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A COVID-19 Geospatial Population Health Risk Assessment for Missouri


Tim Haithcoat







Aim: Design and analyze a multifactor spatial database to assess COVID-19 risk across individual, community, social, infrastructure and culture contexts at the county level to provide quantitative insight and statistical support for potential mitigation areas.

Methods: COVID-19 studies require vast amounts of integrated data to create understanding. MU’s Geospatial Analytical Research Knowledgebase (GeoARK) is a spatially integrated database to support spatial and contextual analytics. Utilizing GeoARK we created six distinct risk databases covering individual susceptibility; transmission; socioeconomic; accessibility; health culture; and exposure. Ordinary least squares regression was used to evaluate combinations of explanatory variables. Selected variables within each risk category then had quintiles calculated to create comparative categorical groups for each risk variable with higher values assigned to worse risk.  Cumulative risk scores were assembled for each risk category as well as an overall composite risk score. These values were then analyzed using Local Moran’s I, Similarity analysis, and spatially constrained multivariate clustering to inform regional grouping outcomes.

Results: Through spatial analytics, differences in both the magnitude of risk, and substance of that risk, among and between rural and urban counties were found. Missouri’s spatial diversity is evident in the variability of overall risk across the six factor areas developed as well as the six region-based groups of counties sharing similar risk traits. We further examined the potential for syndemic interplay of these risk factors in effecting COVID-19 population health impact.

Conclusion: It is important to understand the context and interrelationships of various risk factors occurring within the state in order to better understand the potential pathways for disease as well as what nuances in mitigation strategies are needed to address specific populations. 

Public Health Importance: There is no one-size-fits-all solution for the diversity found through spatial analysis of risk. The ability to address issues that are most influencing the health of a particular region or population is paramount to equality in care.