Associations Between Peaks of Foodborne Infections and Food Recalls

Abstract

Abstract Objectives Food delivery and supply chains represent complex dynamic systems susceptible to pathogen introduction. However, seasonal infections are rarely compared with the timing of food recalls. Gaps between peak infections and recalls illustrate inefficient food inspection and monitoring. We use seasonality features (peak timing and amplitude) to examine associations between peaks of pathogen infectiousness and food recalls. Methods Monthly counts of 3 foodborne infections (Salmonella, Listeria, and Shiga toxin-producing E.coli) were abstracted from the CDC's public database, FoodNet Fast, for 10 representative states (CA, CO, CT, GA, MD, MN, NM, NY, OR, TN) from 1996–2018. We applied Negative Binomial harmonic regression models with the δ-method to derive peak timing and amplitude estimates (with confidence intervals). Food recalls were extracted using the United States Department of Agriculture's (USDA) Food Safety and Inspection Service (FSIS) Recall Case Archive. We extracted recall dates and durations for each infection in each state. We compared lagged associations between infection peaks and recall dates using Spearman correlations. Results Seasonal peaks of Salmonella and Shiga Toxin-producing E.Coli (STEC) are tightly clustered (7.79 [7.71, 7.87], and 7.80 [7.65, 7.95], respectively) while Listeria falls later in August (8.35 [8.05, 8.65]). Peak timing estimates were positively synchronized between New York and Connecticut for Listeria (ρ = 0.780, P = 0.001), and between Georgia and Tennessee for STEC (ρ = 0.666, P = 0.009) indicating possible regional hotspots. From 1996–2018, inspection recalls tended to lag behind peaks of confirmed infections for all infections by 1–2 months, with most recalls occurring during August and September annually. Conclusions Measurements of infection seasonality coupled with food recall information shows lagged associations between population infectiousness and supply chain reporting. This suggests the need for improved food inspection scheduling and monitoring to minimize fiscal and food waste losses. Funding Sources The Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via 2017–17,072,100,002.