title: Spatial-temporal clustering of companion animal enteric syndrome: detection and investigation through the use of electronic medical records from participating private practices.
abstract: There is interest in the potential of companion animal surveillance to provide data to improve pet health and to provide early warning of environmental hazards to people. We implemented a companion animal surveillance system in Calgary, Alberta and the surrounding communities. Informatics technologies automatically extracted electronic medical records from participating veterinary practices and identified cases of enteric syndrome in the warehoused records. The data were analysed using time-series analyses and a retrospective space-time permutation scan statistic. We identified a seasonal pattern of reports of occurrences of enteric syndromes in companion animals and four statistically significant clusters of enteric syndrome cases. The cases within each cluster were examined and information about the animals involved (species, age, sex), their vaccination history, possible exposure or risk behaviour history, information about disease severity, and the aetiological diagnosis was collected. We then assessed whether the cases within the cluster were unusual and if they represented an animal or public health threat. There was often insufficient information recorded in the medical record to characterize the clusters by aetiology or exposures. Space-time analysis of companion animal enteric syndrome cases found evidence of clustering. Collection of more epidemiologically relevant data would enhance the utility of practice-based companion animal surveillance.
authors.affiliation: Faculty of Veterinary Medicine,Department of Ecosystem and Public Health,University of Calgary,AB,Canada.
authors.forename: R M
authors.initials: RM
authors.lastname: Anholt
authors.affiliation: Veterinary Public Health Institute,Vetsuisse Faculty,University of Bern,Bern,Switzerland.
authors.forename: J
authors.identifier: http://orcid.org/0000-0003-3343-908X
authors.initials: J
authors.lastname: Berezowski
authors.affiliation: Department of Geography and Environmental Studies,Wilfrid Laurier University,Waterloo,ON,Canada.
authors.forename: C
authors.initials: C
authors.lastname: Robertson
authors.affiliation: Faculty of Veterinary Medicine,Department of Ecosystem and Public Health,University of Calgary,AB,Canada.
authors.forename: C
authors.initials: C
authors.lastname: Stephen
mesh_terms: D000416:Alberta / Q000453:epidemiology; D000818:Animals; D002371:Cat Diseases / Q000453:epidemiology*; D002415:Cats; D057225:Data Mining; D003967:Diarrhea / Q000453:epidemiology / Q000209:etiology / Q000662:veterinary*; D004283:Dog Diseases / Q000453:epidemiology*; D004285:Dogs; D057286:Electronic Health Records; D062665:Epidemiological Monitoring / Q000662:veterinary; D005289:Ferrets; D007410:Intestinal Diseases / Q000453:epidemiology / Q000209:etiology / Q000662:veterinary*; D057805:Pets; D011331:Private Practice / Q000706:statistics & numerical data; D011817:Rabbits; D012189:Retrospective Studies; D012377:Rodentia; D012621:Seasons; D018571:Sentinel Surveillance / Q000662:veterinary; D013029:Space-Time Clustering; D062211:Spatio-Temporal Analysis
keywords: Community outbreaks; GP surveillance systems; gastrointestinal infections; spatial modelling; veterinary epidemiology