Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting. Issue 2 (August 2016)

• Record Type: Journal Article
• Authors: Glatman-Freedman, Aharona; Kaufman, Zalman; Kopel, Eran; Bassal, Ravit; Taran, Diana; Valinsky, Lea; Agmon, Vered; Shpriz, Manor; Cohen, Daniel; Anis, Emilia; Shohat, Tamy
• Is Part Of: Journal of infection. Volume 73:Issue 2(2016)
• Page Start: 99
• Page End: 106
• Publication Date: 2016-08
• Subjects: Cluster analysis -- Public health -- Enteric pathogens -- Outbreak detection -- Geographical information system (GIS); Infection -- Periodicals; Bacterial Infections -- Periodicals; Communicable Diseases -- Periodicals; Electronic journals; 616.905
• DOI: 10.1016/j.jinf.2016.04.038
• Languages: English
• ISSNs: 0163-4453

Abstract

Summary: 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.