Unveiling Spatial Associations between COVID-19 Severe Health Index, Racial/Ethnic Composition, and Community Factors in the United States

Ruaa Al Juboori (), Divya S. Subramaniam, Leslie Hinyard and J. S. Onésimo Sandoval
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Ruaa Al Juboori: School of Applied Sciences, The University of Mississippi, Oxford, MS 38677, USA
Divya S. Subramaniam: Department of Health and Clinical Outcomes Research, Advanced HEAlth Data (AHEAD) Institute, Saint Louis University, St. Louis, MO 63103, USA
Leslie Hinyard: Department of Health and Clinical Outcomes Research, Advanced HEAlth Data (AHEAD) Institute, Saint Louis University, St. Louis, MO 63103, USA
J. S. Onésimo Sandoval: Department of Sociology and Anthropology, Saint Louis University, St. Louis, MO 63103, USA

IJERPH, 2023, vol. 20, issue 17, 1-17

Abstract: There are limited efforts to incorporate different predisposing factors into prediction models that account for population racial/ethnic composition in exploring the burden of high COVID-19 Severe Health Risk Index (COVID-19 SHRI) scores. This index quantifies the risk of severe COVID-19 symptoms among a county’s population depending on the presence of some chronic conditions. These conditions, as identified by the Centers for Disease Control and Prevention (CDC), include Chronic Obstructive Pulmonary Disease (COPD), heart disease, high blood pressure, diabetes, and obesity. Therefore, the objectives of this study were (1) to investigate potential population risk factors preceding the COVID-19 pandemic that are associated with the COVID-19 SHRI utilizing non-spatial regression models and (2) to evaluate the performance of spatial regression models in comparison to non-spatial regression models. The study used county-level data for 3107 United States counties, utilizing publicly available datasets. Analyses were carried out by constructing spatial and non-spatial regression models. Majority White and majority Hispanic counties showed lower COVID-19 SHRI scores when compared to majority Black counties. Counties with an older population, low income, high smoking, high reported insufficient sleep, and a high percentage of preventable hospitalizations had higher COVID-19 SHRI scores. Counties with better health access and internet coverage had lower COVID-19 SHRI scores. This study helped to identify the county-level characteristics of risk populations to help guide resource allocation efforts. Also, the study showed that the spatial regression models outperformed the non-spatial regression models. Racial/ethnic inequalities were associated with disparities in the burden of high COVID-19 SHRI scores. Therefore, addressing these factors is essential to decrease inequalities in health outcomes. This work provides the baseline typology to further explore many social, health, economic, and political factors that contribute to different health outcomes.

Keywords: racial disparities; chronic disease inequalities; comorbidity index; pandemic; spatial analysis; disease burden; COVID-19 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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