Spatial analysis of COVID-19 clusters and contextual factors in New York City
Spatial analysis of COVID-19 clusters and contextual factors in New York City
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DOI：
10.1016/j.sste.2020.100355
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发表时间：
2020-08-01
影响因子：
3.4
通讯作者：
Castro, Marcia C.
中科院分区：
文献类型：
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作者：
Cordes, Jack;Castro, Marcia C.
为了了解 COVID-19 大流行的风险和分配资源，有必要确定检测机会较少和病例负担较高的地区。使用纽约市的邮政编码级别数据，我们分析了检测率、阳性率和阳性比例。空间扫描统计数据确定了高检测率和低检测率、高阳性率和高阳性比例的集群。箱线图和皮尔逊相关性确定了结果、聚类和背景因素之间的关联。测试较少和阳性测试比例低的集群具有较高的收入、教育程度和白人人口，而测试率高和阳性测试比例高的集群中黑人和没有健康保险的比例过高。相关性显示，白人种族、教育和收入与阳性测试比例呈负相关，与黑人种族、西班牙裔和贫困呈正相关。我们建议将检测和医疗保健资源转移到布鲁克林东部，那里的检测率较低，阳性率较高。 (C) 2020 Elsevier Ltd. 保留所有权利。
Identifying areas with low access to testing and high case burden is necessary to understand risk and allocate resources in the COVID-19 pandemic. Using zip code level data for New York City, we analyzed testing rates, positivity rates, and proportion positive. A spatial scan statistic identified clusters of high and low testing rates, high positivity rates, and high proportion positive. Boxplots and Pearson correlations determined associations between outcomes, clusters, and contextual factors. Clusters with less testing and low proportion positive tests had higher income, education, and white population, whereas clusters with high testing rates and high proportion positive tests were disproportionately black and without health insurance. Correlations showed inverse associations of white race, education, and income with proportion positive tests, and positive associations with black race, Hispanic ethnicity, and poverty. We recommend testing and health care resources be directed to eastern Brooklyn, which has low testing and high proportion positives. (C) 2020 Elsevier Ltd. All rights reserved.