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Small Clinical Benefit Seen for Race-Aware Over Unaware Risk Predictions

Smaller than expected benefit partly due to most individuals receiving same decision, regardless of race being included in model

By Elana Gotkine HealthDay Reporter

TUESDAY, Dec. 3, 2024 (HealthDay News) — The clinical benefit of race-aware over race-unaware disease risk predictions is smaller than expected, according to a study published online Dec. 3 in the Annals of Internal Medicine.

Madison Coots, from Harvard University in Cambridge, Massachusetts, and colleagues present a decision analytic framework for considering the potential benefits of race-aware over race-unaware risk predictions. A cross-sectional study involving U.S. adults was conducted using cardiovascular disease, breast cancer, and lung cancer as case studies. Race-unaware predictions were generated via statistical marginalization, starting with risk predictions from clinically recommended race-aware models. The utility gains of the race-aware over the race-unaware models were estimated based on a simple utility function assuming constant costs of screening and constant benefit of disease detection.

The researchers found that compared with the race-aware predictions as the benchmark, the race-unaware predictions were substantially miscalibrated across racial and ethnic groups. At the population level, the clinical net benefit of race-aware over race-unaware predictions was smaller than anticipated. This result stemmed from two patterns: (1) 95 percent or more of individuals would receive the same decision across all three diseases, irrespective of whether race and ethnicity are included in the risk model; and (2) the net benefit of screening or treatment was relatively small for those who received different decisions because these patients have disease risks close to the decision threshold. Race-aware models may have a more substantial net benefit when used to inform rationing.

“Our results suggest that race-aware risk models yield smaller gains in net benefit over race-unaware models than the improvement in predictions might suggest,” the authors write.

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