1. P2-78: Generic Modeling Approach for Quantitative Microbial Risk Assessment Thomas P. Oscar, USDA, ARS/1890 Center of Excellence in Poultry Food Safety Research, Room 2111, Center for Food Science and Technology, University of Maryland Eastern Shore, Princess Anne, MD 21853; 410-651-6062; Thomas.Oscar@ars.usda.gov INTRODUCTION Risk analysis is a holistic approach to food safety that involves three interactive processes: risk assessment, risk management and risk communication. Risk assessment modeling is the foundation of risk analysis and is most often accomplished using Monte Carlo simulation methods to combine existing knowledge and data into a prediction of risk. The prediction of risk is relative rather than absolute because of knowledge, data and model uncertainty. However, through the process of scenario analysis relative risk can be assessed and used to help inform risk management decisions. OBJECTIVE To provide a generic example of how quantitative microbial risk assessment (QMRA) can be used to provide a relative assessment of risk for informing risk management decisions. MATERIALS AND METHODS Case Study. A food company has two processing plants located in different regions of the country but that produce the same food product. End product testing indicated that the food product produced by both plants was contaminated with a single species of microbial hazard. Hazard incidence was higher for food from Plant A (i.e. 25%) but only food from Plant B (i.e. 10%) had caused an outbreak. These observations caused the risk managers to pose the following risk question: Why did food from plant B cause an outbreak when food from plant B has a lower incidence of hazard than food from plant A? QMRA Model. As a first step, the food company determined the initial distribution of the hazard in single food units at packaging. After hazard identification, the food company developed a Monte Carlo simulation model to predict consumer exposure and response. Fig. 1 shows the input settings and design for the hazard identification and exposure assessment module, whereas Fig. 2 shows the input settings and design for the hazard characterization and risk characterization module for the Plant A and Plant B scenarios. Incidence of hazard events was modeled using a discrete distribution, whereas extent of hazard events was modeled using a pert distribution. The model was created in an Excel spreadsheet and was simulated with @Risk using settings of Latin Hypercube sampling, 10 5 iterations, 200 simulations and randomly selected random number generator seeds. Process Step Input IncidenceExtent Hazard Event A B MinMedianMaxUnits PackagingContamination25%10%014log  DistributionGrowth20%40%0.113log  WashingRemoval15%30%-0.1-3log  CookingSurvival10% -0.1-5-7log  Serving Cross- contamination 15%30%-3-2log rate ClassInput HazardFoodHost%A _BRD min RD 50 RD max 1 - NormalNormal 70_304.06.08.0 2 - HighNormal 6_383.05.07.0 3 - NormalHighNormal2_13.55.57.5 4 - HighHighNormal2_12.54.56.5 5 - NormalNormalHigh5_52.04.06.0 6 - HighNormalHigh9_171.03.05.0 7 - NormalHigh 3_41.53.55.5 8 - HighHigh 3_40.52.54.5 TotalA_B Hazard20_60 % High Risk Food10_10 Host20_30 RESULTS AND DISCUSSION Hazard incidence was lower in food from Plant B at packaging, during distribution and after washing but was similar to food from Plant A after cooking and at serving (Fig. 3). Total hazard number per 100,000 food units was lower for food from Plant B than Plant A from packaging through cooking but because of greater cross-contamination during serving it was similar to Plant A at serving (Fig. 4). Because of a higher incidence of a highly virulent strain of the hazard and because of a higher incidence of high risk consumers (Fig. 2), the dose that elicited an adverse health response from 50% of consumers was lower for food from Plant B than food from Plant A (Fig. 5). This resulted in a higher predicted response rate among consumers of food from Plant B than from Plant A (Fig. 6). The median response rate was 3 (range: 0-11) per 100,000 food units for Plant A and 7.5 Hazard number per food unit PlantIterationPackagingDistributionWashingCookingServingClassResponse Dose A9,9041,451300,13036,26005,94573,065 A64,3762024,53927023822 A69,0753165,7143,21402087 A93,0391001,191173028815 B53,459524,045509081667 B65,115625,18834301116 B69,0753165,7143,21402087 B71,65666892,96513,70509486347 B87,8651533,44661101106106 B93,0391001,191173028815 (range: 1-14) per 100,000 food units for Plant B. The primary risk factors were > 50 cells per food unit at packaging, hazard growth during distribution, washing of food before cooking that resulted in higher risk of cross-contamination during serving, presence of a highly virulent strain of the hazard and consumption of the food by a high risk consumer (Table 1). Thus, although food from Plant B had a lower incidence of hazard contamination at packaging, the food from Plant B posed a higher risk of an outbreak because of a higher incidence of hazard growth during distribution, a higher incidence of cross-contamination during serving, a higher incidence of the virulent strain of the hazard and and a higher incidence of high risk consumers. These results demonstrated to the food company the extreme importance of considering post-process risk factors when assessing safety of food at the processing plant.