Cordes Jack, Castro Marcia C
Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston 02115, MA, USA.
Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston 02115, MA, USA.
Spat Spatiotemporal Epidemiol. 2020 Aug;34:100355. doi: 10.1016/j.sste.2020.100355. Epub 2020 Jun 21.
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.
识别检测机会少且病例负担重的区域对于了解新冠疫情中的风险和分配资源至关重要。利用纽约市邮政编码级别的数据，我们分析了检测率、阳性率和阳性比例。空间扫描统计识别出了检测率高低、阳性率高以及阳性比例高的聚集区。箱线图和皮尔逊相关性分析确定了结果、聚集区和背景因素之间的关联。检测少且阳性检测比例低的聚集区收入、教育水平较高，白人人口较多，而检测率高且阳性检测比例高的聚集区黑人比例过高且没有医疗保险。相关性分析表明，白人种族、教育水平和收入与阳性检测比例呈负相关，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资源导向布鲁克林东部，该地区检测少且阳性比例高。
