Bayesian Monte Carlo Uncertainty Analysis of Human Health Risks from Animal Antimicrobial Use in a Dynamic Model of Emerging Resistance†

Abstract

Recent qualitative analyses warn of potential future human health risks from emergence of antibiotic resistance in food-borne pathogens due to the use of similar antimicrobial drugs in both food animals and human medicine. While historical data suggest that human health risks from some animal antimicrobials, such as virginiamycin (VM), have remained low (McDonald et al., 2001), there is a widespread concern that "resistance epidemics" or endemics could arise in the future. How reassuring is the past about the future? This article applies quantitative risk assessment methods to help find out, using human health risks from VM and the nearly identical human antimicrobial quinupristin-dalfopristin (QD) as a case study. A dynamic simulation model is used to predict the risks of emerging resistance to human antimicrobials in human populations from given input assumptions. Bayesian Monte Carlo uncertainty analysis allows past data to constrain and inform selection of input parameter values, and thus to predict the possible future resistance patterns that are consistent with historical data. The results show that health risks from VM use in food animals are highly sensitive to the human prescription rate of QD. For realistic prescription rates, quantitative risks are less than 1 x 10(-6) even for members of the most-threatened (ICU patient) population, while societal risks are <1 excess statistical death per year for the whole U.S. population. Such quantitative estimates complement more qualitative assessments that discuss the possibility of future "resistance epidemics" (or endemics) without quantifying their probabilities.