Abstract
The aim of this study was to perform a quantitative microbial risk assessment (QMRA) of Shiga toxin-producing Escherichia coli hemolytic uremic syndrome (STEC-HUS) linked to the consumption of Kosher beef produced in Argentina and consumed in Israel in children under 14 years. A probabilistic risk assessment model was developed to characterize STEC prevalence and contamination levels in the beef supply chain (cattle primary production, cattle transport, processing and storage in the abattoir, for export and at retail, and home preparation and consumption). The model was implemented in Microsoft Excel 2016 with the @Risk add-on package. Results of 302 surveys with data collected in Israel were as follows: 92.3% of people consumed beef, mostly at home, and 98.2% preferred levels of cooking that ensured STEC removal from the surface of beef cuts. The preferred degree of ground beef doneness was "well-done" (48.2%). Cooking preference ranged from red to "medium-well done" (51.8%). Median HUS probability from Argentinean beef cut and ground beef consumption in children under 14 years old was <10-15 and 8.57x10-10, respectively. The expected average annual number of HUS cases and deaths due to beef cut and ground beef consumption was zero. Risk of infection and HUS probability correlated with salting effect on E. coli count, processing raw beef before vegetables, ways of storage and refrigeration temperature at home, joint consumption of salad and beef cuts, degree of beef doneness and cutting board washing with detergent after each use with beef and vegetables. The STEC-HUS risk in Israel from consumption of bovine beef produced in Argentina was negligible. The current QMRA results were similar to those of previous beef cut consumption QMRA in Argentina and lower than any of the QMRA performed worldwide in other STEC-HUS linked to ground beef consumption. This study confirms the importance of QMRA to estimate and manage the risk of STEC-HUS from beef consumption. The impact variables identified in the sensitivity analysis allowed us to optimize resources and time management, to focus on accurate actions and to avoid taking measures that would not have an impact on the risk of STEC-HUS.