Last week we looked at the challenges of using racial categories for assessing health risks and making treatment decisions. One of the alternatives proposed is to substitute measures of health indicators for racial categories, to move away from contingent causes and towards more proximal biological markers. Among the common and readily obtainable metrics are weight and the related body mass index (BMI). Here again, correlations do exist between weight & BMI and health outcomes, but also, a single measure fails to capture the full diversity of biology and health.
To the discussion in that article, I’d add the fact that when BMI as currently formulated was first introduced in the 1970s, it was introduced to be a metric at the population level and explicitly not for assessing individual health. In other words, BMI might be able to tell you something about why one county/region/nation has a higher (or lower) rate of some outcome like type 2 diabetes diagnoses than another county/region/nation, but it won’t necessarily tell you why one person has that outcome and another person doesn’t. It is easy to conflate the two; isn’t population health just the sum of individual health? In a sense, yes. But going in the opposite direction is not as straightforward; you can’t just divide population health evenly among each individual.
Let’s try an analogy. Level of education correlates with income, and there are relevant causal pathways such as employers paying more for the additional training associated with additional education. So if we were comparing two populations and added up the incomes of each member of each population and got a meaningful difference, we might find that differences in education in the two populations were related to the differences in total income. But now, what if we took that total income and attempted to assign it back to each person in each population? If we just looked at each person’s level of education and gave them the average for that level, we’d be wrong pretty often, sometimes by quite a bit. I suspect this is pretty obvious when it comes to income. And yet basically the same thing is true for any single measure we might employ in health settings. We know there are multiple factors involved and we can explore their separate contributions in aggregate but not for the individual.
As the article discusses, we can be more specific than just “health is complicated and BMI is an oversimplification.” There are different ways to wind up at the same weight or BMI. Different tissues have different densities, physiological differences at a cell or tissue or organ level may lead to different results despite similarities in overall anatomy, and even a category like fat tissue glosses over important distinctions related to placement or composition. Biologically and medically, there are good reasons not to reduce someone to their BMI. That doesn’t mean BMI will completely go away any time soon, but hopefully it means that more people won’t be told they have to lose weight essentially in order to unlock a more comprehensive engagement with their health.
Along the way, we will also have to reckon with the moral models we sometimes apply, knowingly or not. With both income and health, we might see good outcomes as rewards for good behavior. That good behavior might be defined with respect to religious righteousness or some other ethical standard, or it might be defined in terms of ‘living right’ or ‘working hard.’ Versions of these moral models may be attractive in one direction–one’s good health is a product of eating right and exercising–but it is definitely a mistake to try to go in the other direction. Unwanted outcomes can happen to people for all sorts of reasons that we cannot control. Again, I think if asked we know that is true. And yet I know for myself I sometimes fall into the trap of thinking I have ‘earned’ a certain outcome or ‘earned’ avoidance of other outcomes through my behavior, or I blame myself if a unwanted outcome does occur. If I treat myself that way, I have to assume that I might be doing the same to others, even unconsciously, and so I need to make an ongoing, conscious effort to apply more realistic and accurate models that account for the full diversity of causation.
About the author:
Andy has worn many hats in his life. He knows this is a dreadfully clichÃ©d notion, but since it is also literally true he uses it anyway. Among his current metaphorical hats: husband of one wife, father of two teenagers, reader of science fiction and science fact, enthusiast of contemporary symphonic music, and chief science officer. Previous metaphorical hats include: comp bio postdoc, molecular biology grad student, InterVarsity chapter president (that one came with a literal hat), music store clerk, house painter, and mosquito trapper. Among his more unique literal hats: British bobby, captain's hats (of varying levels of authenticity) of several specific vessels, a deerstalker from 221B Baker St, and a railroad engineer's cap. His monthly Science in Review is drawn from his weekly Science Corner posts -- Wednesdays, 8am (Eastern) on the Emerging Scholars Network Blog. His book Faith across the Multiverse is available from Hendrickson.