sources of uncertainty and current practices for addressing them: exposure perspective clarence w....
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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
Outline
1. Definition of terms2. Dietary exposure model3. Sources of uncertainty in a dietary
exposure assessment 4. Chemical concentration and current
practices to address uncertainty 5. Food consumption and current practices to
address uncertainty6. Conclusions
Uncertainty
The imperfect knowledge concerning the present or future state of an organism, system, or (sub)population under consideration.
Variability
The heterogeneity of values over time, space or different members of a population. Variability implies real difference among members of that population.
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.
• 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).
Dietary Exposure
I
(Conc)
(Food Consumption)
X∫Pr(x)dxi
Dietary Exposure Model
Sources of Uncertainty in Dietary Exposure Assessment
Chemical concentration data
Food consumption data
Sources of Uncertainty in Dietary Exposure Assessment
Chemical concentration data
Food consumption data
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.
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.
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.
Non-Detects in Chemical Concentration
Current practices for addressing the uncertainty from non-detects:
Substitution Method Modeling Detected Values
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
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
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
Example: Modeling Detected Values
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
[MeHg] ppm
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Data
Beta
Gamma
Logistic
Normal
Carrington et al. in press
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.
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.
Summary Statistics for Chemical Concentration
One current practice for addressing the uncertainty from summary statistics:
Characterize 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.
Characterization of Summary Statistics with Multiple Distribution Models
Example: Characterization of Summary Statistics with Multiple Distribution Models
Lognormal and gamma distributions were used to model the summarystatistics 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
Sources of Uncertainty in Dietary Exposure Assessment
Chemical concentration data
Food consumption data
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.
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.
Food Consumption Data
Current practices for addressing uncertainty in food consumption data:
Simple Fractional Adjustment
Frequency-Based Adjustment
Simple Fractional Adjustment
LT (p) = ST 1 – ((1 – p) / CR)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
Simple Fractional Adjustment
Carrington and Bolger, Toxicological and Industrial Health 2001;17: 176-179
Frequency-Based Adjustment
LTS = STS * 365
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
Frequency-Based Adjustment
Carrington and Bolger, Toxicological and Industrial Health 2001;17: 176-179
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.