Quantitative Microbiological Risk Assessment on Salmonella in Slaughter and Breeder pigs: Final Report

摘要

In order to facilitate the investigation of interventions at different points of the food chain, a farm-to-consumption framework was adopted, so that we could model the prevalence of infection / contamination and the microbial load from the farm to the point of consumption (exposure). The probability of infection, or illness, could then be estimated by applying a dose-response model using the estimated amount of Salmonella bacteria ingested at consumption as an input. Numerous QMRAs have been developed or are currently underway for Salmonella in pigs within the EU; including QMRAs for the UK (Hill et al. 2003; VLA, 2009); Belgium (Delhalle et al., 2009); Denmark (Alban et al., 2002; Hurd et al., 2008), Ireland (Barron et al., 2009) and the Netherlands (van der Gaag et al., 2004). However, EFSA requested a QMRA for Salmonella in Pigs that characterised the variability between European Union (EU) Member States (MSs) and, in particular, the inclusion of variability between MSs in their pig farms, slaughterhouses and consumption patterns; this presented numerous challenges. These challenges have been overcome by the development of a generic model with a clearly defined set of parameters that may vary between MSs, the values of which can be easily input for any specific EU MS. To demonstrate the parameterisation and use of the model, four MSs were selected as case studies: MS1, MS2, MS3 and MS4. These MSs were selected by performing a cluster analysis for the EU using criteria relating to pig production and consumption patterns to group the MSs into ‘clusters’. Based on which MSs had the most available data, one MS was selected from each cluster. Three product types are included in the QMRA: pork cuts, minced meat and fermented sausage. These products were chosen to represent a range of different production/preparation practices and consumption patterns, which will affect the Salmonella levels within these products at consumption and hence the probability of human illness.