P2-78: Generic Modeling Approach for Quantitative Microbial Risk Assessment Thomas P. Oscar, USDA,...
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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; [email protected] 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 Process Step Input Incidence Extent Hazard Event A B Min Median Max Units Packaging Contaminati on 25% 10% 0 1 4 log Distributi on Growth 20% 40% 0.1 1 3 log Washing Removal 15% 30% -0.1 -1 -3 log Cooking Survival 10% 10% -0.1 -5 -7 log Cross- 30% Class Input Hazard Food Host %A _B RD min RD 50 RD max 1 - Normal Normal Normal 70_30 4.0 6.0 8.0 2 - High Normal Normal 6_38 3.0 5.0 7.0 3 - Normal High Normal 2_1 3.5 5.5 7.5 4 - High High Normal 2_1 2.5 4.5 6.5 5 - Normal Normal High 5_5 2.0 4.0 6.0 6 - High Normal High 9_17 1.0 3.0 5.0 7 - Normal High High 3_4 1.5 3.5 5.5 8 - High High High 3_4 0.5 2.5 4.5 Total A_B Hazard 20_60 % High Risk Food 10_10 Host 20_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 (). 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 Plant Iteration Packaging Distributi on Washing Cooking Serving Class Response Dose A 9,904 1,451 300,130 36,26 0 0 5,945 7 3,065 A 64,376 202 4,539 27 0 23 8 22 A 69,075 316 5,714 3,214 0 20 8 7 A 93,039 100 1,191 173 0 28 8 15 B 53,459 52 4,045 509 0 81 6 67 B 65,115 62 5,188 343 0 111 6 111 B 69,075 316 5,714 3,214 0 20 8 7 B 71,656 668 92,965 13,70 5 0 948 6 347 B 87,865 153 3,446 611 0 110 6 106 0 1 2 3 4 5 6 7 8 0 20 40 60 80 100 PlantB RD 50 = 4.9 PlantA RD 50 = 5.6 H azard D ose (log) R esponse (% ) 0 5 10 15 0.00 0.05 0.10 0.15 0.20 0.25 PlantA PlantB R esponse R ate (per 100,000) Frequency (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. 0 10 20 30 Packaging D istribution W ashing C ooking S erving PlantA PlantB H azard Incidence (% ) 2 3 4 5 6 7 8 Packaging D istribution W ashing C ooking S ervin g PlantA PlantB Hazard Num ber (lo g p er 100,000 u n its)
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Transcript of P2-78: Generic Modeling Approach for Quantitative Microbial Risk Assessment Thomas P. Oscar, USDA,...
P2-78: Generic Modeling Approach for Quantitative Microbial Risk AssessmentThomas 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; [email protected]
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, 105 iterations, 200 simulations and randomly selected random number generator seeds.
Process Step
Input
Incidence Extent
Hazard EventA B Min Median Max Units
PackagingContaminatio
n25%
10%
0 1 4 log
Distribution
Growth20%
40%
0.1 1 3 log
Washing Removal15%
30%
-0.1 -1 -3 log
Cooking Survival10%
10%
-0.1 -5 -7 log
ServingCross-
contamination
15%
30%
-3 -2 -1 log rateClass Input
Hazard Food Host %A _B RDmin RD50 RDmax
1 - Normal Normal Normal 70_30 4.0 6.0 8.0
2 - High Normal Normal 6_38 3.0 5.0 7.0
3 - Normal High Normal 2_1 3.5 5.5 7.5
4 - High High Normal 2_1 2.5 4.5 6.5
5 - Normal Normal High 5_5 2.0 4.0 6.0
6 - High Normal High 9_17 1.0 3.0 5.0
7 - Normal High High 3_4 1.5 3.5 5.5
8 - High High High 3_4 0.5 2.5 4.5
Total A_B
Hazard 20_60% High
Risk
Food 10_10
Host 20_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
Plant Iteration PackagingDistributio
nWashin
g Cooking Serving ClassResponse
Dose
A9,904 1,451 300,130 36,26
00 5,945 7 3,065
A 64,376 202 4,539 27 0 23 8 22A 69,075 316 5,714 3,214 0 20 8 7A 93,039 100 1,191 173 0 28 8 15B 53,459 52 4,045 509 0 81 6 67B 65,115 62 5,188 343 0 111 6 111B 69,075 316 5,714 3,214 0 20 8 7
B71,656 668 92,965 13,70
50 948 6 347
B 87,865 153 3,446 611 0 110 6 106B 93,039 100 1,191 173 0 28 8 15
0 1 2 3 4 5 6 7 8
0
20
40
60
80
100
Plant BRD50 = 4.9
Plant ARD50 = 5.6
Hazard Dose (log)
Res
po
nse
(%
)
0 5 10 150.00
0.05
0.10
0.15
0.20
0.25Plant APlant B
Response Rate (per 100,000)
Fre
qu
ency
(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.
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