Understand
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.
Built on
Reconsidering the consequences of using race to estimate kidney function
Nwamaka Denise Eneanya, Wei Yang, and Peter Philip Reese · 2019
Earlier work this paper cites.
From race-based to race-conscious medicine: how anti-racist uprisings call us to act
Jessica P Cerdeña, Marie V Plaisime, and Jennifer Tsai · 2020
Earlier work this paper cites.
Black kidney function matters: use or misuse of race?
Neil R Powe · 2020
Earlier work this paper cites.
Hidden in plain sight—reconsidering the use of race correction in clinical algorithms
Darshali A Vyas, Leo G Eisenstein, and David S Jones · 2020
Earlier work this paper cites.
Similar
Clinical implications of removing race from estimates of kidney function
James A Diao, Gloria J Wu, Herman A Taylor, John K Tucker, Neil R Powe, Isaac S Kohane, and Arjun K Manrai · 2021
Cited alongside, same era.
Race and gender differences in abnormal blood glucose screening and clinician response to prediabetes: a mixed-methods assessment
Tainayah W Thomas, Carol Golin, Carmen D Samuel-Hodge, M Sue Kirkman, Shelley D Golden, and Alexandra F Lightfoot · 2021
Cited alongside, same era.
Diabetes screening by race and ethnicity in the United States: Equivalent body mass index and age thresholds
Rahul Aggarwal, Kirsten Bibbins-Domingo, Robert W. Yeh, Yang Song, Nicholas Chiu, Rishi K. Wadhera, Changyu Shen, and Dhruv S. Kazi · 2022
Cited alongside, same era.
National Health and Nutrition Examination Survey Data, 2022
Centers for Disease Control and Prevention (CDC); National Center for Health Statistics (NCHS) · 2022
Cited alongside, same era.
Then
Patient-centered appraisal of race-free clinical risk assessment
Charles F Manski · 2022
Later among the works it cites.
Using measures of race to make clinical predictions: Decision making, patient health, and fairness
Charles F Manski, John Mullahy, and Atheendar Venkataramani · 2022
Later among the works it cites.
Use of race in clinical algorithms
Anirban Basu · 2023
Closest in time.
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