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与华盛顿金县 COVID-19 病例和检测公平性相关的社区水平因素。

Community-Level Factors Associated with COVID-19 Cases and Testing Equity in King County, Washington.

机构信息

Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA.

Bordeaux School of Public Health, University of Bordeaux, 33076 Bordeaux, France.

出版信息

Int J Environ Res Public Health. 2020 Dec 18;17(24):9516. doi: 10.3390/ijerph17249516.

Abstract

Individual-level Coronavirus Disease 2019 (COVID-19) case data suggest that certain populations may be more impacted by the pandemic. However, few studies have considered the communities from which positive cases are prevalent, and the variations in testing rates between communities. In this study, we assessed community factors that were associated with COVID-19 testing and test positivity at the census tract level for the Seattle, King County, Washington region at the summer peak of infection in July 2020. Multivariate Poisson regression was used to estimate confirmed case counts, adjusted for testing numbers, which were associated with socioeconomic status (SES) indicators such as poverty, educational attainment, transportation cost, as well as with communities with high proportions of people of color. Multivariate models were also used to examine factors associated with testing rates, and found disparities in testing for communities of color and communities with transportation cost barriers. These results demonstrate the ability to identify tract-level indicators of COVID-19 risk and specific communities that are most vulnerable to COVID-19 infection, as well as highlight the ongoing need to ensure access to disease control resources, including information and education, testing, and future vaccination programs in low-SES and highly diverse communities.

摘要

个体层面的 2019 冠状病毒病(COVID-19)病例数据表明,某些人群可能受到疫情的影响更大。然而,很少有研究考虑到阳性病例高发的社区,以及社区之间检测率的差异。在这项研究中,我们评估了与西雅图金县华盛顿地区社区层面 COVID-19 检测和检测阳性率相关的社区因素,该地区在 2020 年 7 月感染高峰时进行了评估。多变量泊松回归用于估计确诊病例数,根据检测数量进行调整,这些检测数量与社会经济地位(SES)指标相关,如贫困、教育程度、交通成本,以及与有色人种比例较高的社区相关。多变量模型也用于检查与检测率相关的因素,并发现了有色人种社区和交通成本障碍社区之间的检测差异。这些结果表明,有能力识别 COVID-19 风险的社区层面指标以及最容易感染 COVID-19 的特定社区,并强调了继续确保获得疾病控制资源的必要性,包括低 SES 和高度多样化社区的信息和教育、检测以及未来的疫苗接种计划。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7e0/7767300/e57e62f1e0f7/ijerph-17-09516-g001.jpg

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