Coots Madison, Saghafian Soroush, Kent David M, Goel Sharad
Harvard University, Cambridge, Massachusetts (M.C., S.S., S.G.).
Predictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Boston, Massachusetts (D.M.K.).
Ann Intern Med. 2025 Jan;178(1):98-107. doi: 10.7326/M23-3166. Epub 2024 Dec 3.
Accounting for race and ethnicity in estimating disease risk may improve the accuracy of predictions but may also encourage a racialized view of medicine.
To present a decision analytic framework for considering the potential benefits of race-aware over race-unaware risk predictions, using cardiovascular disease, breast cancer, and lung cancer as case studies.
Cross-sectional study.
NHANES (National Health and Nutrition Examination Survey), 2011 to 2018, and NLST (National Lung Screening Trial), 2002 to 2004.
U.S. adults.
Starting with risk predictions from clinically recommended race-aware models, the researchers generated race-unaware predictions via statistical marginalization. They then estimated the utility gains of the race-aware over the race-unaware models, based on a simple utility function that assumes constant costs of screening and constant benefits of disease detection.
The race-unaware predictions were substantially miscalibrated across racial and ethnic groups compared with the race-aware predictions as the benchmark. However, the clinical net benefit at the population level of race-aware predictions over race-unaware predictions was smaller than expected. This result stems from 2 empirical patterns: First, across all 3 diseases, 95% or more of individuals would receive the same decision regardless of whether race and ethnicity are included in risk models; second, for those who receive different decisions, the net benefit of screening or treatment is relatively small because these patients have disease risks close to the decision threshold (that is, the theoretical "point of indifference"). When used to inform rationing, race-aware models may have a more substantial net benefit.
For illustrative purposes, the race-aware models were assumed to yield accurate estimates of risk given the input variables. The researchers used a simplified approach to generate race-unaware risk predictions from the race-aware models and a simple utility function to compare models.
The analysis highlights the importance of foregrounding changes in decisions and utility when evaluating the potential benefit of using race and ethnicity to estimate disease risk.
The Greenwall Foundation.
在估计疾病风险时考虑种族和族裔因素可能会提高预测的准确性，但也可能助长医学中的种族化观念。
以心血管疾病、乳腺癌和肺癌为例，提出一个决策分析框架，以考虑种族敏感风险预测相对于种族不敏感风险预测的潜在益处。
横断面研究。
2011年至2018年的美国国家健康与营养检查调查（NHANES）以及2002年至2004年的国家肺癌筛查试验（NLST）。
美国成年人。
研究人员从临床推荐的种族敏感模型的风险预测开始，通过统计边缘化生成种族不敏感预测。然后，他们基于一个简单的效用函数估计了种族敏感模型相对于种族不敏感模型的效用增益，该函数假设筛查成本恒定且疾病检测收益恒定。
与以种族敏感预测为基准相比，种族不敏感预测在不同种族和族裔群体中存在严重的校准错误。然而，种族敏感预测在人群水平上相对于种族不敏感预测的临床净收益小于预期。这一结果源于两种经验模式：第一，在所有三种疾病中，95%或更多的个体无论风险模型中是否包含种族和族裔因素都会得到相同的决策；第二，对于那些得到不同决策的个体，筛查或治疗的净收益相对较小，因为这些患者的疾病风险接近决策阈值（即理论上的“无差异点”）。当用于指导资源分配时，种族敏感模型可能具有更大的净收益。
为便于说明，假设种族敏感模型在给定输入变量的情况下能准确估计风险。研究人员使用了一种简化方法从种族敏感模型生成种族不敏感风险预测，并使用一个简单的效用函数来比较模型。
该分析强调了在评估使用种族和族裔因素估计疾病风险的潜在益处时，突出决策和效用变化的重要性。
绿墙基金会。
