The most striking puzzle in health policy is the apparent lack of an aggregate empirical relation between medical care and health.

 From Showing That You Care: The Evolution of Health Altruism by Robin Hanson.  From the Abstract:

Publication selection bias makes it hard to be sure, but the vast medical literature on randomized clinical trials certainly suggests that medical care has health benefits, at least when best practice is applied to patients deemed most likely to benefit. This leaves open, however, the question of the average benefit of typical practice on typical patients, especially since the vast majority of medical treatments have yet to be carefully studied with clinical trials. 

 

Perhaps the most striking puzzle in health policy is the apparent lack of an aggregate empirical relation between medical care and health. Observed variations in medical care typically have an insignificant effect on average population health, even when looking at large data sets, sets larger than those which convinced most researchers of the reality of many other influences on health. 

 

One of the first studies on the aggregate health effects of medicine found mortality variations across the 50 US states were unrelated to health care spending, given various controls.  A recent comparison of 21 developed countries also found national life expectancy did not vary significantly with medical care spending, after controlling for income, education, unemployment, animal fat intake, smoking, and consumption of pharmaceuticals.  

 

The most definitive data on this topic comes from the RAND Health Insurance Experiment, which for three to five years in the mid 1970s randomly assigned two thousand non-elderly US families to either free health care or to plans with a substantial copayment.  Those with free care consumed on average about 25-30% more health care, as measured by spending. They went to the doctor and hospital more often, and as a result suffered one more restricted activity day per year, when they could not do their normal activities. The extra hospital visits were rated by physician reviewers to be just as medically appropriate, and to treat just as severe a stage of disease, as the other hospital visits. 

 

Those with free care obtained more eyeglasses, and had more teeth filled. Beyond this, however, there was no significant difference in a general health index, which was the designed outcome measure. There was also no significant difference in physical functioning, physiologic measures, health practices, satisfaction, or the appropriateness of therapy. Blood pressure may have been reduced, but the point estimate was that this produced a 1% reduction in average future mortality rates, which translates to roughly seven weeks of life. And this estimate was not significantly different from no effect.

I am careful about accepting this assumption at face value.  I do believe that 80-95% of improvements in longevity in nations across the OECD over the past 50 and 100 years are due to basic interventions such as clean water, clean air, safe roads, safer equipment, better cleanliness in the food supply chain, etc.  

Medical treatment obviously can make a difference to the individual but it seems like the magnitude of the difference is difficult to demonstrate in aggregated populations, especially when you have controlled for obvious confounds such as controlling for income, education, unemployment, animal fat intake, smoking, and consumption of pharmaceuticals, etc..

You might characterize this as health interventions revert to the mean such that it is difficult to detect sustained benefits.  We see this as well in education.  Preferred interventions typically have only a minimal and short term beneficial impact.  

Same with non-carceral interventions.  Same with any number of other social interventions.

Regardless of the good intentions and the sincerity of belief, it seems like all social and most public policy interventions revert to the mean when controlling for obvious confounds.  In other words, personal dispositions (from genetics), personal behaviors (from culture) and personal choices (from culture and experience) swamp social and public policy interventions.

Or so it seems to me.