Fremont Allen, Weissman Joel S, Hoch Emily, Elliott Marc N
Rand Health Q. 2016 Jun 20;6(1):16.
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 study 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.
美国医疗保健改革的一个关键目标是在提高所有美国人医疗质量的同时确保公平医疗。医疗记录中有限的种族/族裔数据阻碍了实现这一目标的努力。尽管在医疗可及性和质量方面有所改善，但差距依然存在，尤其是在少数种族/族裔和低收入群体中。本研究描述了使用间接估计方法来生成种族/族裔人口的概率估计，以监测医疗保健利用情况和改善情况。所描述的一种方法称为贝叶斯间接姓氏地理编码，它利用一个人的人口普查姓氏及其邻里的种族/族裔构成，来生成给定个体属于一组互斥种族/族裔群体之一的一组概率。估计种族/族裔方法的进步使健康计划和其他医疗保健组织能够克服长期存在的常规监测障碍以及采取行动减少医疗差距。尽管这些新的估计方法很有前景，但关于如何最有效地应用新获得的种族/族裔数据来解决差距的实践知识和指导仍可大幅扩展。
