Transcript Slide 1

Quantitative Microbial Risk Assessment
(QMRA)
Salmonella spp. in broiler chicken
Suphachai Nuanualsuwan
DVM, MPVM, PhD1
Significance and Rationale
• Public Health
• Bacterial foodborne disease
• Food safety
• Food for Export
•World trade organization (WTO)
• Trade barrier
• Salmonella control
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DVM, MPVM, PhD
Risk Analysis
Risk
communication
Risk
assessment
Risk
management
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DVM, MPVM, PhD
CAC's Risk Assessment
1. Hazard Identification
2.Hazard Characterization
3.Exposure Assessment
4. Risk Characterization
Suphachai 4
DVM, MPVM, PhD
CAC's Risk Assessment
1. Hazard Identification
The identification of biological,
chemical, and physical agents capable of
causing adverse health effects and which may
be present in a particular food or group of
foods.
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Hazard in foods
1. Physical Hazard
2. Chemical Hazard
3. Biological Hazard
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Hazard Identification : Salmonella spp.
• Introduction
• Taxonomy and Nomenclature
• Factors affecting growth and survival
• Geographical distribution and transmission
• Human incidence
• Symptoms and illness
• Foodborne illness
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DVM, MPVM, PhD
Hazard Identification
Introduction
• Salmonella spp.
• Gram negative bacterium
• Family : Enterobacteriaceae
• Rod shape
• Non-spore former
• Human and animals are primary habitat
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Hazard Identification
• Taxonomy and Nomenclature
• WHO and Collaborating Center of Reference &
Research on Salmonella (Institute Pasteur, Paris)
• Salmonella enterica (2443) Salmonella bongori (20)
• Salmonella enterica supsp. enterica serovar. (1454)
• Salmonella enterica supsp. enterica serovar.
typhimurium
Salmonella Typhimurium or S.Typhimurium
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Hazard Identification
• Factors affecting growth and survival
• Temperature
• pH
• Water activities : aW
• Atmosphere : O2
• Predictive microbiology
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DVM, MPVM, PhD
Hazard Identification
• Factors affecting growth and survival
1. Temperature
• Optimal range 30-45oC (mesophile)
• Tmax 54oC
• D57.2 (aW 0.9) = 40-55 min
• Mechanism of inactivation above Tmax
• Protein esp. enzymes
• Lipid esp. cell membrane
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Hazard Identification
• Factors affecting growth and survival
2. pH
• Optimum 6.5-7.5
• Growth 4.5-9.5
• Acid tolerance response (ATR)
• Mechanism of inactivation
• energy use up to maintain pH
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Hazard Identification
• Factors affecting growth and survival
3. Water activities (aW)
• moisture vs. water activity
• Optimum > 0.93
• Compatible solutes : glycine betaine,
choline, proline and glutamate
• Not inactivate bacterium
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Hazard Identification
• Factors affecting growth and survival
4. Atmosphere
• Facultative anaerobe
• Respiration via electron transport system
(ETS)
• Fermentation earns less energy than
respiration
• Salmonella do both
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Hazard Identification
• Geographical distribution and transmission
• Worldwide
• Human animal and environment
• Human incidence
• age group < 5 years and 35 years
• S.Enteritidis (12 %) S.Weltevreden (8%)
• S.Typhimurium (3%)
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Pathogenesis of Salmonella
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Hazard Identification
• Symptoms and illness
• Enteric Fever : S.Typhi & S.Paratyphi
• Gastroenteritis
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DVM, MPVM, PhD
CAC's Risk Assessment
1. Hazard Identification
2.Hazard Characterization
3.Exposure Assessment
4. Risk Characterization
Suphachai 18
DVM, MPVM, PhD
Hazard Characterization
The qualitative and/or quantitative
evaluation of the nature of the adverse health
effects associated with the hazard. For the
purpose of Microbiological Risk Assessment
the concerns relate to microorganisms and/or
their toxins.
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Hazard Characterization
• Major related factors
• Pathogenesis
• Modeling concepts
• Dose-response models available
• Epidemiological data of Salmonella
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DVM, MPVM, PhD
Hazard Characterization
• Major related factors
• Microbiological factor
• Host factor
• Food matrix factor
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Fundamental epidemiological concept
Agent
Disease
Host
Environment
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DVM, MPVM, PhD
Hazard Characterization
• Major related factors
• Microbiological
• Survival in environment and host
• Factors affecting growth and survival
• Virulence factors
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DVM, MPVM, PhD
Hazard Characterization
• Major related factors
• Host
• Demographic and socioeconomic
factors
• Genetic factors
• Health and Immunity factors
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DVM, MPVM, PhD
Hazard Characterization
• Major related factors
• Food Matrix
• Food composition
• Food condition
• Consumption
• Micro-environment
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Hazard Characterization
• Pathogenesis
• Exposure
• Infection
• Illness
• Recovery, sequel, or death
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Hazard Characterization
Pathogenesis
Recovery
Exposure
Infection
Illness
Chronic
Death
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Hazard Characterization
• Dose-response models
• Human-feeding trial
• US. Risk assessment of S. Enteritidis
• Health Canada S. Enteritidis
• Epidemiological data worldwide
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Hazard Characterization
• Epidemiological data
• Similar to the real foodborne outbreaks
• water, cheese, ice cream, ham, beef, salad,
soup, chicken etc.
• 33 outbreaks : Japan (9), North America (11)
• 7 serovar. <= S.Enteritidis (12),
S.Typhimurium (3)
• Beta-Poisson
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Outbreak of Salmonella Enteritidis & Salmonella spp.
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Comparison of Dose-response curves
Outbreak curve  = 0.1324  = 51.45
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Hazard Characterization
• Using epidemiological data
• Beta-Possion model
•  = 0.1324 (0.0763 - 0.2274)
•  = 51.45 (38.49 - 57.96)
P(D) =
Dose
1 - [ 1 + ------------ ] –α

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CAC's Risk Assessment
1. Hazard Identification
2.Hazard Characterization
P(D) =
Dose
1 - [ 1 + ------------ ] –α

Suphachai 33
DVM, MPVM, PhD
CAC's Risk Assessment
1. Hazard Identification
2.Hazard Characterization
3.Exposure Assessment
4. Risk Characterization
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DVM, MPVM, PhD
Exposure assessment
The qualitative and/or quantitative
evaluation of the likely intake of biological,
chemical, and physical agents via food as well
as exposures from other sources if relevant.
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DVM, MPVM, PhD
Exposure assessment
• Estimation of how likely it is that and
individual or a population will be exposed to
a microbial hazard and what numbers of
the microorganism are likely to be ingested
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Exposure assessment
• Probability of Exposure to Salmonella (PE)
• Ingested dose of Salmonella (D)
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Exposure assessment
Process Risk Model (PRM)
• Mathematical model predicting the probability
of an adverse effet as a function of multiple
process parameters
• Risk is determined by the process variables
• Mathematical model describes microbial
changes
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Food chain of poultry production
Parent stock
P
Prevalence
P
P
C
Broiler
Concentration
C
Slaughter house
C
Retail
C
P
Consumption
PE & Dose
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Exposure assessment
1. Probability of exposure
• Probability (or Prevalence) of Salmonella
in chicken
• Concentration of Salmonella in chicken
• Mass of chicken consumed
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DVM, MPVM, PhD
Exposure assessment
2. Ingested dose of Salmonella (D)
• Concentration of Salmonella in chicken
• Mass of chicken consumed
• Dose = Concentration x Consumption
(CFU)
(CFU/g)
x
(g)
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Exposure assessment
How to get these data
• Published sources
• Experiment
• Predictive microbiology
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Exposure assessment
Quality of Data
• Lack of knowledge brings about estimation
• Total uncertainty
• Uncertainty (inadequate sample size)
• Variability (natural phenomena)
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Exposure assessment
• Probability distribution
• Point estimate
• Interval estimate
Deterministic
Probabilistic
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Exposure assessment
1. Probability of exposure (PE)
C
-m * 10
PE = P *(1-e
) = 0.3987
PE
= Probability of Exposure
P
= Prevalence in chicken
C
= Concentration in chicken (LogMPN/g)
m
= Mass of chicken ingested (g)
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DVM, MPVM, PhD
Model and Data analysis
Monte Carlo technique
• combine distributions in models
• considering both uncertainty & variablity
Simulation
• do numerous iterations
• converge to a more stable value
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Exposure assessment
1. Probability of exposure (PE)
Distribution for PE/D26
12
Mean=0.3987346
10
8
6
4
2
0
0 .2 5
0 .3 3 7 5
0 .4 2 5
0 .5 1 2 5
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0 .6
CAC's Risk Assessment
1. Hazard Identification
2.Hazard Characterization
3.Exposure Assessment
PE and Dose
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DVM, MPVM, PhD
Hazard Characterization
Probability of illness from dose = P(D)
c
Dose = 10 x m
Dose
P(D) = 1 - [ + ----------- ]
β
-
= 1.62 x 10
-5
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DVM, MPVM, PhD
Hazard Characterization
Probability of illness from dose = P(D)
Distribution for Pi/C41
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Mean=1. 14971E-04
140
120
100
80
60
40
20
0
0
0 .0 1
0 .0 2
0 .0 3
0 .0 4
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CAC's Risk Assessment
1. Hazard Identification
2.Hazard Characterization
P(D) =
Dose
1 - [ 1 + -----------
] –α
3.Exposure Assessment
C
-m * 10
PE = P *(1-e
)
Suphachai 51
DVM, MPVM, PhD
CAC's Risk Assessment
1. Hazard Identification
2.Hazard Characterization
3.Exposure Assessment
4. Risk Characterization
Suphachai 52
DVM, MPVM, PhD
Risk characterization
The process of determining the qualitative
and/or quantitative estimation, including
attendant uncertainties, of the probability of
occurrence and severity of known or potential
adverse health effects in a given population
based on hazard identification, hazard
characterization and exposure assessment.
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Risk characterization
•
Final stage of risk assessment
•
Overall evaluation of the likelihood that the
population will suffer adverse effects as a
result of the hazard; P(D)
•
Integrate steps 2nd and 3rd
2nd Hazard Characterization : P(D)
3rd Exposure assessment
: PE , D
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Risk characterization
• Risk estimate
Pi = PE x P(D)
Pi = 0.4091 x 1.62 x10-5
= 6.63 x 10-6
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DVM, MPVM, PhD
CAC's Risk Assessment
1. Hazard Identification
2.Hazard Characterization
3.Exposure Assessment
4. Risk Characterization
Pi = PE x P(D)
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DVM, MPVM, PhD
Risk characterization
• Output from Monte Carlo Simulation
• Mean of Risk estimate = 4.57 x10-5
Distribution for Risk estimate/C43
400
Mean=4.573928E-05
350
300
250
200
150
100
50
0
0
5
10
Values in 10^-3
15
20
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Sensitivity Analysis for Risk Management
Regression Sensitivity for Risk estimate/C3
0.081
D/J12
-0.017
InactivationTime/K12
Consumption/B14
0.011
0.01
BConcDist/G10
-1
-0.5
0
0.5
1
Std b Coefficients
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Applications
• Likelihood of population or individual to suffer
from adverse effect by Salmonella
• Risk factors contributing exposure, risk estimate
• Suggest control measures for risk management
• Increase food export
• Enhance public health
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