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2020-2022 年大流行期间长期护理机构中 COVID-19 的基因组流行病学变化,华盛顿州。

Changing genomic epidemiology of COVID-19 in long-term care facilities during the 2020-2022 pandemic, Washington State.

机构信息

Department of Health, Washington State, 1610 NE 150th St, Shoreline, Washington, 98155, USA.

University of Washington, 1410 NE Campus Parkway, Seattle, Washington, 98195, USA.

出版信息

BMC Public Health. 2024 Jan 15;24(1):182. doi: 10.1186/s12889-023-17461-2.

Abstract

BACKGROUND

Long-term care facilities (LTCFs) are vulnerable to disease outbreaks. Here, we jointly analyze SARS-CoV-2 genomic and paired epidemiologic data from LTCFs and surrounding communities in Washington state (WA) to assess transmission patterns during 2020-2022, in a setting of changing policy. We describe sequencing efforts and genomic epidemiologic findings across LTCFs and perform in-depth analysis in a single county.

METHODS

We assessed genomic data representativeness, built phylogenetic trees, and conducted discrete trait analysis to estimate introduction sizes over time, and explored selected outbreaks to further characterize transmission events.

RESULTS

We found that transmission dynamics among cases associated with LTCFs in WA changed over the course of the COVID-19 pandemic, with variable introduction rates into LTCFs, but decreasing amplification within LTCFs. SARS-CoV-2 lineages circulating in LTCFs were similar to those circulating in communities at the same time. Transmission between staff and residents was bi-directional.

CONCLUSIONS

Understanding transmission dynamics within and between LTCFs using genomic epidemiology on a broad scale can assist in targeting policies and prevention efforts. Tracking facility-level outbreaks can help differentiate intra-facility outbreaks from high community transmission with repeated introduction events. Based on our study findings, methods for routine tree building and overlay of epidemiologic data for hypothesis generation by public health practitioners are recommended. Discrete trait analysis added valuable insight and can be considered when representative sequencing is performed. Cluster detection tools, especially those that rely on distance thresholds, may be of more limited use given current data capture and timeliness. Importantly, we noted a decrease in data capture from LTCFs over time. Depending on goals for use of genomic data, sentinel surveillance should be increased or targeted surveillance implemented to ensure available data for analysis.

摘要

背景

长期护理机构 (LTCF) 容易发生疾病暴发。在这里,我们共同分析了 2020-2022 年华盛顿州 (WA) 的 LTCF 和周边社区的 SARS-CoV-2 基因组和配对流行病学数据,以评估在不断变化的政策环境下的传播模式。我们描述了整个 LTCF 中的测序工作和基因组流行病学发现,并在一个县进行了深入分析。

方法

我们评估了基因组数据的代表性,构建了系统发育树,并进行了离散特征分析,以估计随时间推移的引入规模,并探索了选定的暴发事件,以进一步描述传播事件。

结果

我们发现,WA 与 LTCF 相关的病例的传播动态在整个 COVID-19 大流行期间发生了变化,LTCF 的引入率不同,但 LTCF 内的扩增减少。在 LTCF 中循环的 SARS-CoV-2 谱系与同一时间在社区中循环的谱系相似。工作人员与居民之间的传播是双向的。

结论

使用广泛的基因组流行病学在 LTCF 内和之间了解传播动态,可以帮助确定政策和预防措施的重点。跟踪设施层面的暴发有助于将设施内暴发与具有重复引入事件的高社区传播区分开来。基于我们的研究结果,建议为公共卫生从业人员生成假设提供常规树构建和流行病学数据叠加的方法。离散特征分析增加了有价值的见解,可以在进行代表性测序时考虑。基于距离阈值的聚类检测工具的使用可能会受到限制,尤其是在当前数据捕获和及时性方面。重要的是,我们注意到 LTCF 中的数据捕获随时间推移而减少。根据使用基因组数据的目标,应增加哨点监测或实施靶向监测,以确保有可用数据进行分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f42a/10789038/ae9b8176ed24/12889_2023_17461_Fig1_HTML.jpg

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