Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA.
BMJ Open. 2020 Sep 1;10(9):e039886. doi: 10.1136/bmjopen-2020-039886.
To illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond.
We identified key individual, household and community characteristics influencing COVID-19 risks of infection and survival, guided by international experiences and consideration of epidemiological parameters of importance. Using publicly available data, we developed an open-access online tool that allows county-specific querying and mapping of risk factors. As an illustrative example, we assess the pairwise intersections of age (individual level), poverty (household level) and prevalence of group homes (community-level) in US counties. We also examine how these factors intersect with the proportion of the population that is people of colour (ie, not non-Hispanic white), a metric that reflects histories of US race relations. We defined 'high' risk counties as those above the 75th percentile. This threshold can be changed using the online tool.
US counties.
Analyses are based on publicly available county-level data from the Area Health Resources Files, American Community Survey, Centers for Disease Control and Prevention Atlas file, National Center for Health Statistic and RWJF Community Health Rankings.
Our findings demonstrate significant intercounty variation in the distribution of individual, household and community characteristics that affect risks of infection, severe disease or mortality from COVID-19. About 9% of counties, affecting 10 million residents, are in higher risk categories for both age and group quarters. About 14% of counties, affecting 31 million residents, have both high levels of poverty and a high proportion of people of colour.
Federal and state governments will benefit from recognising high intrastate, intercounty variation in population risks and response capacity. Equitable responses to the pandemic require strategies to protect those in counties at highest risk of adverse COVID-19 outcomes and their social and economic impacts.
说明影响新冠疫情对美国各县及其应对能力影响的个体、家庭和社区因素的交叉点和各县之间的差异。
我们根据国际经验并考虑到重要的流行病学参数，确定了影响新冠病毒感染风险和生存的关键个体、家庭和社区特征。我们利用公开数据开发了一个开放获取的在线工具，允许对风险因素进行县特定的查询和映射。作为一个说明性示例，我们评估了美国各县中年龄（个体层面）、贫困（家庭层面）和群体家庭（社区层面）流行率的交叉点。我们还研究了这些因素如何与人口中不是非西班牙裔白人的有色人种比例（即反映美国种族关系历史的指标）交叉。我们将“高”风险县定义为高于第 75 百分位数的县。可以使用在线工具更改此阈值。
美国各县。
分析基于从区域卫生资源档案、美国社区调查、疾病控制与预防中心地图文件、国家卫生统计中心和 RWJF 社区健康排名中获得的公开的县级数据。
我们的研究结果表明，影响新冠病毒感染、严重疾病或死亡率的个体、家庭和社区特征在各县之间存在显著差异。约 9%的县，影响到 1000 万居民，在年龄和群体宿舍方面都处于较高风险类别。约 14%的县，影响到 3100 万居民，既存在高水平的贫困，又有大量的有色人种。
联邦和州政府将受益于认识到人口风险和应对能力在州内各县之间存在巨大差异。要想公平应对这一大流行病，就需要制定战略，保护那些处于新冠疫情高风险县的人群，并减轻其对社会和经济的影响。
