ARS | Publication request: A QUANTITATIVE RISK ASSESSMENT MODEL FOR SALMONELLA AND WHOLE CHICKENS AT RETAIL

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: March 15, 2003
Publication Date: August 10, 2003
Citation: OSCAR, T.P. A QUANTITATIVE RISK ASSESSMENT MODEL FOR SALMONELLA AND WHOLE CHICKENS AT RETAIL. MEETING ABSTRACT. 2003. pp. 120.

Technical Abstract: A quantitative risk assessment model (QRAM) was developed for assessing local differences in chicken production, processing and distribution practices on the rate of salmonellosis from whole chickens. The QRAM was created in an Excel spreadsheet and was simulated using @Risk. The retail-to-table pathway was modeled as a series of pathogen events that included initial contamination at retail, growth during consumer transport, thermal inactivation during cooking, cross-contamination during serving and dose-response after consumption. Data from the scientific literature and data from consumer surveys of food handling practices as well as predictive models for growth and thermal inactivation of Salmonella on chicken were used to establish input settings. Simulation results indicated that the most highly contaminated chickens at retail did not result in the greatest exposure to Salmonella. Rather, by random chance, whole chickens with lower levels of Salmonella at retail resulted in greater consumer exposure when they were undercooked and mishandled during serving. Salmonella growth on raw chicken during consumer transport was the only pathogen event that did not impact the rate of salmonellosis. For the scenario simulated, the QRAM predicted a rate of 0.44 cases of salmonellosis per 100,000 consumers, which was consistent with recent epidemiological data that indicates a rate of 0.66 to 0.88 cases of salmonellosis per 100,000 consumers of chicken. Although the QRAM was in agreement with the epidemiological data, many assumptions were made in the QRAM because of data gaps and thus, further refinement of the QRAM is needed before its predictions can be considered reliable.