Race adjustments in clinical algorithms can help correct for racial disparities in data quality

• 成果类型: Article
• 署名作者: Zink, Anna; Obermeyer, Ziad; Pierson, Emma
• 署名单位: University of Chicago; University of California System; University of California Berkeley; Cornell University; Weill Cornell Medicine
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12081
• DOI: 10.1073/pnas.2402267121
• 发表日期: 2024-08-20
• 关键词: colorectal-cancer; HEALTH; BIAS

摘要

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.