Suppr超能文献

在医疗保险行政数据中审查种族和民族信息。

Examining Race and Ethnicity Information in Medicare Administrative Data.

作者信息

Filice Clara E, Joynt Karen E

机构信息

Office of the Assistant Secretary for Planning and Evaluation, United States Department of Health and Human Services, Washington, DC.

出版信息

Med Care. 2017 Dec;55(12):e170-e176. doi: 10.1097/MLR.0000000000000608.

Abstract

Racial and ethnic disparities are observed in the health status and health outcomes of Medicare beneficiaries. Reducing these disparities is a national priority, and having high-quality data on individuals' race and ethnicity is critical for researchers working to do so. However, using Medicare data to identify race and ethnicity is not straightforward. Currently, Medicare largely relies on Social Security Administration data for information about Medicare beneficiary race and ethnicity. Directly self-reported race and ethnicity information is collected for subsets of Medicare beneficiaries but is not explicitly collected for the purpose of populating race/ethnicity information in the Medicare administrative record. As a consequence of historical data collection practices, the quality of Medicare's administrative data on race and ethnicity varies substantially by racial/ethnic group; the data are generally much more accurate for whites and blacks than for other racial/ethnic groups. Identification of Hispanic and Asian/Pacific Islander beneficiaries has improved through use of an imputation algorithm recently applied to the Medicare administrative database. To improve the accuracy of race/ethnicity data for Medicare beneficiaries, researchers have developed techniques such as geocoding and surname analysis that indirectly assign Medicare beneficiary race and ethnicity. However, these techniques are relatively new and data may not be widely available. Understanding the strengths and limitations of different approaches to identifying race and ethnicity will help researchers choose the best method for their particular purpose, and help policymakers interpret studies using these measures.

摘要

医疗保险受益人的健康状况和健康结果存在种族和民族差异。减少这些差异是国家优先事项,对于致力于此的研究人员而言,获取有关个人种族和民族的高质量数据至关重要。然而,利用医疗保险数据来确定种族和民族并非易事。目前,医疗保险在很大程度上依赖社会保障管理局的数据来获取医疗保险受益人种族和民族的信息。虽然为部分医疗保险受益人收集了直接自我报告的种族和民族信息,但并非明确为在医疗保险行政记录中填充种族/民族信息而收集。由于历史数据收集做法,医疗保险关于种族和民族的行政数据质量因种族/民族群体而异;白人及黑人的数据通常比其他种族/民族群体的数据准确得多。通过使用最近应用于医疗保险行政数据库的一种推算算法,对西班牙裔和亚裔/太平洋岛民受益人的识别有所改进。为提高医疗保险受益人种族/民族数据的准确性,研究人员开发了诸如地理编码和姓氏分析等技术,这些技术可间接确定医疗保险受益人的种族和民族。然而,这些技术相对较新,数据可能无法广泛获取。了解不同种族和民族识别方法的优势和局限性将有助于研究人员为其特定目的选择最佳方法,并帮助政策制定者解读使用这些措施的研究。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验