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多州前瞻性军团病聚集性病例检测的 SaTScan 分析,2011-2015 年。

Multistate analysis of prospective Legionnaires' disease cluster detection using SaTScan, 2011-2015.

机构信息

Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.

Colorado Department of Public Health and Environment, Denver, Colorado, United States of America.

出版信息

PLoS One. 2019 May 30;14(5):e0217632. doi: 10.1371/journal.pone.0217632. eCollection 2019.

Abstract

Detection of clusters of Legionnaires' disease, a leading waterborne cause of pneumonia, is challenging. Clusters vary in size and scope, are associated with a diverse range of aerosol-producing devices, including exposures such as whirlpool spas and hotel water systems typically associated with travel, and can occur without an easily identified exposure source. Recently, jurisdictions have begun to use SaTScan spatio-temporal analysis software prospectively as part of routine cluster surveillance. We used data collected by the Active Bacterial Core surveillance platform to assess the ability of SaTScan to detect Legionnaires' disease clusters. We found that SaTScan analysis using traditional surveillance data and geocoded residential addresses was unable to detect many common Legionnaires' disease cluster types, such as those associated with travel or a prolonged time between cases. Additionally, signals from an analysis designed to simulate a real-time search for clusters did not align with clusters identified by traditional surveillance methods or a retrospective SaTScan analysis. A geospatial analysis platform better tailored to the unique characteristics of Legionnaires' disease epidemiology would improve cluster detection and decrease time to public health action.

摘要

军团病是一种主要的水源性肺炎,其聚集性的检测具有挑战性。聚集的规模和范围各不相同,与各种产生气溶胶的设备有关,包括漩涡水疗池和酒店水系统等通常与旅行有关的暴露,并且可能在没有易于识别的暴露源的情况下发生。最近,各司法管辖区已开始前瞻性地使用 SaTScan 时空分析软件作为常规聚集监测的一部分。我们使用主动细菌核心监测平台收集的数据来评估 SaTScan 检测军团病聚集的能力。我们发现,使用传统监测数据和地理编码的居住地址进行 SaTScan 分析,无法检测到许多常见的军团病聚集类型,例如与旅行或病例之间时间延长有关的聚集。此外,旨在模拟实时搜索聚集的分析信号与传统监测方法或回顾性 SaTScan 分析确定的聚集不一致。一个更适合军团病流行病学独特特征的地理空间分析平台将提高聚集检测的效率,并缩短公共卫生行动的时间。

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