Matching with multiple criteria and its application to health disparities research

TL;DR

This paper introduces a novel statistical matching method for health disparities research, enabling the creation of comparison groups that isolate the effects of specific modifiable factors on disparities.

Contribution

The paper proposes a new matching methodology that constructs nested comparison groups based on multiple criteria, allowing for detailed analysis of health disparities.

Findings

Identified widening PSA screening disparities between white and black men.

Quantified the impact of socioeconomic factors on screening disparities.

Provided tools for customized matching in health disparities studies.

Abstract

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.