1. 1 Quantitative Microbial Risk Assessment (QMRA) Salmonella spp. in broiler chicken Suphachai Nuanualsuwa n DVM, MPVM, PhD

2. 2 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

3. 3 Risk Analysis Risk communication Risk assessment Risk management Suphachai DVM, MPVM, PhD

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

5. 5 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. 1. Hazard Identification CAC's Risk Assessment Suphachai DVM, MPVM, PhD

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

7. 7 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

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

9. 9 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

10. 10 Factors affecting growth and survival Temperature pH Water activities : a W Atmosphere : O 2 Predictive microbiology Hazard Identification Suphachai DVM, MPVM, PhD

11. 11 Factors affecting growth and survival 1. Temperature Optimal range 30-45 o C (mesophile) T max 54 o C D 57.2 (a W 0.9) = 40-55 min Mechanism of inactivation above T max Protein esp. enzymes Lipid esp. cell membrane Hazard Identification Suphachai DVM, MPVM, PhD

12. 12 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 Hazard Identification Suphachai DVM, MPVM, PhD

13. 13 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 Hazard Identification Suphachai DVM, MPVM, PhD

14. 14 Factors affecting growth and survival 4. Atmosphere Facultative anaerobe Respiration via electron transport system (ETS) Fermentation earns less energy than respiration Salmonella do both Hazard Identification Suphachai DVM, MPVM, PhD

15. 15 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

16. 16 Pathogenesis of Salmonella

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

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

19. 19 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. Hazard Characterization

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

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

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

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

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

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

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

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

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

29. 29 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

30. 30 Outbreak of Salmonella Enteritidis & Salmonella spp.

31. 31 Comparison of Dose-response curves Outbreak curve  = 0.1324  = 51.45

32. 32 Using epidemiological data Beta-Possion model  = 0.1324 (0.0763 - 0.2274)  = 51.45 (38.49 - 57.96) Hazard Characterization Dose P(D) = 1 - [ 1 + ------------ ] – α 

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

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

35. 35 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. Exposure assessment Suphachai DVM, MPVM, PhD

36. 36 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 Suphachai DVM, MPVM, PhD

37. 37 Probability of Exposure to Salmonella (P E ) Ingested dose of Salmonella (D) Exposure assessment Suphachai DVM, MPVM, PhD

38. 38 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

39. 39 Food chain of poultry production Parent stock Broiler Slaughter house Retail Consumption P E & Dose PPPPPPPP CCCCCCCC PrevalenceConcentration Suphachai DVM, MPVM, PhD

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

41. 41 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 Suphachai DVM, MPVM, PhD

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

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

44. 44 Probability distribution Point estimate Interval estimate DeterministicProbabilistic Exposure assessment

45. 45 1. Probability of exposure (P E ) C -m * 10 P E = P *(1-e ) = 0.3987 P E = Probability of Exposure P = Prevalence in chicken C = Concentration in chicken (LogMPN/g) m = Mass of chicken ingested (g) Exposure assessment Suphachai DVM, MPVM, PhD

46. 46 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 DVM, MPVM, PhD

47. 47 1. Probability of exposure (P E ) Exposure assessment

48. 48 1. Hazard Identification 2.Hazard Characterization 3.Exposure Assessment CAC's Risk Assessment P E and Dose Suphachai DVM, MPVM, PhD

49. 49 Probability of illness from dose = P(D) Dose -  -5 P(D) = 1 - [ + ----------- ] = 1.62 x 10 β c Dose = 10 x m Hazard Characterization Suphachai DVM, MPVM, PhD

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

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

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

53. 53 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. Risk characterization

54. 54 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 : P E, D Risk characterization

55. 55 Risk estimate P i = P E x P(D) Risk characterization P i = 0.4091 x 1.62 x10 -5 = 6.63 x 10 -6 Suphachai DVM, MPVM, PhD

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

57. 57 Output from Monte Carlo Simulation Mean of Risk estimate = 4.57 x10 -5 Risk characterization

58. 58 Sensitivity Analysis for Risk Management Suphachai DVM, MPVM, PhD

59. 59 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 DVM, MPVM, PhD

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