Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting
Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting
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DOI：
10.1016/j.jinf.2016.04.038
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发表时间：
2016-08-01
影响因子：
28.2
通讯作者：
Shohat, Tamy
中科院分区：
文献类型：
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作者：
Glatman-Freedman, Aharona;Kaufman, Zalman;Shohat, Tamy
目的：为了加强对肠道致病菌的及时监测，以色列于2013年5月引入了时空聚类分析。方法：利用ArcGIS和SATScan对某大型卫生保健机构患者的粪便分离数据进行每周一次的分析，并将聚类结果及时发送至当地卫生部门。结果：18个月期间，共发现52株宋内氏志贺菌、2株沙门氏菌、1株弯曲杆菌。松内氏小蜂群历时1-33天，包括3-30只个体。在聚类分析之前，31个(60%)宋内氏链霉菌群已知LDOHs。LDOHs以前不知道的集群促使了流行病学调查。在37个确诊的集群中，31个(84%)涉及教育机构(托儿所、幼儿园和一所小学)。结论：集群分析显示了补充肠道疾病监测的能力。扩大该系统的规模可以进一步加强对疫情的及时检测和控制。(C)2016年英国感染协会。爱思唯尔有限公司出版。保留所有权利。
Objectives: To enhance timely surveillance of bacterial enteric pathogens, space-time cluster analysis was introduced in Israel in May 2013.Methods: Stool isolation data of Salmonella, Shigella, and Campylobacter from patients of a large Health Maintenance Organization were analyzed weekly by ArcGIS and SaTScan, and cluster results were sent promptly to local departments of health (LDOHs).Results: During eighteen months, we identified 52 Shigella sonnei clusters, two Salmonella clusters, and no Campylobacter clusters. S. sonnei clusters lasted from one to 33 days and included three to 30 individuals. Thirty-one (60%) of the S. sonnei clusters were known to LDOHs prior to cluster analysis. Clusters not previously known by the LDOHs prompted epidemiologic investigations. In 31 of the 37 (84%) confirmed clusters, educational institutes (nursery schools, kindergartens, and a primary school) were involved.Conclusions: Cluster analysis demonstrated capability to complement enteric disease surveillance. Scaling up the system can further enhance timely detection and control of outbreaks. (C) 2016 The British Infection Association. Published by Elsevier Ltd. All rights reserved.