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美国的空间综合征监测和 COVID-19:为大流行做准备的局部集群映射。

Spatial Syndromic Surveillance and COVID-19 in the U.S.: Local Cluster Mapping for Pandemic Preparedness.

机构信息

Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.

University Hospitals, Cleveland, OH 44106, USA.

出版信息

Int J Environ Res Public Health. 2022 Jul 22;19(15):8931. doi: 10.3390/ijerph19158931.

DOI:10.3390/ijerph19158931
PMID:35897298
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9330043/
Abstract

Maps have become the de facto primary mode of visualizing the COVID-19 pandemic, from identifying local disease and vaccination patterns to understanding global trends. In addition to their widespread utilization for public communication, there have been a variety of advances in spatial methods created for localized operational needs. While broader dissemination of this more granular work is not commonplace due to the protections under Health Insurance Portability and Accountability Act (HIPAA), its role has been foundational to pandemic response for health systems, hospitals, and government agencies. In contrast to the retrospective views provided by the aggregated geographies found in the public domain, or those often utilized for academic research, operational response requires near real-time mapping based on continuously flowing address level data. This paper describes the opportunities and challenges presented in emergent disease mapping using dynamic patient data in the response to COVID-19 for northeast Ohio for the period 2020 to 2022. More specifically it shows how a new clustering tool developed by geographers in the initial phases of the pandemic to handle operational mapping continues to evolve with shifting pandemic needs, including new variant surges, vaccine targeting, and most recently, testing data shortfalls. This paper also demonstrates how the geographic approach applied provides the framework needed for future pandemic preparedness.

摘要

地图已成为可视化新冠疫情的事实上的主要模式,从识别本地疾病和疫苗接种模式到了解全球趋势。除了广泛用于公共传播外,还为本地化运营需求创建了各种空间方法的进展。由于《健康保险携带和责任法案》(HIPAA)的保护,更细化工作的更广泛传播并不常见,但它对卫生系统、医院和政府机构的大流行应对起到了基础作用。与公共领域中发现的聚合地理数据提供的回顾性视图或常用于学术研究的视图不同,运营响应需要基于不断流动的地址级数据进行近乎实时的映射。本文描述了 2020 年至 2022 年期间俄亥俄州东北部使用动态患者数据应对新冠疫情时出现的疾病动态制图的机会和挑战。更具体地说,它展示了地理学家在大流行初期开发的新聚类工具如何继续发展,以应对不断变化的大流行需求,包括新的变异浪潮、疫苗接种目标,以及最近的检测数据短缺。本文还展示了所应用的地理方法如何为未来的大流行准备提供所需的框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cba/9330043/5d4689f4456c/ijerph-19-08931-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cba/9330043/7efdbdc5ae14/ijerph-19-08931-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cba/9330043/0f382757c2e6/ijerph-19-08931-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cba/9330043/5d4689f4456c/ijerph-19-08931-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cba/9330043/7efdbdc5ae14/ijerph-19-08931-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cba/9330043/0f382757c2e6/ijerph-19-08931-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cba/9330043/5d4689f4456c/ijerph-19-08931-g003.jpg

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The Geographic Context of Racial Disparities in Aggressive Endometrial Cancer Subtypes: Integrating Social and Environmental Aspects to Discern Biological Outcomes.种族差异在侵袭性子宫内膜癌亚型中的地理背景:整合社会和环境方面以辨别生物学结果。
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