An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City
An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City
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DOI：
10.1186/s12916-020-01731-6
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发表时间：
2020-09-04
期刊：
影响因子：
9.3
通讯作者：
Diaz-Artiles, Ana
中科院分区：
文献类型：
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作者：
Whittle, Richard S.;Diaz-Artiles, Ana
背景纽约市是新冠肺炎疫情在美国的第一个主要城市中心。病例集中在该市，某些社区的病例比其他社区更多。我们调查了潜在的社会经济因素是否可以解释新冠肺炎测试阳性率在社区间的差异。方法收集纽约市177个邮政编码制表地区(占总人口的99.9%)的数据。我们使用多个贝叶斯贝萨格-约克-莫利(BYM)混合模型进行拟合，结果为新冠肺炎检测阳性，一组具有代表性的11个人口统计学、经济和医疗保健相关的ZCTA水平参数作为潜在预测因素，新冠肺炎测试的总次数作为暴露。BYM模型既包括空间随机效应，也包括非空间随机效应，以解释聚集和过度分散。结果多元回归分析显示，发现的新冠肺炎病例与被抚养的儿童(18岁以下)、人口密度、家庭收入中位数和种族之间存在一致的、统计学上显著的关联。在最终的模型中，我们发现，年轻人口中仅增加5%，新冠肺炎阳性率就会增加2.3%(95%可信区间为0.4%至4.2%，p=0.021)。每公里增加10,000人(2)与阳性率增加2.4%(95%可信区间0.6至4.2%，p=0.011)相关。家庭收入中位数减少10,000美元与1.6%相关(95%可信区间0.7%至2.4%，p
Background New York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the COVID-19 test positivity rate. Methods Data were collected from 177 Zip Code Tabulation Areas (ZCTA) in New York City (99.9% of the population). We fit multiple Bayesian Besag-York-Mollie (BYM) mixed models using positive COVID-19 tests as the outcome, a set of 11 representative demographic, economic, and health-care associated ZCTA-level parameters as potential predictors, and the total number of COVID-19 tests as the exposure. The BYM model includes both spatial and nonspatial random effects to account for clustering and overdispersion. Results Multiple regression approaches indicated a consistent, statistically significant association between detected COVID-19 cases and dependent children (under 18 years old), population density, median household income, and race. In the final model, we found that an increase of only 5% in young population is associated with a 2.3% increase in COVID-19 positivity rate (95% confidence interval (CI) 0.4 to 4.2%,p=0.021). An increase of 10,000 people per km(2)is associated with a 2.4% (95% CI 0.6 to 4.2%,p=0.011) increase in positivity rate. A decrease of $10,000 median household income is associated with a 1.6% (95% CI 0.7 to 2.4%,p