School of Public Health, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
School of Nursing, Rutgers, The State University of New Jersey, Newark, NJ, USA.
Med Care Res Rev. 2021 Oct;78(5):616-626. doi: 10.1177/1077558720935733. Epub 2020 Jul 7.
The Centers for Medicare and Medicaid Services administrative data contains two variables that are used for research and evaluation of health disparities: the enrollment database (EDB) beneficiary race code and the Research Triangle Institute (RTI) race code. The objective of this article is to examine state-level variation in racial/ethnic misclassification of EDB and RTI race codes compared with self-reported data collected during home health care. The study population included 4,231,370 Medicare beneficiaries who utilized home health care services in 2015. We found substantial variation between states in Medicare administrative data misclassification of self-identified Hispanic, Asian American/Pacific Islander, and American Indian/Alaska Native beneficiaries. Caution should be used when interpreting state-level health care disparities and minority health outcomes based on existing race variables contained in Medicare data sets. Self-reported race/ethnicity data collected during routine care of Medicare beneficiaries may be used to improve the accuracy of minority health and health disparities reporting and research.
参保数据库(EDB)受益人性别代码和研究三角研究所(RTI)性别代码。本文的目的是研究与家庭保健期间收集的自我报告数据相比,EDB 和 RTI 性别代码在州一级的种族/民族分类错误情况。研究人群包括 2015 年使用家庭保健服务的 4,231,370 名医疗保险受益人。我们发现医疗保险管理数据对自我认定的西班牙裔、亚裔美国/太平洋岛民和美洲印第安人/阿拉斯加原住民受益人性别分类错误在各州之间存在很大差异。在基于医疗保险数据集中现有的种族变量解释州一级的医疗保健差异和少数民族健康结果时应谨慎。在医疗保险受益人的常规护理期间收集的自我报告的种族/族裔数据可用于提高少数民族健康和健康差异报告和研究的准确性。