Recently, two leading medical associations recommended ending a decades-old practice among doctors: using race as one of the variables to estimate how well a person’s kidneys filter waste out of their bodies. Before, clinicians would look at the levels of a certain chemical in blood, then multiply it by a factor of approximately 1.15 if their patient was Black. Using race to estimate kidney function contributes to delays in dialysis, kidney transplants, and other life-saving care for people of color, especially Black patients.
To make the recent decision, 14 experts spent approximately a year evaluating dozens of alternative options, interviewing patients, and weighing the impact of keeping race in the equation. Their final recommendation ensures the corrected kidney equation is equally precise for everyone, regardless of race.
Yet other risk equations that include race are still being used—including ones that have been used to deny former NFL players’ payouts in a concussion settlement, ones that might contribute to underdiagnosing breast cancer in Black women, and ones that have miscalculated the lung function of Black and Asian patients. Ending the use of race-based multipliers in these and dozens of other calculators will take more than a task force in one medical specialty. It’ll need researchers to not just believe, but act on the knowledge that race is not biology, and for the biomedical research enterprise to implement clearer standards for how these calculators are used. Otherwise, it’s just a matter of time before another tool that wrongly uses race to make decisions about patients’ bodies trickles into clinical care.
Physicians have relied on risk calculators, which help doctors make quick decisions in the face of uncertainty, for over four decades. Many doctors tend to stick with the versions they first heard about while in medical school or completing their residency, says California-based ER physician Graham Walker. That kidney function equation that was just updated? Many clinicians still use a much older version that doesn’t include the correction. That ancient version, first developed in 1973, is still the most popular equation on MDcalc, a website and smartphone app that Walker and his cofounder, Joseph Habboushe, developed to curate risk calculators and make them easily accessible to clinicians. While they don’t track users closely, usage statistics and a 2018 survey suggest that about 68 percent of doctors in the US use MDCalc at least every week.
And given that scientists have used race to distinguish between people long before modern medicine, it’s not surprising that when risk calculators were developed, race became a part of many equations.
In the kidney function equation and many others, race became a stand-in for differences in the measurements of some biomarker or other that researchers saw among their study participants, who were usually either white or Black. The observed differences are biological. But they are the result of health disparities caused by racism, not a result of race itself. They might also be mere statistical blips, because a study didn’t include sufficient numbers of Black participants.
And while kidney function equations in the US included a multiplier for being Black, similar calculators in other parts of the world were developed to include “Chinese” or “Japanese” coefficients. In the US, non-Black people of color have found their doctors averaging the Black and non-Black values to estimate their kidney function, or simply defaulting to the “normal”—usually the estimates for white individuals.
Scientists developing these types of calculators often rely on long-running databases from the CDC that include a column with demographic details next to biological statistics such as weight or disease stage. Because that demographic information correlates with differences in disease incidence, severity, or death rates, multipliers for race or ethnicity have become a convenient proxy for the unknown, underlying reasons for these differences. The collective burden of this practice is tough to estimate, because, outside of numbers such as those from MDcalc, it’s impossible to know how many times a risk calculator is used, or how every individual doctor uses the results to guide care for each patient. Still, it’s clear that risk equations being developed today still include race as a factor.
Yet there is another way. In November 2020, researchers developed a new risk calculator named the VACO index to predict the odds of dying a month after a positive Covid-19 test. They used data from the Veterans Affairs health care system, which closely tracks preexisting illnesses that might affect the course of a Covid infection. Once the developers included variables to represent an individual’s age, gender, and chronic conditions such as hypertension, race didn’t matter—the race-free equation worked equally well for everyone in the study.
One explanation for why race does not improve the equation’s accuracy, the researchers suggest in a podcast, is that patients in the VA system experience fewer barriers to accessing care. Disparities in health outcomes are often the result of systemic hurdles and unequal access to health care. With fewer barriers, the seemingly race-based difference in risk of death was minimized. Another possibility is the medical history the developers had at hand, which could explain the underlying biology of the disease itself instead of relying on race as a proxy. “Both theories [about the VACO score] argue that Covid may seem worse in underserved populations because we don’t properly know about chronic conditions in these populations or other social determinants of health,” Habboushe says. “It’s not specific to a checkbox of race itself.”
The VACO score and other calculators to gauge Covid risk have been available to clinicians for months. But they have never been subjected to the same rigorous approval process as other tools that doctors routinely use.
New drugs and vaccines must clear many stages of clinical trials before they’re authorized by the FDA. New diagnostic tests, instruments such as MRI scanners, and even the software used to analyze medical images in said scanners are all closely regulated by the FDA or other agencies. But calculators? They bypass the usual checkpoints put in place to protect patients. They’re submitted to scientific journals for peer review and then made available online after a paper is accepted. There are few restrictions on which variables are included, how much data is used to create a risk equation, and whether the data adequately represent people of color. Variables related to race or ethnicity continue to be poorly defined. In 2016, a group of researchers from Stanford found that geneticists and other researchers who track race, ethnicity, and ancestry data to gauge the diversity of their study populations often didn’t agree on what those terms meant or how they should be applied in making clinical decisions.
Yet while medicine has roiled over the issue of race in equations, academic research—the birthplace of these calculators—has remained relatively untouched. Walker and his colleagues receive daily requests from researchers asking for their equations to be added to MDCalc. The MDCalc team vets tools closely and flags ones that don’t meet certain standards, but their criteria are not a general requirement for equations in the scientific literature.
No one markets these calculators, and no one gains financially from their use, at least at first. Once they’re in the research literature, professional societies—much like the ones that made the recent announcement about the kidney function equation—may occasionally endorse certain equations. They’re incorporated in apps and tools for clinicians. As they grow popular and become part of routine care, some equations find their way onto drug labels and into medical instruments and electronic health record systems, or become available as web-based versions. The consequences can be severe: In one study last year, researchers trying to gauge the impact of the race-based kidney function equation found that at one hospital, one-third of Black patients would be reclassified to a more severe stage of disease—and receive speedier referrals for dialysis and transplants—if the race multiplier were removed.
Risk equations aren’t regulated by the FDA because “simple calculations routinely used in clinical practice are not generally the focus of our regulatory oversight,” an agency spokesperson said via email. “The FDA does not regulate the practice of medicine.” Perhaps, suggests Walker, who has spoken with FDA officials in the past, this is because they’re all based on publicly available research. “It might be a little different if they were more of a black box, like AI-based algorithms, where you don’t actually know why the computer suggests a certain drug or test,” Walker adds. But even if they are publicly available, it’s uncertain how many doctors examine the data sources and statistical methods used to develop a calculator before using it.
Without guardrails, there’s little to determine how equations get used once they’re publicly available. Many are used to inform public health decisions, such as suggesting when someone should get a cancer screening test, receive preventive care, or to prioritize communities or individuals for vaccinations. For example, a county public health official could theoretically use Covid risk calculators to “protect” a community that’s at high risk of death—and the data would suggest their residents are largely people of color—with lockdowns and curfews rather than making vaccines or masks available sooner.
Researchers can’t stop collecting data on disparities, nor ignore the evidence that some biological outcomes—such as dying of cancer or Covid-19—appear to affect people of certain races disproportionately. But they must find new ways to figure out why without evoking race and ethnicity. To do so, academia must see demographic columns in decades-old databases for what they are: clues to a problem, not part of the solution.
To build better risk equations, researchers will have to swap out race variables with numbers linked to more objective, biological explanations, such as whether someone had a viral infection that spikes their risk of cancer. For instance, cervical cancer typically occurs more often and causes more deaths in nonwhite populations. “There was a strong relationship between race, ethnicity, and the disease, but it wasn’t necessarily biological,” says clinical genetics researcher Nicolas Wentzensen of the National Cancer Institute. Insufficient screening and follow-up care were key contributors, but were not represented in equations. Once researchers identified the link between HPV infection and cervical cancer and included the viral infection into risk equations, they found demographic variables didn’t matter anymore. “Once we understand the biology of the disease, we can take out some of these factors,” Wentzensen says. “There’s no race in any of the models because all the risk is explained by when you were exposed to the virus.”
But for many conditions where risk calculators are used, such biological explanations for disparities are still a mystery. Here, scientists are beginning to find new ways to map and measure things that aren’t biology but still matter to our health. Some turn to proxies such as using a person’s residence zip code, which can help gauge socio-economic conditions, or exposure to smoke or pollutants. New multipliers might be created if biologists begin to measure how these social, environmental, and other factors influence health.
And while many scientific publishers have issued statements about their commitment to equity, some have also begun to define standards on the use of race in research. For example, in April this year, the Journal of Hospital Medicine began to require that researchers who use race in a study justify how they defined the term, why they used it to analyze results, and identify how racism or other mechanisms of inequity might have affected a study’s findings.
How scientists measure the world and what they choose to measure shapes the problems they identify as well as the solutions they create. If researchers parse their results by race or ethnicity, these terms will continue to remain meaningful in biology. Quantifying social determinants of health—not just acknowledging their existence—is critical to moving science beyond checkboxes of race and ancestry. Until biomedical research can wrap its collective intelligence around better ways to measure social, economic, and environmental factors, people whose health is most severely affected by these variables are poised to remain at risk.