Comparison of Public Health Impact of Listeria monocytogenes Product-to-Product and Environment-to-Product Contamination of Deli Meats at Retail
Abstract:This study compared the relative public health impact in deli meats at retail contaminated with Listeria monocytogenes by either (i) other products or (ii) the retail environment. Modeling was performed using the risk of listeriosis-associated deaths as a public health outcome of interest and using two deli meat products (i.e., ham and turkey, both formulated without growth inhibitors) as model systems. Based on reported data, deli meats coming to retail were assumed to be contaminated at a frequency of 0.4%. Three contamination scenarios were investigated: (i) a baseline scenario, in which no additional cross-contamination occurred at retail, (ii) a scenario in which an additional 2.3% of products were cross-contaminated at retail due to transfer of L. monocytogenes cells from already contaminated ready-to-eat deli meats, and (iii) a scenario in which an additional 2.3% of products were contaminated as a result of cross-contamination from a contaminated retail environment. By using a previously reported L. monocytogenes risk assessment model that uses product-specific growth kinetic parameters, cross-contamination of deli ham and turkey was estimated to increase the relative risk of listeriosis-associated deaths by 5.9- and 6.1-fold, respectively, for contamination from other products and by 4.9- and 5.8-fold, respectively, for contamination from the retail environment. Sensitivity and scenario analyses indicated that the frequency of cross-contamination at retail from any source (other food products or environment) was the most important factor affecting the relative risk of listeriosis-associated deaths. Overall, our data indicate that retail-level cross-contamination of ready-to-eat deli meats with L. monocytogenes has the potential to considerably increase the risk of human listeriosis cases and deaths, and thus precise estimates of cross-contamination frequency are critical for accurate risk assessments.
Document Type: Research Article
Affiliations: 1: Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York 14853, USA; Department of Nutrition and Food Science & Center for Food Safety and Security Systems (CFS3), University of Maryland, College Park, Maryland 20742, USA. email@example.com 2: Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas 77843, USA 3: Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York 14853, USA 4: Computational Biology Service Unit, Cornell University, Ithaca, New York 14853, USA 5: Department of Food Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, New York 14853, USA
Publication date: November 1, 2011
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