S1: I’m going to tell you the story of Kevin Henry, you might have heard of him. He is a former NFL player, played for the Pittsburgh Steelers, retired in 2000. Like a lot of players, after he left the game, he found himself forgetting things and losing his temper. His body was a wreck, so he filed a claim with the NFL. You probably remember back in 2013, the league reached a settlement agreement that allowed players who can prove they’ve sustained brain injuries to get payouts. But Kevin’s claim was denied twice. The administrator said it was because Kevin was evaluated using inappropriate norms. His doctor hadn’t factored in Kevin’s race. He’s black. That’s how Kevin and another player ended up in court
S2: in a lawsuit. They say the NFL has avoided paying claims based on a discriminatory practice that assumes black players start from a lower cognitive level than white players.
S3: It’s a controversial practice known as race norming, a practice called race norming practice known as race norming. And in a nutshell, here’s what it is.
S1: Race norming is part of how a doctor tells how sick you are. It’s used in a lot of medical tests. A physician takes some measurements and puts them into a computer, then punches in some other information. Your gender, your age and a lot of times your race. The algorithm spits out a score. It’s all pretty invisible. But Kevin gave race, norming a face.
S4: Football doesn’t give you an expiration date. You just expire. I’ve had 10 concussions or more. I’ve had at least 17 surgeries. 17. And I’m still getting them,
S1: Kevin also gave race, norming a cost, he said he may have missed out on millions of dollars in settlement money.
S4: I felt so betrayed and I still feel that way. Two different systems. How could how can that be OK? Why should that be OK?
S1: At first, it seemed like Kevin Henry’s lawsuit wasn’t going anywhere. A judge kicked it out of court, sent the parties to mediation. The NFL called the allegations misguided. But last week, all that changed.
S3: Now to the major announcement from the NFL tonight. The league revealing it will support now the end of the controversial practice of race norming. Now, all claims previously made under the concussion settlement will be re-opened and reassessed without the discriminatory practice. The NFL, though, ending its use of race, norming quite notably, did not apologize. Joining me now is Jemele Hill, contributing writer.
S1: Listening to this story, it’s easy to pick out a villain, the NFL,
S3: the NFL, this is on Brand or who they are and who they have always been.
S1: But today on the show, we’re going to do something a little different. We’re going to talk about why what looks like a victory for football players might be part of something much bigger, a reassessment of how all of us are seen when we go to the doctor. I’m Mary Harris. You’re listening to what next? Stick around. To understand this one way that race factors into the care we get, I called up their lives. Dahli is a resident physician at Mass General. She’s also part of a year long fight to eliminate the most harmful race norming in medicine. Dossani says race norming it’s baked into her job as an internist. She sees it mostly in these digital tools that help her decide on treatment options. There’s a tool that lets doctors predict the likelihood that a patient will have a kidney stone, a tool to measure lung function, even a tool to predict how likely it is that a patient will survive a hospital stay. All of those tools factor in race
S5: more and more as our technology improves and increases. There is sort of a movement to move towards like an online calculator or an algorithm or a risk score that helps doctors make difficult decisions. You know, there are some decisions that are clear cut and some decisions that are more in a grey area. And one we’re trying to make decisions like that. It could be when to start a patient on a certain kind of medication or how to counsel a patient towards or away from a procedure or sort of when to seek additional testing or imaging. There are some grey areas. And then in those cases, it can be helpful to have a tool that helps us individualize a patient’s risk or a patient’s risk factors and sort of guide decision making. And in some ways, it’s helpful to have that because it helps doctors be more objective.
S1: You’re not just going on your gut, right.
S5: It can be helpful to standardize decision making in that way, especially when there is a gray area.
S1: But when race is a factor, Daschle is going to make a call, plug the data in or leave it out. It’s a decision that’s complicated by the fact that oftentimes there isn’t a clear answer on what her patient’s race is.
S5: There is no clear guidelines on how to answer that question. And there’s a lot of room for error in judgment to go into that decision. It also these tools that ask for race, you know, the typically the the last four very constrained categories of race, they’ll either say black, white, Asian, the patients I take care of have racial identities that don’t fall neatly into those categories. So clinicians often will have to make an assumption based on it can be skin color. It can be what you think they’re identified as. If the patient’s in front of you, you can ask them what race they identify as. But again, there’s they’re very strict categories. And I think one one problem that these tools don’t comment on at all is what to do if a patient is a multiracial patient or identifies with multiple ethnic backgrounds. Do you pick one? Do you say other? You know, and how does that affect what output you get from the tool?
S1: And these tools are based on previous information, right. Like outcomes of patients who have come before. And it’s like I think about race norming is this kind of closed loop of information. Where it’s both documenting a reality, but then there’s this question of whether by documenting the reality, you are then creating a reality, right, because you’ve given this score, which now is going to impact how you treat the patient. And so you’ve sort of used a stereotype to capture someone in a way. Right.
S5: And then to use it and predictive modeling. And so you’re sort of using a current snapshot of a disparity and using it for a predictive tool to almost continue that disparity into the future. Yeah, it becomes this warped circle of logic.
S1: So I’m a little curious what race norming looks like in this NFL situation in particular, like if I was a player looking for compensation for a brain injury, I’m wondering like what kind of tests would I get and how would they be corrected for race?
S5: Basically, to decide about the settlements, you need to assess what the damage done is to the cognition into the brain function. And so these players undergo tests with the way they’re interpreted, differs based on race. And the way they differ is the tests assume that black players have lower cognitive function at baseline. And so to order to qualify for the settlement, they have to they have to show a larger decrement in cognitive function. And it’s kind of based in this. You know what? The NFL has defended the practice in the past, saying this was based on sort of long established tests and widely accepted scoring methodologies. But there’s no scientific evidence to show that that black patients have lower cognitive function, of course. And that’s kind of at odds with all of our genetic understanding of race to begin with.
S1: Race often pops up in these tools the same way a biological characteristic might like blood pressure or cholesterol. The problem, Daschle says, is that race is a social construct, not a biological condition.
S5: Just because something correlates with an outcome doesn’t mean it’s a causation. And just because race correlates with an outcome of interest doesn’t make it part of the causal pathway. It’s not something about being black that makes people more or less likely to have an outcome of interest. It’s the experience of being black. And so and, you know, in some cases it’s easier for us to recognize a social factor that that doesn’t end up in the model. Like for a lot of these analysis, people will find that insurance type also correlates with the outcome of interest insurance type doesn’t end up in the final tool because we can recognize that insurance status is a social determinants of health. But when race ends up with a signal, it often ends up in the final model. And that does kind of imply that we’re using it in a biological or genetic way.
S1: It’s interesting because. I’m sure the argument that someone coming up with one of these tools might use as well the signal is so loud, like we have to include it because, you know, race was just the loudest signal we had. And so obviously we must include it. And I wonder if if you might see that differently where you like. Yeah, that’s like a clanging bell for the racism in medicine, not, you know, some kind of indication that we need to be sorting people in this way.
S5: Right. And I think right. When you see the signal for race, that should be sort of a call to action, that these racial disparities are really stark and that they need to be addressed at their root cause, not that we should correct for them and to just adjust our models around the disparity,
S1: which means in a way, kind of accepting the disparity.
S5: Right. And in the worst case scenario, perpetuating the disparity forward if we’re just correcting our tools around them.
S1: When we come back, at the same time, a race norming battle has been raging at the NFL there, Shelly and her colleagues have been waging a fight of their own and they just won. I want to talk about your personal story, because part of the reason I wanted to talk to you is that you’ve really dug into how race norming is all over medicine. Like if you had to tick off the kinds of tests that are race normed, like, could you do it?
S5: I mean, I think that what has been so striking of this work is like it started with just a few examples that stood out to me and to my classmates. But it has just shown that it’s ubiquitous across all fields of medicine. And it’s not just one field, it’s surgery, it’s obstetrics, it’s general medicine. And so what I I think it’s important to emphasize the ones that we talk about are sort of a collection of them. But it’s really it’s a really common practice throughout medicine.
S1: There really first got interested in race and health care back in medical school. One of the first things she learned is that genetic variation is greater within racial groups than between them. But when she entered the hospital, Dahli saw race used again and again to determine what kind of treatments patients should receive.
S5: And when I was on my obstetrics rotation, there was another example of race correction kind of right in front of me through the vaginal birth after caesarean section tool or the feedback tool that also corrects by race
S1: this VBC tool. It’s another one of those calculators for doctors, for women who have given birth once by caesarean section, but want to try for a vaginal birth next time. That’s a V back. This tool lets you plug in all kinds of information. Then you get a score. It tells you how successful a vaginal birth is likely to be. The thing is, telling the tool you’re black or Hispanic lowered your score.
S5: Anecdotally, we heard from practitioners who would use the tool and have a cut off in their mind. Like this calculator gives me a percentage that’s less than 50 percent. I’m not going to offer you back.
S1: And that could mean a more dangerous birth for a black or Hispanic mom. A successful VBC has far fewer complications than a repeat C-section, but the tool wasn’t really factoring that in.
S5: The equity concern there is that it may be directing clinicians to steer women of color towards repeat caesarean sections.
S1: Yeah, I wonder when you were studying obstetrics, did you see this in immediately a light bulb went off like, hold it. This is what I’ve been talking about with my friends and colleagues already. And here it is again. And my professors are saying we’re going to use this tool like each and every day.
S5: Yeah, I actually remember using it in, like, you know, preparing to see a patient with one of the obstetricians I was working with. And like, we pulled it up before we went in to see the patient, like just on the website and entered the patient’s characteristics into the tool. And then that day at our news conference teaching session, like someone had put up the equation for back on the on the screen in front of the whole room. And it had these subtraction factors for African-American race and Hispanic race. And it was just like projected onto the screams like, oh, this is another example of the same logic.
S1: What was the logic and why didn’t it make sense?
S5: Basically, that that there was a group of researchers who wanted to create a tool to help clinicians decide who’s a good candidate for a buyback. They looked at huge data sets and found a bunch of factors that correlated with having a successful buyback. And they found a lot of characteristics that correlate. And a lot of them ended up in the tool like BMI, prior labor history, things that have sort of a clear, mechanistic connection to a vaginal birth. They also found, interestingly, other factors that they also saw correlated that they did not include in the tool. And this is they found marital status correlated with successful vaginal birth. They found insurance type correlated and they found race correlated. They they didn’t end up using marital status or insurance type, but they did include race. And to me, that kind of points to our ability to identify some factors as socially mediated. But we can’t make that connection to race for some reason that we assume race is still biologically relevant.
S1: So in a couple of colleagues wrote a paper, they urged their peers to reconsider the use of race in this tool. For a while, it was difficult to find an audience. It took them over a year to find a journal willing to publish them. But since then, Dali’s seen some progress.
S5: Just a couple of weeks ago, actually, the feedback calculator online has officially changed to a version of the same tool that does not use race. And what’s really interesting and exciting is that the same group that validated the original feedback calculator revalidated the tool removing race.
S1: Why was that meaningful for you?
S5: I think two things about it stand out to me. The first is that the VAT calculator has officially become the first instance of race correction in a clinical algorithm. That’s. And systematically reconsidered, revalidated and actually abandoned with an explicit concern for equity, but also it’s a powerful demonstration that equity work can look like this willingness to respond and incorporate critique and to reconsider old practices and to be explicit. You know, the same developers who made the first move that calculator have now made a new one without risk of ethnicity that they also feel is a confident predictor of risk for these women. And so I think the development of this new model exemplifies that, yes, our clinical tools can still be scientifically rigorous and clinically useful without race correcting. And it’s powerful to see a group sort of rethink a decision they made years and years ago and kind of respond to an equity concern that was raised and do it in a scientifically rigorous way. Huh?
S1: OK, so the NFL has said it won’t use race norming in its compensation process. And your work just in the last couple of weeks has led to this change in the scoring for women who are trying to decide whether there are candidates for Reback. I wonder if you feel like this relief right now that the gears are turning, I guess, and things are changing a little bit?
S5: Yeah, I think it does feel like, you know, these do feel like two winds. There’s been a lot of momentum over the past year. In particular, I think the NFL and the buyback are the two big changes over the past few weeks. I will just say in March, there was another big development around the use of race correction in the EGFR formula for kidney function. There was a national task force that was assembled and they made their official recommendation in March that we should end race correcting EGFR, too. And I do think there are you know, this was a year that was really well conditioned to having these broader discussions about race. And it’s it’s very exciting to see momentum. Like you said, there’s still a lot of work to be done both in the remainder of these tools and sort of implementing changes that come from higher up levels and also sort of setting new standards moving forward.
S1: I mean, something that struck me is that even though the NFL is not using these race corrected scores for cognitive function. I wonder doctors can still use those scores, and that means if you’re a patient. Like, do you even know if you’re being raised normed at the moment?
S5: I mean, I think in general, patients often don’t know when they’re being raised normed. Like I you know, some of the tools are ones that maybe a doctor will do in front of the patient. But like EGFR, for example, that calculation happens at the lab. That’s the kidney function. SOIRE Yeah, that that happens at the lab level. That gets just sent to us in our in our chart in the morning. So there are a lot of examples of race norming that patients wouldn’t be aware of. And even like the Vehbi calculator, you know, sometimes clinicians will maybe pull up the calculator and do it with the patient in front of them. But often it’s done before the visit even starts. And so it is you know, there is an element of this that’s patient advocacy and sort of, you know, empowering patients to ask about scores that are being used to help guide decisions about their care. Ms.
S1: I wonder how you deal with that as a physician, someone who’s trying to give care to patients but make yourself feel hemmed in by these scores, that you may you may have a choice about whether to use them. You may not like if you have a patient in front of you and you’re sort of clicking in their electronic medical record and trying to figure out their risk of whatever. Do you find yourself making decisions in the moment, like I’m going to leave race off of this one or maybe I’ll put race in this one? Because I do think it’s important. Like, how do you navigate those?
S5: Yeah, no, it’s really tough. And I think what makes it tough is that the decisions that we’re making about whether to include race or not in a tool are also based on this these faulty ideas about about how to identify race to begin with. Anecdotally, we hear physicians do all sorts of things to try to make this decision more fair until these tools are revised. And that can mean, you know, entering a patient in a tool as as white or not selecting race and then also selecting race and showing the range of values that that means. I think what it often ends up looking like is is talking about how race is being used in the tool with the patient directly. And so I think what it can do is open up a conversation around, like around talking about race, connection with the patient themselves and sort of having the discussion around what are your actual risk factors for disease. Like forget about this category of the tools making me a sign, but like, let’s just talk to the patient in front of us.
S1: There, Charlie Vias, thank you so much for joining me.
S5: Thanks for having me
S1: there, Charlie. This is a resident physician at Massachusetts General Hospital and that is our show. What Next is produced by Mary Wilson Davis Land. Kamal Dilshad, Daniel Hewett and Elena Schwartz were led by Alice in Benedictine, Alicia Montgomery and Mary Harris. Thanks for listening. I’ll catch you back here tomorrow. Well.