Second Principle of Iatrogenics: Nonlinearity in Response

SECOND PRINCIPLE OF IATROGENICS (NONLINEARITY IN RESPONSE)

Taleb’s second principle of iatrogenics concerns nonlinearity: the benefit of an intervention may change disproportionately with the severity of a condition. He argues for greater caution when expected benefits are small and greater willingness to accept treatment risks when the untreated danger is high.

The source illustrates its argument with hypertension. For blood pressure marginally above the range it calls “normotensive,” it gives a treatment-benefit figure of 5.6 percent, or roughly one person in eighteen. For the “high” and “severe” categories, it gives 26 and 72 percent, or roughly one person in four and two persons out of three. Taleb uses these figures to illustrate benefits increasing with severity, then assumes that treatment harms remain constant across the groups. Under that assumption, the balance improves as benefit rises. The excerpt does not identify the drug, outcome, follow-up period, or study behind the figures, and treatment harms need not be constant across patients.

The argument here is based on the structure of conditional survival probabilities, similar to the one that we used to prove that harm needs to be nonlinear for porcelain cups. Consider that Mother Nature had to have tinkered through selection in inverse proportion to the rarity of the condition. Of the hundred and twenty thousand drugs the author describes as available at the time of writing, I can hardly find a via positiva one that makes a healthy person unconditionally “better” (and if someone shows me one, I will be skeptical of yet-unseen side effects). Once in a while we come up with drugs that enhance performance, such as, say, steroids, only to discover what people in finance have known for a while: in a “mature” market there is no free lunch anymore, and what appears as a free lunch has a hidden risk. When you think you have found a free lunch, say, steroids or trans fat, something that helps the healthy without visible downside, it is most likely that there is a concealed trap somewhere. Actually, my days in trading, it was called a “sucker’s trade.”

Taleb also offers an evolutionary explanation: selection may have had more opportunity to address common conditions than rare ones. This is an argument about how to think about intervention, rather than a statistical law connecting an illness’s severity, rarity, and treatability. Those relationships require evidence for the condition in question.

At the time of writing, Taleb argued that medical decision-making did not adequately formalize this nonlinearity of benefits relative to harms. Even risks seem to be linearly extrapolated, causing both underestimation and overestimation, most certainly miscalculation of degrees of harm—for instance, a paper on the effect of radiation states the following: “The standard model currently in use applies a linear scale, extrapolating cancer risk from high doses to low doses of ionizing radiation.” Further, pharmaceutical companies are under financial pressures to find diseases and satisfy the security analysts. They have been scraping the bottom of the barrel, looking for disease among healthier and healthier people, lobbying for reclassifications of conditions, and fine-tuning sales tricks to get doctors to overprescribe. Now, if your blood pressure is in the upper part of the range that used to be called “normal,” you are no longer “normotensive” but “pre-hypertensive,” even if there are no symptoms in view. There is nothing wrong with the classification if it leads to healthier lifestyle and robust via negativa measures—but what is behind such classification, often, is a drive for more medication.

I am not against the function and mission of pharma, rather, its business practice: they should focus for their own benefit on extreme diseases, not on reclassifications or pressuring doctors to prescribe medicines. Indeed, pharma plays on the interventionism of doctors.

The practical question is how the patient’s baseline risk changes the expected benefit and harm of a particular intervention. Severity alone does not establish that a speculative treatment is worthwhile or that prevention is unnecessary.