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Quantitative Microbial Risk Assessment (QMRA) Salmonella spp. in broiler chicken

Suphachai Nuanualsuwan DVM, MPVM, PhD. Quantitative Microbial Risk Assessment (QMRA) Salmonella spp. in broiler chicken. Significance and Rationale. Public Health Bacterial foodborne disease Food safety Food for Export World trade organization (WTO) Trade barrier

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Quantitative Microbial Risk Assessment (QMRA) Salmonella spp. in broiler chicken

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

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

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

  5. 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 DVM, MPVM, PhD

  6. Hazard in foods • Physical Hazard • Chemical Hazard • Biological Hazard Suphachai DVM, MPVM, PhD

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

  9. Hazard Identification • Taxonomy and Nomenclature • WHO andCollaborating 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 orS.Typhimurium

  10. Hazard Identification • Factors affecting growth and survival • Temperature • pH • Water activities : aW • Atmosphere : O2 • Predictive microbiology Suphachai DVM, MPVM, PhD

  11. Hazard Identification • Factors affecting growth and survival • 1. Temperature • Optimal range 30-45oC(mesophile) • Tmax54oC • D57.2 (aW 0.9) = 40-55 min • Mechanism of inactivation above Tmax • Protein esp. enzymes • Lipid esp. cell membrane Suphachai DVM, MPVM, PhD

  12. 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 Suphachai DVM, MPVM, PhD

  13. 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 Suphachai DVM, MPVM, PhD

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

  15. Hazard Identification • Geographical distribution and transmission • Worldwide • Human animal and environment • Human incidence • age group <5 yearsand 35 years • S.Enteritidis (12 %) S.Weltevreden (8%) • S.Typhimurium (3%) Suphachai DVM, MPVM, PhD

  16. Pathogenesis of Salmonella

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

  18. CAC's Risk Assessment 1. Hazard Identification 2.Hazard Characterization3.Exposure Assessment 4. Risk Characterization Suphachai DVM, MPVM, PhD

  19. 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.

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

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

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

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

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

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

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

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

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

  29. 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

  30. Outbreak of Salmonella Enteritidis&Salmonella spp.

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

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

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

  34. CAC's Risk Assessment 1. Hazard Identification 2.Hazard Characterization3.Exposure Assessment 4. Risk Characterization Suphachai DVM, MPVM, PhD

  35. 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 DVM, MPVM, PhD

  36. 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 DVM, MPVM, PhD

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

  38. 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 DVM, MPVM, PhD

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

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

  41. 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 DVM, MPVM, PhD

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

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

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

  45. 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) Suphachai DVM, MPVM, PhD

  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. Exposure assessment 1. Probability of exposure (PE)

  48. CAC's Risk Assessment 1. Hazard Identification 2.Hazard Characterization3.Exposure Assessment PE and Dose Suphachai DVM, MPVM, PhD

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

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

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