2. Partner for progress Gertjan Medema Quantitative Microbial Risk Assessment (QMRA) CAMRA Summer Institute, MSU, August 2006

3. © Kiwa 2006 2 Outline What is QMRA? QMRA framework Case studies Relation QMRA – risk management

4. © Kiwa 2006 3 Quantitative Microbial Risk Assessment

5. © Kiwa 2006 4 Quantitative Microbial Risk Assessment

6. © Kiwa 2006 5 Quantitative Microbial Risk Assessment Probability of hazardous event x adverse effect of event

7. © Kiwa 2006 6 Quantitative Microbial Risk Assessment Probability of hazardous event x adverse effect of event

8. © Kiwa 2006 7 Risk assessment

9. © Kiwa 2006 8 Risk assessment

10. © Kiwa 2006 9 Quantitative Microbial Risk Assessment Probability of hazardous event x adverse effect of event

11. © Kiwa 2006 10 Quantitative risk assessment

12. © Kiwa 2006 11 Quantitative risk assessment

13. © Kiwa 2006 12 Quantitative Microbial Risk Assessment Probability of exposure to pathogens x health effect of exposure

14. © Kiwa 2006 13 Outline What is QMRA? QMRA framework Case studies Relation QMRA – risk management

15. © Kiwa 2006 14 ILSI Framework for QMRA

16. © Kiwa 2006 15 ILSI QMRA Framework Problem formulation Analysis Exposure assessment Effect assessment Risk characterization

17. © Kiwa 2006 16 ILSI QMRA Framework Problem formulation Analysis Exposure assessment Effect assessment Risk characterization

18. © Kiwa 2006 17 Problem formulation Interaction risk manager – risk assesser Define the purpose of the QMRA Define the breadth of the QMRA Explore the context of the QMRA Develop a conceptual model Route(s) of exposure for QMRA System under evaluation Pathogens (characteristics) Exposed population; immunity Health outcome Data needs Preliminar, exploratory assessment Incorporate control alternatives (optional)

19. © Kiwa 2006 18 ILSI QMRA Framework Problem formulation Analysis Exposure assessment Effect assessment Risk characterization

20. © Kiwa 2006 19 Analysis - Exposure assessment Pathogen characterisation (relevant for exposure) Die-off rate Removal rate Inactivation rate Growth rates Pathogen occurrence Frequency, Concentrations, Seasonality Methodological issues Pathogen behaviour in system Removal/inactivation System variability

21. © Kiwa 2006 20 Analysis - Exposure assessment 2 Exposure analysis Calculation of pathogen concentration in matrix Consumption data Combination of all information into exposure data Include variability (ranges, probabilistic) Include uncertainty analysis Include sensitivity analyis

22. © Kiwa 2006 21 Analysis – Effect assessment Host characterization Age, pregnancy, immune-status, nutritional status etc. Human health effects Duration Severity (morbidity, mortality, sequellae) Secondary transmission Dose response Human (or animal) dose-response data Type of response: infection, morbidity, mortality, DALY’s Inoculum used (type, preparation, administration) Study population characteristics Strain variation Statistical model

23. © Kiwa 2006 22 Analysis – Effect assessment 2 Host pathogen profile Include variability Include sensitivity analysis

24. © Kiwa 2006 23 ILSI QMRA Framework Problem formulation Analysis Exposure assessment Effect assessment Risk characterization

25. © Kiwa 2006 24 Risk characterization Combine data from exposure assessment and effect assessment into characterization of risk Include variability (probabilistic) Discuss assumptions: include sensitivity analysis Discuss how outcome answers problem formulation (Compare control scenario’s) Identify data needs/improvements

26. © Kiwa 2006 25 QMRA is iterative proces

27. © Kiwa 2006 26 Outline What is QMRA? QMRA framework Case studies Relation QMRA – risk management

28. © Kiwa 2006 27 Case study 1 Setting priorities for risk management Risk assessment by J.F. Loret

29. © Kiwa 2006 28 Problem formulation Suez Environnement operates >1700 water systems in France. After the waterborne outbreaks of cryptosporidiosis reported in the USA and UK, Suez wanted to develop an approach to evaluate the risk of Cryptosporidium for each water system. Such an approach would allow Suez to: demonstrate compliance with the EU drinking water directive know if any of these systems was at risk to Cryptosporidium; prioritise investments (if needed). Specific risk assessment goal What is the risk of Cryptosporidium in the (>1700) water systems?

30. © Kiwa 2006 29 Hazard identification Cryptosporidium absence of specific therapy resistance to chemical disinfection. Questionnaire operators of each system volume of water produced, the type of source water used, including information on the type of environment (urban, rural, presence of cattle etc.) and data on general water quality parameters (coliforms, turbidity, ammonium and nitrate), and about the type of treatment processes. The returned information covered treatment facilities that supply 9 million people with drinking water.

31. © Kiwa 2006 30 Exposure assessment Cryptosporidium occurence in source water (data)

32. © Kiwa 2006 31 Exposure assessment 2 Cryptosporidium occurence in source water (classes)

33. © Kiwa 2006 32 Exposure assessment 3 Cryptosporidium removal by water treatment (literature data)

34. © Kiwa 2006 33 Effect assessment Dose response data and model (literature)

35. © Kiwa 2006 34 Risk characterization

36. © Kiwa 2006 35 Risk management Systems at risk: small (<5000) and groundwater under surface water influence Validation: monitoring at sites in different classes: consistent Upgrading program for risk sites

37. © Kiwa 2006 36 Case study 2 Does my system meet the health target?

38. © Kiwa 2006 37 Problem formulation A Water Company in the Netherlands is the owner and operator of a plant for drinking water production from surface water. Given the occurrence of outbreaks of cryptosporidiosis through drinking water in neighbouring countries the water company and the drinking water inspectorate want to know if the population served by the treatment plant is adequately protected against cryptosporidiosis. The new Dutch Drinking Water Act states that for pathogenic micro- organisms, a health risk should not exceed 1 infection per 10.000 consumers per year (VROM, 2001). Specific risk assessment goal Does this treatment plant produce drinking water that meets the 10 -4 infection risk-level?

39. © Kiwa 2006 38 Hazard identification Cryptosporidium present in source water absence of specific therapy resistance to chlorination

40. © Kiwa 2006 39 System description

41. © Kiwa 2006 40 Exposure assessment Pathogens in source water Cryptosporidium data 1998-2004 N=48 15 Crypto detected 0.7-19/100L

42. © Kiwa 2006 41 Exposure assessment Pathogen monitoring - QA Cryptosporidium recovery data N=30 Average 22%

43. © Kiwa 2006 42 Exposure assessment Removal by treatment Select significant barriers in system Rapid Sand Filtration Ozonation Slow sand filtration Data collection

44. © Kiwa 2006 43 Removal by treatment Step 1 - Rapid sand filters Clostridium spore removal 1995-2004 N=380 Average 1.29 log P2.5% 0.74 log Good fit

45. © Kiwa 2006 44 Removal by treatment Step 2: Ozone Clostridium spore removal 1995 - 2004 N=363 Average 1.04 log P2.5% 0.24 log Adequate fit

46. © Kiwa 2006 45 Removal by treatment Step 3: Slow sand filtration Clostridium spore removal 1995 - 2004 N=1031 95% = 0 Part of the samples out>in Data provided no reliable quantification of removal Questions about validity of using anaerobic spores for Cryptosporidium for slow sand filtration Solution: Pilot testing Removal 5 logs

47. © Kiwa 2006 46 Exposure assessment Distribution Not included Research program ongoing

48. © Kiwa 2006 47 Exposure assessment Consumption

49. © Kiwa 2006 48 Effect assessment Dose response Integration of dose response data of multiple C. parvum strains

50. © Kiwa 2006 49 Risk characterization: QMRA tool

51. © Kiwa 2006 50 Statistical data analysis - output Cryptosporidium in source water and after treatment

52. © Kiwa 2006 51 Statistical data analysis - output Probability of Cryptosporidium infection of the Amsterdam system

53. © Kiwa 2006 52 Statistical data analysis - output

54. © Kiwa 2006 53 Risk management First iteration: good data missing on Crypto removal by slow sand filtration Pilot plant study Crypto on slow sand filter. Second iteration: report (high) compliance to standard to inspectorate and consumer Monitoring source water for Crypto (genotyping) Triangular distribution to include variability and uncertainty in slow sand filtration data Validate use of SSRC as surrogate for Crypto for ozone

55. © Kiwa 2006 54 Outline What is QMRA? QMRA framework Case studies Relation QMRA – risk management

56. © Kiwa 2006 55 Relation with risk management

57. © Kiwa 2006 56 Risk management HACCP / Water safety plan

58. © Kiwa 2006 57 Risk management questions that need quantitative answers

59. © Kiwa 2006 58 Risk 1Risk 2 Risk management: balancing the risks Expert judgement

60. © Kiwa 2006 59 SafetyCosts Risk management: balance between consumer safety and (consumer)cost. Expert judgement

61. © Kiwa 2006 60 SafetyCosts QMRA Risk management: balance between consumer safety and (consumer)cost. QMRA

62. © Kiwa 2006 61 The value of Quantitative Risk Assessment “the Commission needs to find the balance between the freedom and rights of individuals, industry and organisations with the need to reduce the risk of adverse effects to human health and the environment. This balance should be: science-based, proportionate, non-discriminatory, transparent and coherent and requires a structured decision-making process with detailed scientific, objective information within an overall framework of risk analysis.” Address by D. Byrne (Commissioner for Health & Consumer Safety) on the Precautionary Principle in the domain of human health and food safety. The Economist conference, Nov. 9, 2000, Paris. QMRA

63. © Kiwa 2006 62 Take home messages

64. © Kiwa 2006 63 Framework for QMRA Help the risk manager Define goal and scope carefully, together with risk manager(s) Good QMRA Practice Tracebility of data Include variability (also include the odd peak event) Describe and test assumptions (sensitivity analysis) Present outcome to risk manager You are not the risk manager

65. © Kiwa 2006 64 QMRA is iterative proces

66. © Kiwa 2006 65 Relation with risk management There is so much uncertainty…. Zero uncertainty does not exist The uncertainty is there, QMRA makes it visible Weak spots  research program Risk management has learned to deal with uncertainty