University of Louisville School of Public Health and Information Sciences, Department of Epidemiology and Population Health, United States of America.
University of Louisville School of Public Health and Information Sciences, Department of Health Management and System Sciences, United States of America; Louisville Metro Department of Public Health and Wellness, Center for Health Equity, United States of America.
Sci Total Environ. 2021 Sep 10;786:147495. doi: 10.1016/j.scitotenv.2021.147495. Epub 2021 May 3.
The US COVID-19 epidemic impacted counties differently across space and time, though large-scale transmission dynamics are unclear. The study's objective was to group counties with similar trajectories of COVID-19 cases and deaths and identify county-level correlates of the distinct trajectory groups.
Daily COVID-19 cases and deaths were obtained from 3141 US counties from January through June 2020. Clusters of epidemic curve trajectories of COVID-19 cases and deaths per 100,000 people were identified with Proc Traj. We utilized polytomous logistic regression to estimate Odds Ratios for trajectory group membership in relation to county-level demographics, socioeconomic factors, school enrollment, employment and lifestyle data.
Six COVID-19 case trajectory groups and five death trajectory groups were identified. Younger counties, counties with a greater proportion of females, Black and Hispanic populations, and greater employment in private sectors had higher odds of being in worse case and death trajectories. Percentage of counties enrolled in grades 1-8 was associated with earlier-start case trajectories. Counties with more educated adult populations had lower odds of being in worse case trajectories but were generally not associated with worse death trajectories. Counties with higher poverty rates, higher uninsured, and more living in non-family households had lower odds of being in worse case and death trajectories. Counties with higher smoking rates had higher odds of being in worse death trajectory counties.
In the absence of clear guidelines and personal protection, smoking, racial and ethnic groups, younger populations, social, and economic factors were correlated with worse COVID-19 epidemics that may reflect population transmission dynamics during January-June 2020. After vaccination of high-risk individuals, communities with higher proportions of youth, communities of color, smokers, and workers in healthcare, service and goods industries can reduce viral spread by targeting vaccination programs to these populations and increasing access and education on non-pharmaceutical interventions.
美国 COVID-19 疫情在空间和时间上对各县产生了不同的影响，尽管大规模传播动态尚不清楚。本研究的目的是对 COVID-19 病例和死亡轨迹相似的县进行分组，并确定不同轨迹组的县一级相关因素。
从 2020 年 1 月至 6 月的 3141 个美国县获取每日 COVID-19 病例和死亡人数。使用 Proc Traj 确定 COVID-19 每 10 万人的病例和死亡人数的流行曲线轨迹集群。我们利用多变量逻辑回归估计与县一级人口统计学、社会经济因素、学校入学率、就业和生活方式数据相关的轨迹组隶属关系的优势比。
确定了六个 COVID-19 病例轨迹组和五个死亡轨迹组。年轻的县、女性比例较高、黑人和西班牙裔人口比例较高、私营部门就业比例较高的县，发生更严重病例和死亡轨迹的可能性更高。1-8 年级入学率较高的县与较早开始的病例轨迹相关。成年人口受教育程度较高的县发生更严重病例轨迹的可能性较低，但与更严重的死亡轨迹一般没有关联。贫困率较高、未参保率较高、非家庭住户比例较高的县发生更严重病例和死亡轨迹的可能性较低。吸烟率较高的县发生更严重死亡轨迹的可能性更高。
在缺乏明确指导方针和个人保护的情况下，吸烟、种族和族裔群体、年轻人口、社会和经济因素与 2020 年 1 月至 6 月期间更严重的 COVID-19 疫情相关，这可能反映了人群传播动态。在对高危人群进行疫苗接种后，青年人口比例较高、有色人种社区、吸烟者以及医疗保健、服务和商品行业的劳动者较多的社区，可以通过针对这些人群的疫苗接种计划，并增加非药物干预措施的获取和教育来减少病毒传播。
