Matching is a popular nonparametric covariate adjustment strategy in empirical health services research. Matching helps construct two groups comparable in many baseline covariates but different in some key aspects under investigation. In health disparities research, it is desirable to understand the contributions of various modifiable factors, like income and insurance type, to the observed disparity in access to health services between different groups. To single out the contributions from the factors of interest, we propose a statistical matching methodology that constructs nested matched comparison groups from, for instance, White men, that resemble the target group, for instance, black men, in some selected covariates while remaining identical to the white men population before matching in the remaining covariates. Using the proposed method, we investigated the disparity gaps between white men and black men in the US in prostate-specific antigen (PSA) screening based on the 2020 Behavioral Risk Factor Surveillance System (BFRSS) database. We found a widening PSA screening rate as the white matched comparison group increasingly resembles the black men group and quantified the contribution of modifiable factors like socioeconomic status. Finally, we provide code that replicates the case study and a tutorial that enables users to design customized matched comparison groups satisfying multiple criteria.

翻译：匹配是实证卫生服务研究中一种常用的非参数协变量调整策略。该方法有助于构建在多个基线协变量上可比但在所研究的关键方面存在差异的两组人群。在健康差异研究中，需要理解各种可改变因素（如收入和保险类型）对不同群体间观察到的卫生服务可及性差异的贡献。为分离出所关注因素的贡献，我们提出一种统计匹配方法，该方法从例如白人男性群体中构建嵌套式匹配比较组，这些比较组在选定的协变量上与目标群体（如黑人男性）相似，同时在其余协变量上保持与匹配前白人男性群体一致。利用所提方法，我们基于2020年行为风险因素监测系统（BFRSS）数据库，研究了美国白人男性与黑人男性在前列腺特异性抗原（PSA）筛查中的差异。我们发现，随着白人匹配比较组与黑人男性群体的相似度提高，PSA筛查率差距逐步扩大，并量化了社会经济地位等可改变因素的贡献。最后，我们提供了复现该案例研究的代码以及帮助用户设计满足多重标准的定制化匹配比较组的教程。