When Race/Ethnicity Data Are Lacking: Using Advanced Indirect Estimation Methods to Measure Disparities

doi:10.7249/RR1162

GSTDTAP > 资源环境科学

DOI	10.7249/RR1162
报告编号	RR-1162-COMMASS
	When Race/Ethnicity Data Are Lacking: Using Advanced Indirect Estimation Methods to Measure Disparities
	Allen Fremont; Joel S. Weissman; Emily Hoch; Marc N. Elliott
	2016
出版年	2016
页数	8
语种	英语
国家	美国
出版者	RAND Corporation
领域	资源环境
英文摘要	A key aim of U.S. health care reforms is to ensure equitable care while improving quality for all Americans. Limited race/ethnicity data in health care records hamper efforts to meet this goal. Despite improvements in access and quality, gaps persist, particularly among persons belonging to racial/ethnic minority and low-income groups. This report describes the use of indirect estimation methods to produce probabilistic estimates of racial/ethnic populations to monitor health care utilization and improvement. One method described, called Bayesian Indirect Surname Geocoding, uses a person's Census surname and the racial/ethnic composition of their neighborhood to produce a set of probabilities that a given person belongs to one of a set of mutually exclusive racial/ethnic groups. Advances in methods for estimating race/ethnicity are enabling health plans and other health care organizations to overcome a long-standing barrier to routine monitoring and actions to reduce disparities in care. Though these new estimation methods are promising, practical knowledge and guidance on how to most effectively apply newly available race/ethnicity data to address disparities can be greatly extended.
英文关键词	Health Care Access Health Disparities Health Care Quality Statistical Analysis Methodology
URL	查看原文
来源平台	Rand Corporation
引用统计
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/4544
专题	资源环境科学
推荐引用方式 GB/T 7714	Allen Fremont,Joel S. Weissman,Emily Hoch,et al. When Race/Ethnicity Data Are Lacking: Using Advanced Indirect Estimation Methods to Measure Disparities:RAND Corporation,2016.