Sources of Uncertainty and Current Practices for Addressing Them: Exposure Perspective

1. Sources of Uncertainty and Current Practices for Addressing Them: Exposure Perspective Clarence W. Murray, III, Ph.D. Center for Food Safety and Applied Nutrition June 15, 2011

2. Outline • Definition of terms • Dietary exposure model • Sources of uncertainty in a dietary exposure assessment • Chemical concentration and current practices to address uncertainty • Food consumption and current practices to address uncertainty • Conclusions

3. Uncertainty • The imperfect knowledge concerning the present or future state of an organism, system, or (sub)population under consideration.

4. Variability • The heterogeneity of values over time, space or different members of a population. Variability implies real difference among members of that population.

5. Dietary Exposure Assessment • The qualitative and/or quantitative evaluation of the likely intake of chemicals (including nutrients) via food, beverage, drinking water, and food supplements.

6. Dietary Exposure Model I ∫ Dietary Exposure Pr(x)dx (Conc)  (Food Consumption)  X i • Yields dietary exposure estimates for a total population or a specific subpopulation • (Conc) - Analytical results for a chemical that is measured in a specific food • (Food Consumption) - food consumption data is most likely obtained from the most recent National Health and Nutrition Examination Survey (NHANES) or from the Continuing Survey of Food Intakes by Individuals (CSFII).

7. Sources of Uncertainty in Dietary Exposure Assessment • Chemical concentration data • Food consumption data

8. Sources of Uncertainty in Dietary Exposure Assessment • Chemical concentration data • Food consumption data

9. Sources of Uncertainty in Chemical Concentration Data • Sources of uncertainty: • Analytical measurements resulting in non-detect values for the chemical concentration in foods. • Summary statistics used to describe the chemical concentration in foods.

10. Sources of Uncertainty in Chemical Concentration Data • Sources of uncertainty: • Analytical measurements resulting in non-detect values for the chemical concentration in foods. • Summary statistics used to describe the chemical concentration in foods.

11. Non-Detects in Chemical Concentration • Problem: • Analytical techniques are unable to measure chemical concentrations below its limit of detection. • Non-detect analytical result does not imply that the chemical is not present in the sample.

12. Non-Detects in Chemical Concentration • Current practices for addressing the uncertainty from non-detects: • Substitution Method • Modeling Detected Values

13. Substitution Method • Non-detects are substituted with the following values: • Non-detect = 0 • Non-detect = ½ Limit of detection • Non-detect = Limit of detection • Upper and lower bounds are derived

14. Example: Substitution Method • Example: Perchlorate analyses in shredded wheat cereal – FDA’s Total Diet Study (TDS) (TDS food # 73) Taken from: http://www.fda.gov/Food/FoodSafety/FoodContaminantsAdulteration/ChemicalContaminants/Perchlorate/ucm077615.htm

15. Modeling Detected Values • Non-detect values are removed from the data set • Detected values are modeled with distributions • Probability tree is used to decide which model provides the best fit for the data

16. Example: Modeling Detected Values Carrington et al. in press

17. Sources of Uncertainty in Chemical Concentration Data • Sources of uncertainty: • Analytical measurements resulting in non-detect values for the chemical concentration in foods. • Summary statistics used to describe the chemical concentration in foods.

18. Summary Statistics for Chemical Concentration • Problem : • In some cases, the full description of the data sets are unavailable. • Limited information may lead to unsubstantiated assumption in the selection of the appropriate distribution model to describe the summary statistics.

19. Summary Statistics for Chemical Concentration • One current practice for addressing the uncertainty from summary statistics: • Characterize summary statistics with multiple distribution models

20. Characterization of Summary Statistics with Multiple Distribution Models • The summary statistics are fitted to multiple distribution models. • Use parameter information from a surrogate empirical distribution to model the parameter values for the multiple distribution models.

21. Example: Characterization of Summary Statistics with Multiple Distribution Models Lognormal and gamma distributions were used to model the summary statistics from the National Marine Fisheries Survey data for tilefish, butterfish, and mackerel. Uniform distribution from shark, tuna, and swordfish were used to represent the magnitude of the shape parameter in the tilefish, butterfish, and mackerel distributions. Carrington and Bolger, Risk Analysis, Vol. 22, No. 4, 2002

22. Sources of Uncertainty in Dietary Exposure Assessment • Chemical concentration data • Food consumption data

23. Food Consumption Data • Source of uncertainty: • Typically, food consumption data is characterized as the variability of a population consumption for a specific food; however uncertainty arises in this data when a long-term characterization of a specific food is required.

24. Food Consumption Data • Problem: • Short term surveys have the tendency to misrepresent infrequent consumers of a food because the survey does not count a consumer who did not eat a specific food during the survey. • Short term survey may project higher consumption for an infrequent consumer of a food. • As a result the short term survey may underestimate the numbers of eaters and overestimate the daily consumption for eaters for longer periods of time since the survey fails to count many consumers who consume a product infrequently.

25. Food Consumption Data • Current practices for addressing uncertainty in food consumption data: • Simple Fractional Adjustment • Frequency-Based Adjustment

26. Simple Fractional Adjustment ( ) 1 – ((1 – p) / CR) LT (p) = ST CR LT () – long-term consumption distribution ST – short-term consumption distribution CR – Consumer ratio ( the long-term to short-term consumer population) Carrington and Bolger, Toxicological and Industrial Health 2001;17: 176-179

27. Simple Fractional Adjustment Carrington and Bolger, Toxicological and Industrial Health 2001;17: 176-179

28. Frequency-Based Adjustment STS * 365 LTS = CR (α/DS)β LTS – projected annual servings ( the long-term estimate) STS – daily servings (from the short-term survey) CR – Consumer ratio ( the long-term to short-term consumer population) α- inversely related to consumption frequency β- determines the shape of the function Carrington and Bolger, Toxicological and Industrial Health 2001;17: 176-179

29. Frequency-Based Adjustment Carrington and Bolger, Toxicological and Industrial Health 2001;17: 176-179

30. Conclusions • Uncertainty is the imperfect knowledge concerning the present or future state of an organism, system, or (sub)population under consideration. • Sources of uncertainty in a dietary exposure assessment are from either the chemical concentration data, the food consumption data, or from both • Current practices used to address uncertainty simply represents its presence in the chemical concentration data and food consumption data. • Because uncertainty is identified and represented it allows for dietary exposure estimates to be characterized for uncertainty. • Finally, in order to reduce uncertainty in the chemical concentration data and food consumption data more sampling and analyses is needed however variability will still be present.