在电子健康记录数据中估计种族和种族的评估方法
Sarah Conderino1, Jasmin Divers1,2, John A Dodson1
1Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA.
Health services research
|May 27, 2025
概括
非匿名贝叶斯改进姓氏地理编码 (BISG) 提供比匿名方法更准确的种族和种族归因,特别是当数据不随机丢失时. 慢性疾病负担研究显示,在各种归算技术中,结果一致.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 准确的种族和民族数据对于了解慢性疾病负担至关重要.
- 电子健康记录 (EHR) 是有价值的数据来源,但往往缺乏人口统计信息.
- 推算方法旨在填补缺少的数据,但它们的性能可能有所不同.
研究的目的:
- 在EHR数据中比较种族和种族的匿名和非匿名归算方法.
- 评估不同归算技术的准确性和一致性.
- 评估归算方法对慢性疾病负担估计的影响.
主要方法:
- 使用各种缺失数据机制进行模拟分析.
- 输入方法包括贝叶斯改进的姓氏地理编码 (BISG),单次输入,随机森林和链式方程 (MICE) 多重输入.
- 性能通过灵敏度,精度,准确性和科恩的卡帕 (κ) 与自我报告数据的一致性来衡量. 方法应用于两个EHR数据集.
主要成果:
- 在模拟中,非匿名的BISG显示了最高的准确性 (66-73%).
- 当数据缺失时,匿名方法的一致性下降,而不是随机 (MNAR).
- 尽管模拟的差异,种族/种族分布和疾病负担估计在电子健康记录数据中的方法之间是一致的. 与完整的案例分析相比,所有归算方法都提高了估计精度.
结论:
- 非匿名的BISG可以提供优越的种族和民族分类,特别是在MNAR条件下.
- 关于慢性疾病负担的描述性研究可能对选择种族和种族的归算方法具有强大影响.
- 进一步的研究可以探索不同EHR群体中归算的细微差别.
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