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Reevaluating the Role of Race and Ethnicity in Diabetes Screening

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arxiv 2306.10220 v1 pith:HCFCD3FG submitted 2023-06-17 stat.AP

classification stat.AP
keywords ethnicityracescreeningriskclinicalgainsaccuracydiabetes
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There is active debate over whether to consider patient race and ethnicity when estimating disease risk. By accounting for race and ethnicity, it is possible to improve the accuracy of risk predictions, but there is concern that their use may encourage a racialized view of medicine. In diabetes risk models, despite substantial gains in statistical accuracy from using race and ethnicity, the gains in clinical utility are surprisingly modest. These modest clinical gains stem from two empirical patterns: first, the vast majority of individuals receive the same screening recommendation regardless of whether race or ethnicity are included in risk models; and second, for those who do receive different screening recommendations, the difference in utility between screening and not screening is relatively small. Our results are based on broad statistical principles, and so are likely to generalize to many other risk-based clinical decisions.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reconsidering Fairness Through Unawareness From the Perspective of Model Multiplicity

    cs.LG 2025-05 conditional novelty 7.0 of 10

    Omitting protected attributes can cut disparate impact substantially with negligible accuracy loss, and new model-multiplicity bounds explain when fairer unaware models exist.

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