Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA.
Environmental Health Service, Department of Family and Community Medicine, Baylor College of Medicine, Houston, TX 77030, USA.
Int J Environ Res Public Health. 2021 Feb 4;18(4):1495. doi: 10.3390/ijerph18041495.
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.
开发有效的 COVID-19 大流行控制措施的核心是了解社区传播的流行病学。基于位置的邻里数据的地理空间分析可以深入了解感染的驱动因素。在对德克萨斯州哈里斯县的当前分析中，我们使用 GIS 中的自定义插值工具，将邮政编码的 COVID-19 发病率估计值分解为普查地段的估计值——这更好地代表了邻里层面的估计值。我们使用一系列非空间和空间模型评估了 29 个邻里特征与 COVID-19 发病率之间的关系。在我们的最终非空间模型中保持与 COVID-19 发病率显著正相关的变量，以及后来在地理加权回归模型中表示的变量，包括黑人/非裔美国人人口的百分比、外国出生人口的百分比、区域派生指数（ADI）、每普查地段无车辆的家庭比例和 65 岁以上人口的比例。相反，我们观察到教育领域就业比例与 COVID-19 发病率呈负相关且显著。值得注意的是，空间模型表明 ADI 的影响在整个研究区域内是均匀的，但其他风险因素因邻里而异。当前的研究结果可以增强当地公共卫生官员应对 COVID-19 大流行的决策能力。通过了解驱动社区传播的因素，我们可以更好地针对疾病控制措施。
