Race and Ethnicity Data Quality and Imputation Using U.S. Census Data in an Integrated Health System
Race and Ethnicity Data Quality and Imputation Using U.S. Census Data in an Integrated Health System
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在综合卫生系统中使用美国人口普查数据进行种族和民族数据质量和估算
DOI：
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发表时间：
2013
影响因子：
2.5
通讯作者：
S. Jacobsen
中科院分区：
文献类型：
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作者：
S. Derose;R. Contreras;K. Coleman;C. Koebnick;S. Jacobsen
使用卫生系统数据库对种族和民族差异进行研究可以揭示大量个人的日常医疗保健和结果，从而更好地理解和解决健康不平等问题。此类研究通常受到种族/民族数据质量的限制。我们检查了一个大型、多样化、综合卫生系统中种族/民族数据的质量，该系统反复收集有关服务利用的这些数据。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。通过与成人的自我报告进行比较来判断，行政种族/民族数据是准确的。对于卫生系统中四个最常见的种族/民族群体：白人、西班牙裔、黑人和亚洲人，贝叶斯改进姓氏地理编码方法产生的插补结果远好于机会分配。这些结果支持重新努力对大型卫生系统中的种族和民族差异进行研究。
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.
通过趋势估计和卡尔曼滤波改进稀有种族/族裔群体的差异估计：国家健康访谈调查的应用。
影响因子：
3.4
作者：
Elliott,MarcN;McCaffrey,DanielF;Finch,BrianK;Klein,DavidJ;Orr,Nate;Beckett,MeganK;Lurie,Nicole
通讯作者：
Lurie,Nicole
影响因子：
19.6
作者：
Derose, Stephen F.;Rutkowski, Mark P.;Crooks, Peter W.
通讯作者：
Crooks, Peter W.