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Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States

Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 coun...

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Autores principales: Yue, Han, Hu, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8296863/
https://www.ncbi.nlm.nih.gov/pubmed/34202168
http://dx.doi.org/10.3390/ijerph18136832
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author Yue, Han
Hu, Tao
author_facet Yue, Han
Hu, Tao
author_sort Yue, Han
collection PubMed
description Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study’s findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19.
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spelling pubmed-82968632021-07-23 Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States Yue, Han Hu, Tao Int J Environ Res Public Health Article Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study’s findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19. MDPI 2021-06-25 /pmc/articles/PMC8296863/ /pubmed/34202168 http://dx.doi.org/10.3390/ijerph18136832 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yue, Han
Hu, Tao
Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
title Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
title_full Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
title_fullStr Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
title_full_unstemmed Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
title_short Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States
title_sort geographical detector-based spatial modeling of the covid-19 mortality rate in the continental united states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8296863/
https://www.ncbi.nlm.nih.gov/pubmed/34202168
http://dx.doi.org/10.3390/ijerph18136832
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