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