Epidemiological approaches to multivariable models of health inequity: A study of race, rurality, and occupation during the COVID-19 pandemic

Abstract

Purpose: Methods for assessing the structural mechanisms of health inequity are not well established. This study applies a phased approach to modeling racial, occupational, and rural disparities on the county level. Methods: Rural counties with disparately high rates of COVID-19 incidence or mortality were randomly paired with in-state control counties with the same rural-urban continuum code. Analysis was restricted to the first six months of the pandemic to represent the baseline structural reserves for each county and reduce biases related to the disruption of these reserves over time. Conditional logistic regression was applied in two phases—first, to examine the demographic distribution of disparities and then, to examine the relationships between these disparities and county-level social and structural reserves. Results: In over 200 rural county pairs (205 for incidence, 209 for mortality), disparities were associated with structural variables representing economic factors, healthcare infrastructure, and local industry. Modeling results were sensitive to assumptions about the relationships between race and other social and structural variables measured at the county level, particularly in models intended to reflect effect modification or mediation. Conclusions: Multivariable modeling of health disparities should reflect the social and structural mechanisms of inequity and anticipate interventions that can advance equity.