P2-239 Identifying and Modeling Multi-scale Risk Factors for Contamination by Foodborne Pathogens in Mixed Farms

Monday, July 27, 2015
Exhibit Hall (Oregon Convention Center)
Hao Pang, University of Maryland, College Park, MD
Elisabetta Lambertini, University of Maryland, College Park, MD
Abani Pradhan, University of Maryland, College Park, MD
Introduction: An increasing number of foodborne outbreaks attributed to produce has led to the recognition of this class of products as vehicle for foodborne pathogens. The production of vegetable crops in a mixed farming environment (produce grown in the same premises with farm animals) is also gradually increasing in the U.S.  

Purpose: The objectives of this study were to: 1) identify possible risk factors for pathogen contamination in produce at pre-harvest level, and 2) compare different modelling tools that can be used to analyze and identify risk factors in order to control and manage the pathogen contamination risk at farm level. 

Methods: A broad literature search was carried out, and studies that investigated possible risk factors for contamination from Listeria, Salmonella, and pathogenic E. coli in a variety of produce at pre-harvest level were summarized and discussed. Potential pre-harvest risk factors were identified and divided into three categories: farm management factors, weather factors, and environmental factors.

Results: Presence and survival of pathogens in wild and domestic animals, water, soil, and manure are well documented. Weather factors such as temperature, freeze-thaw cycle, and rainfall have been investigated as possible risk factors, although consistent evidence is lacking to conclusively support the association between these factors and contamination on produce, especially in mixed farms. Classification trees and logistic regression are the primary statistical modelling tools that have been used to identify potential risk factors for contamination.

Significance: This study analyzed possible risk factors of microbial contamination in mixed produce farms and discussed statistical tools that can be used to evaluate and determine those risk factors. The information provided in this study could serve as a useful resource to evaluation and rank risk factors in produce production.