Department of Medicine, Brigham and Women's Hospital, WHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Boston, MA 02115, USA.
Epidemiol Infect. 2010 Jun;138(6):873-83. doi: 10.1017/S0950268809990884. Epub 2009 Oct 2.
Antimicrobial resistance is a priority emerging public health threat, and the ability to detect promptly outbreaks caused by resistant pathogens is critical for resistance containment and disease control efforts. We describe and evaluate the use of an electronic laboratory data system (WHONET) and a space-time permutation scan statistic for semi-automated disease outbreak detection. In collaboration with WHONET-Argentina, the national network for surveillance of antimicrobial resistance, we applied the system to the detection of local and regional outbreaks of Shigella spp. We searched for clusters on the basis of genus, species, and resistance phenotype and identified 19 statistical 'events' in a 12-month period. Of the six known outbreaks reported to the Ministry of Health, four had good or suggestive agreement with SaTScan-detected events. The most discriminating analyses were those involving resistance phenotypes. Electronic laboratory-based disease surveillance incorporating statistical cluster detection methods can enhance infectious disease outbreak detection and response.
抗菌药物耐药性是一个新出现的优先公共卫生威胁，而迅速发现耐药病原体引起的暴发的能力对于耐药性控制和疾病控制工作至关重要。我们描述并评估了电子实验室数据系统 (WHONET) 和时空置换扫描统计在半自动化疾病暴发检测中的应用。我们与阿根廷 WHONET 合作，该网络是监测抗菌药物耐药性的国家网络，我们将该系统应用于检测志贺氏菌属的本地和区域暴发。我们根据属、种和耐药表型搜索集群，并在 12 个月内确定了 19 个统计“事件”。在向卫生部报告的六起已知暴发中，四起与 SaTScan 检测到的事件具有良好或提示性的一致性。最具鉴别力的分析是涉及耐药表型的分析。基于电子实验室的传染病监测结合统计集群检测方法，可以提高传染病暴发检测和应对能力。
