Matching with Multiple Criteria and Its Application to Health Disparities Research.

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. The Institute of Medicine (IOM) defines a health services disparity as the difference in accessing health services between members of racial or ethnic minorities not justified by the difference in health status or patients' preference. To estimate a disparity measure consistent with the IOM definition, we propose a statistical matching methodology that constructs 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. Finally, we provide code that replicates the case study and a tutorial that enables users to design customized matched comparison groups satisfying multiple criteria.