Modeling the relationship between food animal health and human foodborne illness

Abstract

To achieve further reductions in foodborne illness levels in humans, effective pre-harvest interventions are needed. The health status of food animals that are destined to enter the human food supply chain may be an important, although often overlooked, factor in predicting the risk of human foodborne infections. The health status of food animals can potentially influence foodborne pathogen levels in three ways. First, diseased animals may shed higher levels of foodborne pathogens. Second, animals that require further handling in the processing plant to remove affected parts may lead to increased microbial contamination and cross-contamination. Finally, certain animal illnesses may lead to a higher probability of mistakes in the processing plant, such as gastrointestinal ruptures, which would lead to increased microbial contamination and cross-contamination. Consequently, interventions that reduce the incidence of food animal illnesses might also help reduce bacterial contamination on meat, thereby reducing human illness. Some of these interventions, however, might also present a risk to human health. For example, the use of antibiotics in food animals can reduce rates of animal illness but can also select for antibiotic-resistant bacteria which can threaten human treatment options. In this study, we present a mathematical model to evaluate human health risks from foodborne pathogens associated with changes in animal illness. The model is designed so that potential human health risks and benefits from interventions such as the continued use of antibiotics in animal agriculture can be evaluated simultaneously. We applied the model to a hypothetical example of Campylobacter from chicken. In general, the model suggests that very minor perturbations in microbial loads on meat products could have relatively large impacts on human health, and consequently, small improvements in food animal health might result in significant reductions in human illness.