Predicting Food-Safety Risk and Determining Cost-Effective Risk-Reduction Strategies

Abstract

Food safety is a major risk for agribusiness firms. According to the Centers for Disease Control and Prevention (CDC), approximately 5000 people die annually, and 36,000 people are hospitalized as a result of foodborne outbreaks in the United States. Globally, the death estimate is about 42,000 people per year. A single outbreak could cost a particular segment of the food industry hundreds of millions of dollars due to recalls and liability; these instances might amount to billions of dollars annually. Despite U.S. advancements and regulations, such as pathogen reduction/hazard analysis critical control points (PR/HACCP) in 1996 and the Food Modernization Act in 2010, to reduce food-safety risk, retail meat facilities continue to experience recalls and major outbreaks. We developed a stochastic-optimization framework and used stochastic-dominance methods to evaluate the effectiveness for three strategies that are used by retail meat facilities. Copula value-at-risk (CVaR) was utilized to predict the magnitude of the risk exposure associated with alternative, cost-effective risk-reduction strategies. The results showed that optimal retail-intervention strategies vary by meat and pathogen types, and that having a single Salmonella performance standard for PR/HACCP could be inefficient for reducing other pathogens and food-safety risks.