Analysis of Meat Juice ELISA Results and Questionnaire Data to Investigate Farm-Level Risk Factors for Salmonella Infection in UK Pigs

Authors


Richard Piers Smith. Centre of Epidemiology and Risk Analysis, VLA Weybridge, Addlestone, UK. Tel.: +44 1932 359465;
Fax: +44 1932 359429;
E-mail: r.smith@vla.defra.gsi.gov.uk

Summary

The study set out to explore risk factors for Salmonella infection in pigs, based on seroprevalence amongst slaughtered pigs, using a large study population of holdings and a comprehensive list of farm characteristics. Farm data were collected from pig quality assurance schemes and supplemented by a postal questionnaire. These data were used with meat juice serology results from ongoing abattoir Salmonella surveillance, for a multivariable risk factor analysis, modelling the ELISA sample to positive ratio directly (ELISA ratio). The study population contained 566 farms, covering a geographically representative spread of farms within the United Kingdom, with a mean average of 224 sample results per holding over a 4-year period. The model highlighted that temporal factors (quarterly and yearly cycles) and monthly meteorological summaries for rainfall, sunshine and temperature were associated with Salmonella presence (< 0.01). The ELISA ratio was found to be highest in autumn and lowest in spring and summer, whereas yearly averages showed a greater degree of variation than seasonal. Two feed variables (homemix and barley) were found to be protective factors, as was a conventional, rather than organic or freedom foods, farm enterprise type. The number of annual pig deliveries and dead stock collections, and the main cause of pig mortality on the farm were found to be associated with Salmonella infection. Scottish farms had a lower ELISA ratio than other regions, and an increased number of pig farms within a 10-km radius was associated with a higher ELISA ratio. The study demonstrated that the analysis of routinely collected data from surveillance and quality assurance schemes was cost-effective, with sufficient power to detect modest associations between Salmonella and exposure variables. The model results can be used to inform on-farm Salmonella control policies and could target-specific geographical regions and seasons to assist the efficiency of surveillance.

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