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Article

Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina

1
Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA
2
Department of Statistics, College of Arts and Sciences, University of South Carolina, Columbia, SC 29208, USA
3
Department of Geography, College of Arts and Sciences, University of South Carolina, Columbia, SC 29208, USA
4
Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA
*
Author to whom correspondence should be addressed.
Equal contribution.
Academic Editors: Dimitrios Paraskevis, Maria Yavropoulou and Sotirios Tsiodras
Int. J. Environ. Res. Public Health 2021, 18(18), 9673; https://doi.org/10.3390/ijerph18189673
Received: 3 August 2021 / Revised: 9 September 2021 / Accepted: 10 September 2021 / Published: 14 September 2021
(This article belongs to the Topic Burden of COVID-19 in Different Countries)
Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal–geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including USAFacts, U.S. Census Bureau, and the Population Estimates Program. The percentage of males and the unemployed population were positively associated with geodistributions of COVID-19 infection (p values < 0.05) in global models throughout the time. The percentage of the white population and the obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models fit better than global models, suggesting nonstationary correlations between a region and its neighbors. Characterized by temporal–geospatial patterns, disparities in COVID-19 infection rate and their PIDRs are different from the mortality and morbidity of COVID-19 patients. Our findings suggest the importance of prioritizing different populations and developing tailored interventions at different times of the pandemic. View Full-Text
Keywords: COVID-19; healthcare disparities; social determinants of health; spatial analysis; Post-Acute Sequelae of SARS-CoV-2 infection COVID-19; healthcare disparities; social determinants of health; spatial analysis; Post-Acute Sequelae of SARS-CoV-2 infection
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MDPI and ACS Style

Lyu, T.; Hair, N.; Yell, N.; Li, Z.; Qiao, S.; Liang, C.; Li, X. Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina. Int. J. Environ. Res. Public Health 2021, 18, 9673. https://doi.org/10.3390/ijerph18189673

AMA Style

Lyu T, Hair N, Yell N, Li Z, Qiao S, Liang C, Li X. Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina. International Journal of Environmental Research and Public Health. 2021; 18(18):9673. https://doi.org/10.3390/ijerph18189673

Chicago/Turabian Style

Lyu, Tianchu, Nicole Hair, Nicholas Yell, Zhenlong Li, Shan Qiao, Chen Liang, and Xiaoming Li. 2021. "Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina" International Journal of Environmental Research and Public Health 18, no. 18: 9673. https://doi.org/10.3390/ijerph18189673

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