Inequities in healthcare and unequal risk correlated with racial categories have received a lot of attention in the past year and a half. From early in the COVID-19 pandemic, it was apparent that the risks of severe disease were not borne equally. Age is clearly related to risk, with plausible biological underpinnings; we have observed independently that the behavior of our immune systems changes throughout our lives. Variation in risk is also correlated with racial categories, but here the story is complex, involving unequal exposure risks related to job type & living conditions, differing access to healthcare, and varied prevalence of preexisting conditions, along with other possible factors. If we were looking to predict risk, we might therefore consider racial categories as one predictor. But will that improve or exacerbate inequity related to racial categories in healthcare and public health?
That question is far from hypothetical. As detailed in this article, racial categories are used in a number of risk calculators and other assessment tools that are routinely used to make individual treatment decisions and to inform public health planning and resource allotment. Even though our categories based on skin color are minimally informative about the rest of someone’s genetics, those categories frequently correlate with various healthcare outcomes. If those outcome differences are not the result of differences present at conception, then they are logically the result of experiences afterwards which can also shape biology and thus health. If we treat people differently because of the color of their skin or if the opportunities available to them people are different for that same reason, we should not be surprised that those differences have consequences. So when racial outcomes correlate with healthcare outcomes, we can plausibly hypothesize a causal connection that will be predictive.
And yet, as you can see from the discussion in the article, there is not broad agreement on how to properly design these assessment tools to address historical and ongoing inequities to bring about a more equitable healthcare system. If I had to summarize, I’d say the case for including these categories is that they can be causal and thus meaningful predictors, and that colorblind approaches do not always resolve inequities.
On the other hand, those causal connections are highly contingent and therefore subject to change; relying on them can reinforce tendencies towards a misleading essentialist view of racial categories. An example of contingency shows up in the article: the relationship between racial categories and bone density is different in the United States than in African nations. Using more proximal causal connections might be more reliable and less contingent. Further, we don’t want to repeat the mistake of reducing the richness of each individual’s distinctiveness to the color of their skin simply for expediency. We will always need to group people to some extent to have enough data for robust analysis, but that doesn’t mean we need to keep using categories that have so often been used to cause harm.
Going forward, the solution(s) is likely to be complicated and contextual. While there is a unifying theme, renal function is not adult bone density is not infant urinary health. The appropriate way to address inequity while retaining valid predictions may vary between assessment tools. Practical considerations may also come into play. In my experience in public health informatics, sometimes basic demographics are all that is available. Sometimes that may just mean we have to work harder to get the data we need to make the changes we have decided are worthwhile, but there may also be times when privacy considerations or consistency over time are appropriate to consider. I appreciate that this kind of complexity leads to the impression that science is fickle, which leads to its own effects on how people interact with healthcare. Nevertheless, the contingent nature of these relationships makes some amount of complexity unavoidable, so perhaps discussing it up front will help foster understanding.
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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.