USDA, ARS Workshop Poultry 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 Cooking Safe 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 APlant BPlant CPlant DPlant E Lm Se Cj Waldroup et al. (1992) J. Appl. Poultry Res. 1: Cj levels are higher than Lm and Se, which are similar

Relative differences among pathogens are simulated in Poultry FARM

Plant Packaging Contamination Distribution Abuse Growth/Death Preparation Contamination Transfer Cooling Abuse Growth/Death Cooking Under-cooking Survival Serving Contamination Transfer Table Consumption 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:

Predictive models can be developed for each pathogen event

No response InfectionMild illness Illness Determines whether or not an illness occurs

Depends on the outcome of the interaction between the pathogen, food and host Disease Triangle PathogenHost Food

IllnessHospitalDeath Determines the severity of illness

There are important differences in severity among pathogens OutcomeListeriaSalmonellaCampylobacter Hospital92.2%22.1%10.2% Death20.0%0.78%0.1% Mead et al. (1999)

Illness (C 1 ) Hospital (C 2 ) Death (C 3 ) Severity = C 1 + 2C C 3 Weight factor Cases

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 : 10,000 Discrete 1 0 : 0 Pert (0,2,4) : 2.2 Antilog : 0 Round 63 0 : 0 IncidenceExtent Pathogen Number =IF(RiskDiscrete=0,0,RiskPert) Poultry FARM simulates pathogen-free servings

Poultry FARM Tour

Lot ALot B Which is higher risk?

InputPathogenABCDE 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. 1:

QuestionABCDE Q45% Q510% Q615% 5%15%10% Q720% 10%20%10% Q825% 15%25%15% Q920%10% 30%20% Q1035%10%15%35% Q1125%5%15%25%15% Q1220%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

Listeria monocytogenesABCDELmSeCj0%16.7%78.1%0%28.1%53.1%29.2%47.9%90.6%18.8%24%100%0%9.4%82.3%

Salmonella entericaABCDELmSeCj0%16.7%78.1%0%28.1%53.1%29.2%47.9%90.6%18.8%24%100%0%9.4%82.3%

Campylobacter jejuniABCDELmSeCj0%16.7%78.1%0%28.1%53.1%29.2%47.9%90.6%18.8%24%100%0%9.4%82.3%

Lm + Se + CjABCDELmSeCj0%16.7%78.1%0%28.1%53.1%29.2%47.9%90.6%18.8%24%100%0%9.4%82.3%

It is important to consider multiple pathogens and post-process risk factors when assessing food safety