Prev Vet Med. 2016 Jun 1;128:41-50. doi: 10.1016/j.prevetmed.2016.03.010. Epub 2016 Mar 17.
Network, cluster and risk factor analyses for porcine reproductive and respiratory syndrome using data from swine sites participating in a disease control program.
- 1Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada. Electronic address: arrud002@umn.edu.
- 2Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada.
- 3Woodstock, ON N4S 6N8, Canada.
- 4Strategic Solutions Group, Puslinch, ON N0B 2J0, Canada.
Abstract
The objectives of this study were to describe networks of Ontario swine sites and their service providers (including trucking, feed, semen, gilt and boar companies); to categorize swine sites into clusters based on site-level centrality measures, and to investigate risk factors for porcine reproductive and respiratory syndrome (PRRS) using information gathered from the above-mentioned analyses. All 816 sites included in the current study were enrolled in the PRRS area regional control and elimination projects in Ontario. Demographics, biosecurity and network data were collected using a standardized questionnaire and PRRS status was determined on the basis of available diagnostic tests and assessment by site veterinarians. Two-mode networks were transformed into one-mode dichotomized networks. Cluster and risk factor analyses were conducted separately for breeding and growing pig sites. In addition to the clusters obtained from cluster analyses, other explanatory variables of interest included: production type, type of animal flow, use of a shower facility, and number of neighboring swine sites within 3km. Unadjusted univariable analyses were followed by two types of adjusted models (adjusted for production systems): a generalizing estimation equation model (GEE) and a generalized linear mixed model (GLMM). Results showed that the gilt network was the most fragmented network, followed by the boar and truck networks. Considering all networks simultaneously, approximately 94% of all swine sites were indirectly connected. Unadjusted risk factor analyses showed significant associations between almost all predictors of interest and PRRS positivity, but these disappeared once production system was taken into consideration. Finally, the vast majority of the variation on PRRS status was explained by production system according to GLMM, which shows the highly correlated nature of the data, and raises the point that interventions at this level could potentially have high impact in PRRS status change and/or maintenance.
Copyright © 2016 Elsevier B.V. All rights reserved.
KEYWORDS:
Cluster analysis; Disease control programs; Porcine reproductive and respiratory syndrome; Porcine reproductive and respiratory syndrome control programs; Risk factor analysis; Service provider networks
- PMID:
- 27237389
- [PubMed - in process]
