Purpose: More people suffer and die from malnutrition than foodborne illness. Therefore, it is important that models that predict the risk of foodborne illness provide reliable predictions so that safe food that could benefit public health by combating malnutrition is not labelled as unsafe. In the current study, a process risk model (PRM) for Salmonella and chicken parts was developed and validated and shown to provide reliable predictions of the risk of foodborne illness.
Materials and method: The PRM was developed in an Excel spreadsheet and was simulated with @Risk. It consisted of four unit operations (pathogen events): 1) Preparation (contamination); 2) Cooking (death); 3) Serving (cross-contamination); and 4) Consumption (dose-response). Data for model development were acquired by whole sample enrichment, real-time polymerase chain reaction (WSE-qPCR).
Results: Salmonella prevalence on raw chicken parts at meal preparation as determined by WSE-qPCR was 15.6% (25/160) whereas incidence of Salmonella cross-contamination of cooked chicken during serving was 12.5% (5/40). Six serotypes of Salmonella were isolated with most (83%; 25/30) being high risk serotypes Typhimurium and Typhimurium var 5-. Mean number of Salmonella on raw chicken parts was 0.36 log (range: 0-0.93 log) whereas mean number of Salmonella that cross-contaminated cooked chicken was 0.36 log (range: 0.13-0.67 log). Predictions of the PRM were validated against outbreak data. Sensitivity and scenario analyses indicated that the primary risk scenario for salmonellosis was cross-contamination of cooked chicken with a high risk serotype of Salmonella during serving.
Conclusion: Reduction of high risk Salmonella o n chicken during production and processing and consumer education to reduce the incidence of cross-contamination during serving are interventions that could reduce this important risk to public health.