Abstract Understanding how changes in the prevalence of Salmonella-positive chicken carcasses affects the prevalence of Salmonella-positive chicken parts samples across slaughter establishments is necessary to model the risk of foodborne… Click to show full abstract
Abstract Understanding how changes in the prevalence of Salmonella-positive chicken carcasses affects the prevalence of Salmonella-positive chicken parts samples across slaughter establishments is necessary to model the risk of foodborne illness. When developing new Salmonella performance standards for chicken parts—that were implemented following existing chicken carcass performance standards—FSIS made assumptions regarding the correlation between these two forms of chicken products marketed to consumers. These assumptions were necessary because data were not available at the time to measure this correlation. Using recent FSIS sampling data and covariate information concerning antimicrobials applied during chicken processing, regression analysis suggests a slight negative relationship between the prevalence of Salmonella-positive chicken parts in slaughter establishment and the use of peracetic acid on carcasses, as well as a large positive relationship between prevalence of Salmonella-positive chicken parts and carcasses. Nevertheless, using a repeated random sub-sampling cross-validation approach, the regression model has very limited predictive value. In lieu of a predictive model, estimation of the correlation between prevalence of Salmonella-positive chicken carcass and parts samples is useful for understanding the public health value of performance standards and other interventions applied to these chicken products. After adjusting the observed correlation of the sampling evidence to account for the underlying beta and binomial distribution errors inherent in observed results, the estimated correlation of prevalence of Salmonella-positive chicken carcass and parts is 0.54. This implies a moderate degree of relatedness but may still understate the true correlation because of limitations in the available data.
               
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