# Presentation: 741791

## Session on Assessment of Food Exposure

## Food Exposure

**William O. Smith, Ph.D.**
- Chemist
- Health Effects Division
- Office of Pesticide Programs

**Food Exposure**

## Outline of Presentation

- Exposure Input parameters
- Residues (USDA/PDP & FDA/CFSAN)
- Consumption (USDA CSFII 1994-96/1998)
-  Residue Manipulations
- Index Equivalent Residues (RPF method)
- Probabilistic Exposure/Risk Assessment

**Notes:**
The purpose of this presentation is to briefly describe the methodology and results of  a cumulative dietary risk case study for organophosphorus pesticides using an RPF approach and the DEEM model. 
RPF is an acronym for Relative Potency Factor and reflects a simple mathematical procedure  to convert  chemical specific residues on a food sample to a residue equivalence.
DEEM is acronym for Dietary Exposure Evaluation Model,  which is a probablistic model used by OPP to estimate dietary risk to pesticides.

## Sources of Residue Data

- USDA Pesticide Data Program (PDP)
- _http://www.ams.usda.gov/science/pdp_
- FDA Center for Food Safety & Applied Nutrition
- _http://www.cfsan.fda.gov/~lrd/pestadd.html_
- 	Pesticide Residue Monitoring Program
- 	Total Diet Study (TDS)

## OPs Included in Current Food Assessment

- acephate, azinphos methyl, chlorpyrifos, chlorpyrifos-methyl, disulfoton, diazinon, dichlorvos, dimethoate, ethoprop, fenamiphos, malathion, methidathion, methamidophos, mevinphos, oxydemeton-methyl, methyl parathion, phorate, phosolone, phosmet, pirimiphos methyl, terbufos, &  tribufos

## PDP Foods Included in Assessment

**Apples ****	**
**Apple Juice****	**
**Bananas****	**
**Broccoli****	**
**Celery****	**
**Cantaloupe****	**
**Carrots**
**Sweet Corn****	**
**Cucumbers****	**
**Corn Syrup **
**Cherries**
**Rice****	**

**Green Beans****	**
**Grapes**
**Grape Juice****	**
**Lettuce****	**
**Milk**
**Oats****	**
**Oranges****	**
**Orange Juice****	**
**Peaches****	**
**Pears**
**Nectarines**
**Pineapple**

**Potatoes**
**Bell Peppers**
**Strawberries**
**Sweet Potatoes**
**Soybean**
**Spinach**
**Sweet Peas**
**Tomatoes****	**
**Wheat****	**
**Winter Squash**
**Poultry**
**Peanut Butter**

## Foods Based on Translated PDP Data

- Eggplant
- Pepper(other than green)
- Citrus (other than orange)
- Apricots
- Plums/Prunes
- Rye
- Beets-garden
- Horseradish
- Parsnips
- Radishes

- Rutabagas
- Turnips
- Various leafy greens
- Brussels sprouts
- Cabbage
- Cauliflower
- Melons (other than cantaloupe)
- Pumpkins
- Squash-summer

## Foods Based on FDA Monitoring Data

- Eggs
- Assume negligible based on FDA monitoring data
- Seafood
- Assume negligible based on FDA monitoring data

- Meat from Beef, Pork, Sheep & Goats
- Used maximum residues found in FDA/TDS

## Foods Assumed Negligible Contributors

- Sugarcane, Sugar Beet & Maple
- Molasses, syrup & sugar
- Assume negligible residues because...
- Highly processed/refined
- No residues in sugar or pancake syrup analyzed by FDA/TDS
- No residues in corn syrup analyzed by PDP

## The Proportion of the Diet of Children (3-5) Covered in the Cumulative Assessment   Source of  Residue Estimate	% of Diet	PDP (RACs & processed)       		85.7    Translation of PDP		1.3    FDA                           		6.3    Assumed negligible	 	3.1    Not included              		3.6

## The Proportion of the Diet of Children (3-5) Covered in the Cumulative Assessment   

- Top 30 foods are included in this assessment
- Top 95% of Diet=56 foods
- 52 of 56 top foods included

## Foods Not Included in Preliminary Assessment

- Not expected to impact significantly on assessment
- Many are highly processed or blended foods
- Infrequently detected residues and/or low levels

## Cumulative Dietary Exposure

- Exposure=Residue  X Consumption

*Cumulative Residues*

- MOE=Point of Departure/Exposure

## Relative Potency Factor Method of Estimating Cumulative Residues

- Converted chemical specific residues on food samples to a common residue

- Index Equivalent Residue  (Residue)

## Calculation of Cumulative Residues 

**Residue:  **PDP residue data by sample
**PF:  ****	**Processing factor (if applicable)
**RPF:  ****	**Relative potency factor

- ResidueIE=Residue  X  PF  X  RPF

- Cumulative ResidueIE= ResidueIE (per PDP sample)

## Residue Inputs for Cumulative Assessment 

- Distributions of cumulative residues
**OR**
- Average cumulative residues for highly blended foods

## Cumulative Dietary Exposure

- Exposure=Residue  X  Consumption

- CSFII 94-96/98

## CSFII 1994-96/1998 

- Intakes of individuals residing in U.S.
- 20,607 individual participants interviewed over two discontinuous days (~3-10 days apart)
- 1998 Supplemental Children’s Survey
- 5,559 additional children
- Birth through 9 years old

## CSFII 1994-96/1998

- The 1994-96/1998 CSFII significantly increases the number of children in the survey compared to the 1989-91 survey data currently being used by OPP

## Comparison of  Number of 1-day and Complete Intakes, by Children’s Age Group 1994-96/1998 vs. 1989-91 CSFII

| Age Group | 1994-96/1998 CSFII |  | 1989-91 CSFII |  |
| --- | --- | --- | --- | --- |
|  | No. Individuals | No. Person-days | No. 
Individuals | No. Person-days |
| <1 | 1151 | 2940 | 197 | 579 |
| 1-2 | 2179 | 4114 | 574 | 1314 |
| 3-5 | 4579 | 8464 | 806 | 1812 |
| 6-11 | 2000 | 3706 | 1476 | 3441 |

## Populations Groups Assessed

- Separate assessments were based on survey information on the following age groups:
- Children 1-2 years old
- Children 3-5 years old
- Adults 20-49  years old
- Adults 50+ years old

## Exposure Assessment Models

- DEEMTM and CalendexTM were used to estimate cumulative food exposure and risk
- The use of CalendexTM and related issues will be discussed next by David Miller

## DEEM Model 

- Dietary Exposure Evaluation Model:   DEEM-FCIDTM
- Probabilistic (Monte-Carlo) procedure
- Input:
- Distributions for consumption
- Distributions or point estimates for residue concentrations
- Output:
- Distribution of one-day dietary exposures
- Distribution of associated risks, i.e., MOEs

**Notes:**
Add information about DEEM-fcid
Different food codes 
separates out baby foods
What else

Uses new usda epa translation recipes that will be publicly available

## Dietary Exposure Evaluation Model DEEM-FCIDTM

- Uses EPA's Food Commodity Intake Database
- Commodity definitions are based on EPA's Food Commodity Vocabulary
- Recipes for relating foods consumed to raw commodities are publicly available

**Notes:**
Add information about DEEM-fcid
Different food codes 
separates out baby foods
What else

Uses new usda epa translation recipes that will be publicly available

## Preliminary Cumulative Food Exposure

## Preliminary Cumulative Food Exposure-Upper Portion of Distribution 

## Preliminary Cumulative Exposure Assessment

**Margin of Exposure (MOE) at Upper-End of Distribution **

- Percentile

- 125

- 264

- 393

- 720

- 1228

- 2321

- Adults 50+

- 127

- 281

- 415

- 755

- 1280

- 2415

- Adults 20-49

- 62

- 133

- 196

- 348

- 562

- 994

- Children 3-5

- 51

- 108

- 160

- 280

- 454

- 800

- Children 1-2

- 99.9th

- 99.5th

- 99th

- 97.5th

- 95th

- 90th

- MOE = POD/exposure; POD = 0.08 mg/kg body wt/day

## Final

- Further refinements to inputs
- Interpretation of results

*Work to be completed...*

*Preliminary*

## Potential Effects of Input Assumptions & Refinements on the Assessment

- Translation of PDP residue data to other foods
- Use of processing factors to estimate residues in processed food forms (cooked, dried, juices, etc.)

## Effect of Translation of PDP Data 

**MOEs for Children 1-2 at Selected Points in the Exposure Distribution**

- 53

- 114

- 165

- 470

- 2082

- Only PDP Foods

- 52

- 110

- 160

- 457

- 2001

- Full Assessment

- 99.9th

- 99.5th

- 99th

- 95th

- Mean

- MOE=POD/exposure; POD=0.08 mg/kg body wt/day

## Effect of  Baby Foods

**MOEs for Children 1-2 at Selected Points in the Exposure Distribution**

- MOE=POD/exposure; POD=0.08 mg/kg body wt/day

## Impact of  Processing & Translations

**MOEs for Children 1-2 at Selected Points in the Exposure Distribution**

- MOE=POD/exposure; POD=0.08 mg/kg body wt/day

## Cumulative Exposure Distribution-Effect of Translations and Extrapolation of Data

## Effect of Older Data on Assessment

- PDP foods analyzed 1994-2000
- One to 5 years PDP data per food
- Evaluating later data to see if they better represent current use practices

## Effect of Older Data on the Assessment 

**MOEs for Children 1-2 at Selected Points in the Exposure Distribution**

- MOE=POD/exposure; POD=0.08 mg/kg body wt/day

## Effect of Older Data on Assessment

- Analysis not complete
- Complex factors
- Multiple distributions representing different segments of time

## Analysis of Contributors to the Exposure Distribution

- DEEM Critical Exposure Commodity Analysis identifies major contributors to exposure
- Currently analyzing the top food contributors & the factors related to their contributions
- Chemical residues (potency, frequency, levels)
- Sampling details (origin & date of sample)

## Analysis of Contributors to the Exposure Distribution

- Foods A, B, and C
- Highest contributors to exposure between 99.8th  & 100th percentile
- Examined impact of removal of OP residues from all forms of each of these foods from the cumulative assessment

## Impact of  Foods A, B, & C  on Assessment

**MOEs for Children 1-2 at Selected Points in the Exposure Distribution**

## Impact of Foods A,B, & C on Assessment

## Impact of  Foods A, B, & C  on Assessment

**Ratios of MOE****n****/MOE****full assessment**

## Interpretation of The Food Risk Results

- PDP residue data cover the major food consumption items
- Further refinements of PDP data not likely to drastically alter the results at the higher end of the exposure distribution
- Complex factors are contributing to the exposure distribution

## Calendar-Based Exposure Assessment

-  CalendexTM was used to estimate cumulative exposure from food, water, and residential uses as a series of daily exposure distributions for a year
- This time-series food exposure assessment and related issues will be discussed next by David Miller

## Time-Frame Considerations

**David Miller**
- Chemist (USPHS)
- Health Effects Division

**Time-Frame Considerations**

## Outline of Presentation

- Introduction/Background information
- DEEM(FCID)TM vs. DEEM(FCID)/CalendexTM
- Time-Frame Considerations
- Modes in which CalendexTM can be used for a cumulative risk assessment
- Consecutive daily estimates
- Rolling (or sliding) time-frame
- Strengths and limitations of these modes
- Questions  for the SAP

## Points to Remember...

- Presentation will not review step-by-step mechanics of DEEM(FCID)/CalendexTM algorithms:
- DEEM/CalendexTM reviewed in previous SAPs
- Presentation concentrates on exposure through food
- Principles apply to all routes
- No decision has been made on an appropriate MOE or threshold percentile for regulation

## Introduction

- DEEM(FCID)/CalendexTM provides a probabilistic assessment of exposures through food, water, and residential pathways
- DEEM(FCID)/CalendexTM incorporates concept of a Calendar to evaluate aggregate or cumulative exposures
- Time-based approach
- Calendar-based approach looks at each individual day of the year
- Calendar-based approach allows appropriate “temporal matching” of exposures through food, drinking water, and residential pathways
- Temporal aspect of exposure important for OP’s due to expected seasonal use-patterns

## Introduction

- CalendexTM uses probabilistic techniques to appropriately combine exposures from the food, water, and residential pathways in a manner which incorporates:
- probabilities of exposure,
- use and application practices,
- human activity patterns,
- etc.
- and considers their associated seasonality and timing
- Result is a collection (or distribution) of aggregate exposures (food, residential, and drinking water combined) for each day of the year for the relevant region

- These _**exposures**_ can be plotted as a **“**_**time-line**_**”** or profile of daily exposures for any **given percentile** in the distribution

## Slide 49

## How Does DEEM(FCID)/CalendexTM differ from DEEM(FCID)TM? 

- DEEM(FCID)TM analysis assesses exposure from food alone without respect to timing/seasonality issues
- Randomly matches reported food consumption, by individual, with residue data
- No time component
- Result is a single distribution of exposures and a single-valued estimate of risk at any given percentile of exposure

## How Does DEEM(FCID)/CalendexTM differ from DEEM(FCID)TM?

- In an aggregate or cumulative assessment in which pathways are combined, timing considerations are important
- DEEM(FCID)/CalendexTM performs analysis in a manner in which timing considerations are incorporated
- Does this by performing separate analyses for each day of the year
- Result is 365 separate distributions of exposures for each of the day of the  year
- Exposures at any given percentile can be plotted as a time-based exposure profile

## DEEM(FCID)TM vs. DEEM(FCID)/ CalendexTM

- _DEEM (FCID)_TM
- Considers Food Alone
- Timing is not considered
- No day-to-day variation
- Single exposure estimate at any given percentile

- _DEEM (FCID)/Calendex_TM
- Considers all pathways
- Timing is considered
- Some Day-to-day variation in diet
- 365 sequential daily exposure estimates at any given percentile

## Time-Frame Considerations	

- Important to consider how estimated exposure is compared with toxicity endpoint
- Toxicity endpoint in PCRA is based on BMD10  which reflects multi-day dosing studies

## Time-Frame Considerations

- September 2001 SAP Meeting considered this issue of how to compare time component of toxicity endpoints and exposure:
- CRA should ideally compare toxicity endpoints and exposure durations of same time-frame
- To the extent possible, comparison should take into account the pattern of human exposure

## Time-Frame Considerations

- DEEM/CalendexTM program can perform analysis using a variety of time-frames
- This presentation considers two specific modes of analysis:
- Single Consecutive daily estimates
- Rolling (or sliding) time-frame
- Examples here are illustrative only

## Single (Consecutive) Day Analysis

- Single Consecutive Day Option
- Separate independent exposure and risk (MOE) estimates made for each day of the year
- Estimates are arrayed chronologically into an exposure time line for any selected percentile and graphed
- These represent independent, daily estimates of risk on each day of the year
- Not necessarily the same individual on consecutive days

## Single (Consecutive) Day Analysis

*99.9**th** Percentile*
*(Individual #10456)*

**January 1**

- Exposure

- Frequency

## Single (Consecutive) Day Analysis

*99.9**th** Percentile*
*(Individual #1492)*

**January 2**

- Exposure

- Frequency

## Single (Consecutive) Day Analysis

*99.9**th** Percentile*
*(Individual #18912)*

**December 31**

- Exposure

- Frequency

## Single (Consecutive) Day Analysis

- Plot, for each day, a fixed population exposure percentile (e.g., 99.9th)
- Resulting time-based exposure profile represents 99.9th percentile exposure for each day of the year
- each day of year considered independently

## Example Exposure Profile for Food 

- 232

- 243

- 254

- 265

- 276

- 287

- 298

- 309

- 320

- 331

- 342

- 353

- 364

**Julian Days**

**MOEs at the 99.9th Percentile**

- Food MOE

## Example Exposure Profile for Food

- On Day 31, the MOEF at the 99.9th percentile is 58
- Translation:
- Exposure to the 99.9th percentile individual on Day 31 is 58 times lower than the POD
- On Day 32, the MOEF at the 99.9th percentile is 66
- Translation:
- Exposure to the 99.9th percentile individual on Day 32 is 66 times lower than the POD
- This is likely to be a different individual than the 99.9th percentile individual on Day 31

## Pros and Cons for Single-Day Based Estimate

**Pros:**
- Easier to identify risk contributors
- Health protective
- unlikely to underestimate average exposure over multiple days

**Cons:**
- Point of Departure is based on multi-day exposure
- Relevance of comparing a series of single-day exposures to multi-day endpoint
- Consecutive _daily_ estimates are likely to overestimate _multi-day_ exposures to an individual (at higher percentiles)
- e.g., not possible to interpret extended series of elevated exposures on consecutive days as representing extended period of exposure to the _same_ individual.

## Rolling Time-Frame Approach 

- Multiple Sequential Day Option (a.k.a. rolling time-frame)
- In a rolling time-frame approach, average exposures over multiple days are calculated for each individual
- e.g., January 1 through 7, then January 2 through 8, January 3 through 9, etc.
- This series of _**multi-day average exposures**_ serves as basis of comparison with POD
- This distribution of multi-day average exposures serves as basis of comparison with (multi-day) BMD10

## Rolling Time-frame Approach

**0**

**0.002**

**0.004**

**0.006**

**0.008**

**0.01**

**0.012**

**0.014**

**1-Jan**

**2-Jan**

**3-Jan**

**4-Jan**

**5-Jan**

**6-Jan**

**7-Jan**

**Exposure**

- Individual #1

## Rolling Time-frame Approach

**0**

**0.002**

**0.004**

**0.006**

**0.008**

**0.01**

**0.012**

**0.014**

**1-Jan**

**2-Jan**

**3-Jan**

**4-Jan**

**5-Jan**

**6-Jan**

**7-Jan**

**Exposure**

**Individual #1**

**Average #1**

**(Jan 1-7)**

## Rolling Time-Frame Approach

**0**

**0.002**

**0.004**

**0.006**

**0.008**

**0.01**

**0.012**

**0.014**

**1-Jan**

**2-Jan**

**3-Jan**

**4-Jan**

**5-Jan**

**6-Jan**

**7-Jan**

**Exposure**

## Rolling Time-Frame Approach

**0**

**0.002**

**0.004**

**0.006**

**0.008**

**0.01**

**0.012**

**0.014**

**1-Jan**

**2-Jan**

**3-Jan**

**4-Jan**

**5-Jan**

**6-Jan**

**7-Jan**

**Exposure**

## Rolling Time-Frame Approach

**0**

**0.002**

**0.004**

**0.006**

**0.008**

**0.01**

**0.012**

**0.014**

**1-Jan**

**2-Jan**

**3-Jan**

**4-Jan**

**5-Jan**

**6-Jan**

**7-Jan**

**Exposure**

## Rolling Time-Frame Approach

**0**

**0.002**

**0.004**

**0.006**

**0.008**

**0.01**

**0.012**

**0.014**

**2-Jan**

**3-Jan**

**4-Jan**

**5-Jan**

**6-Jan**

**7-Jan**

**8-Jan**

**Exposure**

## Pros and Cons for Rolling-Average Based Estimates

**Pros: **
- Incorporate variability in exposure for an individual _across multiple days_
- Likely to provide a more realistic estimate of exposures across multiple days
- Flexible with respect to matching time-frame associated with POD

## Pros and Cons for Rolling-Average Based Estimates

**Cons:**
- USDA’s CSFII does not provide consumption data across the multiple consecutive days which would be of interest
- Limited to two days of reported intake
- Two days are not consecutive (3-10 days apart)
- Multi-day average exposure for any individual uses only two days of reported consumption data for that individual
- and repeats these (randomly) through the time-frame of interest
- No “longitudinal” residue data is available

## Issues:  Food Consumption

- Any consecutive day period of interest for an individual will contain a series of repeated diets which tend to underestimate variability
- This will tend to overstate potential exposure at upper tails of distribution to the extent that reported diets (food choices) are associated with higher exposure

## Issues:  Residues

- Since residue values are drawn (anew) at random for each day during the time-frame of interest, true correlations that may exist between residues on subsequent days may not be accurately reflected
- This will tend to understate potential exposure at the upper tails of the distribution to the extent that an individual’s exposure is associated with pesticide residue concentrations
- Juices
- Bags of Produce

## Comparison of MOE Using Different Time-Frames

- DEEM(FCID)/CalendexTM software has various pre-defined options for duration of time-frame
- 7-, 14-, 21-, and 28-day rolling time-frames
- Multi-day averaging process
- Increases in time-frame over which averaging performed results in:
- Attenuation of variability
- Increase in the MOE (decrease in exposure)
- Degree to which these changes occur dependent upon selected percentile

## Comparison of Exposure Windows in the Cumulative PCRA Higher Percentile Example

- 1

- 10

- 100

- 1000

- 1

- 12

- 23

- 34

- 45

- 56

- 67

- 78

- 89

- 100

- 111

- 122

- 133

- 144

- 155

- 166

- 177

- 188

- 199

- 210

- 221

- 232

- 243

- 254

- 265

- 276

- 287

- 298

- 309

- 320

- 331

- 342

- 353

- 364

**Julian Days**

**MOEs in Logscale at the 99.9****th**** Percentile**

- Food MOE 1 day

- Food MOE 7 day

- Food MOE 14 day

- Food MOE 21 day

## Comparison of Exposure Windows in the Cumulative PCRA Lower Percentile Example

*Comparison of Exposure Windows in the Cumulative PCRA Lower Percentile Example*

## Questions for the SAP on Assessment of Food Exposure

**Questions for the SAP on Assessment of Food Exposure**

## Question 1

- 	In the In the Preliminary OP Cumulative Risk Assessment, OPP used all available PDP monitoring data generated since 1994 as the basis for the residue distributions of pesticides in treated foods. As a result, some foods have multiple years of data (as many as 5), while others have only a single year of data. All years of data were included to provide the most robust residue data set possible. These data were extended to cover foods and processed forms of foods for which data are not directly available. Additionally, some foods were included in the analysis based on less robust data from FDA.
- 	OPP is conducting a sensitivity analysis in which the residue contributions from specific foods (either one at a time or in combination with other foods) are removed from the analysis. This analysis is being conducted as part of an effort to determine the contributions of specific food commodities and chemicals to the upper tail of the exposure distribution. Some preliminary results are shown in Table 1 of the addendum to this document.

## Question 1

- 	Partly as a result of this exercise, OPP has observed that the more variables (e.g., commodities, chemicals, years of data) that are included in the exposure distribution, the more difficult it becomes to affect the tail of the distribution by removing commodity/pesticide combinations from the calculations.  While removal of most exposure contributors results in a demonstrable change in the lower portion of the distribution, the exposures at the upper end of the tail (for example the 99.9th percentile) are relatively unaffected by removal of a single commodity, even if it is identified by DEEM as a frequent contributor to the high end of the exposure distribution.

*Please discuss the significance of this observation and its potential impact on interpretation of the output distributions and results from highly complex distributional analyses such as the Preliminary OP Cumulative Risk Assessment.*

## Question 2A

- 	The Calendex model can be used in a number of modes to develop a profile of exposure estimates.  In the current assessment, OPP conducted a series of single-day assessments arrayed chronologically to develop a response surface of exposures.  A constant percentile of exposure was selected to represent the potential exposure to a given percentile of the population.  For example, the 99th percentile for each day would be arrayed for 365 days to reflect the population estimate across the calendar year.  Calendex can also be used in a multi-day sequential series analysis, also referred to as a “rolling time-frame mode.”  A rolling time-frame provides an estimate of the average of daily exposures for an individual calculated over multiple (7, 14, 21, or 28) days, for each multiple day period over the course of a year, (e.g., days 1-7, then days 2-8, then days 3-9, etc.).  In this mode, an individual's food exposure is tracked across the calendar year by randomly selecting day one or day two of that individual’s reported consumption from the CSFII and combining each commodity which comprises that consumption with randomly selected residue values for each day of the calendar year.  These rolling averages  for each individual are assembled to develop a distribution of rolling average exposures.

## Question 2A (continued)

- 	During previous SAP meetings, the Panel has expressed concern about the use of CSFII records to represent longitudinal consumption patterns for individuals.  Concern arose as a result of the design of the CSFII study, in which two **nonconsecutive** days of data (separated by 3 to 10 days) were collected for each individual.

*Please comment on the use of CSFII data to support each of these two modes of Calendex as they pertain to the cumulative risk assessment of pesticides in foods. *

## Question 2B

- The random selection of PDP residue values assumes that the residues in foods consumed across a series of days are independent of each other.  In other words, foods consumed are from unrelated sources and there is no carryover from one day to another.  This assumption may be inappropriate given that many consumers obtain food in bulk (i.e., multi-day) quantities that may have similar treatment history and would typically consume this food over a short multi-day period (e.g., leftovers).  In such a case the residues contained in the foods would violate the assumption of independence.

*Please comment on the use of PDP data to support each of these two modes of Calendex as they pertain to the cumulative risk assessment of pesticides in foods.  What issues are likely to accrue from the assumption of independence in residue data?*

## Slide 84