Automated use of WHONET and SaTScan to detect outbreaks of Shigella spp. using antimicrobial resistance phenotypes.

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.

抗菌素耐药性是一种新兴的重点公共卫生威胁，而迅速检测由耐药病原体引起的疫情暴发的能力对于遏制耐药性和疾病控制工作至关重要。我们描述并评估了一个电子实验室数据系统（WHONET）以及一种时空排列扫描统计方法在半自动疾病暴发检测中的应用。我们与阿根廷抗菌药物耐药性监测国家网络WHONET - Argentina合作，将该系统应用于志贺氏菌属局部和区域暴发的检测。我们根据菌属、菌种和耐药表型搜索聚集性病例，并在12个月期间确定了19个统计学上的“事件”。在向卫生部报告的6起已知疫情中，有4起与SaTScan检测到的事件具有良好或提示性的一致性。最具判别力的分析是那些涉及耐药表型的分析。基于电子实验室的疾病监测结合统计聚集性检测方法可以加强传染病暴发的检测和应对。