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Article
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
International Journal of Environmental Research and Public Health
  • Holly L. Richmond, Georgia Southern University, Jiann-Ping Hsu College of Public Health
  • Joana Tome, Georgia Southern University, Jiann-Ping Hsu College of Public Health
  • Haresh Rochani, Georgia Southern University, Jiann-Ping Hsu College of Public Health
  • Isaac Chun-Hai Fung, Georgia Southern University, Jiann-Ping Hsu College of Public Health
  • Gulzar H. Shah, Georgia Southern University, Jiann-Ping Hsu College of Public Health
  • Jessica S Schwind, Georgia Southern University, Jiann-Ping Hsu College of Public Health
Document Type
Article
Publication Date
10-31-2020
DOI
http://doi.org/10.3390/ijerph17218036
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.

Comments

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Citation Information
Holly L. Richmond, Joana Tome, Haresh Rochani, Isaac Chun-Hai Fung, et al.. "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" International Journal of Environmental Research and Public Health Vol. 17 Iss. 21 (2020) p. 1 - 12 ISSN: 1660-4601
Available at: http://works.bepress.com/isaac_fung1/177/