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.

翻译：关于在疾病风险评估中是否应考虑患者种族与族裔因素，目前存在活跃的学术争论。通过纳入种族与族裔信息，虽然可能提升风险预测的准确性，但学界担忧这可能助长种族化医学观念。在糖尿病风险模型中，尽管使用种族与族裔变量能显著提升统计精度，但临床实用性的改善却出人意料地有限。这种临床效益的局限性源于两种经验性规律：其一，无论风险模型是否包含种族或族裔变量，绝大多数个体都会获得相同的筛查建议；其二，对于因模型差异而获得不同筛查建议的群体，进行筛查与不进行筛查之间的效用差异相对较小。本研究基于广泛的统计学原理，其结论很可能适用于其他基于风险模型的临床决策场景。