Causal hidden variable model of pathogenic contamination from pig to pork

Risk assessments relating to food safety over more than one step along a production chain are frequently hampered by lack of detailed quantitative data. This study set out to develop a Bayesian hidden variable model to integrate available limited data of the combined occurrence of three bacterial pathogens, Listeria monocytogenes, Yersinia enterocolitica and Yersinia pseudotuberculosis, with causal assumptions along three steps of pork production chain. The pathogen occurrence data were animal specific both on conventional and organic pig farms and at the abattoir, but merely farm specific at meat cutting plants. The model was able to incorporate all data concerning different types of testing at different steps of the chain, and missing data values were dealt with in a straightforward manner. It provides a tool for quantitative risk assessments and for estimating the causal risk mitigation effects by combining external data with the specific follow-up data. Intervention effects are provided with Bayesian credible intervals indicating the uncertainty due to all information sources included in the model. Combined prevalence in Finnish pork was estimated to be 1–11% and it could be reduced to 0–2% if head was removed intact and rectum sealed off.