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COVID-19 Surveillance Data: A Primer for Epidemiology and Data Science.新冠病毒疾病监测数据:流行病学与数据科学入门
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基于宾夕法尼亚州费城邻里感染发病率的 COVID-19 和 HIV 发病差异。

Disparities of COVID-19 and HIV Occurrence Based on Neighborhood Infection Incidence in Philadelphia, Pennsylvania.

机构信息

All of the authors are with the Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA.

出版信息

Am J Public Health. 2022 Mar;112(3):408-416. doi: 10.2105/AJPH.2021.306538.

DOI:10.2105/AJPH.2021.306538
PMID:35196028
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8887150/
Abstract

To evaluate the occurrence of HIV and COVID-19 infections in Philadelphia, Pennsylvania, through July 2020 and identify ecological correlates driving racial disparities in infection incidence. For each zip code tabulation area, we created citywide comparison -score measures of COVID-19 cases, new cases of HIV, and the difference between the scores. Choropleth maps were used to identify areas that were similar or dissimilar in terms of disease patterning, and weighted linear regression models helped identify independent ecological predictors of these patterns. Relative to COVID-19, HIV represented a greater burden in Center City Philadelphia, whereas COVID-19 was more apparent in Northeast Philadelphia. Areas with a greater proportion of Black or African American residents were overrepresented in terms of both diseases. Although race is a shared nominal upstream factor that conveys increased risk for both infections, an understanding of separate structural, demographic, and economic risk factors that drive the overrepresentation of COVID-19 cases in racial/ethnic communities across Philadelphia is critical. Difference-based measures are useful in identifying areas that are underrepresented or overrepresented with respect to disease occurrence and may be able to elucidate effective or ineffective mitigation strategies. (. 2022;112(3):408-416. https://doi.org/10.2105/AJPH.2021.306538).

摘要

评估 2020 年 7 月前在宾夕法尼亚州费城发生的 HIV 和 COVID-19 感染情况,并确定导致感染发病率种族差异的生态相关因素。对于每个邮政编码区,我们创建了全市范围内的 COVID-19 病例、新的 HIV 病例以及分数之间差异的比较分数衡量标准。使用等值线地图来确定在疾病模式方面相似或不同的区域,加权线性回归模型有助于确定这些模式的独立生态预测因素。与 COVID-19 相比,HIV 在费城中心城的负担更大,而 COVID-19 在费城东北区更为明显。黑人和非裔美国人居民比例较高的地区在这两种疾病中都有较高的代表性。尽管种族是一个共同的名义上游因素,会增加这两种感染的风险,但了解导致 COVID-19 病例在费城不同种族/族裔社区中过度出现的单独的结构性、人口统计学和经济风险因素至关重要。基于差异的衡量标准有助于识别在疾病发生方面代表性不足或过高的区域,并且可能能够阐明有效或无效的缓解策略。(2022;112(3):408-416。https://doi.org/10.2105/AJPH.2021.306538)。