@article{TEXTUAL,
      recid = {13159},
      author = {Zink, Anna and Obermeyer, Ziad and Pierson, Emma},
      title = {Race adjustments in clinical algorithms can help correct   for racial disparities in data quality},
      journal = {PNAS},
      address = {2024-08-13},
      number = {TEXTUAL},
      abstract = {Despite ethical and historical arguments for removing race  from clinical algorithms, the consequences of removal  remain unclear. Here, we highlight a largely undiscussed  consideration in this debate: varying data quality of input  features across race groups. For example, family history of  cancer is an essential predictor in cancer risk prediction  algorithms but is less reliably documented for Black  participants and may therefore be less predictive of cancer  outcomes. Using data from the Southern Community Cohort  Study, we assessed whether race adjustments could allow  risk prediction models to capture varying data quality by  race, focusing on colorectal cancer risk prediction. We  analyzed 77,836 adults with no history of colorectal cancer  at baseline. The predictive value of self-reported family  history was greater for White participants than for Black  participants. We compared two cancer risk prediction  algorithms—a race-blind algorithm which included standard  colorectal cancer risk factors but not race, and a  race-adjusted algorithm which additionally included race.  Relative to the race-blind algorithm, the race-adjusted  algorithm improved predictive performance, as measured by  goodness of fit in a likelihood ratio test (P-value:  <0.001) and area under the receiving operating  characteristic curve among Black participants (P-value:  0.006). Because the race-blind algorithm underpredicted  risk for Black participants, the race-adjusted algorithm  increased the fraction of Black participants among the  predicted high-risk group, potentially increasing access to  screening. More broadly, this study shows that race  adjustments may be beneficial when the data quality of key  predictors in clinical algorithms differs by race group.},
      url = {http://knowledge.uchicago.edu/record/13159},
}