Why is Race So Central To Medical Practice?
I’d like you to think about the last time you gave a formal patient presentation. You know, for a talk, or during rounds or whatever. You probably said something like:
This is a 38 year old, black man who presented with a chief complaint of fever…
-Typical case presentation
And it went on from there. Age, race, gender. I know that style was engrained in my head. But… why race?
I got to thinking about this when I read this research letter, appearing in JAMA, talking about our eGFR estimating equation.
GFR, or glomerular filtration rate, is an important metric for kidney function, but its really hard to measure directly — it requires a timed infusion of a drug like iothalomate and pretty advanced analytics — you’re not getting this at your local lab.
So, brilliant scientists, some of whom are my friends and colleagues, developed equations that can take just a few easily-measured variables and predict GFR. The best such equation, known as CKD-Epi, looks like this:
It requires knowing four things: the patient’s creatinine level, age, sex… and race.
There’s race again. Why does race matter? Why is it so central in our understanding of our patients?
In the context of GFR estimation, I was taught that creatinine comes from muscle, so people with more muscle have a higher creatinine value at the same GFR. And, on average black people have more muscle. So we need to account for that.
We are using race to infer something biological. What I never thought to ask is, well, if we are accounting for muscle mass, why don’t we just measure muscle mass?
But the truth is, race doesn’t really account for much biologically. Race is a label we assign based on skin color. That is a biological factor, but so is eye color, so is hair color, so is toenail length. Why has race been given so much credence? Why is it the second thing we say about a patient?
If your answer is that it is a proxy for a variety of genetic differences that do have medical relevance, well, it doesn’t seem to work that well. Allelic variation is greater within a race, as typically defined, than between races.
What about kidney disease? Well, that eGFR estimation is really important to nephrologists like me. You can’t get evaluated for transplant, for instance, until the eGFR is below 20. We might think about starting dialysis when the eGFR is below 10.
Given the same age, sex, and creatinine value, someone who identifies as black will have a higher eGFR than someone who identifies as white. Take this hypothetical individual.
If he describes himself as white, he is eligible for a transplant. If he describes himself as black, he isn’t.
But what does the data show? The JAMA article compares the measured “true” GFR of 2,601 self-described black individuals to the estimated GFR, with and without the use of race in the estimating equation. You can see the results here.
Without using the race coefficient, the eGFR is systematically too low. Using the coefficient, the estimate is better, especially in the range south of 75 which is what we tend to focus on.
Using height and weight instead of weight did not rescue the accuracy of the equation.
So does this show that we need race?
Not really — it just shows that race, which is a weak reflection of some biological stuff relevant to GFR, is better than height and weight — which are weaker reflections of some biological stuff relevant to GFR.
Why shouldn’t we use race? The main reason is that the use of race in medical models or for medical therapy amplifies the idea that there are relevant biological differences between the races, that race may dictate response to a variety of interventions.
In other words, we see that race is in a model and conclude that race is important as opposed to the reality which is that race is convenient.
Of course, black individuals do have worse outcomes in various disease, but with rare exceptions it’s not because of genetics, it’s because they are exposed to the systemically toxic effects of racism.
So instead of putting the convenient race term in our models, we need to invest in the measurement of those social factors that really drive the differences in outcomes — poverty, isolation, poor education. Then we can know what to try to fix.
In other words, maybe our case presentations should begin:
This is a 38-year old uninsured man with a chief-complaint of fever.
-Atypical case presentation
This commentary first appeared on medscape.com.