quantitative microbial risk assessment (qmra) salmonella spp. in broiler chicken

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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


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1. Hazard Identification

2.Hazard Characterization 3.Exposure Assessment

4. Risk Characterization

CAC's Risk Assessment


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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.




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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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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


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• Factors affecting growth and survival

• Temperature

• pH

• Water activities : aW

• Atmosphere : O2

• Predictive microbiology



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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



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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



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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



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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



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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%)



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Pathogenesis of Salmonella

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• Symptoms and illness

• Enteric Fever : S.Typhi & S.Paratyphi

• Gastroenteritis



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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



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• Major related factors• Microbiological factor• Host factor• Food matrix factor



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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



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• Major related factors• Host

• Demographic and

socioeconomic factors• Genetic factors• Health and Immunity factors



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• Major related factors• Food Matrix

• Food composition• Food condition• Consumption• Micro-environment



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• Pathogenesis• Exposure• Infection• Illness• Recovery, sequel, or death



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Exposure Infection Illness Chronic

Death

Pathogenesis


Recovery


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• ‑ Dose response models

• Human-feeding trial

• US. Risk assessment of S. Enteritidis

• Health Canada S. Enteritidis

• Epidemiological data worldwide



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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


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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)


DoseP(D) = - 1 1[ +------------ ] –α

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2.Hazard Characterization


DoseP(D) = - 1 1[ +------------ ] –α


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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


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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


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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


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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


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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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PE and Dose


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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



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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)


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DoseP(D) = - 1 1[ +------------ ] –α

C -m * 10 PE = P *(1-e )


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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


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• Risk estimate

Pi = PE x P(D)


Pi = 0.4091 x 1.62 x10-5

= 6.63 x 10-6


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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


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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

quantitative microbial risk assessment (qmra) salmonella spp. in broiler chicken

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