Spatio‐Temporal Scan Statistics for the Detection of Outbreaks Involving Common Molecular Subtypes: Using Human Cases of Escherichia coli O157:H7 Provincial PFGE Pattern 8 (National Designation ECXAI.0001) in Alberta as an Example. (7th September 2012)

• Record Type: Journal Article
• Authors: So, H. C.; Pearl, D. L.; von Königslöw, T.; Louie, M.; Chui, L.; Svenson, L. W.
• Is Part Of: Zoonoses and public health. Volume 60:Number 5(2013)
• Page Start: 341
• Page End: 348
• Publication Date: 2012-09-07
• Subjects: Zoonoses -- Periodicals; Public health -- Periodicals; 636.0896959
• DOI: 10.1111/zph.12012
• Languages: English
• ISSNs: 1863-1959

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Summary</title> <p>Molecular typing methods have become a common part of the surveillance of foodborne pathogens. In particular, pulsed‐field gel electrophoresis (PFGE) has been used successfully to identify outbreaks of <italic>Escherichia coli</italic> O157:H7 in humans from a variety of food and environmental sources. However, some PFGE patterns appear commonly in surveillance systems, making it more difficult to distinguish between outbreak and sporadic cases based on molecular data alone. In addition, it is unknown whether these common patterns might have unique epidemiological characteristics reflected in their spatial and temporal distributions. Using <italic>E. coli</italic> O157:H7 surveillance data from Alberta, collected from 2000 to 2002, we investigated whether <italic>E. coli</italic> O157:H7 with provincial PFGE pattern 8 (national designation ECXAI.0001) clustered in space, time and space–time relative to other PFGE patterns using the spatial scan statistic. Based on our purely spatial and temporal scans using a Bernoulli model, there did not appear to be strong evidence that isolates of <italic>E. coli</italic> O157:H7 with provincial PFGE pattern 8 are distributed differently from other PFGE patterns. However, we did identify space–time clusters of isolates with PFGE pattern 8, using a Bernoulli model and a space–time permutation model, which included known outbreaks and potentially unrecognized outbreaks or additional outbreak cases. There were differences between the two models in the space–time clusters identified, which suggests that the use of both models could increase the sensitivity of a quantitative surveillance system for identifying outbreaks involving isolates sharing a common PFGE pattern.</p> </abstract>