Can J Vet
Res. 2013 Oct;77(4):241-53.
Bottoms K1,
Poljak Z, Friendship R, Deardon R, Alsop J, Dewey C.
1Department
of Population Medicine, Ontario Veterinary College (Bottoms, Poljak,
Friendship, Dewey) and Department of Mathematics and Statistics (Deardon),
University of Guelph, Guelph, Ontario; Ontario Ministry of Agriculture, Food
and Rural Affairs, Guelph, Ontario (Alsop).
Abstract
Risk-based
surveillance is becoming increasingly important in the veterinary and public
health fields. It serves as a means of increasing surveillance sensitivity and
improving cost-effectiveness in an increasingly resource-limited environment.
Our approach for developing a tool for the risk-based geographical surveillance
of contagious diseases of swine incorporates information about animal density
and external biosecurity practices within swine herds in southern Ontario. The
objectives of this study were to group the sample of herds into discrete
biosecurity groups, to develop a map of southern Ontario that can be used as a
tool in the risk-based geographical surveillance of contagious swine diseases,
and to identify significant predictors of biosecurity group membership. A
subset of external biosecurity variables was selected for 2-step cluster
analysis and latent class analysis (LCA). It was determined that 4 was the best
number of groups to describe the data, using both analytical approaches. The
authors named these groups: i) high biosecurity herds that were open with
respect to replacement animals; ii) high biosecurity herds that were closed
with respect to replacement animals; iii) moderate biosecurity herds; and iv)
low biosecurity herds. The risk map was developed using information about the
geographic distribution of herds in the biosecurity groups, as well as the
density of swine sites and of grower-finisher pigs in the study region.
Finally, multinomial logistic regression identified heat production units
(HPUs), number of incoming pig shipments per month, and herd type as significant
predictors of biosecurity group membership. It was concluded that the ability
to identify areas of high and low risk for disease may improve the success of
surveillance and eradication projects.
PMID:
24124266 [PubMed - indexed for MEDLINE] PMCID: PMC3788655
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