Development of a System Model to Predict the Impact of Pre-harvest Contamination Sources on a Possible Leafy Greens-related E. coli O157:H7 Outbreak

Introduction: Consumption of leafy greens contaminated with pathogens remains a major cause of foodborne illnesses in the United States. For many outbreaks associated with pre-harvest contamination of leafy greens, the source of pathogens origination is unclear because of challenges involved in conducting traceback investigations. Salinas Valley in California is a major producer of leafy greens, but at the same time, has been linked to more than ten Escherichia coli O157:H7 outbreaks associated with leafy greens produced in this region since 1999.

Purpose: The objective of this study was to develop a pre-harvest system model to understand the ecology of E. coli O157:H7 in leafy greens production.

Methods: A dynamic system model consisting of subsystems (soil, irrigation, livestock operations, rainfall, and wildlife) simulating a farm in Salinas Valley was developed. This model assumed two crops of lettuce in a year. The model was simulated assuming the events of plantation, irrigation, harvesting, tillage, contamination of soil and plants, and survival of E. coli O157:H7.

Results: The concentrations of E. coli O157:H7 in the crops harvested in different months as predicted by the baseline model for conventional fields estimated that 11 out of 221 first crops harvested in July will have at least one plant with more than 1 CFU of E. coli O157:H7. The concentrations were higher in the second crops than the first crops, with the probability of having at least one plant with more than 1 CFU of E. coli O157:H7 in a crop predicted as 21/253, 4/333, 11/307, and 6/105 in August, September, October, and November, respectively. For organic fields, the probabilities of having at least one plant with more than 1 CFU of E. coli O157:H7 in a crop were predicted to be higher than those for the conventional fields.

Significance: These results are in close agreement with the known outbreaks and could be useful in developing metrics to mitigate the risks of leafy greens associated outbreaks.