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Author ORCID Identifier

https://orcid.org/0000-0002-2593-0795

Date Available

8-5-2027

Year of Publication

2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

Education

Department/School/Program

Education Sciences

Faculty

Xin Ma

Faculty

Kathleen Aspiranti

Abstract

This dissertation bridges the disconnect between intersectionality theory and quantitative methodology. Current health disparities research suffers from a critical gap between concept (i.e., multidimensional inequity) and method (e.g., unidimensional analysis). While theory posits that social identities and structural context are inseparable, traditional regression frameworks often treat social identities as distinct independent variables. To address this gap, this study moves beyond a traditional single-axis approach by adopting a quantitative intersectional approach to redefine how risks associated with one’s social position are modeled while modeling the complex interactions of various social contexts (i.e., intersectional strata) as the primary unit of analysis. Furthermore, this study advances intersectional quantitative methods by extending intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) into a multivariate and cross-classified structure. This advanced method allows us to model broader social mechanisms (e.g., structural context), hospital context, and clinical factors simultaneously.  Using New York State Inpatient Database 2022 and 2023, this study employs intersectional multivariate CC-MAIHDA to quantify and decompose the drivers of intersectional group disparities in adult patients hospitalized with five medical conditions simultaneously (congestive heart failure, acute myocardial infarction, stroke, chronic obstructive pulmonary disease, and pneumonia). Variance partitioning through the series of analyses isolated the sources of intersectional disparity, including hospital performance context, clinical complexity, and additive main effects versus intersectional interaction effects.  This study makes significant contributions to the methodological literature on quantitative intersectional methods by offering effective and efficient statistical frameworks for analyzing complex, high-dimensional data. Beyond its methodological contributions, this study also has a potential to offer new insights for health policy. By modeling five medical conditions simultaneously, this study seeks to find whether the pattern of structural inequities is consistent across diseases. The presence of shared systemic patterns or complex interaction effects informs policy makers and organizational leaders of the need for broader structural intervention or hospital performance evaluation reforms.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2026.402

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