Analysis of COVID-19 cases' spatial dependence in US counties reveals health inequalities

Saffary, T., Adegboye, Oyelola, Gayawan, E., Elfaki, F., Kuddus, Md Abdul, and Saffary, R. (2020) Analysis of COVID-19 cases' spatial dependence in US counties reveals health inequalities. Frontiers in Public Health, 8. 579190.

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On March 13, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus SARS-CoV2 a pandemic. Since then the virus has infected over 9.1 million individuals and resulted in over 470,000 deaths worldwide (as of June 24, 2020). Here, we discuss the spatial correlation between county population health rankings and the incidence of COVID-19 cases and COVID-19 related deaths in the United States. We analyzed the spread of the disease based on multiple variables at the county level, using publicly available data on the numbers of confirmed cases and deaths, intensive care unit beds and socio-demographic, and healthcare resources in the U.S. Our results indicate substantial geographical variations in the distribution of COVID-19 cases and deaths across the US counties. There was significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran I = 0.174 and 0.264, p < 0.0001). A similar result was found for the global spatial correlation between the percentage of Black American and deaths due to COVID-19 at the county level in the U.S. (Moran I = 0.264, p < 0.0001). There was no significant spatial correlation between the Hispanic population and COVID-19 cases and deaths; however, a higher percentage of non-Hispanic white was significantly negatively spatially correlated with cases (Moran I = –0.203, p < 0.0001) and deaths (Moran I = –0.137, p < 0.0001) from the disease. This study showed significant but weak spatial autocorrelation between the number of intensive care unit beds and COVID-19 cases (Moran I = 0.08, p < 0.0001) and deaths (Moran I = 0.15, p < 0.0001), respectively. These findings provide more detail into the interplay between the infectious disease and healthcare-related characteristics of the population. Only by understanding these relationships will it be possible to mitigate the rate of spread and severity of the disease.

Item ID: 65426
Item Type: Article (Research - C1)
ISSN: 2296-2565
Keywords: Coronavirus, COVID-19, Spatial autocorrelation, Health rankings, Neighborhood
Copyright Information: © 2020 Saffary, Adegboye, Gayawan, Elfaki, Kuddus and Saffary. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Date Deposited: 07 Jan 2021 23:23
FoR Codes: 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490501 Applied statistics @ 40%
42 HEALTH SCIENCES > 4202 Epidemiology > 420210 Social epidemiology @ 30%
32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320211 Infectious diseases @ 30%
SEO Codes: 92 HEALTH > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920109 Infectious Diseases @ 100%
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