Quantitative Microbial Risk Assessment QMRA Salmonella spp in

Quantitative Microbial Risk Assessment (QMRA) Salmonella spp. in broiler chicken Suphachai Nuanualsuwan DVM, MPVM, Ph. D 1

Significance and Rationale • Public Health • Bacterial foodborne disease • Food safety • Food for Export • World trade organization (WTO) • Trade barrier • Salmonella control Suphachai 2 DVM, MPVM, Ph. D

Risk Analysis Risk communication Risk assessment Risk management Suphachai 3 DVM, MPVM, Ph. D

CAC's Risk Assessment 1. Hazard Identification 2. Hazard Characterization 3. Exposure Assessment 4. Risk Characterization Suphachai 4 DVM, MPVM, Ph. D

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. Suphachai 5 DVM, MPVM, Ph. D

Hazard in foods 1. Physical Hazard 2. Chemical Hazard 3. Biological Hazard Suphachai 6 DVM, MPVM, Ph. D

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 7 DVM, MPVM, Ph. D

Hazard Identification Introduction • Salmonella spp. • Gram negative bacterium • Family : Enterobacteriaceae • Rod shape • Non-spore former • Human and animals are primary habitat 8

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 9

Hazard Identification • Factors affecting growth and survival • Temperature • p. H • Water activities : a. W • Atmosphere : O 2 • Predictive microbiology Suphachai 10 DVM, MPVM, Ph. D

Hazard Identification • Factors affecting growth and survival 1. Temperature • Optimal range 30 -45 o. C (mesophile) • Tmax 54 o. C • D 57. 2 (a. W 0. 9) = 40 -55 min • Mechanism of inactivation above Tmax • Protein esp. enzymes • Lipid esp. cell membrane Suphachai 11 DVM, MPVM, Ph. D

Hazard Identification • Factors affecting growth and survival 2. p. H • Optimum 6. 5 -7. 5 • Growth 4. 5 -9. 5 • Acid tolerance response (ATR) • Mechanism of inactivation • energy use up to maintain p. H Suphachai 12 DVM, MPVM, Ph. D

Hazard Identification • Factors affecting growth and survival 3. Water activities (a. W) • moisture vs. water activity • Optimum > 0. 93 • Compatible solutes : glycine betaine, choline, proline and glutamate • Not inactivate bacterium Suphachai 13 DVM, MPVM, Ph. D

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 Suphachai 14 DVM, MPVM, Ph. D

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%) Suphachai 15 DVM, MPVM, Ph. D

Pathogenesis of Salmonella 16

Hazard Identification • Symptoms and illness • Enteric Fever : S. Typhi & S. Paratyphi • Gastroenteritis Suphachai 17 DVM, MPVM, Ph. D

CAC's Risk Assessment 1. Hazard Identification 2. Hazard Characterization 3. Exposure Assessment 4. Risk Characterization Suphachai 18 DVM, MPVM, Ph. D

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. 19

Hazard Characterization • Major related factors • Pathogenesis • Modeling concepts • Dose-response models available • Epidemiological data of Salmonella Suphachai 20 DVM, MPVM, Ph. D

Hazard Characterization • Major related factors • Microbiological factor • Host factor • Food matrix factor Suphachai 21 DVM, MPVM, Ph. D

Fundamental epidemiological concept Agent Disease Host Environment Suphachai 22 DVM, MPVM, Ph. D

Hazard Characterization • Major related factors • Microbiological • Survival in environment and host • Factors affecting growth and survival • Virulence factors Suphachai 23 DVM, MPVM, Ph. D

Hazard Characterization • Major related factors • Host • Demographic and socioeconomic factors • Genetic factors • Health and Immunity factors Suphachai 24 DVM, MPVM, Ph. D

Hazard Characterization • Major related factors • Food Matrix • Food composition • Food condition • Consumption • Micro-environment Suphachai 25 DVM, MPVM, Ph. D

Hazard Characterization • Pathogenesis • Exposure • Infection • Illness • Recovery, sequel, or death Suphachai 26 DVM, MPVM, Ph. D

Hazard Characterization Pathogenesis Recovery Exposure Infection Illness Chronic Death Suphachai 27 DVM, MPVM, Ph. D

Hazard Characterization • Dose‑response models • Human-feeding trial • US. Risk assessment of S. Enteritidis • Health Canada S. Enteritidis • Epidemiological data worldwide Suphachai 28 DVM, MPVM, Ph. D

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 29

Outbreak of Salmonella Enteritidis & Salmonella spp. 30

Comparison of Dose-response curves Outbreak curve = 0. 1324 = 51. 45 31

Hazard Characterization • Using epidemiological data • Beta-Possion model • = 0. 1324 (0. 0763 - 0. 2274) • = 51. 45 (38. 49 - 57. 96) P(D) = Dose + 1 ] –α - 1 ------ ] 32

CAC's Risk Assessment 1. Hazard Identification 2. Hazard Characterization P(D) = Dose + 1 ] - 1 ------] –α Suphachai 33 DVM, MPVM, Ph. D

CAC's Risk Assessment 1. Hazard Identification 2. Hazard Characterization 3. Exposure Assessment 4. Risk Characterization Suphachai 34 DVM, MPVM, Ph. D

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. Suphachai 35 DVM, MPVM, Ph. D

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 Suphachai 36 DVM, MPVM, Ph. D

Exposure assessment • Probability of Exposure to Salmonella (PE) • Ingested dose of Salmonella (D) Suphachai 37 DVM, MPVM, Ph. D

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 Suphachai 38 DVM, MPVM, Ph. D

Food chain of poultry production Parent stock P Prevalence P P Broiler C Concentration C Slaughter house C Retail C P Consumption PE & Dose Suphachai 39 DVM, MPVM, Ph. D

Exposure assessment 1. Probability of exposure • Probability (or Prevalence) of Salmonella in chicken • Concentration of Salmonella in chicken • Mass of chicken consumed Suphachai 40 DVM, MPVM, Ph. D

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) Suphachai 41 DVM, MPVM, Ph. D

Exposure assessment How to get these data • Published sources • Experiment • Predictive microbiology Suphachai 42 DVM, MPVM, Ph. D

Exposure assessment Quality of Data • Lack of knowledge brings about estimation • Total uncertainty • Uncertainty (inadequate sample size) • Variability (natural phenomena) Suphachai 43 DVM, MPVM, Ph. D

Exposure assessment • Probability distribution • Point estimate • Interval estimate Deterministic Probabilistic 44

Exposure assessment 1. Probability of exposure (PE) PE = P *(1 -e -m * 10 C ) = 0. 3987 PE = Probability of Exposure P = Prevalence in chicken C = Concentration in chicken (Log. MPN/g) m = Mass of chicken ingested (g) Suphachai 45 DVM, MPVM, Ph. D

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 Suphachai 46 DVM, MPVM, Ph. D

Exposure assessment 1. Probability of exposure (PE) 47

CAC's Risk Assessment 1. Hazard Identification 2. Hazard Characterization 3. Exposure Assessment PE and Dose Suphachai 48 DVM, MPVM, Ph. D

Hazard Characterization Probability of illness from dose = P(D) c Dose = 10 x m 5 Dose P(D) = 1 - [ + ------ ] β - - = 1. 62 x 10 Suphachai 49 DVM, MPVM, Ph. D

Hazard Characterization Probability of illness from dose = P(D) 50

CAC's Risk Assessment 1. Hazard Identification 2. Hazard Characterization P(D) = 3. Exposure Assessment Dose + 1 ] - 1 ------] –α PE = P *(1 -e -m * 10 C ) Suphachai 51 DVM, MPVM, Ph. D

CAC's Risk Assessment 1. Hazard Identification 2. Hazard Characterization 3. Exposure Assessment 4. Risk Characterization Suphachai 52 DVM, MPVM, Ph. D

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. 53

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 2 nd and 3 rd 2 nd Hazard Characterization : P(D) 3 rd Exposure assessment : PE , D 54

Risk characterization • Risk estimate Pi = PE x P(D) Pi = 0. 4091 x 1. 62 x 10 -5 = 6. 63 x 10 -6 Suphachai 55 DVM, MPVM, Ph. D

CAC's Risk Assessment 1. Hazard Identification 2. Hazard Characterization 3. Exposure Assessment 4. Risk Characterization Pi = PE x P(D) Suphachai 56 DVM, MPVM, Ph. D

Risk characterization • Output from Monte Carlo Simulation • Mean of Risk estimate = 4. 57 x 10 -5 57

Sensitivity Analysis for Risk Management Suphachai 58 DVM, MPVM, Ph. D

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 Suphachai 59 DVM, MPVM, Ph. D

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