Investigating the relationships between concentrated disadvantage, place connectivity, and COVID-19 fatality in the United States over time.

Investigating the relationships between concentrated disadvantage, place connectivity, and COVID-19 fatality in the United States over time.
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DOI:
10.1186/s12889-022-14779-1
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发表时间:
2022-12-14
期刊:
影响因子:
4.5
通讯作者:
--
中科院分区:
医学2区
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--
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集中的弱势地区受到美国COVID-19爆发的不成比例的影响。与此同时,高度连通的地区可能导致更高的人员流动,导致更高的COVID-19病例和死亡。这项研究考察了随着时间的推移,集中劣势、地点连通性和美国COVID-19死亡率之间的关联。集中劣势的评估是基于低社会经济地位居民的空间集中。地点连通性被定义为一年内(Y = 2019)该县与美国相邻的所有其他县之间共享Twitter用户的标准化数量。2019冠状病毒病死亡率按累计2019冠状病毒病死亡人数除以累计2019冠状病毒病病例计算。使用四个时间段(截至2021年10月31日)的县级(N = 3,091)COVID-19死亡人数,我们进行了混合效应负二项回归,以研究集中劣势、地点连通性和COVID-19死亡人数之间的关联,并考虑到潜在的州一级变化。分析了县级地方连通性和集中劣势的调节效应。COVID-19死亡率的空间滞后变量被添加到模型中,以控制COVID-19死亡率的空间自相关效应。在四个时间段内,集中劣势与COVID-19死亡率的增加显著相关(p < 0.01)。更重要的是,适度性分析表明,地点连通性在三个时期显著加剧了集中劣势对COVID-19死亡率的有害影响(p <0. 01),并且这种显著的适度性影响随着时间的推移而增强。当使用前一年的地点连通性数据时,调节效应也很显著。生活在高度集中的劣势和高地方连通性的县的人口可能面临更高的COVID-19死亡率风险。在集中的贫困县发生的COVID-19死亡人数增加,部分原因可能是通过地方连通性增加了人员流动。为应对COVID-19及未来其他传染病爆发,鼓励政策制定者利用历史劣势,将连通性数据用于疫情监测和高度连通的弱势地区的监测,并针对弱势人群和社区进行额外干预。在线版本包含补充材料,可通过10.1186/s12889-022-14779-1获得。
Concentrated disadvantaged areas have been disproportionately affected by COVID-19 outbreak in the United States (US). Meanwhile, highly connected areas may contribute to higher human movement, leading to higher COVID-19 cases and deaths. This study examined the associations between concentrated disadvantage, place connectivity, and COVID-19 fatality in the US over time. Concentrated disadvantage was assessed based on the spatial concentration of residents with low socioeconomic status. Place connectivity was defined as the normalized number of shared Twitter users between the county and all other counties in the contiguous US in a year (Y = 2019). COVID-19 fatality was measured as the cumulative COVID-19 deaths divided by the cumulative COVID-19 cases. Using county-level (N = 3,091) COVID-19 fatality over four time periods (up to October 31, 2021), we performed mixed-effect negative binomial regressions to examine the association between concentrated disadvantage, place connectivity, and COVID-19 fatality, considering potential state-level variations. The moderation effects of county-level place connectivity and concentrated disadvantage were analyzed. Spatially lagged variables of COVID-19 fatality were added to the models to control for the effect of spatial autocorrelations in COVID-19 fatality. Concentrated disadvantage was significantly associated with an increased COVID-19 fatality in four time periods (p < 0.01). More importantly, moderation analysis suggested that place connectivity significantly exacerbated the harmful effect of concentrated disadvantage on COVID-19 fatality in three periods (p < 0.01), and this significant moderation effect increased over time. The moderation effects were also significant when using place connectivity data from the previous year. Populations living in counties with both high concentrated disadvantage and high place connectivity may be at risk of a higher COVID-19 fatality. Greater COVID-19 fatality that occurs in concentrated disadvantaged counties may be partially due to higher human movement through place connectivity. In response to COVID-19 and other future infectious disease outbreaks, policymakers are encouraged to take advantage of historical disadvantage and place connectivity data in epidemic monitoring and surveillance of the disadvantaged areas that are highly connected, as well as targeting vulnerable populations and communities for additional intervention. The online version contains supplementary material available at 10.1186/s12889-022-14779-1.
DOI: 10.1007/s40615-020-00940-2
发表时间: 2022-03
影响因子: 3.9
作者:
Yellow Horse AJ;Yang TC;Huyser KR
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