  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.
