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Structured Expert Elicitation About Listeria monocytogenes Cross-Contamination in the Environment of Retail Deli Operations in the United States


Karin Hoelzer, Department of Food Science, Cornell University, 410 Stocking Hall, Ithaca, NY 14850, USA; tel: 607-255-1266;


Listeria monocytogenes is among the foodborne pathogens with the highest death toll in the United States. Ready-to-eat foods contaminated at retail are an important source of infection. Environmental sites in retail deli operations can be contaminated. However, commonly contaminated sites are unlikely to come into direct contact with food and the public health relevance of environmental contamination has remained unclear. To identify environmental sites that may pose a considerable cross-contamination risk, to elucidate potential transmission pathways, and to identify knowledge gaps, we performed a structured expert elicitation of 41 experts from state regulatory agencies and the food retail industry with practical experience in retail deli operations. Following the “Delphi” method, the elicitation was performed in three consecutive steps: questionnaire, review and discussion of results, second questionnaire. Hands and gloves were identified as important potential contamination sources. However, bacterial transfers to and from hands or gloves represented a major data gap. Experts agreed about transfer probabilities from cutting boards, scales, deli cases, and deli preparation sinks to product, and about transfer probabilities from floor drains, walk-in cooler floors, and knife racks to food contact surfaces. Comparison of experts' opinions to observational data revealed a tendency among experts with certain demographic characteristics and professional opinions to overestimate prevalence. Experts’ votes clearly clustered into separate groups not defined by place of employment, even though industry experts may have been somewhat overrepresented in one cluster. Overall, our study demonstrates the value and caveats of expert elicitation to identify data gaps and prioritize research efforts.

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