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USDA, ARS Workshop

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Title: USDA, ARS Workshop


1
USDA, ARS Workshop Poultry Food Assess Risk
Model (Poultry FARM)
2
What is Poultry FARM?
A quantitative microbial risk assessment model
(QMRA) for Listeria, Salmonella, Campylobacter
and chicken meat.
3
What does Poultry FARM do?
Predicts the public health impact of chicken meat
destined for specific distribution channels and
consumer populations
4
What is the goal of Poultry FARM?
Packaging
Unsafe
Cooking
Safe
Distribution Channel
Consumption
To maximize the public health benefit of chicken
by ensuring its safety consumption
5
What is QMRA?
  • Hazard Identification
  • Exposure Assessment
  • Hazard Characterization
  • Risk Characterization

Holistic
How many people will get sick and die?
6
Hazard Identification
Initial distribution of pathogens among servings
Campylobacter (Cj)
Salmonella (Se)
Listeria (Lm)
7
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
Cj levels are higher than Lm and Se, which are
similar
Waldroup et al. (1992) J. Appl. Poultry Res.
1226-234.
8
Simon Sez!!!
Relative differences among pathogens are
simulated in Poultry FARM
9
Exposure Assessment
Plant Packaging Contamination
Distribution Abuse Growth/Death
Preparation Contamination Transfer
Cooling Abuse Growth/Death
Cooking Under-cooking Survival
Predicts how pathogen levels change from
farm-to-table
Serving Contamination Transfer
Table Consumption Dose-response
Key Unit Operation _ Human Action _ Pathogen
Event
10
Physiological Differences
Se grows, whereas Cj dies at ambient temperatures
Burnette and Yoon (2004) Food Sci. Biotechnol.
13796-800
11
Simon Sez!!!
Predictive models can be developed for each
pathogen event
12
Hazard Characterization
No response
Infection
Mild illness
Illness
Determines whether or not an illness occurs
13
Simon Sez!!!
Depends on the outcome of the interaction between
the pathogen, food and host
Disease Triangle
Pathogen
Host
Food
14
Risk Characterization
Illness
Hospital
Death
Determines the severity of illness
15
Simon Sez!!!
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/v
ol5no5/mead.htm
16
Severity Assessment
Illness (C1)
Hospital (C2)
Death (C3)
Severity C1 2C2 10C3
Cases
Weight factor
17
Simon Sez!!!
Foodborne illness is a random event
18
Simon Sez!!!
Monte Carlo simulation is a good method for
modeling foodborne illness
19
What is Monte Carlo Simulation?
A B C
20
Simon Sez!!!
Foodborne illness is a rare event
21
Rare Events' Modeling
Pathogen Number
Incidence
Extent
Discrete 1 0 0 0
Iteration 1 2 3 10,000
Pert (0,2,4) 1.8 1.2 0.2 2.2
Antilog 63.1 0 0 0
Round 63 0 0 0
IF(RiskDiscrete0,0,RiskPert)
Poultry FARM simulates pathogen-free servings
22
Poultry FARM Tour
23
Scenario Analysis
What IF?
24
Which is higher risk?
Lot A
Lot B
25
Scenario Settings
Input Pathogen A B C D E
Q1 Q2 Q3 Lm Se Cj 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.
1226-234.
26
Scenario Settings
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
27
Simulation Settings
QMRA Model Poultry FARM 3.0 Iterations 10,000
servings Simulations 100 Sampling Latin
Hypercube Random Number Generator Seed Random
Selection
28
Simon Sez!!!
Each random number generator seed produces a
unique outcome of the scenario
29
Listeria monocytogenes
A B C D E
Lm Se Cj 0 16.7 78.1 0 28.1 53.1 29.2 47.9 90.6 18.8 24 100 0 9.4 82.3
30
Salmonella enterica
A B C D E
Lm Se Cj 0 16.7 78.1 0 28.1 53.1 29.2 47.9 90.6 18.8 24 100 0 9.4 82.3
31
Campylobacter jejuni
A B C D E
Lm Se Cj 0 16.7 78.1 0 28.1 53.1 29.2 47.9 90.6 18.8 24 100 0 9.4 82.3
32
Lm Se Cj
A B C D E
Lm Se Cj 0 16.7 78.1 0 28.1 53.1 29.2 47.9 90.6 18.8 24 100 0 9.4 82.3
33
Simon Sez!!!
It is important to consider multiple pathogens
and post-process risk factors when assessing food
safety
34
The End
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