The Use of Penalized Regression Analysis to Identify County-Level Demographic and Socioeconomic Variables Predictive of Increased COVID-19 Cumulative Case Rates in the State of Georgia

Holly L. Richmond, Joana Tome, Haresh Rochani, Isaac Chun-Hai Fung, Gulzar H. Shah and Jessica S. Schwind
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Holly L. Richmond: Jiann-Ping Hsu College of Public Health, Department of Biostatistics, Epidemiology & Environmental Health Sciences, Georgia Southern University, Statesboro, GA 30458, USA
Joana Tome: Jiann-Ping Hsu College of Public Health, Department of Biostatistics, Epidemiology & Environmental Health Sciences, Georgia Southern University, Statesboro, GA 30458, USA
Haresh Rochani: Jiann-Ping Hsu College of Public Health, Department of Biostatistics, Epidemiology & Environmental Health Sciences, Georgia Southern University, Statesboro, GA 30458, USA
Isaac Chun-Hai Fung: Jiann-Ping Hsu College of Public Health, Department of Biostatistics, Epidemiology & Environmental Health Sciences, Georgia Southern University, Statesboro, GA 30458, USA
Gulzar H. Shah: Jiann-Ping Hsu College of Public Health, Department of Health Policy and Community Health, Georgia Southern University, Statesboro, GA 30458, USA
Jessica S. Schwind: Jiann-Ping Hsu College of Public Health, Department of Biostatistics, Epidemiology & Environmental Health Sciences, Georgia Southern University, Statesboro, GA 30458, USA

IJERPH, 2020, vol. 17, issue 21, 1-12

Abstract: Systemic inequity concerning the social determinants of health has been known to affect morbidity and mortality for decades. Significant attention has focused on the individual-level demographic and co-morbid factors associated with rates and mortality of COVID-19. However, less attention has been given to the county-level social determinants of health that are the main drivers of health inequities. To identify the degree to which social determinants of health predict COVID-19 cumulative case rates at the county-level in Georgia, we performed a sequential, cross-sectional ecologic analysis using a diverse set of socioeconomic and demographic variables. Lasso regression was used to identify variables from collinear groups. Twelve variables correlated to cumulative case rates (for cases reported by 1 August 2020) with an adjusted r squared of 0.4525. As time progressed in the pandemic, correlation of demographic and socioeconomic factors to cumulative case rates increased, as did number of variables selected. Findings indicate the social determinants of health and demographic factors continue to predict case rates of COVID-19 at the county-level as the pandemic evolves. This research contributes to the growing body of evidence that health disparities continue to widen, disproportionality affecting vulnerable populations.

Keywords: COVID-19; social determinants of health; county; race; demographics; socioeconomic; community health rankings (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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