Race and Ethnicity Data Quality and Imputation Using US Census Data in an Integrated Health System: The Kaiser Permanente Southern California Experience
Race and Ethnicity Data Quality and Imputation Using US Census Data in an Integrated Health System: The Kaiser Permanente Southern California Experience
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DOI：
10.1177/1077558712466293
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发表时间：
2013-06-01
影响因子：
2.5
通讯作者：
Jacobsen, Steven J.
中科院分区：
文献类型：
--
作者：
Derose, Stephen F.;Contreras, Richard;Jacobsen, Steven J.
利用卫生系统数据库对种族和族裔差异进行研究，可以揭示大量个人的通常保健和结果，以便更好地理解和解决卫生不平等问题。这种研究往往受到种族/族裔数据质量的限制。我们研究了一个大型的，多样化的，综合的卫生系统，反复收集这些数据的服务利用的种族/民族数据的质量。我们测试了贝叶斯改进姓氏地理编码对种族/民族数据插补的准确性。通过与成人自我报告进行比较，判定管理人种/种族数据准确。贝叶斯改进的姓氏地理编码方法产生的插补结果远远好于卫生系统中四个最常见的种族/民族群体的机会分配：白人，西班牙裔，黑人和亚洲人。这些结果支持了对大型卫生系统中种族和民族差异进行研究的新努力。
Research on racial and ethnic disparities using health system databases can shed light on the usual health care and outcomes of large numbers of individuals so that health inequities can be better understood and addressed. Such research often suffers from limitations in race/ethnicity data quality. We examined the quality of race/ethnicity data in a large, diverse, integrated health system that repeatedly collects these data on utilization of services. We tested the accuracy of Bayesian Improved Surname Geocoding for imputation of race/ethnicity data. Administrative race/ethnicity data were accurate as judged by comparison with self-report in adults. The Bayesian Improved Surname Geocoding method produced imputation results far better than chance assignment for the four most common race/ethnicity groups in the health system: Whites, Hispanics, Blacks, and Asians. These results support renewed efforts to conduct studies of racial and ethnic disparities in large health systems.