Application of Bayesian Techniques to Model the Burden of Human Salmonellosis Attributable to U.S. Food Commodities at the Point of Processing: Adaptation of a Danish Model

Chuanfa Guo, Robert M. Hoekstra, Carl M. Schroeder, Sara Monteiro Pires, Kanyin Liane Ong, Emma Hartnett, Alecia Naugle, Jane Harman, Patricia Bennett, Paul Cieslak, Elaine Scallan, Bonnie Rose, Kristin G. Holt, Bonnie Kissler, Evelyne Mbandi, Reza Roodsari, Frederick J. Angulo, Dana Cole

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Abstract

Mathematical models that estimate the proportion of foodborne illnesses attributable to food commodities at specific points in the food chain may be useful to risk managers and policy makers to formulate public health goals, prioritize interventions, and document the effectiveness of mitigations aimed at reducing illness. Using human surveillance data on laboratory-confirmed Salmonella infections from the Centers for Disease Control and Prevention and Salmonella testing data from U.S. Department of Agriculture Food Safety and Inspection Service's regulatory programs, we developed a point-of-processing foodborne illness attribution model by adapting the Hald Salmonella Bayesian source attribution model. Key model outputs include estimates of the relative proportions of domestically acquired sporadic human Salmonella infections resulting from contamination of raw meat, poultry, and egg products processed in the United States from 1998 through 2003. The current model estimates the relative contribution of chicken (48%), ground beef (28%), turkey (17%), egg products (6%), intact beef (1%), and pork (
Original languageEnglish
JournalFoodborne Pathogens and Disease
Volume8
Issue number4
Pages (from-to)509-516
ISSN1535-3141
DOIs
Publication statusPublished - 2011

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