我们评估了社会人口统计学的地理差异,移动性,并建立了与COVID-19测试相关的环境因素,case,纽约市(NYC)的死亡率。COVID-19费率(截至2020年6月10日),相关的社会人口统计信息,和建筑环境特征由邮政编码制表区(ZCTA)汇总。拟合空间调整的多变量回归模型以解释空间自相关。结果表明,不同的邻域特征集与COVID-19测试独立相关,case,和死亡率。例如,ZCTA中黑人和西班牙裔人的比例与COVID-19病例率呈正相关。与传统假设相反,低密度住房的社区出现了更高的COVID-19病例率。此外,大流行期间的人口统计学变化(例如外移)可能会使COVID-19发病率的估计出现偏差.未来的研究应该进一步调查这些邻域水平的因素及其随着时间的推移的相互作用,以更好地了解它们影响COVID-19的机制。
We assessed the geographic variation in socio-demographics, mobility, and built environmental factors in relation to COVID-19 testing,
case, and death rates in New York City (NYC). COVID-19 rates (as of June 10, 2020), relevant socio-demographic information, and built environment characteristics were aggregated by ZIP Code Tabulation Area (ZCTA). Spatially adjusted multivariable regression models were fitted to account for spatial autocorrelation. The results show that different sets of
neighborhood characteristics were independently associated with COVID-19 testing,
case, and death rates. For example, the proportions of Blacks and Hispanics in a ZCTA were positively associated with COVID-19
case rate. Contrary to the conventional hypothesis, neighborhoods with low-density housing experienced higher COVID-19 case rates. In addition, demographic changes (e.g. out-migration) during the pandemic may bias the estimates of COVID-19 rates. Future research should further investigate these
neighborhood-level factors and their interactions over time to better understand the mechanisms by which they affect COVID-19.