Working Paper Article Version 1 This version is not peer-reviewed

Predictors of Death Rate During the COVID-19 Pandemic

Version 1 : Received: 14 August 2020 / Approved: 14 August 2020 / Online: 14 August 2020 (12:28:18 CEST)
Version 2 : Received: 9 September 2020 / Approved: 11 September 2020 / Online: 11 September 2020 (09:48:57 CEST)

A peer-reviewed article of this Preprint also exists.

Journal reference: Healthcare 2020, 8, 339
DOI: 10.3390/healthcare8030339

Abstract

COVID-19 is a potentially fatal viral infection. This study investigates geography, demography, socioeconomics, health conditions, hospital characteristics, and politics as potential explanatory variables for death rates at the state and county levels. Data from the Centers for Disease Control and Prevention, the Census Bureau, and other sources were used to evaluate spatial regression models. Yearly pneumonia and flu death rates (state level, 2014-2018) were evaluated as a function of the governors’ political party using repeated measures analysis. Spatial regression at the county level discovered a statistically significant model that included only geography, racial and ethnic status along with a political factor. State level analysis was consistent with this finding. The political factor did not, however, appear in a subsequent analysis of 2014-2018 pneumonia and flu death rates. This study suggests racial/ethnic composition and geographic relationships with the outbreak are important considerations but do not fully explain death rates without inclusion of political factors. The pathogenesis of COVID-19 has greater and disproportionate effect within racial and ethnic minority groups. While population density was not found to be significant, political influence on the reporting of COVID-19 mortality was a significant finding.

Subject Areas

COVID-19; Geospatial Regression; Health Disparities; Public Health

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