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Quantitative Microbial Risk Assessment (QMRA)
Salmonella spp. in broiler chicken
Suphachai Nuanualsuw
anDVM, MPVM,
PhD
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Significance and Rationale• Public Health
• Bacterial foodborne disease• Food safety
• Food for Export•World trade organization (WTO)• Trade barrier
• Salmonella control
Suphachai DVM, MPVM, PhD
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Risk Analysis
Risk communication
Risk assessment
Riskmanagement
Suphachai DVM, MPVM, PhD
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1. Hazard Identification
2.Hazard Characterization 3.Exposure Assessment
4. Risk Characterization
CAC's Risk Assessment
Suphachai DVM, MPVM, PhD
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The identification of biological, chemical, an
d physical agents capable of causing advers
e health effects and which may be present in
a particular food or group of foods.
1. Hazard Identification
CAC's Risk Assessment
Suphachai DVM, MPVM, PhD
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Hazard in foods
1. Physical Hazard
2. Chemical Hazard
3. Biological Hazard
Suphachai DVM, MPVM, PhD
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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
Suphachai DVM, MPVM, PhD
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Introduction• Salmonella spp.• Gram negative bacterium• Family : Enterobacteriaceae
• Rod shape • Non-spore former• Human and animals are primary habitat
Hazard Identification
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• 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
Hazard Identification
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• Factors affecting growth and survival
• Temperature
• pH
• Water activities : aW
• Atmosphere : O2
• Predictive microbiology
Hazard Identification
Suphachai DVM, MPVM, PhD
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• Factors affecting growth and survival1. 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
Hazard Identification
Suphachai DVM, MPVM, PhD
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• Factors affecting growth and survival2. pH
• Optimum 6.5-7.5• Growth 4.5-9.5• Acid tolerance response (ATR)• Mechanism of inactivation
• energy use up to maintain pH
Hazard Identification
Suphachai DVM, MPVM, PhD
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• Factors affecting growth and survival3. Water activities (aW)
• moisture vs. water activity• Optimum > 0.93• Compatible solutes : glycine betaine, choline, proline and glutamate• Not inactivate bacterium
Hazard Identification
Suphachai DVM, MPVM, PhD
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• Factors affecting growth and survival4. Atmosphere
• Facultative anaerobe• Respiration via electron transport system (ETS)• Fermentation earns less energy than respiration• Salmonella do both
Hazard Identification
Suphachai DVM, MPVM, PhD
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• 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%)
Hazard Identification
Suphachai DVM, MPVM, PhD
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Pathogenesis of Salmonella
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• Symptoms and illness
• Enteric Fever : S.Typhi & S.Paratyphi
• Gastroenteritis
Hazard Identification
Suphachai DVM, MPVM, PhD
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1. Hazard Identification
2.Hazard Characterization 3.Exposure Assessment
4. Risk Characterization
CAC's Risk Assessment
Suphachai DVM, MPVM, PhD
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The qualitative and/or quantitative evaluation
of the nature of the adverse health effects ass
ociated with the hazard. For the purpose of Mi
crobiological Risk Assessment the concerns rel
ate to microorganisms and/or their toxins.
Hazard Characterization
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• Major related factors
• Pathogenesis
• Modeling concepts
• Dose-response models available
• Epidemiological data of Salmonella
Hazard Characterization
Suphachai DVM, MPVM, PhD
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• Major related factors• Microbiological factor• Host factor• Food matrix factor
Hazard Characterization
Suphachai DVM, MPVM, PhD
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Agent
Disease
Host Environment
Fundamental epidemiological concept
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• Major related factors• Microbiological
• Survival in environment and host• Factors affecting growth and survival• Virulence factors
Hazard Characterization
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• Major related factors• Host
• Demographic and
socioeconomic factors• Genetic factors• Health and Immunity factors
Hazard Characterization
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• Major related factors• Food Matrix
• Food composition• Food condition• Consumption• Micro-environment
Hazard Characterization
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• Pathogenesis• Exposure• Infection• Illness• Recovery, sequel, or death
Hazard Characterization
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Exposure Infection Illness Chronic
Death
Pathogenesis
Hazard Characterization
Recovery
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• ‑ Dose response models
• Human-feeding trial
• US. Risk assessment of S. Enteritidis
• Health Canada S. Enteritidis
• Epidemiological data worldwide
Hazard Characterization
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• 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
Hazard Characterization
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Outbreak of Salmonella Enteritidis & Salmonella spp.
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Comparison of Dose-response curvesOutbreak curve = 0.1324 = 51.45
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• Using epidemiological data• Beta-Possion model• = 0.1324 (0.0763 - 0.2274)• = 51.45 (38.49 - 57.96)
Hazard Characterization
DoseP(D) = - 1 1[ +------------ ] –α
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1. Hazard Identification
2.Hazard Characterization
CAC's Risk Assessment
DoseP(D) = - 1 1[ +------------ ] –α
Suphachai DVM, MPVM, PhD
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1. Hazard Identification
2.Hazard Characterization 3.Exposure Assessment
4. Risk Characterization
CAC's Risk Assessment
Suphachai DVM, MPVM, PhD
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The qualitative and/or quantitative evaluatio
n of the likely intake of biological, chemical,
and physical agents via food as well as expo
sures from other sources if relevant.
Exposure assessment
Suphachai DVM, MPVM, PhD
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• 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
Exposure assessment
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• Probability of Exposure to Salmonella
(PE)
• Ingested dose of Salmonella (D)
Exposure assessment
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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
Exposure assessment
Suphachai DVM, MPVM, PhD
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Food chain of poultry productionParent stock
Broiler
Slaughter house
Retail
ConsumptionPE & Dose
P
P
P
P
C
C
C
C
Prevalence Concentration
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1. Probability of exposure• Probability (or Prevalence) of Salmonella
in chicken• Concentration of Salmonella in chicken• Mass of chicken consumed
Exposure assessment
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2. Ingested dose of Salmonella (D)
• Concentration of Salmonella in chicken
• Mass of chicken consumed
• Dose = Concentration x Consumption
(CFU) (CFU/g) x (g)
Exposure assessment
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How to get these data• Published sources• Experiment• Predictive microbiology
Exposure assessment
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Quality of Data
• Lack of knowledge brings about
estimation
• Total uncertainty
• Uncertainty (inadequate sample size)
• Variability (natural phenomena)
Exposure assessment
Suphachai DVM, MPVM, PhD
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• Probability distribution• Point estimate• Interval estimate
Deterministic Probabilistic
Exposure assessment
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1. Probability of exposure (PE) C -m * 10 PE = P *(1-e ) = 0.3987
PE = Probability of ExposureP = Prevalence in chicken C = Concentration in chicken (LogMPN/g)
m = Mass of chicken ingested (g)
Exposure assessment
Suphachai DVM, MPVM, PhD
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Model and Data analysisMonte Carlo technique
• combine distributions in models• considering both uncertainty &
variablity
Simulation• do numerous iterations• converge to a more stable value Suphachai
DVM, MPVM, PhD
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Distribution for PE/D26
0
2
4
6
8
10
12
Mean=0.3987346
0.25 0.3375 0.425 0.5125 0.6
Mean=0.3987346
1. Probability of exposure (PE)Exposure assessment
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1. Hazard Identification
2.Hazard Characterization 3.Exposure Assessment
CAC's Risk Assessment
PE and Dose
Suphachai DVM, MPVM, PhD
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Probability of illness from dose = P(D)
Dose - -5P(D) = 1 - [ -------+ ---- ] = 1.62 x 10
β
c Dose = 10 x m
Hazard Characterization
Suphachai DVM, MPVM, PhD
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Distribution for Pi/C41
0
20
40
60
80
100
120
140
160
Mean=1.14971E-04
0 0.01 0.02 0.03 0.04
Mean=1.14971E-04
Probability of illness from dose = P(D)
Hazard Characterization
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1. Hazard Identification
2.Hazard Characterization 3.Exposure Assessment
CAC's Risk Assessment
DoseP(D) = - 1 1[ +------------ ] –α
C -m * 10 PE = P *(1-e )
Suphachai DVM, MPVM, PhD
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1. Hazard Identification
2.Hazard Characterization 3.Exposure Assessment
4. Risk Characterization
CAC's Risk Assessment
Suphachai DVM, MPVM, PhD
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The process of determining the qualitative and
/or quantitative estimation, including attendan
t uncertainties, of the probability of occurrence
and severity of known or potential adverse hea
lth effects in a given population based on hazar
d identification, hazard characterization and ex
posure assessment.
Risk characterization
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• 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
Risk characterization
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• Risk estimate
Pi = PE x P(D)
Risk characterization
Pi = 0.4091 x 1.62 x10-5
= 6.63 x 10-6
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1. Hazard Identification
2.Hazard Characterization 3.Exposure Assessment
4. Risk Characterization
CAC's Risk Assessment
Pi = PE x P(D)Suphachai DVM, MPVM, PhD
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• Output from Monte Carlo Simulation• Mean of Risk estimate = 4.57 x10-5
Distribution for Risk estimate/C43
Values in 10^-3
0
50
100
150
200
250
300
350
400
Mean=4.573928E-05
0 5 10 15 20
Mean=4.573928E-05
Risk characterization
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Regression Sensitivity for Risk estimate/C3
0.081
-0.017
0.011
0.01
-1 -0.5 0 0.5 1
D/J12
InactivationTime/K12
Consumption/B14
BConcDist/G10
Std b Coefficients
Sensitivity Analysis for Risk Management
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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 healthSuphachai DVM, MPVM, PhD
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