27 November 2012
Why is it that those that faithfully take their sugar pill placebo consistently have better outcomes than those that don’t? I’m really glad a friend of mine sent me this NY Times article by Gary Taubes: Do We Really Know What Makes Us Healthy?
I had not adequately appreciated the bias of healthy users and compliance: “At its simplest, the problem is that people who faithfully engage in activities that are good for them — taking a drug as prescribed, for instance, or eating what they believe is a healthy diet — are fundamentally different from those who don’t. One thing epidemiologists have established with certainty, for example, is that women who take H.R.T. {hormone replacement therapy} differ from those who don’t in many ways, virtually all of which associate with lower heart-disease risk: they’re thinner; they have fewer risk factors for heart disease to begin with; they tend to be more educated and wealthier; to exercise more; and to be generally more health conscious.”
Science produces a constantly revised version of reality based off of new data. This can be vexing for those looking to draw health recommendations, as often our views become more enlightened and change. But this causes an extra problem for preventive medicine: how can one know that a recommendation is not doing harm? If vitamin Z was good for me yesterday, why is it not recommended now?
Preventive medicine epidemiology has several glaring, inherent flaws. It’s actually very easy to find positive correlations (A and B occur together) between pretty much anything. Conversely, it’s nearly impossible to establish causation (A leads to B) for anything. The reason why is an ethical one: how can one be certain that a preventive medicine being tested won’t do more hard than good? It’s a bit of a catch-22; there’s really no way of predicting harmful effects beforehand, and as a result, well controlled clinical trials are rarely done on healthy humans for preventive purposes. I.e. vitamin Z could be extremely harmful if taken at 1mg/kg body weight for all we know.
Media bias can be especially damning to the process. The first report in any epidemiological study of new association is almost always incorrect, or at least that’s what the statistics say. It takes time for any new idea or association to be re-tested and analyzed by other groups than the one doing the study. Unfortunately, these first reports are often the ones reported in the news media (see above comic). Even worse, the news media often spices up their own interpretations to get their viewers’ attention, further confounding the truth, then fail to follow-up on more nuanced, less sensationalized studies that follow in the wake of an initial discovery. I.e. the ones that would provide better health information or advice.
It’s all very frustrating to this scientific apprentice. But at least forcing myself to write about it helps me keep track of important ideas as I move forward into positions of influence. For that, I have you to thank, dear reader. Without you I might not have taken the time to educate myself as comprehensively!
Ryon