Comparing foodborne illness risks among meat commodities in the United States

Abstract Food-safety regulatory agencies are often tasked with oversight of a broad range of food commodities. For these agencies to regulate multiple commodities effectively, they need to develop policies and allocate resources that consider the varying magnitudes of the risk of illness that each of the commodities poses to the broad population of consumers. Process modeling is used in risk assessment to estimate the likelihood of illness by modeling contamination of raw foods, the microbial dynamics of pathogens between production and consumption, and dose–response relationships for the pathogen to estimate the risk and total number of illnesses for a specific commodity. Nevertheless, these models are usually unique to each commodity and constructed using different models and data sources, which can produce estimates that are difficult to compare. An alternative approach is presented that stems primarily from public health data. It uses simple methods to estimate various risk metrics simultaneously for multiple pathogens and commodities. This alternative approach is used to compare multiple risk metrics for beef, lamb, pork, and poultry for both Salmonella and Escherichia coli O157:H7. The implications of the different risk metrics are discussed with respect to current regulatory efforts in the United States.