Cargando…
Risk factors associated with mortality of COVID-19 in 3125 counties of the United States
BACKGROUND: The number of cumulative confirmed cases of COVID-19 in the United States has risen sharply since March 2020. A county health ranking and roadmaps program has been established to identify factors associated with disparity in mobility and mortality of COVID-19 in all counties in the Unite...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780082/ https://www.ncbi.nlm.nih.gov/pubmed/33397470 http://dx.doi.org/10.1186/s40249-020-00786-0 |
_version_ | 1783631447627661312 |
---|---|
author | Tian, Ting Zhang, Jingwen Hu, Liyuan Jiang, Yukang Duan, Congyuan Li, Zhongfei Wang, Xueqin Zhang, Heping |
author_facet | Tian, Ting Zhang, Jingwen Hu, Liyuan Jiang, Yukang Duan, Congyuan Li, Zhongfei Wang, Xueqin Zhang, Heping |
author_sort | Tian, Ting |
collection | PubMed |
description | BACKGROUND: The number of cumulative confirmed cases of COVID-19 in the United States has risen sharply since March 2020. A county health ranking and roadmaps program has been established to identify factors associated with disparity in mobility and mortality of COVID-19 in all counties in the United States. The risk factors associated with county-level mortality of COVID-19 with various levels of prevalence are not well understood. METHODS: Using the data obtained from the County Health Rankings and Roadmaps program, this study applied a negative binomial design to the county-level mortality counts of COVID-19 as of August 27, 2020 in the United States. In this design, the infected counties were categorized into three levels of infections using clustering analysis based on time-varying cumulative confirmed cases from March 1 to August 27, 2020. COVID-19 patients were not analyzed individually but were aggregated at the county-level, where the county-level deaths of COVID-19 confirmed by the local health agencies. Clustering analysis and Kruskal–Wallis tests were used in our statistical analysis. RESULTS: A total of 3125 infected counties were assigned into three classes corresponding to low, median, and high prevalence levels of infection. Several risk factors were significantly associated with the mortality counts of COVID-19, where higher level of air pollution (0.153, P < 0.001) increased the mortality in the low prevalence counties and elder individuals were more vulnerable in both the median (0.049, P < 0.001) and high (0.114, P < 0.001) prevalence counties. The segregation between non-Whites and Whites (low: 0.015, P < 0.001; median:0.025, P < 0.001; high: 0.019, P = 0.005) and higher Hispanic population (low and median: 0.020, P < 0.001; high: 0.014, P = 0.009) had higher likelihood of risk of the deaths in all infected counties. CONCLUSIONS: The mortality of COVID-19 depended on sex, race/ethnicity, and outdoor environment. The increasing awareness of the impact of these significant factors may help decision makers, the public health officials, and the general public better control the risk of pandemic, particularly in the reduction in the mortality of COVID-19. GRAPHIC ABSTRACT: [Image: see text] |
format | Online Article Text |
id | pubmed-7780082 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77800822021-01-04 Risk factors associated with mortality of COVID-19 in 3125 counties of the United States Tian, Ting Zhang, Jingwen Hu, Liyuan Jiang, Yukang Duan, Congyuan Li, Zhongfei Wang, Xueqin Zhang, Heping Infect Dis Poverty Research Article BACKGROUND: The number of cumulative confirmed cases of COVID-19 in the United States has risen sharply since March 2020. A county health ranking and roadmaps program has been established to identify factors associated with disparity in mobility and mortality of COVID-19 in all counties in the United States. The risk factors associated with county-level mortality of COVID-19 with various levels of prevalence are not well understood. METHODS: Using the data obtained from the County Health Rankings and Roadmaps program, this study applied a negative binomial design to the county-level mortality counts of COVID-19 as of August 27, 2020 in the United States. In this design, the infected counties were categorized into three levels of infections using clustering analysis based on time-varying cumulative confirmed cases from March 1 to August 27, 2020. COVID-19 patients were not analyzed individually but were aggregated at the county-level, where the county-level deaths of COVID-19 confirmed by the local health agencies. Clustering analysis and Kruskal–Wallis tests were used in our statistical analysis. RESULTS: A total of 3125 infected counties were assigned into three classes corresponding to low, median, and high prevalence levels of infection. Several risk factors were significantly associated with the mortality counts of COVID-19, where higher level of air pollution (0.153, P < 0.001) increased the mortality in the low prevalence counties and elder individuals were more vulnerable in both the median (0.049, P < 0.001) and high (0.114, P < 0.001) prevalence counties. The segregation between non-Whites and Whites (low: 0.015, P < 0.001; median:0.025, P < 0.001; high: 0.019, P = 0.005) and higher Hispanic population (low and median: 0.020, P < 0.001; high: 0.014, P = 0.009) had higher likelihood of risk of the deaths in all infected counties. CONCLUSIONS: The mortality of COVID-19 depended on sex, race/ethnicity, and outdoor environment. The increasing awareness of the impact of these significant factors may help decision makers, the public health officials, and the general public better control the risk of pandemic, particularly in the reduction in the mortality of COVID-19. GRAPHIC ABSTRACT: [Image: see text] BioMed Central 2021-01-04 /pmc/articles/PMC7780082/ /pubmed/33397470 http://dx.doi.org/10.1186/s40249-020-00786-0 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Tian, Ting Zhang, Jingwen Hu, Liyuan Jiang, Yukang Duan, Congyuan Li, Zhongfei Wang, Xueqin Zhang, Heping Risk factors associated with mortality of COVID-19 in 3125 counties of the United States |
title | Risk factors associated with mortality of COVID-19 in 3125 counties of the United States |
title_full | Risk factors associated with mortality of COVID-19 in 3125 counties of the United States |
title_fullStr | Risk factors associated with mortality of COVID-19 in 3125 counties of the United States |
title_full_unstemmed | Risk factors associated with mortality of COVID-19 in 3125 counties of the United States |
title_short | Risk factors associated with mortality of COVID-19 in 3125 counties of the United States |
title_sort | risk factors associated with mortality of covid-19 in 3125 counties of the united states |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780082/ https://www.ncbi.nlm.nih.gov/pubmed/33397470 http://dx.doi.org/10.1186/s40249-020-00786-0 |
work_keys_str_mv | AT tianting riskfactorsassociatedwithmortalityofcovid19in3125countiesoftheunitedstates AT zhangjingwen riskfactorsassociatedwithmortalityofcovid19in3125countiesoftheunitedstates AT huliyuan riskfactorsassociatedwithmortalityofcovid19in3125countiesoftheunitedstates AT jiangyukang riskfactorsassociatedwithmortalityofcovid19in3125countiesoftheunitedstates AT duancongyuan riskfactorsassociatedwithmortalityofcovid19in3125countiesoftheunitedstates AT lizhongfei riskfactorsassociatedwithmortalityofcovid19in3125countiesoftheunitedstates AT wangxueqin riskfactorsassociatedwithmortalityofcovid19in3125countiesoftheunitedstates AT zhangheping riskfactorsassociatedwithmortalityofcovid19in3125countiesoftheunitedstates |