www.overcomingbias.com/2007/05/rand_health_ins.html
How useful is medicine, to the average person, wondering if he should go to the doctor or skip it? We have perhaps a million medical studies, but how do we combine them into a total estimate of the value of medicine? It is hard to see how to correct for many potential biases such as fraud, funding bias, treatment selection bias, publication selection bias, and so on. These biases can be partially overcome by focusing on studies of the aggregate effects of medicine on the general population, some of which compare millions of people over years.
usually find no health effect of more medicine, but most are correlation studies, so one may doubt if they controlled for enough relevant factors. Fortunately, there has been one large randomized experiment on aggregate medicine.
RAND health insurance experiment, where from 1974 to 1982 the US government spent $50 million to randomly assign 7700 people in six US cities to three to five years each of either free or not free medicine, provided by the same set of doctors. The plan was to compare five measures of general health, and also 23 physiologic health measures.
New England Journal of Medicine article: For the average person enrolled in the experiment, we observed two significant positive effects of free care relative to cost-sharing: corrected far vision ... was better by 01 Snellen lines (p = 0001) and diagnostic blood pressure was lower by 08mm HG (p = 003). any true differences would be clinically and socially negligible. For the five general health measures, we could detect no significant positive effect of free care for persons who differed by income .. Among participants who were judged to be at elevated risk with respect to smoking habits, cholesterol levels, and weight, free care had no detectable effect. For persons who were in the upper quartile of the distribution of risk factors included in the risk of dying index, the risk of dying was 10 percent lower on the free than the cost-sharing plans (p = 002). It has long been obvious that eyeglasses help people see better, and eyeglasses are basically physics, not "medicine," so that result should be set aside. Since this experiment looked at thirty measures in total then just by chance one of them should seem significant at the three percent level, explaining the blood pressure result.
The bottom line is that thousands of people randomly given free medicine in the late 1970s consumed 30-40% more medical services, paid one more "restricted activity day" per year to deal with the medical system, but were not noticeably healthier! So unless the marginal value of medicine has changed in the last thirty years, if you would not pay for medicine out of your own pocket, then don't bother to go when others offer to pay; on average such medicine is as likely to hurt as to help. Why is this shocking news unknown to most readers of the weekly health section of the newspaper?
RAND Health Insurance Experiment: Comments I don't think that's quite what they conclude. Here's a paper written by two profs from U of Chicago that summarizes the RAND study and other studies as well. A few quotes: If you are talking about whether or not the people currently not covered by health care would benefit from it, I think the answer is yes... "Another lesson from this literature is that the size of the effect of health insurance on health depends very much on whose health we are talking about. Vulnerable populations such as infants and children on the fringes of Medicaid eligibility or low-income individuals in the RAND experiment have the most to gain from more resources, and do appear to benefit from them." Furthermore, when you say, "These biases can be partially overcome by focusing on studies of the aggregate effects of medicine on the general population, some of which compare millions of people over years. Such studies usually find no health effect of more medicine, but most are correlation studies, so one may doubt if they controlled for enough relevant factors. Fortunately, there has been one large randomized experiment on aggregate medicine." "The results of small quasi-experimental studies provide only mixed evidence that health insurance affects health, while larger quasi-experimental studies and the RAND Health Insurance Experiment provide consistent evidence that health insurance improves health. Only one large-scale quasi-experimental study (Perry and Rosen) fails to show a relationship between health insurance and health, and this study may not have adequate power to rule out the possibility that health insurance improves health. Taken as a whole, these high-quality studies of the health effects of health insurance strongly suggest that policies to expand insurance can also promote health."
May 08, 2007 at 10:06 AM More tomorrow on the RAND experiment. One issue (that is often touched upon in the weekly health section of the newspaper) is given by the line in the study "Confidence intervals were wider for subgroups of persons with low income or initially in poor health; therefore we cannot rule out clinically meaningful changes in particular subgroup".
May 08, 2007 at 10:13 AM Technically, if you would not pay out of pocket for medicine *if it was offered at 1970s prices and with 1970s quality* you shouldn't use it if someone else is paying. However, healthcare price has gone WAY up relative to median income and quality has also probably risen, so a this modifier is relevant.
May 08, 2007 at 10:15 AM Ah, thanks Chuck, you answered my question before I asked it. Just to be sure - "more resources" means free or lower cost health insurance?
May 08, 2007 at 10:17 AM Chuck, I am talking about our actual data, not about commentary from "two profs from U of Chicago." My post above shows the inaccuracy of the claim you quote, that "RAND Health Insurance Experiment provide consistent evidence that health insurance improves health." But if the ratio of helpful to harmful medicine has not changed, for medicine that price subsidizes induce folks to consume, the claim stands.
May 08, 2007 at 10:39 AM "Chuck, I am talking about our actual data, not about commentary from "two profs from U of Chicago." "" The paper from the University of Chicago (link might have been stripped or I forgot to include it, I'll try to include it again) is by Helen Levy and David Meltzer. Both have PhD's in economics, one from the University of Chicago and the other from Princeton, and also various other degress from Yale, etc, in economics and health. Metzler is also an MD Their paper is titled "WHAT DO WE REALLY KNOW ABOUT WHETHER HEALTH INSURANCE AFFECTS HEALTH?" These two educated specialists in this field are commenting on the same data as you in a scholarly article and seeming to reach different conclusions. I wonder, for example, if the *average* person in the quote you cited is average for the study, or average for the population as a whole? Furthermore, what is the distinction between "free care vs. Are we comparing two groups of people who have coverage, but it is paid for in different ways? I was under the impression we were comparing people without insurance to people with insurance.
paper you cite cites the blood pressure result I mentioned, but notes "One caveat is that the analyses do not control for the presence of multiple comparisons (that is, hypothesis tests for multiple health outcomes)." That is, they are aware of but just choose to ignore the fact that we should have expected some such result by chance.
May 08, 2007 at 11:16 AM Robin, not to dispute your overall conclusion, but the pragraph you wrote seems suspicious: It has long been obvious that eyeglasses help people see better, and eyeglasses are basically physics, not "medicine," so that result should be set aside. Since this experiment looked at thirty measures in total then just by chance one of them should seem significant at the three percent level, explaining the blood pressure result. The "risk of dying index" effect is mainly just the blood pressure effect, and the index came from a 1976 paper on heart attack risk, which was chosen well after the RAND experiment started, so the statistical significances...
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