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

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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.