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USDA, ARS WorkshopPoultry Food Assess Risk Model
(Poultry FARM)
A quantitative microbial risk assessment model (QMRA) for Listeria, Salmonella, Campylobacter and chicken meat.
Predicts the public health impact of chicken meat destined for specific distribution channels and consumer populations
Packaging
Consumption
Distribution Channel
CookingSafe
Unsafe
To maximize the public health benefit of chicken by ensuring its safety & consumption
Hazard Identification
Exposure Assessment
Hazard Characterization
Risk Characterization
How many people will get sick and die?
Holistic
Salmonella (Se)Campylobacter
(Cj)
Initial distribution of pathogens among servings
Listeria (Lm)
Pathogen levels on chicken meat(mean log MPN/carcass)
Plant A Plant B Plant C Plant D Plant E
Lm 0 0 1.01 0.90 0
Se 0.11 0.17 0.74 0.19 0.48
Cj 3.65 2.37 3.86 4.19 3.45
Waldroup et al. (1992) J. Appl. Poultry Res. 1:226-234.
Cj levels are higher than Lm and Se, which are similar
Relative differences among pathogens are simulated in Poultry FARM
PlantPackaging
Contamination
DistributionAbuse
Growth/Death
PreparationContamination
Transfer
CoolingAbuse
Growth/Death
CookingUnder-cooking
Survival
ServingContamination
Transfer
TableConsumption
Dose-response
Predicts how pathogen levels change from farm-to-table
Key: Unit Operation _ Human Action _ Pathogen Event
Physiological Differences
Se grows, whereas Cj dies at ambient temperatures
Burnette and Yoon (2004) Food Sci. Biotechnol. 13:796-800
Predictive models can be developed for each pathogen event
Noresponse
Infection Mildillness
Illness
Determines whether or not an illness occurs
Depends on the outcome of the interaction between the pathogen, food and host
DiseaseTriangle
Pathogen Host
Food
Illness Hospital Death
Determines the severity of illness
There are important differences in severity among pathogens
Outcome Listeria Salmonella Campylobacter
Hospital 92.2% 22.1% 10.2%
Death 20.0% 0.78% 0.1%
Mead et al. (1999) http://www.cdc.gov/ncidod/eid/vol5no5/mead.htm
Illness(C1)
Hospital(C2)
Death(C3)
Severity = C1 + 2C2 + 10C3
Weight factorCases
Foodborne illness is a random event
Monte Carlo simulation is a good method for modeling foodborne illness
A + B = C
Foodborne illness is a rare event
Iteration
1
2
3
:
10,000
Discrete
1
0
0
:
0
Pert (0,2,4)
1.8
1.2
0.2
:
2.2
Antilog
63.1
0
0
:
0
Round
63
0
0
:
0
Incidence Extent Pathogen Number
=IF(RiskDiscrete=0,0,RiskPert)
Poultry FARM simulates pathogen-free servings
Poultry FARM Tour
Lot A Lot B
Which is higher risk?
Input Pathogen
A B C D E
Q1Q2Q3
LmSeCj
0.0%16.7%78.1%
0.0%28.1%53.1%
29.2%47.9%90.6%
18.8%24.0%100.0%
0.0%9.4%82.3%
Waldroup et al (1992) J. Appl. Poultry Res. 1:226-234.
Question A B C D E
Q4 5% 5% 5% 5% 5%
Q5 10% 10% 10% 10% 10%
Q6 15% 15% 5% 15% 10%
Q7 20% 20% 10% 20% 10%
Q8 25% 25% 15% 25% 15%
Q9 20% 10% 10% 30% 20%
Q10 35% 10% 15% 35% 35%
Q11 25% 5% 15% 25% 15%
Q12 20% 60% 20% 10% 80%
QMRA Model = Poultry FARM 3.0
Iterations = 10,000 servings
Simulations = 100
Sampling = Latin Hypercube
Random Number Generator Seed = Random Selection
Each random number generator seed produces a unique outcome of the
scenario
A B C D E0
20
40
60
Scenario
Sev
erit
yListeria monocytogenes
AA BB CC DD EE
LmLm
SeSe
CjCj
0%0%
16.7%16.7%
78.1%78.1%
0%0%
28.1%28.1%
53.1%53.1%
29.2%29.2%
47.9%47.9%
90.6%90.6%
18.8%18.8%
24%24%
100%100%
0%0%
9.4%9.4%
82.3%82.3%
A B C D E0
20
40
60
Scenario
Sev
erit
ySalmonella enterica
AA BB CC DD EE
LmLm
SeSe
CjCj
0%0%
16.7%16.7%
78.1%78.1%
0%0%
28.1%28.1%
53.1%53.1%
29.2%29.2%
47.9%47.9%
90.6%90.6%
18.8%18.8%
24%24%
100%100%
0%0%
9.4%9.4%
82.3%82.3%
A B C D E0
20
40
60
Scenario
Sev
erit
yCampylobacter jejuni
AA BB CC DD EE
LmLm
SeSe
CjCj
0%0%
16.7%16.7%
78.1%78.1%
0%0%
28.1%28.1%
53.1%53.1%
29.2%29.2%
47.9%47.9%
90.6%90.6%
18.8%18.8%
24%24%
100%100%
0%0%
9.4%9.4%
82.3%82.3%
A B C D E0
20
40
60
Scenario
Sev
erit
yLm + Se + Cj
AA BB CC DD EE
LmLm
SeSe
CjCj
0%0%
16.7%16.7%
78.1%78.1%
0%0%
28.1%28.1%
53.1%53.1%
29.2%29.2%
47.9%47.9%
90.6%90.6%
18.8%18.8%
24%24%
100%100%
0%0%
9.4%9.4%
82.3%82.3%
It is important to consider multiple pathogens and post-process risk factors
when assessing food safety