v2\/posts\/308635\/revisions"}],"wp:attachment":[{"href":"https:\/\/mereja.media\/index\/wp-json\/wp\/v2\/media?parent=308635"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mereja.media\/index\/wp-json\/wp\/v2\/categories?post=308635"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mereja.media\/index\/wp-json\/wp\/v2\/tags?post=308635"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nthat no one dies from lack of insurance. The data is messy, and the
\nstudies often contradict each other. Intuitively, I feel as if there
\nshould be some effect. But if the results are this messy, I would
\nguess that the effect is not very big. At minimum, I think we should
\nbe pretty cautious about stating that we know how many people die from
\nlack of insurance. We don’t, and worse, we may never.
\n
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