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

International Journal of Environmental Research and Public Health

Zhenlong Li¹  Shan Qiao²  Xiaoming Li²  Nicole Hair³  Tianchu Lyu³  Chen Liang³  Nicholas Yell⁴ 

[1] Department of Geography, College of Arts and Sciences, University of South Carolina, Columbia, SC 29208, USA;Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA;Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA;Department of Statistics, College of Arts and Sciences, University of South Carolina, Columbia, SC 29208, USA;

关键词: COVID-19;    healthcare disparities;    social determinants of health;    spatial analysis;    Post-Acute Sequelae of SARS-CoV-2 infection;   

DOI  :  10.3390/ijerph18189673

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown