Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States.
Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States.
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DOI：
10.1186/s12889-021-11149-1
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发表时间：
2021-06-14
影响因子：
4.5
通讯作者：
Kharrazi H
中科院分区：
文献类型：
--
作者：
Chang HY;Tang W;Hatef E;Kitchen C;Weiner JP;Kharrazi H
COVID-19的传播凸显了美国长期存在的健康不平等，因为资源较少的社区与COVID-19传播率较高有关。虽然居家令是遏制其蔓延的最有效方法之一，但低收入社区的居民在保持社交距离方面面临障碍。我们旨在量化居家政策对COVID-19传播的不同影响，以及不同社会经济劣势社区居民的流动性。这是一个比较中断的时间序列分析在国家一级。我们包括来自38个州的2087个县，这些县都实施和解除了全州范围内的居家令。每个县根据其不利程度被分配到四个同等大小的组中的一个，由地区贫困指数表示。2019冠状病毒病的患病率是通过将每日累计确诊的2019冠状病毒病病例数除以2010年人口普查的居民人数计算得出。我们使用源自COVID-19影响分析平台的社会距离指数（SDI）来衡量流动性。对于实施评价，观察时间从2020年3月1日开始至起吊前1天;对于起吊，观察时间范围为实施后1天至2020年7月5日。我们分别计算了实施和解除居家令前后，三个最高劣势水平县与最低劣势水平县之间COVID-19患病率和社会距离指数的每日趋势变化。在居家实施和解除日期，不利水平最高或最低的县的COVID-19患病率要高得多，而流动性随着不利水平的增加而下降。与拥有最多资源的县相比，最贫困县的流动性受到居家实施和放松的影响最小;然而，在居家实施和放松后，贫困县的COVID-19感染相对增加最大。不同社会经济劣势程度的社区对COVID-19缓解政策的实施和放松反应不同。政策制定者应该考虑在贫困县投入更多资源，因为在大多数社区控制住疫情之前，疫情可能不会停止。在线版本包含补充材料，可通过10.1186/s12889-021-11149-1获得。
The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents’ mobility across neighborhoods of different levels of socioeconomic disadvantage. This was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately. On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation. Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control. The online version contains supplementary material available at 10.1186/s12889-021-11149-1.
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影响因子：
29.9
作者：
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通讯作者：
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影响因子：
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影响因子：
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通讯作者：
Kharrazi H