COVID-19 Community Incidence and Associated Neighborhood-Level Characteristics in Houston, Texas, USA.
COVID-19 Community Incidence and Associated Neighborhood-Level Characteristics in Houston, Texas, USA.
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美国得克萨斯州休斯敦COVID-19社区发病率和相关社区水平特征。
DOI：
10.3390/ijerph18041495
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发表时间：
2021-02-04
影响因子：
--
通讯作者：
Amos C
中科院分区：
文献类型：
--
作者：
Oluyomi AO;Gunter SM;Leining LM;Murray KO;Amos C
为COVID-19大流行制定有效控制措施的核心是了解社区传播的流行病学。对社区一级数据进行地理空间分析可以深入了解感染的驱动因素。在目前对德克萨斯州哈里斯县的分析中，我们使用GIS中的自定义插值工具将COVID-19发病率估计从邮政编码分解为人口普查区估计-更好地代表了社区水平的估计。我们使用一系列空间和空间模型评估了29个社区水平特征与COVID-19发病率之间的关联。在我们最终的无空间模型中，与COVID-19发病率保持显著正相关的变量以及后来在地理加权回归模型中表示的变量是黑人/非洲裔美国人人口的百分比，外国出生人口的百分比，面积推导指数（ADI），没有车辆的家庭百分比以及每个人口普查区域内65岁以上人口的百分比。相反，我们观察到与教育部门就业百分比的负相关和显著相关。值得注意的是，空间模型表明，ADI的影响在整个研究区域是均匀的，但其他风险因素因邻近地区而异。目前的研究结果可以加强当地公共卫生官员在应对COVID-19大流行时的决策。通过了解推动社区传播的因素，我们可以更好地针对疾病控制措施。
Central to developing effective control measures for the COVID-19 pandemic is understanding the epidemiology of transmission in the community. Geospatial analysis of neighborhood-level data could provide insight into drivers of infection. In the current analysis of Harris County, Texas, we used custom interpolation tools in GIS to disaggregate COVID-19 incidence estimates from the zip code to census tract estimates—a better representation of neighborhood-level estimates. We assessed the associations between 29 neighborhood-level characteristics and COVID-19 incidence using a series of aspatial and spatial models. The variables that maintained significant and positive associations with COVID-19 incidence in our final aspatial model and later represented in a geographically weighted regression model were the percentage of the Black/African American population, percentage of the foreign-born population, area derivation index (ADI), percentage of households with no vehicle, and percentage of people over 65 years old inside each census tract. Conversely, we observed negative and significant association with the percentage employed in education. Notably, the spatial models indicated that the impact of ADI was homogeneous across the study area, but other risk factors varied by neighborhood. The current findings could enhance decision making by local public health officials in responding to the COVID-19 pandemic. By understanding factors that drive community transmission, we can better target disease control measures.
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影响因子：
4.9
作者：
Desjardins, M. R.;Hohl, A.;Delmelle, E. M.
通讯作者：
Delmelle, E. M.
影响因子：
5
作者：
HAAN, M;KAPLAN, GA;CAMACHO, T
通讯作者：
CAMACHO, T
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5.5
作者：
Gottlieb, Laura M.;Tirozzi, Karen J.;Sandel, Megan T.
通讯作者：
Sandel, Megan T.
影响因子：
3.4
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Butler, Danielle C.;Petterson, Stephen;Bazemore, Andrew W.
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Bazemore, Andrew W.
影响因子：
6.3
作者：
Cuomo, Raphael E.
通讯作者：
Cuomo, Raphael E.