Laboratory-Based Prospective Surveillance for Community Outbreaks of Shigella spp. in Argentina
Viñas, María R.
Brengi, Silvina P.
Della Gaspera, Anabella
van der Ploeg, Claudia
Caffer, María I.
Galas, MarceloNote: Order does not necessarily reflect citation order of authors.
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CitationViñas, M. R., E. Tuduri, A. Galar, K. Yih, M. Pichel, J. Stelling, S. P. Brengi, et al. 2013. “Laboratory-Based Prospective Surveillance for Community Outbreaks of Shigella spp. in Argentina.” PLoS Neglected Tropical Diseases 7 (12): e2521. doi:10.1371/journal.pntd.0002521. http://dx.doi.org/10.1371/journal.pntd.0002521.
AbstractBackground: To implement effective control measures, timely outbreak detection is essential. Shigella is the most common cause of bacterial diarrhea in Argentina. Highly resistant clones of Shigella have emerged, and outbreaks have been recognized in closed settings and in whole communities. We hereby report our experience with an evolving, integrated, laboratory-based, near real-time surveillance system operating in six contiguous provinces of Argentina during April 2009 to March 2012. Methodology To detect localized shigellosis outbreaks timely, we used the prospective space-time permutation scan statistic algorithm of SaTScan, embedded in WHONET software. Twenty three laboratories sent updated Shigella data on a weekly basis to the National Reference Laboratory. Cluster detection analysis was performed at several taxonomic levels: for all Shigella spp., for serotypes within species and for antimicrobial resistance phenotypes within species. Shigella isolates associated with statistically significant signals (clusters in time/space with recurrence interval ≥365 days) were subtyped by pulsed field gel electrophoresis (PFGE) using PulseNet protocols. Principal Findings In three years of active surveillance, our system detected 32 statistically significant events, 26 of them identified before hospital staff was aware of any unexpected increase in the number of Shigella isolates. Twenty-six signals were investigated by PFGE, which confirmed a close relationship among the isolates for 22 events (84.6%). Seven events were investigated epidemiologically, which revealed links among the patients. Seventeen events were found at the resistance profile level. The system detected events of public health importance: infrequent resistance profiles, long-lasting and/or re-emergent clusters and events important for their duration or size, which were reported to local public health authorities. Conclusions/Significance: The WHONET-SaTScan system may serve as a model for surveillance and can be applied to other pathogens, implemented by other networks, and scaled up to national and international levels for early detection and control of outbreaks.
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