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Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina.
Lyu, Tianchu; Hair, Nicole; Yell, Nicholas; Li, Zhenlong; Qiao, Shan; Liang, Chen; Li, Xiaoming.
  • Lyu T; Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA.
  • Hair N; Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA.
  • Yell N; Department of Statistics, College of Arts and Sciences, University of South Carolina, Columbia, SC 29208, USA.
  • Li Z; Department of Geography, College of Arts and Sciences, University of South Carolina, Columbia, SC 29208, USA.
  • Qiao S; Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA.
  • Liang C; Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA.
  • Li X; Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA.
Int J Environ Res Public Health ; 18(18)2021 09 14.
Article in English | MEDLINE | ID: covidwho-1409571
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans / Male Country/Region as subject: North America Language: English Year: 2021 Document Type: Article Affiliation country: Ijerph18189673

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans / Male Country/Region as subject: North America Language: English Year: 2021 Document Type: Article Affiliation country: Ijerph18189673