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

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 f

SUBMITTER: Zink A 

PROVIDER: S-EPMC11348319 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature