Pediatric Institute, Cleveland Clinic, Cleveland, OH, USA.
J Prev Med Public Health. 2021 Jul;54(4):238-244. doi: 10.3961/jpmph.21.071. Epub 2021 Jul 2.
Previous pandemics have demonstrated that several demographic, geographic, and socioeconomic factors may play a role in increased infection risk. During this current coronavirus disease 2019 (COVID-19) pandemic, our aim was to examine the association of timing of lockdown at the county level and aforementioned risk factors with daily case rate (DCR) in the United States.
A cross-sectional study using publicly available data was performed including Americans with COVID-19 infection as of May 24, 2020. The United States counties with >100 000 population and >50 cases per 100 000 people were included. The independent variable was the days required from the declaration of lockdown to reach the target case rate (50/100 000 cases) while the dependent (outcome) variable was the DCR per 100 000 on the day of statistical calculation (May 24, 2020) after adjusting for multiple confounding socio-demographic, geographic, and health-related factors. Each independent factor was correlated with outcome variables and assessed for collinearity with each other. Subsequently, all factors with significant association to the outcome variable were included in multiple linear regression models using stepwise method. Models with best R2 value from the multiple regression were chosen.
The timing of mandated lockdown order had the most significant association on the DCR per 100 000 after adjusting for multiple socio-demographic, geographic and health-related factors. Additional factors with significant association with increased DCR include rate of uninsured and unemployment.
The timing of lockdown order was significantly associated with the spread of COVID-19 at the county level in the United States.
以往的大流行表明,一些人口统计学、地理和社会经济因素可能会增加感染风险。在当前的 2019 年冠状病毒病(COVID-19)大流行期间,我们的目的是检验县级封锁时间以及上述风险因素与美国每日病例数(DCR)之间的关联。
本研究采用了横断面研究设计,使用了截至 2020 年 5 月 24 日的美国 COVID-19 感染者的公开数据。纳入了美国人口超过 10 万且每 10 万人中有超过 50 例病例的县。独立变量是从宣布封锁到达到目标病例数(每 10 万人 50 例)所需的天数,而因变量是在统计计算日(2020 年 5 月 24 日)每 10 万人的 DCR,调整了多种混杂的社会人口统计学、地理和与健康相关的因素后。每个独立因素与因变量相关联,并相互评估是否存在共线性。随后,使用逐步法将与因变量有显著关联的所有因素纳入多元线性回归模型。选择多元回归中 R2 值最佳的模型。
在调整了多种社会人口统计学、地理和与健康相关的因素后,强制封锁令的时间安排对每 10 万人的 DCR 具有最显著的关联。与 DCR 增加显著相关的其他因素包括未参保率和失业率。
封锁令的时间安排与美国县级 COVID-19 的传播显著相关。