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.
2.
3.
4.
5.
6.
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
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.
Dietary Exposure Model
∫
I
Dietary
 Pr(x)dx  (Conc) X (Food Consumption)
Exposure
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).
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
concentration in foods.
to
describe
the
chemical
Sources of Uncertainty in Chemical
Concentration Data

Sources of uncertainty:


Analytical measurements resulting in nondetect 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
100%
90%
Cumulative Frequency.
80%
70%
60%
Data
Beta
Gamma
Logistic
Normal
50%
40%
30%
20%
10%
0%
0
0.1
0.2
0.3
0.4
[MeHg] ppm
0.5
0.6
0.7
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
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.
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
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
=
β
(α/DS)
CR
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.