在肺癌查资格预测模型中使用种族和种族的方法
Rebecca Landy1, Isabel Gomez1,2, Tanner J Caverly3
1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland.
JAMA network open
|September 18, 2023
概括
从肺癌查模型中删除种族和种族可以加剧差异. 反事实方法提高了准确性,增加了非洲裔美国人的资格,证明了公平护理的潜力.
科学领域:
- 健康差距 研究 研究 研究 研究
- 临床预测建模模型
- 癌症查 癌症查
背景情况:
- 结合种族和种族的临床预测模型可以延续或减少健康差异.
- 准确的肺癌查资格确定对于早期检测和改善结果至关重要.
- 现有的模型可能无法公平地为多样化的人口提供服务.
研究的目的:
- 为了比较肺癌查资格,使用没有种族/种族的模型与反事实方法进行比较.
- 评估消除种族和族裔对查计算机断层扫描 (LYFS-CT) 模型所获得的生命年数的影响.
- 确保种族和少数民族群体获得公平的查机会.
主要方法:
- 使用大型临床试验和国家健康访谈调查 (NHIS) 数据,重新调整了LYFS-CT模型 (LYFS-CT NoRace),排除了种族和种族.
- 开发了一种反事实资格方法,重新计算使用白人种族作为代理人的少数群体的预期寿命.
- 经过验证的模型和在美国代表性人口中比较查资格 (NHIS 2015-2018).
主要成果:
- 不包括种族/族裔错误校准的非洲裔美国人,西班牙裔美国人和亚裔美国人的死亡风险预测.
- LYFS-CT NoRace模型减少了非洲裔美国人的查资格39%,同时增加了西班牙裔美国人 (108%) 和亚裔美国人 (73%) 的资格.
- 反事实方法保持了跨群体的模型校准,并增加了13%的非裔美国人资格,而不会降低其他人的资格.
结论:
- 从LYFS-CT模型中删除种族和种族,显著降低了非洲裔美国人的肺癌查资格.
- 反事实性资格方法显示了保持模型准确性和减少差异的潜力.
- 公平的临床预测模型对于促进癌症查中的健康公平至关重要.
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