Uncertainties in Dietary Exposure Analysis A Challenge to Be Addressed Dr David Tennant Food Chemical Risk Analysis, UK
Conflict of interest regarding this presentation: I wish to declare a potential conflict of interest, and that I have received indirect industry support (travel expenses) in relation to all or part of the results presented here.
Uncertainties in Dietary Exposure Analysis A Challenge to Be Addressed Uncertainty what is it? Why is uncertainty analysis important? How has it been addressed in the past? ILSI Europe s contribution A tiered approach Mapping uncertainties Recommendations for further research. 3
Uncertainties in Dietary Exposure Analysis Variability vs. uncertainty Uncertainty = Imperfect data or knowledge about data Can be reduced with more information Variability = Inherent variation in data such as amounts consumed, concentrations in foods, etc. Cannot be reduced with more information 4
Uncertainties in Dietary Exposure Analysis Why is it important? Dietary exposure analysis is critical step in risk assessment for chemicals in food. Conservatism in face of uncertain knowledge is a fundamental principle. Unclear if risk is real or hypothetical. Key objective: High consumer protection within the context of a realistic analysis 5
Uncertainties in Dietary Exposure Analysis Previous guidance WHO/IPCS (International Program on Chemical Safety) 2008 Guidance on characterizing and communicating uncertainty in exposure assessment FDSA 2006 Guidance of the Scientific Committee on a request from EFSA related to Uncertainties in Dietary Exposure Assessment EFSA 2012 Guidance on the use of probabilistic methodology for modelling dietary exposure to pesticides each scientific output should describe the types of uncertainties encountered and indicate their relative importance and influence on the assessment outcome EFSA SC Guidance on Transparency, 2009 6
Uncertainties in Dietary Exposure Analysis Previous guidance Sources of uncertainty: 1. Scenario uncertainty: True exposure scenario unknown particular sub-population, season, etc.? Origin of chemical? 2. Model uncertainty: Gaps in scientific knowledge, oversimplified model, assumptions embedded in model, etc. 3. Parameter uncertainty: Unknown values for the factors that determine the exposure food consumed, levels in food, who by, etc. 7
Uncertainties in Dietary Exposure Analysis Previous guidance Semi-quantitative analysis of uncertainties EFSA 2012 8
Uncertainties in Dietary Exposure Analysis ILSI Europe s contribution Workshop November 2014 IDENTIFYING PRACTICAL WAYS OF DETERMINING UNCERTAINTIES IN FOOD INTAKE Providing a scientific basis and generating consensus for the qualitative and quantitative evaluation of uncertainties and their classification in order to support realistic food intake assessments and improved communication of uncertainties. Publication: 9
Uncertainties in Dietary Exposure Analysis A tiered approach Tier 0 (screening) uncertainty analysis Tier 1 (qualitative) uncertainty analysis Tier 2 (deterministic) uncertainty analysis Tier 3 (probabilistic) uncertainty analysis More Conservative assumptions Less Corresponds to JECFA tiered approach to exposure analysis 10
Uncertainties in Dietary Exposure Analysis Mapping uncertainties Sources of uncertainties are similar for all tiers and can be classified into exposure scenario, parameter and model uncertainties. General and model-independent uncertainties were identified Uncertainties introduced and influenced by the specific model during the tiered approach. 11
Uncertainties in Dietary Exposure Analysis Further research Uncertainties template Substance: Uncertainties Analysis Reporting Template Impact on overall exposure Source of uncertainty Scenario uncertainties Nature of uncertainty Variable affected Impact on variable Mid-range (25-75%) Top 5% General comments Parameter uncertainties Model uncertainties Overestimation means the uncertainty causes the estimated value to be HIGHER than the true value of the parameter or exposure; Underestimation means the estimate would be LOWER than the true value; Pairs of symbols (e.g. SO/MU) can be used to represent a range of possible impacts for the uncertainty. Large over-estimate LO Moderate over-estimate MO Small over-estimate SO Neutral N Small under-estimate SU Moderate under-estimate MU Large under-estimate LU 12
Uncertainties in Dietary Exposure Analysis Further research Uncertainties template Summary of uncertainties common to deterministic models for estimation of dietary exposure to food additives Source of Uncertainty Nature of Uncertainty Variable Effected Parameter uncertainties Food consumption data Levels of additive present in food and beverages Frequency of occurrence of additive in food and beverages Food categorisation system Duration of food consumption surveys Model permits use only of Maximum Permitted Level or maximum reported level 100% occurrence of additive assumed in all food categories at all times Limited number of broad food categories Quantity consumed Additive concentration Additive concentration Quantity of food consumed and Additive concentration Impact on Variable Impact on overall exposure Impact on midrange Exposures Impact on upper range of Exposures MO N/A MO LO LO N/A MO LO LO N/A MO LO SO N/A MO Etc 13
KEY CONCLUSIONS 14
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