Author:
Tian Ting,Zhang Jingwen,Hu Liyuan,Jiang Yukang,Duan Congyuan,Li Zhongfei,Wang Xueqin,Zhang Heping
Abstract
Abstract
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
Publisher
Springer Science and Business Media LLC
Subject
Infectious Diseases,Public Health, Environmental and Occupational Health,General Medicine
Reference34 articles.
1. Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Ann Intern Med. 2020;172(9):577–82. https://doi.org/10.7326/M20-0504.
2. China, CDC. Distribution of COVID-19 cases in the world.2020. http://2019ncov.chinacdc.cn/2019-nCoV/global.html. Accessed 30 Aug 2020.
3. China, CDC. Coronavirus disease 2019 (COVID-19). 2020. https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html. Accessed 30 Aug 2020.
4. Hadler JL, Yousey-Hindes K, Pérez A, Anderson EJ, Bargsten M, Bohm SR, et al. Influenza-related hospitalizations and poverty levels—United States, 2010–2012. Morb Mortal Wkly Rep. 2016;65(5):101–5.
5. Noppert GA, Yang Z, Clarke P, Ye W, Davidson P, Wilson ML. Individual-and neighborhood-level contextual factors are associated with Mycobacterium tuberculosis transmission: genotypic clustering of cases in Michigan, 2004–2012. Ann Epidemiol. 2017;27(6):371-376.e5. https://doi.org/10.1016/j.annepidem.2017.05.009.
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