Developing a risk management framework to improve public health outcomes by enumerating Salmonella in ground turkey

Abstract

Salmonella spp. continue to be a leading cause of foodborne morbidity worldwide. To assess the risk of foodborne disease, current national regulatory schemes focus on prevalence estimates of Salmonella and other pathogens. The role of pathogen quantification as a risk management measure and its impact on public health is not well understood. To address this information gap, a quantitative risk assessment model was developed to evaluate the impact of pathogen enumeration strategies on public health after consumption of contaminated ground turkey in the USA. Public health impact was evaluated by using several dose-response models for high- and low-virulent strains to account for potential under- or overestimation of human health impacts. The model predicted 2705-21 099 illnesses that would result in 93-727 reported cases of salmonellosis. Sensitivity analysis predicted cooking an unthawed product at home as the riskiest consumption scenario and microbial concentration the most influential input on the incidence of human illnesses. Model results indicated that removing ground turkey lots exceeding contamination levels of 1 MPN/g and 1 MPN in 25 g would decrease the median number of illnesses by 86-94% and 99%, respectively. For a single production lot, contamination levels higher than 1 MPN/g would be needed to result in a reported case to public health officials. At contamination levels of 10 MPN/g, there would be a 13% chance of detecting an outbreak, and at 100 MPN/g, the likelihood of detecting an outbreak increases to 41%. Based on these model prediction results, risk management strategies should incorporate pathogen enumeration. This would have a direct impact on illness incidence linking public health outcomes with measurable food safety objectives.

Keywords: Ground turkey; pathogen enumeration; risk assessment; salmonella.

Fig. 1.

Risk assessment model framework. (a) Thawing and cooking scenarios. (b) Dose–response approach.

Fig. 2.

Sensitivity analysis of the influence of the model inputs on the predicted number of illnesses.

Fig. 3.

Number of illnesses and reported cases predicted by the feeding trial dose–response model (a) and the outbreak data dose–response model (b) at various contamination levels of a single 2000 lb lot of contaminated ground turkey. Shaded bars correspond to predicted illnesses. Black bars correspond to reported cases. Dotted lines correspond to the mean Salmonella spp. concentration in a positive lot (log MPN/g). Percentages correspond to the probability of a later investigation identifying an outbreak source.