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The use of outbreak information in the interpretation of clustering of reported cases of Escherichia coli O157 in space and time in Alberta, Canada, 2000–2002

Published online by Cambridge University Press:  03 January 2006

D. L. PEARL
Affiliation:
Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada
M. LOUIE
Affiliation:
Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada
L. CHUI
Affiliation:
Provincial Laboratory for Public Health (Microbiology), Edmonton, Alberta, Canada
K. DORÉ
Affiliation:
Division of Enteric, Foodborne and Waterborne Diseases, Public Health Agency of Canada, Guelph, Ontario, Canada
K. M. GRIMSRUD
Affiliation:
Alberta Health and Wellness, Edmonton, Alberta, Canada
D. LEEDELL
Affiliation:
Provincial Laboratory for Public Health (Microbiology), Edmonton, Alberta, Canada
S. W. MARTIN
Affiliation:
Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada
P. MICHEL
Affiliation:
Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Saint-Hyacinthe, Québec, Canada
L. W. SVENSON
Affiliation:
Alberta Health and Wellness, Edmonton, Alberta, Canada
S. A. McEWEN
Affiliation:
Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada
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Abstract

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We obtained a list of all reported cases of Escherichia coli O157 in Alberta during the 2000–2002 period, and using scan statistics we identified yearly temporal and spatial clusters of reported cases of E. coli O157 during the summer and in southern Alberta. However, the location of the spatial cluster in the south was variable among years. The impact of using both outbreak and sporadic data or only sporadic data on the identification of spatial and temporal clusters was small when analysing individual years, but the difference between spatial clusters was pronounced when scanning the entire study period. We also identified space-time clusters that incorporated known outbreaks, and clusters that were suggestive of undetected outbreaks that we attempted to validate with molecular data. Our results suggest that scan statistics, based on a space-time permutation model, may have a role in outbreak investigation and surveillance programmes by identifying previously undetected outbreaks.

Type
Research Article
Copyright
2006 Cambridge University Press