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

© Kiwa Outline What is QMRA? QMRA framework Case studies Relation QMRA – risk management

© Kiwa Quantitative Microbial Risk Assessment

© Kiwa Quantitative Microbial Risk Assessment

© Kiwa Quantitative Microbial Risk Assessment Probability of hazardous event x adverse effect of event

© Kiwa Quantitative Microbial Risk Assessment Probability of hazardous event x adverse effect of event

© Kiwa Risk assessment

© Kiwa Risk assessment

© Kiwa Quantitative Microbial Risk Assessment Probability of hazardous event x adverse effect of event

© Kiwa Quantitative risk assessment

© Kiwa Quantitative risk assessment

© Kiwa Quantitative Microbial Risk Assessment Probability of exposure to pathogens x health effect of exposure

© Kiwa Outline What is QMRA? QMRA framework Case studies Relation QMRA – risk management

© Kiwa ILSI Framework for QMRA

© Kiwa ILSI QMRA Framework Problem formulation Analysis Exposure assessment Effect assessment Risk characterization

© Kiwa ILSI QMRA Framework Problem formulation Analysis Exposure assessment Effect assessment Risk characterization

© Kiwa 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)

© Kiwa ILSI QMRA Framework Problem formulation Analysis Exposure assessment Effect assessment Risk characterization

© Kiwa 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

© Kiwa 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

© Kiwa 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

© Kiwa Analysis – Effect assessment 2 Host pathogen profile Include variability Include sensitivity analysis

© Kiwa ILSI QMRA Framework Problem formulation Analysis Exposure assessment Effect assessment Risk characterization

© Kiwa 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

© Kiwa QMRA is iterative proces

© Kiwa Outline What is QMRA? QMRA framework Case studies Relation QMRA – risk management

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

© Kiwa 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?

© Kiwa 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.

© Kiwa Exposure assessment Cryptosporidium occurence in source water (data)

© Kiwa Exposure assessment 2 Cryptosporidium occurence in source water (classes)

© Kiwa Exposure assessment 3 Cryptosporidium removal by water treatment (literature data)

© Kiwa Effect assessment Dose response data and model (literature)

© Kiwa Risk characterization

© Kiwa 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

© Kiwa Case study 2 Does my system meet the health target?

© Kiwa 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 consumers per year (VROM, 2001). Specific risk assessment goal Does this treatment plant produce drinking water that meets the infection risk-level?

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

© Kiwa System description

© Kiwa Exposure assessment Pathogens in source water Cryptosporidium data N=48 15 Crypto detected /100L

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

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

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

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

© Kiwa Removal by treatment Step 3: Slow sand filtration Clostridium spore removal N= % = 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

© Kiwa Exposure assessment Distribution Not included Research program ongoing

© Kiwa Exposure assessment Consumption

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

© Kiwa Risk characterization: QMRA tool

© Kiwa Statistical data analysis - output Cryptosporidium in source water and after treatment

© Kiwa Statistical data analysis - output Probability of Cryptosporidium infection of the Amsterdam system

© Kiwa Statistical data analysis - output

© Kiwa 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

© Kiwa Outline What is QMRA? QMRA framework Case studies Relation QMRA – risk management

© Kiwa Relation with risk management

© Kiwa Risk management HACCP / Water safety plan

© Kiwa Risk management questions that need quantitative answers

© Kiwa Risk 1Risk 2 Risk management: balancing the risks Expert judgement

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

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

© Kiwa 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

© Kiwa Take home messages

© Kiwa 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

© Kiwa QMRA is iterative proces

© Kiwa 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