Abstract

Social determinants of health are associated with a variety of negative health outcomes, including COVID-19 morbidity and mortality. However, most research evaluating this relationship have been case studies, retrospective cohort studies, and case series studies and/or have used use analytic techniques, such as linear regression, that can struggle to adequately model the social determinants' complex nature. This study used United States county-level social determinants of health data and March 2020-December 2020 COVID-19 morbidity and mortality data. Structural equation modeling was used to develop a latent measurement model for the social determinants of health. Substantial cross-loadings among the social determinants of health precluded the estimation of the originally proposed measurement model. However, a more parsimonious model was estimated, with adequate factor loadings and model fit statistics. A multi-level, two-part structural equation model further validated the relationship between social determinants of health and COVID-19 morbidity and mortality. The model's predictive performance was moderate to strong, which validates and extends previous research using structural equation modeling to evaluate the relationship between social determinants of health and COVID-19 morbidity. The study adds to the theoretical and empirical foundation supporting the use of structural equation modeling to study the social determinants of health.

Degree

College and Department

Family, Home, and Social Sciences; Sociology

Rights

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