Tian Ting, Zhang Jingwen, Hu Liyuan, Jiang Yukang, Duan Congyuan, Li Zhongfei, Wang Xueqin, Zhang Heping
School of Mathematics, Sun Yat-Sen University, 135 Xingang Xi Road, Guangzhou, 510275, Guangdong, China.
School of Management, Sun Yat-Sen University, 135 Xingang Xi Road, Guangzhou, 510275, Guangdong, China.
Infect Dis Poverty. 2021 Jan 4;10(1):3. doi: 10.1186/s40249-020-00786-0.
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.
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.
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.
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.
自2020年3月以来，美国新冠病毒病（COVID-19）累计确诊病例数急剧上升。已建立了一项县卫生排名和路线图计划，以确定与美国各县COVID-19流动性和死亡率差异相关的因素。不同流行水平的与县级COVID-19死亡率相关的风险因素尚不清楚。
本研究使用从县卫生排名和路线图计划获得的数据，对截至2020年8月27日美国县级COVID-19死亡计数应用负二项式设计。在该设计中，根据2020年3月1日至8月27日随时间变化的累计确诊病例，使用聚类分析将受感染的县分为三个感染水平类别。未对COVID-19患者进行个体分析，而是在县级进行汇总，其中县级COVID-19死亡由当地卫生机构确认。聚类分析和Kruskal-Wallis检验用于我们的统计分析。
共有3125个受感染县被分为对应低、中、高感染流行水平的三类。几个风险因素与COVID-19死亡计数显著相关，其中空气污染水平较高（0.153，P<0.001）会增加低流行县的死亡率，老年人在中流行（0.049，P<0.001）和高流行（0.114，P<0.001）县中更易感染。非白人和白人之间的隔离（低：0.015，P<0.001；中：0.025，P<0.001；高：0.019，P=0.005）以及较高的西班牙裔人口比例（低和中：0.020，P<0.001；高：0.014，P=0.009）在所有受感染县中死亡风险更高。
COVID-19的死亡率取决于性别、种族/民族和户外环境。对这些重要因素影响的认识不断提高，可能有助于决策者、公共卫生官员和公众更好地控制大流行风险，特别是降低COVID-19的死亡率。
