# The S tochastic H uman E xposure and

D ose Si mulation Model for Multimedia, Multipathway Chemicals (SHEDS-Multimedia):Dietary Module

SHEDS-Dietary version 1

Draft Technical Manual

June 16, 2010

Prepared by: Jianping Xue 1 

### , Valerie Zartarian 1 

### , and Steve Nako 2

1 US Environmental Protection Agency, Office of Rese arch and Development, National Exposure Research Laboratory 2 US Environmental Protection Agency, Office of Pesti cide Programsi 

# Disclaimer

EPA's SHEDS-Dietary model is a probabilistic, popul ation-based dietary exposure assessment model that simulates individual exposures to chemic als in food and drinking water over different time periods (e.g., daily, yearly). SHEDS-Dietary i s one module (along with the separate SHEDS-Residential module) of EPA’s more comprehensi ve human exposure model, the Stochastic Human Exposure and Dose Simulation model for multimedia, multipathway chemicals (SHEDS-Multimedia), which can simulate ag gregate or cumulative exposures over time via multiple routes of exposure (dietary & non -dietary) for different types of chemicals and scenarios. SHEDS-Residential and SHEDS-Dietary will be merged together in a future version of SHEDS-Multimedia.

SHEDS-Dietary version 1 includes case study example s for illustrative purposes, as described in the the Technical Manual and User Guide. All input values used in the SHEDS-Dietary model for a given application should be entered or review ed by the researcher so that the model results are based on appropriate data sources for the given application.

The United States Environmental Protection Agency t hrough its Office of Research and Development developed and funded the SHEDS-Dietary model with assistance from contractor Alion Science and Technology. SHEDS-Dietary Version 1 will undergo external peer review by EPA's Scientific Advisory Panel July, 2010, and sho uld be considered draft at this time.



# Acknowledgments
We gratefully acknowledge our colleagues Andrew M. Geller in EPA’s Office of Research and Development, and David J. Miller and members of the HED Dietary Exposure Science Advisory Committee (DE SAC) in EPA’s Office of Pesticide Pro grams, for providing technical input and review of the SHEDS-Dietary model and technical man ual.ii 

# Table of Contents

Disclaimer.........................................

................................................... ......................................... ...i Acknowledgments....................................

................................................... ..................................... i List of Figures ....................................

................................................... ......................................... iv List of Tables .....................................

................................................... ......................................... iv Acronyms and Abbreviations.........................

................................................... ............................. vi 1 Background .......................................

................................................... ..................................... 1 1.1 Purpose of SHEDS-Dietary .......................

................................................... .......... 1 1.2 Overview of SHEDS-Dietary......................

................................................... ......... 3 1.2.1 Key Features and Model Options ............... ............................................... 4 1.2.1.1 Eating Occasion Analyses................... ......................................... 6 1.2.1.2 Half-life Analyses ......................... ............................................... 6 1.2.1.3 Uses CSFII or NHANES/WWEIA Food Consumption Data ...... 6 1.2.1.4 Options for Simulating Longitudinal Consump tion Data............ 6 1.2.1.5 Multi-chemical (Cumulative Exposure Assessm ent) Capability. 8 1.2.1.6 Linkage to PBPK Models ..................... ....................................... 8 1.2.1.7 Sensitivity and Uncertainty Analyses ....... ................................... 8 1.2.2 Plans and Future Research Needs.............. ................................................ 8 2 SHEDS-Dietary Methodology ........................

................................................... ..................... 10 2.1 Algorithm for Estimating Exposure to Pesticides and Other Toxicants from Food Consumption........................................

................................................... .............. 12 2.1.1 Consumption ..................................

................................................... ...... 13 2.1.2 Residues and Process Factors................. ................................................. 13 2.1.3 Sample calculation...........................

................................................... ..... 14 2.2 Drinking Water Exposure Algorithm..............

................................................... ... 15 2.3 Inputs.........................................

................................................... ......................... 15 2.3.1 Food and Indirect Water Consumption Data..... ...................................... 15 2.3.2 Recipe Files .................................

................................................... ......... 16 2.3.3 Direct Water Consumption Data ................ ............................................. 20 Pesticide Use (% crop treated).....................

................................................... ...... 21 2.3.4 Process Factors ..............................

................................................... ....... 21 2.3.5 Food Residue Data ............................

................................................... ... 21 2.3.6 Drinking Water Concentration Data............ ............................................ 22 2.4 Methods Issues.................................

................................................... .................. 22 2.4.1 Issues Regarding Selection of Consumption Dat abase ........................... 22 2.4.2 Issues Regarding Sampling Food Residues...... ....................................... 22 2.4.3 Modeling Longitudinal Food Consumption ....... ..................................... 24 2.4.4 Considering Persisting Effects ............... ................................................. 27 2.4.5 Algorithm for Matching (Behavioral) Diaries: Food Consumption and Activity Patterns..................................

................................................... ... 29 2.4.6 Considering Timing and Amounts of Drinking Wa ter Consumption ..... 30 2.4.7 Considering Number of Person-Days (or Person- Years) to Simulate ..... 34 2.4.8 Sensitivity Analysis Issues ..................

................................................... . 34iii 2.4.9 Uncertainty Analysis Issues..................

................................................... 34 2.5 Output Capabilities ............................

................................................... ................ 34 2.6 Sensitivity Analysis Methods and Results .......

................................................... .. 37 2.6.1 Sensitivity Analyses Methods ................. ................................................ 37 2.6.2 Sensitivity Analyses on Food Consumption ‘Out liers’ ........................... 37 2.6.3 Drinking Water Consumption Outliers .......... ......................................... 38 2.7 Uncertainty Analysis Methods and Results .......

................................................... 40 2.7.1 Uncertainty Analysis Methods ................. ............................................... 40 2.7.2 Uncertainty Analyses on Selection of Food Con sumption Diaries ......... 41 2.8 Quality Assurance ..............................

................................................... ................ 42 2.8.1 Comparison SHEDS vs. DEEM (Model-to-Model Eva luation) ............. 43 3 Applications/Case Studies to Date................

................................................... ....................... 48 3.1 Arsenic (As) ...................................

................................................... .................... 48 3.1.1 Arsenic Uncertainty Analysis Results ......... ............................................ 52 3.2 Methyl Mercury from Fish Consumption (MeHg) .... ........................................... 53 3.3 Permethrin.....................................

................................................... ..................... 57 4 References.......................................

................................................... ..................................... 63

APPENDIX A. BACKGROUND INFORMATION ON THE FOOD AND WATER CONSUMPTION DATA USED IN SHEDS DIETARY............. .............................................A-1 APPENDIX B. BACKGROUND INFORMATION ON THE WATER CON CENTRATION DATA USED IN SHEDS DIETARY.........................

................................................... ............A-5 APPENDIX C. CSFII FOOD LIST IN RAC CODE ORDER...... ............................................ A-6 APPENDIX D. CSFII FOOD LIST IN ALPHABETICAL ORDER . ......................................A-10 APPENDIX E. CSFII FOOD DEFINITIONS ................

................................................... ....A-105 APPENDIX F. NEW FOODS IN 2005 NHANES COMPARED WITH CSFII....................A-108 APPENDIX G: ANNOTATED SHEDS-DIETARY SAS CODE...... ...................................A-116iv 

# List of Figures

Figure 2-1. SHEDS-Dietary Methodology (modified fro m Xue et al. 2010) ............................... 10 Figure 2-2. Monte Carlo Simulation in Dietary Expos ure Modeling ....................................... .... 11 Figure 2-3. Different methods for assembling longit udinal diaries from cross-sectional data..... 24 Figure 2-4. Hypothetical Figure to Illustrate Persi sting Effect for an Individual ..................... .... 28 Figure 2-5. Example SHEDS-Dietary modeled longitudi nal dietary exposure profile for 1 individual .........................................

................................................... .................................. 29 Figure 2-6. Total Number of Occasions of Direct Dri nking Water Consumption........................ 32 Figure 2-7. Distribution of Direct Drinking Water C onsumption, By Time of Day..................... 32 Figure 2-8. Box-and-Whisker Plot of Potato Consumpt ion by Day for Children 1-2 Yrs. Old ... 38 Figure 2-9. Box-Cox Plot of Infant Direct Water Con sumption (mL/kg bw/day)........................ 39 Figure 2-10. Uncertainty analysis profiles for dai ly dietary cis-permethrin exposure................. 41 Figure 2-11. Comparison of SHEDS and DEEM on 99.9 th percentiles of drinking water. Chemical ABC exposure (mg/kg/day) of 17 different s cenarios. ......................................... 4 7 Figure 3-1. Contribution of Inorganic Arsenic Intak e by Foods......................................... .......... 50 Figure 3-2. Contribution of Total Arsenic Intake by Foods............................................. ............. 50 Figure 3-3. SHEDS-Dietary Exposure Model Evaluation for Arsenic Against Duplicate Food Data...............................................

................................................... ..................................... 51 Figure 3-4. Total Arsenic Model Evaluation for SHED S-Dietary linked with MENTOR PBPK Model and Compared Against NHANES Urinary Biomarker Data. .................................... 51 Figure 3-5. Example SHEDS-Dietary Uncertainty Analy ses................................................ ........ 53 Figure 3-6. SHEDS-Dietary Methyl Mercury Exposure by Ethnicity Using 1999-2006 NHANES Data...............................................

................................................... ..................................... 54 Figure 3-7. Contribution of Methyl Mercury Exposur e from Different Fish Types for Asians, Native Americans, and Pacific Islanders Self-Report ed in NHANES.................................. 55 Figure 3-8. Contribution of Foods to Cis-Permethri n Exposure Using SHEDS-Dietary ............ 59 Figure 3-9. Comparison of SHEDS-Dietary Estimates Against CTEPP Duplicate Diet Exposure Data for Cis- and Trans- Permethrin. (Note: The cl oud of yellow lines illustrating 100 modeled variability runs contains the observed data .)................................................. ......... 60 Figure 3-10. SHEDS-Dietary Modeled Exposure of Cis -Permethrin with NHANES and CSFII for 3-5 year-olds..................................

................................................... ............................... 61



# List of Tables

Table 1-1 Key Features of SHEDS-Dietary............

................................................... ..................... 4 Table 2-1. An Example CSFII Food Diary (CSFII ID=28 517-2-2: 1 yr, M, 13.6 kg). ................. 13 Table 2-2. Example CSFII Food Consumption Diary....

................................................... ............ 16 Table 2-3. Example Recipe File for Two Food Items..

................................................... .............. 18 Table 2-4. Example CSFII Food Consumption Data for Two Days........................................... .. 19 Table 2-5. Total Number of DWCS Diaries, By Age Gro up, Gender and Season ....................... 33 Table 2-6. Potential Applications of the SHEDS-Diet ary model .......................................... ....... 35 Table 2-7. Bootstrap uncertainty analyses for cis- permethrin ......................................... ............ 42v

Table 2-8. A Comparison of DEEM-FCID and SHEDS-Die tary Exposure Results for Chemical ABC. ...............................................

................................................... ................................... 45 Table 2-9. Contribution to exposure by commodities for 1-2 year-olds................................. ..... 46 Table 3-1. SHEDS-Dietary Methyl Mercury Exposure by Ethnicity and Age from 1999-2006 NHANES Data........................................

................................................... ........................... 56 Table 3-2 Summary statistics of organic blood level by age group and ethnicity ( μ g/liter) from 1999-2006 NHANES data ..............................

................................................... ................... 57 Table 3-3. Cis-permethrin exposure by age groups fr om SHEDS-Dietary with CSFII Data ....... 59 Table 3-4. SHEDS-Dietary Evaluation against CTEPP D uplicate Diet Data for Cis- and TransPermethrin with CSFII Data.........................

................................................... ...................... 60 Table 3-5. SHEDS-Dietary Modeled Contribution of R AC to Total Food Consumption for CisPermethrin and 3-5 year-olds.......................

................................................... ...................... 62vi 

# Acronyms and Abbreviations

ai/kg bw Active ingredient per kilogram body weight ATUS American Time Use Survey CCA Copper Chromated Arsenic CHAD Consolidated Human Activity Database CRA Cumulative Risk Assessment CSFII USDA Continuing Survey of Food Intakes by Ind ividuals CTEPP Children’s Total Exposure to Persistent Pollu tants study D &A Diversity and Autocorrelation DEEM Dietary Exposure Evaluation Model DWCS Drinking Water Consumption Survey EATS Eating at America’s Table Study ERDEM Exposure Related Dose Estimating Model FCID Food Commodity Intake Database FDA Food and Drug Administration FF Food form FFQ Food Frequency Question FIFRA Federal Insecticide, Fungicide, and Rodentici de Act FQPQ Food Quality Protection Act LOD Limit of detection MEC Mobile Examination Centers NCI National Cancer Institute NHANES National Health and Nutrition Survey NMC N-methyl carbamate insecticide OP Organophosphorous insecticide OPP Office of Pesticide Programs PCT Percent of crop treated PD Persisting Dose PDP USDA Pesticide Data Program PRZM-EXAMS Pesticide Root Zone Model – Exposure Ana lysis Modeling System RAC Raw agricultural commodity SAP Science Advisory Panel SCIGROW Screening Concentration in Ground Water SHEDS Stochastic Human Exposure and Dose Simulation TDS FDA Total Dietary Study US EPA United States Environmental Protection Agenc y USDA United States Department of Agriculture WWEIA NHANES what We Eat in America1 

# 1 Background

1.1 Purpose of SHEDS-Dietary

The EPA’s Office of Pesticide Programs (OPP) is res ponsible for registering all uses of pesticides ( http://www.epa.gov/pesticides/regulating/laws.htm .). The Agency must ensure that a pesticide, when used according to label directions, can be used with a reasonable certainty of no harm to human health and without posing unreasonabl e risks to the environment. The Agency also sets tolerances (maximum pesticide residue lev els) for the amount of the pesticide that can legally remain in or on foods when a pesticide may be used on food or feed crops. Under the Food Quality and Protection Act of 1996 (FQPA), “th e term ‘safe’, with respect to a tolerance for a pesticide chemical residue, means that the Admini strator has determined that there is a reasonable certainty that no harm will result from aggregate exposure to the pesticide chemical residue, including all anticipated dietary exposure s and all other exposures for which there is reliable information.” FQPA specifies ‘all anticip ated dietary exposures’ as the potential for concurrent exposures from ‘all other tolerances in effect for the pesticide’, and ‘all other exposures’ as the potential for concurrent exposure s from ‘non-occupational uses’, such as lawn care and other residential uses of pesticides.

Since the passage of FQPA, the Agency has conducted three types of dietary risk assessments: acute (1-day), chronic, and cancer. Chronic and c ancer risk assessments have been based traditionally on deterministic calculations at the per capita level, using DEEM-FCID TM to calculate exposure by combining food consumption an d residue data (US EPA, FIFRA SAP 1997, 1998). For higher tier, refined acute dietar y risk assessments, OPP has generally used DEEM-FCID (U.S. EPA 2000a) with Monte Carlo simulat ions to obtain an estimate of total daily dietary exposure to a pesticide. To conduct s everal cumulative risk assessments (OP CRA, NMC CRA, Triazine CRA), OPP has used longitudinal a ggregate exposure models (e.g., Calendex-FCID, CARES, Lifeline), peer-reviewed by O PP’s FIFRA SAP (U.S. EPA 1999, 2000b, 2000c, 2000d).

EPA’s Office of Research and Development (ORD), Nat ional Exposure Research Laboratory (NERL) has developed the Stochastic Human Exposure and Dose (SHEDS)-Dietary model version 1 (v1), a probabilistic, population-based d ietary exposure assessment model that simulates individual exposures to chemicals in food and drinking water over different time periods (e.g., daily, yearly) (Xue et al., 2010). SHEDS-Dietary is a module, along with SHEDSResidential, of ORD/NERL’s more comprehensive human exposure model, SHEDS-Multimedia (Zartarian et al., 2008; http://www.epa.gov/heasd/p roducts/sheds_multimedia/sheds_mm.html ; http://www.epa.gov/scipoly/SAP/meetings/2007/081407 _mtg.htm ).

The Stochastic Human Exposure and Dose Simulation m odel for multimedia, multiroute/pathway chemicals (SHEDS-Multimedia) is being developed as a state-of-science computer model for improving estimates of aggregate (single-chemical, multi-route/pathway) and cumulative (multi-chemical, multi-route/pathway ) human exposure and dose. SHEDS-2

Multimedia is the EPA/ORD’s principal model for sim ulating human exposures to a variety of multimedia, multipathway environmental chemicals su ch as pesticides, metals, and persistent bioaccumulative toxins.

SHEDS-Multimedia version 4 is comprised of both the dietary module, SHEDS-Dietary version 1 (Xue, 2010) described in this technical manual an d related user guide (Isaacs et al., 2010a), and a residential module, SHEDS-Residential version 4.0 , described in a separate technical manual and user guide (Glen et al., 2010, Isaacs et al., 2 010b). SHEDS-Residential is a physically-based, probabilistic model that predicts, for user-specifi ed population cohorts, exposures incurred in the residential environment over time via inhaling cont aminated air, touching contaminated surface residues, and ingesting residues from hand- or obje ct- to-mouth activities. To do this, it combines information on chemical usage, human activ ity data (e.g., from time/activity diary surveys, videography studies), environmental residu es and concentrations, and exposure factors to generate time series of exposure for simulated i ndividuals. One-stage or two-stage Monte Carlo simulation is used to produce distributions o f exposure for various population cohorts (e.g., age/gender groups) that reflect the variability and /or uncertainty in the input parameters.

A methodology for linking the residential and dieta ry modules for simulated individuals (based on age, gender, body weight, total caloric intake/M ETS, race, season, weekday and region) will be peer reviewed by EPA’s July 20-22, 2010 FIFRA Sc ientific Advisory Panel. This methodology, described later in this manual in the section entitled, “Algorithm for Matching (Behavioral) Diaries: Food Consumption and Activity Patterns.” has been tested through “soft linking” the two modules with a permethrin pesticid e case study. In the next version of SHEDSMultimedia, the dietary and residential module SAS codes will be merged, so that both types of exposure can be calculated for the same individual after food consumption and activity pattern diaries are appropriately matched. A common Graphi cal User Interface (GUI) will also allow the user to run either module separately, or to run the m both together. The focus of this Technical Manual is the standalone SHEDS-Dietary model.

The SHEDS-Multimedia model, including the SHEDS-Die tary and SHEDS-Residential modules, represent an advancement in science over existing m odels, given some of the key features described below. SHEDS-Dietary allows conducting a dditional analyses for pesticides; quantifying uncertainty in acute dietary risk asses sments; and enhancing chronic and cumulative risk assessments. This model can be applied to othe r chemicals as well as pesticides, and therefore may be useful to other Program Offices an d Agencies.

This Technical Manual describes the algorithms, met hodologies, data sources, and input and output options and capabilities of the SHEDS-Dietar y model v1. ORD, in conjunction with OPP, developed this Agency state-of-the-science model to probabilistically estimate dietary exposures to inform regulatory risk assessments as well as ad dress science questions for research purposes. ORD’s SHEDS-Dietary modeling research focused on en hancing the science of probabilistic dietary exposure assessments. OPP collaboration on SHEDS-Dietary model development has considered criteria for regulatory use: peer-revie wed / transparent (algorithms); publicly available (free or nominal cost); and consistent wi th EPA/OPP policies and guidelines.3

One major purpose of the July, 2010 FIFRA SAP meeti ng is to review SHEDS-Dietary version 1 and SHEDS-Residential (cumulative or aggregate) ver sion 4 modules, and methodology for linking them in the next version of SHEDS-Multimedi a, so they can be used for regulatory decision-making in EPA. Peer review of SHEDS-Multim edia, including its modules, methodologies, and case studies, is necessary for b road regulatory applications in EPA and potentially other Agencies. In 2007 the EPA FIFRA S AP reviewed the residential module of the SHEDS-Multimedia model (version 3), and provided pe er consult of the conceptual dietary module (http://www.epa.gov/scipoly/SAP/meetings/200 7/081407_mtg.htm).

SHEDS-Dietary is a publicly available, transparent model that uses the SAS platform (requires a SAS license for version 9.1 and higher); see specif ic computer requirements in the User Guide, Isaacs et al., 2010a), which provides model adaptab ility and the ability to view, query, analyze, and update the underlying databases (e.g., food con sumption, recipes, residues). It also facilitates food consumption data (NHANES) and recipe updates, and development of alternate exposure modeling assumptions (e.g., stochastic assumption o n residues, by eating occasion or day). SAS Output Tables provide flexibility to develop altern ate contribution analyses, and facilitate linkage with PBPK models. This flexibility contributes to various features of SHEDS-Dietary to allow for exposure analyses in addition to standard dieta ry exposure model results (i.e., exposure at the 95th , 99th, and 99.9 th percentiles of the population).

The SHEDS-Dietary model is consistent with EPA/OPP policies and guidelines in that it addresses FQPA requirements for acute and longitudi nal aggregate and cumulative exposure assessments to pesticides residues in food, drinkin g water, and water used in food preparation while fulfilling the criteria described above for r egulatory-use models.

The following sections of this manual describe the technical details of the SHEDS-Dietary model v1.



## 1.2 Overview of SHEDS-Dietary

SHEDS-Dietary can produce population percentiles of dietary exposure by source and age-gender group; quantify contribution to total exposure by f ood, commodity, and chemical; and be used for eating occasion, sensitivity, and uncertainty analy ses. In general terms, it combines information about food and drinking water consumption data for each reported eating occasion with corresponding chemical residue/concentration data t o estimate human dietary exposures. The model can use either USDA’s Continuing Survey of Fo od Intake by Individuals (CSFII) (1994- 96, 1998) or the NHANES/WWEIA (What We Eat in Ameri ca) dietary consumption data (1999- 2006), along with EPA/USDA recipe translation files (FCID; Food Commodity Intake Database), and available food and water concentration data. S pecifics about combining this information require a number of technical considerations, such as translating foods reported as eaten into raw agricultural commodities using recipe files, sampli ng residues within a day and over time, considering non-detects, and allocating total drink ing water consumption into within-day drinking water events. The goals of the SHEDS-Diet ary model are to use state-of-the-science algorithms, to enhance the science of probabilistic dietary exposure assessments by allowing4

additional analyses, and to better characterize and quantify uncertainty in Agency risk

assessments.

1.2.1 Key Features and Model Options

Some of the key features of SHEDS-Dietary are prese nted in Table 1-1 and described below:

Table Table 1 1 - 1 Key Features of SHEDS Key Features of SHEDS Key Features of SHEDS- Dietary Dietary Dietary

SHEDS -Dietary Option/Feature Available in SHEDS-Dietary? Notes [Option linked to 2007 FIFRA SAP Question] Food Consumption Data Sources CSFII (1994-96, 1998 Children supplemental) Yes Data used in Agency risk assessments (e.g., DEEMFCID TM ) NHANES (1999-2006), Preliminary data Yes Food recipes not available for new foods

Modeling Longitudinal Consumption (Food, Water) Pat terns Within Day Direct DW Consumption: 6 Equal Amounts, Fixed Times (6 am, 9, 12, 3, 6, 9 pm) Yes [ Q3 FIFRA SAP 2007 ] Within Day Consumption of Direct DW: Bayer DW Consumption Survey Yes [ Q3 FIFRA SAP 2007 ]

2-Diary Yes Similar to Method used in Agency risk assessments ( e.g., Calendex-FCID TM )

8-Diary Yes [ Q2 & Q5 FIFRA SAP 2007 ; option available but not recommended; will be dropped in next update since d ata not included in NHANES] Diary Assembly (DA) Yes Currently based on Total C aloric Intake Residues (Food & Drinking Water)

Commodity (FCID) Residues Yes Method used in OPP risk assessments (e.g., DEEMFCID TM )

Food Residue (vs. Commodity) No Option used to assess Arsenic (Journal article); Ca se study assigns residues to FCID commodities; Difficult to Incorporate in GUI Drinking Water Concentrations Yes Single Distribut ions only (e.g., DEEM-FCID TM )

Drinking Water Concentrations – Calendar Year No Randomly Select Year, then apply to corresponding Modeled Day (e.g., Calendex-FCID TM and CARES TM use of 30 years of PRZM-EXAMS predicted DW concentrations) Modeling Food Residues Select Single Residue for all Eating Occasions, by Commodity (RAC-FF) Yes Method used in Agency risk assessments (e.g., DEEMFCID TM ) Select New Residue for differentEating Occasions, by FoodRAC-FF Yes [ Q1 FIFRA SAP 2007 , 32; Option often has little effect for food-only analyses; may ‘add’ uncertainty] Correlation across commodities, across multiple chemicals (products) applied to foods, and No Minimal data to implement; (FIFRA SAP 2007, p.25)5

SHEDS -Dietary Option/Feature Available in SHEDS-Dietary? Notes [Option linked to 2007 FIFRA SAP Question] over subsequent days Multiple Distributions for Commodity (RAC-FF) No E.g., probability of ‘Domest ic’ or ‘Import’; Multiple Distributions for Commodity (RAC-FF), By Season No [ Q5 FIFRA SAP 2007 ] E.g., linking food consumption with seasonal (and/or regional) residues Modeling Drinking Water Concentrations Randomly select new DW concentration each day Yes Method used in Agency risk assessments (e.g., DEEMFCID TM ) Randomly select Year for each Person-iteration, then apply Predicted DW based on Calendar (365) date No Retain seasonal patterns (autocorrelation) in DW concentrations. Method used in Agency risk assessm ents (e.g., Calendex-FCID TM , CARES TM ) Sensitivity/Uncertainty Analyses

Sensitivity Analyses * Requires supplemental routine (e.g., effect of consumption outliers on infant DW exposures – aldic arb memo) Uncertainty Analyses * [ Q4 FIFRA SAP 2007 ] Requires supplemental routine Compiling/Viewing Summary Statistics Total Daily Exposures (99.9 th ) Yes Measure used in OPP assessments Average Daily Exposures (99.9 th ) * Need supplemental routine Eating Occasions (99.9 th ) based on Maximum Exposure over all Eating Occasions Yes Method used to characterize ex posures (NMC CRA)

Eating Occasions with Chemical-Specific Half-Life (99.9 th ) * Method used in DEEM-Based Eating Occasions analyses ; Need supplemental routine. Supplemental program to calculate per capita 99.9 th for single-chemical, single-day was recently incorporated into GUI; needs QC. Plotting Person-Day Exposures Yes Visualize Exposur e Patterns/Persisting Dose Contribution Analyses: Shares of Total Exposure, by Commodity Yes Used to develop risk mitigation optio ns Contribution Analyses: Shares of Total Consumption, by Food (Commodity) Yes Output Summary Results (99.9 th

, CEC, etc.) to File (MS Excel/MS Word) * SAS Editor/Wizard allow users to e xport results View/Query Data (Food Diaries, Recipes, etc.) * SAS Editor allows users to view/qu ery data New Aggregate Contribution Analyses * Need supplemental routine * not implemented in GUI but can be conducted using SAS code6

1.2.1.1 Eating Occasion Analyses In addition to providing estimates of total daily d ietary exposure, SHEDS-Dietary provides the Agency with a capability to conduct ‘Eating Occasi ons’ analyses to refine risks for pesticides and other chemicals; such analyses have been discus sed by several Panels (US EPA FIFRA SAP 1999, 2003, 2005). Research suggests that eating o ccasion analyses may refine the risk assessments for some compounds with short half-live s (U.S. EPA. 2007; Nako et al., 2007). Use of the Bayer Drinking Water Consumption Survey (DWC S) data in SHEDS-Dietary (and in the future, time of drinking water consumption data fro m NHANES) can be used to refine previous drinking water exposure analyses (e.g., revised OP CRA, NMC CRA; http://www.epa.gov/pesticides/cumulative/). Eatin g and drinking occasion algorithms in SHEDS-Dietary enhance the ability to model dietary exposures over short-term durations. Detailed information by eating occasion also allows conducting analyses to determine the contribution to exposure of different food types, c hemicals, and other factors for different agegender groups.

1.2.1.2 Half-life Analyses SHEDS-Dietary v1 includes longitudinal algorithms t o enhance the ability to model dietary exposures over short-term durations - less than a d ay, and up to one year, and to assess the impact of a chemical’s half-life on the exposure results ( e.g., “persisting effects” for organophosphates). For example, the SHEDS-Dietary longitudinal analys es can be used to assess exposure bioindicators persisting across multiple exposure even ts (e.g., on cholinesterase inhibition for organophosphates). By including recovery half-live s, fraction of the peak effect persisting from one exposure event is considered when a second expo sure event occurs later.

1.2.1.3 Uses CSFII or NHANES/WWEIA Food Consumption Data SHEDS-Dietary can use the USDA’s CSFII 1994-96, 199 8 or the NHANES/WWEIA 1999-2006 food consumption data. The 1994-1996, 1998 CSFII d ata base included 5,845 food items consumed by respondents. The NHANES respondents re ported consuming many of those same foods, as well as approximately 580 new foods that were not reported during the CSFII survey. As part of the transition to using the newer NHANES data, the Office of Pesticide Programs is currently planning to update the food recipe data b ase (FCID) to include new foods that were not reported by respondents in the CSFII survey. Appro ximately 20g (or approximately 1% of all food eaten by individuals) of new NHANES foods are not matched to CSFII foods (see Appendix F); however, our analyses have shown this does not affect results. Using this Agency model provides OPP a quick and economical means to assess the National Health and Nutrition Examination Survey (NHANES) food consumption data f or modeling dietary exposures.

1.2.1.4 Options for Simulating Longitudinal Consumption Dat a While the main focus to date in SHEDS-Dietary has b een on the cross-sectional algorithms, the model is capable of modeling longitudinal dietary e xposures to chemicals. For this purpose, SHEDS-Dietary requires the construction of human co nsumption diaries that cover the entire simulation period of a model run. This period is of ten several months, a year, or even longer. For a simulated individual, SHEDS-Diet ary constructs a longitudinal profile of food consumption over a 365 day period with 3 options: a cross-sectional or 2-diary approach; an 8-7

diary approach, and the “D&A” approach (Glen et al. , 2007) described below. Issues relating to longitudinal diary construction are described later in this manual.

In both CSFII and NHANES, there are two-day dietary consumption data for the subjects. Therefore, we can use two-day data for the same per son together with many run interactions to assemble the longitudinal data. For example, the re sidues are randomly assigned to one person with two-day dietary consumption data, which is cou nted as one interaction. The same two-day data will be randomly assigned with different resid ue concentrations. This is another interaction. In this way, many interactions can be generated. Th en, data from the first interaction will be counted as day 1 and 2, the second as day 3 and 4 a nd so on. In this way, longitudinal data will be assembled.

The 8-diary longitudinal algorithm (Xue et al., 200 4) is the same approach used in the SHEDSMultimedia model, which constructs longitudinal act ivity profiles from human activity diaries drawn from EPA’s CHAD (Consolidated Human Activity Database; McCurdy et al., 2000; http://www.epa.gov/chadnet1 ). CHAD typically includes just one day (24 hours) of activities from each person. SHEDS-Dietary creates modeled in dividuals (reference population) by randomly drawing a person from the Census data. Th e food consumption diaries used by SHEDS-Dietary (NHANES or CSFII; see later sections) are grouped by age and gender, and for each of these age-gender cohorts, ‘diary pools’ are created based on Season and Day of Week (weekday or weekend). For each modeled individual, SHEDS-Dietary constructs a longitudinal profile of food consumption by randomly selecting 8 food diaries (one weekend and one weekday, for each of the four seasons) from the app ropriate cohort-diary pools. NHANES/WWEIA does not provide dates, so SHEDS-Dieta ry randomly draws consumption diaries from that survey to use this approach.

The August 2007 FIFRA SAP reviewed this approach fo r dietary exposure assessment, and found 8 diaries to be insufficient. Thus, a more d etailed and desirable option for assembling year-long diaries is given in Glen et al., 2007. Th e user chooses target behavior and statistics to control within- and between- person variance, and d ay-to-day autocorrelation. Diaries are preferentially sampled to produce the target behavi or. This method, referred to as the “D&A approach”, requires a few additional inputs to be d esignated by the user, but allows for more control over the properties of the assembled diarie s. This diary assembly method requires the

user to: 1) select the diary property most relevant to expos ure for the current application (e.g., Total Calories); 2) specify the “D” (diversity) statistic, which rel ates the within-person and between-person variances for this diary property; and 3) specify the 1-day lag autocorrelation “A” in thi s diary property.

Guideline values for the D and A statistics for a n umber of diary properties have been calculated using the steps below for the permethrin case study , but other values can be used as more data become available: 1) Calculate the total amount of major vegetables cont ributing to dietary permethrin exposure. Those vegetables are spinach, cabbage, le ttuce, parsley, celery and tomato.8

2) Use the total amount of those vegetables consumptio n (grams) per day as index to calculate D and A statistics. 3) Results: D=0.27 and A=0.06 4) Total calories consumption was used with 0.3 for D and 0.1 for A statistics, based on data from Lu et al. (2006a,b) (Alex Lu, personal communi cation), to assemble the longitudinal diary for one year.

1.2.1.5 Multi-chemical (Cumulative Exposure Assessment) Cap ability There are two key differences between the single ch emical and cumulative exposures in the SHEDS-Dietary model: 1) co-occurrence of the chemic als; and 2) addition of exposures among chemicals with similar mode of action. Different SA S code modules in the model are used to accommodate these differences even though they shar e a common algorithm. For pesticides, there is a small data set storing pesticide codes, usually in three letters or digits. Selected pesticides (pesticide codes) will be used to merge the residue data. Due to co-occurrence, pesticides measured in the same raw agricultural co mmodity (RAC) or food item will be stored in the same place labeled by the same identifier so that it will be selected as whole by Monte Carlo simulation. For adding exposures to obtain cu mulative exposure, relative potency factors are used so that exposures of different pesticides can be added, weighted by the relative toxicities.

1.2.1.6 Linkage to PBPK Models The timing of exposures throughout a simulated day becomes important as the Agency moves toward integrating dietary exposure models with phy siologically-based pharmacokinetic models. To account for eating occasions, SHEDS-Dietary pre serves information from the detailed food diaries and corresponding exposure calculations. L inkage between SHEDS-Dietary exposure outputs, which preserve variability of exposures wi thin a day

, and physiologically-based pharmacokinetic (PBPK) models allows for refined do se and risk analyses, and evaluation of SHEDS-Dietary model performance against NHANES biom onitoring data (Xue et al., 2010).

1.2.1.7 Sensitivity and Uncertainty Analyses SHEDS modeling research has involved developing and applying new methods for sensitivity and uncertainty analyses (Xue et al., 2006; Zartari an et al., 2007; http://www.epa.gov/heasd/products/sheds_multimedia/ sheds_mm.html ; http://www.epa.gov/scipoly/SAP/meetings/2007/081407 _mtg.htm). These methods can be applied to different model applications for identif ying key factors, outliers, and data needs.

1.2.2 Plans and Future Research Needs Plans and future research needs for SHEDS-Dietary i nclude the following:

 Apply to other case studies with PBPK linkage, sens itivity and uncertainty analyses, model evaluation;

 Expand model applications to local/community scale for different chemicals;

 Refine longitudinal algorithms based on available d ata;

 Merge dietary & residential modules (match food con sumption and activity diaries);9

 Analyze impact of different residue sampling: same vs. different residues within a day for same foods eaten by an individual;

 Possible refinements to drinking water allocations;

 Explore enhancements to uncertainty analyses;

 Expand the model to local scale applications for di fferent chemicals, seasons, regions; methods (including analyses of CSFII and NHANES) to examine importance of regionand season- specific dietary consumption amounts an d patterns on dietary exposure estimates;

 Consider other data sets for considering enhancing within-day modeling of exposures e.g. Child Development Supplement to Panel Study of Inco me Dynamics ( http://psidonline.isr.umich.edu/CDS/time_diary_read me.html ), American Time Use Survey (ATUS), http://www.bls.gov/tus/#overview , University of Maryland archive on recent and historical data sets on individual time use and activity patterns, http://www.webuse.umd.edu ); and

 Conduct more research on sampling drinking water co ncentrations (e.g., randomly select a year then apply daily concentrations throughout t he modeled calendar year).10 

# 2 SHEDS-Dietary Methodology

The SHEDS-Dietary Module overview is illustrated in Figure 2-1, and details on the algorithms are given in the following sections and the annotat ed code in Appendix G.

Monte Carlo sampling applied

NHANES Consumption: Food consumption data from NHAN ES Residue Concentration: Residue concentration data b y food item or commodity from TDS Distribution fitting: fittings of residue data into suitable statistical distribution Food Item: food products people in the survey consu med such pizza, raw apple Commodity: raw agriculture commodity (RAC) Usage factors: Pesticide usage percentages by RAC f rom USDA. Process factors: concentration or dilution factors d ue to processes of food from RAC into food products. Recipe files (EPA FCID): data base for percents of v arious RACs for the food products.

Figure Figure 2 2 - 1 . SHEDS . SHEDS . SHEDS- Dietary Methodology (modified from Xue et al. 2010 Dietary Methodology (modified from Xue et al. 2010)11



### Normal Uniform 

# C onsumption (g food/kg bw) 

# x 

# R esidue (mg pesticide/gram food) 

# =



# E xposure (mg pesticide/kg bw)

MONTE CARLO SIMULATION each MC trial is an iteration => simulated exposure event a series of trials => simulated distribution of exp osures

Exposure distribution Exposure Outcomes

Figure Figure 2 2 - 2 . Monte Carlo Simulation in Dietary Exposure Model ng . Monte Carlo Simulation in Dietary Exposure Modeli

Xue et al., 2010 describes the SHEDS-Dietary method ology shown in Figure 2-1. For estimating daily dietary exposure, detailed NHANES (or CSFII) food diaries are used by the SHEDSDietary model to simulate food ingestion exposures by separ ate eating occasions for a simulated individual (Figure 2-1). SHEDS-Dietary can use residues for food items as consumed, as wel l as residues of raw agricultural commodities (RAC). Th e reported food items are matched with food items in the FDA’s Total Diet Study (TDS) where pos sible (see step 1 in Figure 2-1). If TDS residues are available for a particular food (e.g., rice, chicken), then SHEDS-Dietary randomly draws a TDS residue from that corresponding residue distribution of the same food. Otherwise, the model applies the FCID recipe files to the NHAN ES or CSFII food items and randomly selects a residue for each of the RAC ingredients a ccording to the recipe (see step 2 in Figure 2- 1). Note that SHEDS-Dietary version 1 does not impl ement the option of sampling residues from food items as consumed, e.g., TDS.

Through the recipe files, the unmatched foods consu med are matched by RAC so that residues for those foods can be calculated. One option in SH EDS-Dietary version 1 for sampling residues within a day is to draw the same residue value if t hat RAC is found in the same foods. A second option is to draw different residue values for the same foods within a day. For non-detects, the model can assign zero or 1⁄2 LOD, depending on the ch emical usage information (see details below). For each NHANES food diary, SHEDS-Dietary s elects a residue value from an empirical distribution for each TDS food or RAC. While a part icular commodity may be used in multiple12

foods, the cooking method may differ, and thus, it will have a different food form. Process factors can then be applied (see step 3 in Figure 2 -1). These factors account for food changes and related concentration changes due to dilution, dryi ng, etc.. Each simulated individual’s exposure for each commodity is calculated by multiplying eat ing occasion consumption with corresponding residues. Summation of exposures from every eating occasion for one day yields the individual’s total daily exposure (see step 4 i n Figure 2-1). Monte Carlo simulation is applied to generate population estimates of dietary exposure (see Figure 2-2). More details on the food and drinking water ingestion exposure algorith ms are given below.



## 2.1 Algorithm for Estimating Exposure to Pesticides and Other Toxicants from Food Consumption

SHEDS-Dietary v1 incorporates the data and decision s illustrated in Figure 2-1 above, and calculations shown in Equations 1 and 2 below, to c alculate food ingestion exposure. The model uses food consumption diaries to simulate individua ls’ food ingestion exposures by separate eating occasions. Reported consumption data are co mbined with sampled chemical residues in foods consumed, and concentration or dilution facto rs that adjust the residues for changes due to food processing (Figure 2-1). This section briefly introduces the equations used for calculating dietary exposure and their inputs, which are detail ed in subsequent sections.

Total daily exposure is calculated by summing expos ures across all commodities, as depicted in Equation (1). Each simulated individual’s exposure for each commodity is calculated by multiplying the eating occasion consumption with th e corresponding residues and process factors:

Equation (1) – SHEDS-Dietary Equation for Estimatin g an Individual’s Exposure from a Single Eating Occasion

Individual’s Dietary Exposure for a Single Eating O ccasion [mass chemical] = amount of food item consumed [mass food] x concentration in the f ood item [mass chemical/ mass food] x process factors

Equation (2) – SHEDS-Dietary Equation for Estimatin g an Individual’s Total Daily Exposure from All Eating Occasions

Individual’s Total Daily Exposure = Σ ( Individual’s Dietary Exposure for a Single Eating

Occasion) over all eating occasions in a given day.

Population estimates are obtained by applying Monte Carlo simulation as shown in Figure 2-2; the algorithms for computing an individual’s total daily exposure are repeated thousands of times to obtain a population cumulative density function (CDF).13

2.1.1 Consumption The food consumption diaries used by SHEDS-Dietary and other dietary exposure models (such as those shown in Table 2-1) contain information on the timing and amounts consumed as reported by the survey respondents. Note that food and water consumption quantities are recorded in units of ounces, cups, or by count (cf. Egg, whole, Table 2-1). These units are converted to grams and ml for calculations. Append ix A contains details on food consumption data used in SHEDS-Dietary version 1.

Table Table 2 2 - 1 . An Example CSFII Food . An Example CSFII Food . An Example CSFII Food Diary (CSFII ID=28517 Diary (CSFII ID=28517 Diary (CSFII ID=28517- 2 - 2: 1 yr, M, 13.6 kg). 2: 1 yr, M, 13.6 kg). 2: 1 yr, M, 13.6 kg).

SEQN Time of

Day Food Description Amount (unit code) Consump

tion (gm) Food

Source /1

1 7:00 AM Milk, cow's, fluid, whole 6 fl.oz (10205) 183 Store

2 Egg, whole, fried W/ LARD 2 XX (60919) 92 Store

3 10:15 AM White potato, home fries W/ LARD 2 C (10205) 388 Store

4 Chicken, drumstick, with or without bone, roasted, skin eaten 1 XX (61343) 52 Store

5 6:00 PM White potato, home fries W/ LARD 2 C (10205) 388 Store

6 8:00 PM Milk, cow's, fluid, whole 6 fl.oz (10205) 183 Store
/1 The Food Source variable is based on the questi on, ‘Where was the food item obtained?’ (1=store, e tc.).

2.1.2 Residues and Process Factors In principle, food residues as well as drinking wat er concentrations may also vary by eating occasion and/or foods consumed throughout the day. With that modeling assumption, the SHEDS-Dietary eating occasion approach tracks expos ures throughout the simulated day based on the food diary data. Currently in SHEDS-Dietary , the user has 2 options for sampling residues consumed by an individual on multiple eati ng occasions within a day: (1) same residue sampled for same RAC and food items within a given day; (2) different residues sampled for all RAC and food items within in a given day.

In SHEDS-Dietary version 1.0, empirical distributio ns are be used for raw agricultural commodities (RAC; see Appendix C). Reported food i tems are matched with food items (e.g., 2% milk, raw apple) in the Total Diet Study (TDS; http://www.fda.gov/Food/FoodSafety/FoodContaminants Adulteration/TotalDietStudy/default.ht m) where possible; the model randomly draws a resid ue from that corresponding TDS residue distribution of same food. For unmatched foods, th e model applies FCID recipe files to the food14

items and randomly selects a residue for each of th e RAC ingredients according to the recipe so that residues for those foods can be calculated (th is approach is used for pesticides).

The current version of SHEDS-Dietary does not inclu de the option of matching reported foods (note that modifications were conducted for the ars enic case study to allow food items as consumed as depicted in Figure 2-1) where possible with foods reported “as eaten” (e.g., pizza rather than the pizza RAC tomatoes, flour, etc.) (e .g., in the FDA in the TDS), so that residues for those foods can be sampled; the model randomly draws a residue from that corresponding food residue distribution. A particular commodity m ay be used in multiple foods, with different cooking methods; thus, it will have a different foo d form reported. Process factors can be applied that account for food changes and related concentra tion changes due to dilution, drying, etc. Through recipe files (see Appendix E), unmatched fo ods consumed are matched by RAC so that residues for those foods can be calculated. SHEDS-D ietary randomly selects a residue from the corresponding RAC-food form distributions for those unmatched foods, according to the recipe amounts of those RAC. The exposure from each commod ity-food form (RAC-FF) is calculated by multiplying that residue value with the amount c onsumed.

Assignment of RAC residues for non-detects depends on the percent detected in PDP for the commodity and the percent of crops using that chemi cal. For example, if 20% of a crop is treated with Chemical X, and 5% of samples in PDP had detec table residues, then SHEDS-Dietary used the actual values for the 5%, assumes 1⁄2 LOD for 15% , and 0 for 80%. If the crop is not treated with Chemical X, the LOD is assigned zero.

2.1.3 Sample calculation Total daily exposure is calculated in SHEDS-Dietary by summing chemical exposures across all commodities. Summation of chemical exposures from every commodity and every eating occasion for one day yields the individual’s daily total dietary exposure.

This equivalence is illustrated with the following simple numerical example using the diary from a 1 yr old child (Table 2-1; CSFII ID=28517-2-2). If we assume that 47 grams of potatoes was consumed at 10:15 am from food, “White potato, home fries W/ LARD” and a residue of 1 ppm was drawn only for ‘potatoes’, then the exposure is 0.047 mg for this eating occasion. At 6:00 pm, the same amount of the same food was consumed, therefore, 1 ppm of residue for the potato was used again due to the same food, then exposure for this eating occasion is also 0.047 mg: Exposure = 47 gms x (1/1000) x 1 (mg/kg) = 0.047 m g.

If exposure from egg was 0.05 mg and that was the o nly other food to contribute any exposure that particular day, then total daily exposure for the subject will be the summation of 0.047,0.047 and 0.05, i.e. 1.44 mg/day. Options for modeling fo od residues (i.e., randomly drawing different residues) are discussed in a subsequent section. A model specification could select a new residue for the same RAC-FF consumed through different food s (e.g., milk versus other dairy products).

This process is repeated for many simulated individ uals (for each food consumption diary, or simulated person-day) via Monte Carlo sampling (see Figure 2-2) to generate population15

estimates of dietary exposure (Xue et al., 2010). For any particular diary, a Monte Carlo simulation is performed to select a residue concent ration for each food commodity (raw agricultural commodity – food form; RAC-FF).



## 2.2 Drinking Water Exposure Algorithm
The SHEDS-Dietary drinking water exposure algorithm is similar to that for food exposure. Because the CSFII data does not provide information on timing and amounts of direct water intake throughout the day, SHEDS-Dietary currently distributes total direct water consumption from this database in 6 equal amounts at 6 fixed ti mes (6am, 9am, 12pm, 3pm, 6pm, 9pm). The more recent NHANES 2005-2006 did collect informatio n on timing and amounts of direct water intake throughout the day, so that information can also be used directly in SHEDS-Dietary in the future to assess timing and amounts of direct drink ing water (e.g., tap, bottled) and indirect drinking water (e.g., infant formula, ‘kool aid’, c offee, tea, water used in cooking) intake within a simulated person-day (see Appendix A). Total drink ing water consumed (both direct and indirect water consumption) is assumed to contain t he same concentration, i.e., only one concentration value is selected in the Monte Carlo simulation for each eating occasion. SHEDSDietary randomly draws a drinking water concentrati on for each person-day (similar to DEEMFCID). One residue value is randomly selected and multiplied by total water intake to obtain drinking water exposures. In principle, drinking wa ter concentrations may vary based on source (e.g., tap, bottled, other source); this is an area of future research. Currently, SHEDS-Dietary randomly draws drinking water concentration for any given day (no seasonality).



## 2.3 Inputs This section summarizes the data used by SHEDS-Diet ary to assess dietary exposures.

2.3.1 Food and Indirect Water Consumption Data The primary sources of consumption data used in SHE DS-Dietary to model dietary exposures to pesticides are the food consumption diaries in the U.S. Department of Agriculture’s Continuing Survey of Food Intakes by Individuals (CSFII) datab ase and in the NHANES/What We Eat in America (WWEIA) 1999-2006 database (see Appendices A, C, D, E). These surveys contain information regarding the real-time reported amount of food and water consumed by individuals, i.e. amounts of food and drinking water reported by individuals for each separate eating occasion.

The CSFII food diaries contain information collecte d through a multiple pass 24-hour dietary recall instrument that was administered by trained interviewers in the respondents’ homes (Day 1) or by phone interview (Day 2). Individuals were asked to provide food intake on 2 nonconsecutive days (3 to 10 days apart) as well as socioeconomic and health-related information. A total of 20,607 individuals provide d two 24 hour food diaries (total of 41,214 diaries) during the initial survey period, 1994-1996, and th rough a children’s supplemental survey conducted in 1998 to address FQPA requiremen ts that the USDA provide food intake data for a statistically adequate sample of children for use by the EPA to estimate exposure to pesticide residues. Table 2-2 shows an example diar y from CSFII.16

The NHANES/WWEIA 1999-2006 food consumption data (5 3,522 diaries) are also 24 hour recalls. The first day (Day 1) diary was collected through in-person interviews in the Mobile Examination Centers (MEC), while the second day 24 hour recall diary is collected by telephone, approximately 10 days after the in-person interview

.

Table Table 2 2 - 2 . Example CSFII Food Consumption Diary . Example CSFII Food Consumption Diary . Example CSFII Food Consumption Diary

DAYCODE OCC_TIME foodname FOODAMT (grams)

1 800 MILK, COW'S, FLUID, 1% FAT 183 1 800 APPLE JUICE, W/ ADDED VITAMIN C 186 1 800 APPLE, RAW 138 1 800 CHEERIOS 30 1 800 MILK, COW'S, FLUID, 1% FAT 122 1 930 CRACKERS, CHEESE 124 1 1230 WHITE POTATO, CHIPS (INCL FLAVORED) 40.5 1 1230 PEPPER, SWEET, RED, RAW 74.5 1 1230 PEAR, RAW 166 1 1230 APPLE, RAW 69 1 1230 MILK, COW'S, FLUID, 1% FAT 122 1 1230 BREAD, POTATO 52 1 1230 TUNA SALAD 26 1 1530 APPLE JUICE, W/ ADDED VITAMIN C 186 1 1830 RICE, FRIED, W/ MEAT/POULTRY 198 1 1830 PORK, SPARERIBS, COOKED, LEAN ONLY 72 1 1830 CHICKEN PATTY/FILLET/TENDERS, BREADED, COOKED 176 1 1830 MILK, COW'S, FLUID, 1% FAT 122 1 1830 CRANBERRY JUICE DRINK W/VIT C ADDED(INCL COCKT AIL) 126.5 1 1900 ICE CREAM, REGULAR, NOT CHOCOLATE 44 2 800 MILK, COW'S, FLUID, 1% FAT 122 2 800 TEA, MADE FROM POWDERED INSTANT, PRESWEETENED 11 8.4 2 800 CHEERIOS 37.5 2 800 MILK, COW'S, FLUID, 1% FAT 183 2 1000 PUFFED RICE CAKE 9 2 1000 TEA, MADE FROM POWDERED INSTANT, PRESWEETENED 1 18.4 2 1200 CHICKEN VEGETABLE SOUP,W/RICE,MEXICAN(SOPA / C ALDO DE POLLO) 242 2 1200 BREAD, ITALIAN, GRECIAN, ARMENIAN 20 2 1200 MILK, COW'S, FLUID, 1% FAT 122 2 1500 APPLE, RAW 138 2 1830 CORN DOG (FRANKFURTER/HOT DOG W/ CORNBREAD COA TING) 88 2 1830 MILK, COW'S, FLUID, 1% FAT 122 2 1830 COUSCOUS, PLAIN, COOKED, FAT ADDED IN COOKING 81 2 1830 PORK & VEG (W/ CAR/DK GREEN, NO POTATO), NO SA UCE 81 Example of food consumption data for HHID=11328 and SPNUM=2 for two days (Female and 1 years old)

2.3.2 Recipe Files For the purpose of assessing food tolerance, the EP A developed the Food Commodity Intake Database (FCID) that converts CSFII food items (e.g ., apple pie, hamburger, milk and other diary products) into Raw Agricultural Commodities (RAC) b ased on likely cooking method and food form (FF) (Appendix E). The FCID database contains recipes or each food item reported in the 1994-1996, 1998 CSFII diaries. These recipes (see Table 2-3) allow the model to calculate contributions from each food (e.g., pork and vegeta bles, fried rice) to aggregate exposures. FCID17

recipe files break down foods into 553 RAC. Recipe s are being developed by OPP for new NHANES/WWEIA food items (anticipated release, Fall 2010).

The FCID commodity diaries may underestimate dietar y exposures from some food items. For example, the FCID recipe decomposes an 8 oz. glass of whole milk (244 g) into three components: water (88%), fat (3.3%), and non-fat so lids (8.7%). A simulation based on the food recipes entails randomly selecting a residue for ea ch of the three components, and calculating contributions based on the corresponding weights: 2 14 g, 9 g, and 21 g, respectively. The assumption that residues are independent may lead t o underestimating exposures to the extent that some components (e.g., water and fat) contain residues (treated), while the other components (non-fat solids) do not. Ideally, the user may wan t to directly apply the PDP data, because milk samples, collected from distribution centers and su permarkets, reflect foods as consumed by persons, and thus, do not require additional modeli ng assumptions regarding correlations (independence) across the components. The advantag e of the FCID commodity diaries is that it facilitates developing anticipated residues for hun dreds of other diary products, since the recipes account for different contributions from water, fat , and non-fat solids. SHEDS-Dietary version 2 will allow the user to specify residues for both fo ods as eaten and/or commodities - as applied in Xue et al., 2010.18

Table Table 2 2 - 3 . Example Recipe File for Two Food Items . Example Recipe File for Two Food Items . Example Recipe File for Two Food Items

Food Items RAC CM_name CS_name FF_name Percent

Bean, lima, succulent Not specified Cooked Fresh or N/ S 3.96 Bean, snap, succulent Not specified Cooked Fresh or N/ S 10.45 Carrot Not specified Cooked Fresh or N/S 15.84 Corn, sweet Not specified Cooked Fresh or N/S 15.84 Pea, succulent Not specified Cooked Fresh or N/S 10.45 Pork, fat Not specified Cooked Fresh or N/S 5.995 Pork, meat Not specified Cooked Fresh or N/S 37.127 Bean, mung, seed Fried Cooked Fresh or N/S 0.486 Chicken, fat Fried Cooked Fresh or N/S 0.504 Chicken, meat Fried Cooked Fresh or N/S 6.285 Corn, field, oil Not specified Refined Not Applicable 0.201 Cottonseed, oil Not specified Refined Not Applicable 0.319 Egg, whole Fried Cooked Fresh or N/S 10.741 Olive, oil Not specified Refined Not Applicable 0.082 Onion, green Fried Cooked Fresh or N/S 2.8 Pea, succulent Fried Cooked Fresh or N/S 3.42 Peanut, oil Not specified Refined Not Applicable 0.055 Rapeseed, oil Not specified Refined Not Applicable 0.169 Rice, white Fried Cooked Fresh or N/S 24.24 Safflower, oil Not specified Refined Not Applicable 0.001 Sesame, oil Not specified Refined Not Applicable 0.001 Soybean, oil Not specified Refined Not Applicable 3.556 Soybean, seed Fried Cooked Fresh or N/S 0.956 Sunflower, oil Not specified Refined Not Applicable 0.007 Water, indirect, all sources Fried Cooked Fresh or N/S 39.31 Wheat, flour Fried Cooked Fresh or N/S 0.376 PORK & VEG (W/ CAR/DK GREEN, NO POTATO), NO

SAUCE

'RICE, FRIED, W/ MEAT/POULTRY19

Table Table 2 2 - 4 . Example CSFII Food Consumption Data for Two Days Example CSFII Food Consumption Data for Two Days Example CSFII Food Consumption Data for Two Days

DAYCODE OCC_TIME foodname RAC CM_name CS_name FF_name amount_gram

1 800 MILK, COW'S, FLUID, 1% FAT Milk, fat Not specified Uncooked Fresh or N/S 1.94 1 800 MILK, COW'S, FLUID, 1% FAT Milk, nonfat solids Not specified Uncooked Fresh or N/S 16.21 1 800 MILK, COW'S, FLUID, 1% FAT Milk, water Not specified Uncooked Fresh or N/S 164.85 1 800 APPLE JUICE, W/ ADDED VITAMIN C Apple, juice Not specified Uncooked Fresh or N/S 185.93 1 800 APPLE, RAW Apple, fruit with peel Not specified Uncooked Fresh or N/S 138.00 1 800 CHEERIOS Beet, sugar Not specified Refined Not Applicable 0.40 1 800 CHEERIOS Cassava Not specified Cooked Dried 0.01 1 800 CHEERIOS Corn, field, starch Not specified Cooked Dried 0.93 1 800 CHEERIOS Oat, groats/rolled oats Not specified Cooked Dried 29.94 1 800 CHEERIOS Potato, flour Not specified Cooked Dried 0.01 1 800 CHEERIOS Rice, flour Not specified Cooked Dried 0.01 1 800 CHEERIOS Sugarcane, sugar Not specified Refined Not Applicable 0.51 1 800 CHEERIOS Wheat, flour Not specified Cooked Dried 0.01 1 800 MILK, COW'S, FLUID, 1% FAT Milk, fat Not specified Uncooked Fresh or N/S 1.29 1 800 MILK, COW'S, FLUID, 1% FAT Milk, nonfat solids Not specified Uncooked Fresh or N/S 10.81 1 800 MILK, COW'S, FLUID, 1% FAT Milk, water Not specified Uncooked Fresh or N/S 109.90 1 930 CRACKERS, CHEESE Barley, flour Baked Cooked Fresh or N/S 0.45 1 930 CRACKERS, CHEESE Cottonseed, oil Not specified Refined Not Applicable 2.08 1 930 CRACKERS, CHEESE Milk, fat Baked Cooked Fresh or N/S 4.49 1 930 CRACKERS, CHEESE Milk, nonfat solids Baked Cooked Fresh or N/S 4.08 1 930 CRACKERS, CHEESE Milk, water Baked Cooked Fresh or N/S 1.15 1 930 CRACKERS, CHEESE Pepper, nonbell, dried Baked Cooked Fresh or N/S 0.27 1 930 CRACKERS, CHEESE Soybean, oil Not specified Refined Not Applicable 23.91 1 930 CRACKERS, CHEESE Wheat, flour Baked Cooked Fresh or N/S 92.66

1 1230 WHITE POTATO, CHIPS (INCL

FLAVORED) Corn, field, oil Not specified Refined Not Applicable 1 .30

1 1230 WHITE POTATO, CHIPS (INCL

FLAVORED) Cottonseed, oil Not specified Refined Not Applicable 0. 97

1 1230 WHITE POTATO, CHIPS (INCL

FLAVORED) Potato, chips Fried Cooked Fresh or N/S 26.14

1 1230 WHITE POTATO, CHIPS (INCL

FLAVORED) Rapeseed, oil Not specified Refined Not Applicable 0.54

1 1230 WHITE POTATO, CHIPS (INCL

FLAVORED) Safflower, oil Not specified Refined Not Applicable 0.0 0

1 1230 WHITE POTATO, CHIPS (INCL

FLAVORED) Soybean, oil Not specified Refined Not Applicable 11.02

1 1230 WHITE POTATO, CHIPS (INCL

FLAVORED) Sunflower, oil Not specified Refined Not Applicable 0.0 4 1 1230 PEPPER, SWEET, RED, RAW Pepper, bell Not specified Uncooked Fresh or N/S 74.50 1 1230 PEAR, RAW Pear Not specified Uncooked Fresh or N/S 166.00 1 1230 APPLE, RAW Apple, fruit with peel Not specified Uncooked Fresh or N/S 69.00 1 1230 MILK, COW'S, FLUID, 1% FAT Milk, fat Not specified Uncooked Fresh or N/S 1.29 1 1230 MILK, COW'S, FLUID, 1% FAT Milk, nonfat solids Not specified Uncooked Fresh or N/S 10.81 1 1230 MILK, COW'S, FLUID, 1% FAT Milk, water Not specified Uncooked Fresh or N/S 109.90 1 1230 BREAD, POTATO Beet, sugar Not specified Refined Not Applicable 1.29 1 1230 BREAD, POTATO Cottonseed, oil Not specified Refined Not Applicable 0.13 1 1230 BREAD, POTATO Guar, seed Baked Cooked Fresh or N/S 0.06 1 1230 BREAD, POTATO Milk, fat Baked Cooked Fresh or N/S 0 .49 1 1230 BREAD, POTATO Milk, nonfat solids Baked Cooked Fresh or N/S 2.27 1 1230 BREAD, POTATO Milk, water Baked Cooked Fresh or N/S 0.17 1 1230 BREAD, POTATO Potato, flour Baked Cooked Fresh or N/S 1.17 1 1230 BREAD, POTATO Soybean, flour Baked Cooked Fresh or N/S 0.29 1 1230 BREAD, POTATO Soybean, oil Not specified Refined Not Applicable 1.50 1 1230 BREAD, POTATO Sugarcane, sugar Not specified Refined Not Applicable 1.64 1 1230 BREAD, POTATO Wheat, flour Baked Cooked Fresh or N/S 33.30 1 1230 TUNA SALAD Beet, sugar Not specified Refined Not Applicable 0.02 1 1230 TUNA SALAD Celery Not specified Uncooked Fresh or N/S 1.90 1 1230 TUNA SALAD Coriander, leaves Not specified Cooked Canned 0.01 1 1230 TUNA SALAD Coriander, seed Not specified Cooked Canned 0.01 1 1230 TUNA SALAD Corn, field, syrup Not specified Cooked Canned 1.68 1 1230 TUNA SALAD Cucumber Not specified Cooked Canned 2.00 1 1230 TUNA SALAD Egg, whole Not specified Cooked Canned 0.16 1 1230 TUNA SALAD Egg, yolk Not specified Cooked Canned 0.11 1 1230 TUNA SALAD Fish-saltwater finfish, tuna Not specified Cooked Canned 14.08 1 1230 TUNA SALAD Ginger, dried Not specified Cooked Canned 0.01 1 1230 TUNA SALAD Herbs, other Not specified Cooked Canned 0.01 1 1230 TUNA SALAD Lemon, juice Not specified Cooked Canned 0.05 1 1230 TUNA SALAD Onion, dry bulb Not specified Uncooked Fresh or N/S 2.54

Example of food consumption data for HHID=11328 and SPNUM=2 for two days (Female and 1 years old)20

Table 2-2 provides an example of real dietary consu mption data for one person over two days. Table 2-3 shows an example recipe files for two foo d items consumed by the subjects (there are many other food items in the recipe files not shown ). Through recipe files, consumption data in Table 2-2 can be converted into the format in Table 2-4, so that the exact amount of RAC consumed by the subject will be used to be assigned with residue concentrations by RAC.

2.3.3 Direct Water Consumption Data As discussed above, SHEDS-Dietary version 1 distrib utes total direct water consumption from the CSFII database in 6 equal amounts at 6 fixed ti mes (6am, 9am, 12pm, 3pm, 6pm, 9pm). The more recent NHANES 2005-2006 did collect informatio n on timing and amounts of direct water intake throughout the day, so that information can also be used directly in SHEDS-Dietary in future versions.

Another option for drinking water consumption data in SHEDS-Dietary is available. For any modeled individual, a drinking water diary is rando mly selected from the Bayer DWCS data based on similar socioeconomic characteristics (age , gender, season). Bayer CropScience sponsored a study on direct drinking water consumpt ion entitled “Drinking Water Consumption Survey” (DWCS), to evaluate this issue (Barraj et.a l. 2004). The objective of this study was to obtain a distribution of water intake for a 24-hour time period that was nationally representative sample of the US population. The DWCS was conducte d in two waves, in August 2000 (wave 1=- summer), and March 2001 (wave 2 = winter). The report provides the following description on the study design (Barraj et.al. 2004, pp.9-10):

“The National Product Database group (NPD) was cho sen to conduct this survey because of its experience in tracking the consumpti on habits of the US population since 1980 through its National Eating Trends (NET ® ) service (NET

® , 2004).” “ Two nationally representative samples (one for each wav e) were extracted from a core sample of 250,000 households from NPD’s Home Testing Insti tute (HTI) consumer panel. The sample for wave 1 included 3,000 households randoml y selected from the core sample of 250,000 households, while in an effort to increase the number of children in the survey, the sample for wave 2 included 650 households rando mly selected from households with children less than 6 years of age in addition to 3, 000 households randomly selected from the core sample.” “One thousand nine hundred ninety -two participants in 994 households (33% response rate) completed the first wave of the survey, and 2,950 participants in 1,320 households (36% response rate ) completed the second wave of the survey.”

Participants recorded their water consumption (time of day and amount consumed) over a oneweek (7 day) period. The following information was collected in the DWCS diaries:

• Date and day of the week;

• Age and gender of the household member;

• Source of the home’s drinking water (municipal, wel l);

• Time period of water consumption episode (18 hourly intervals starting at 6 am, and one 6 hr interval corresponding to the midnight -6 am period);21 • Number of ounces of water consumed per time period (in 2-ounce bins);

• Where the consumption episode occurred (home/work o r school/other);

• Whether the water was consumed with a meal; and

• The type of water consumed (tap/bottled).

A number of diaries were not used due to incomplete or missing information. The resulting database contained data from 4,198 individuals from 2,154 households, providing a total of 27,282 person-day diaries (approximately 83% of the total of all participants returned diaries for all 7 days).

Pesticide Use (% crop treated) The pesticide use information, in particular the pe rcent of crop treated (PCT) with a particular chemical, is used to determine how many samples wer e not treated and may be assumed to have no residues (true zero). This variable may come fro m either the USDA National Agricultural Statistics Service or proprietary data.

2.3.4 Process Factors

“Process factors” include concentration or dilution factors due to cooking or processing of food from RAC into food products. These data used in SH EDS-Dietary may come from registrant submission and the peer reviewed literature.

2.3.5 Food Residue Data SHEDS-Dietary can use point estimates or (empirical ) distributions from any source, modeled or measured (e.g., Field Trials, USDA/PDP, FDA/TDS; PR ZM-EXAMS). Field Trial Studies are tests conducted by registrants to determine toleran ce on Raw Agricultural Commodities. Field trial residues may exceed anticipated residues when the RAC: (i) includes inedible portions (e.g., banana and orange peel, watermelon rind, etc.), (ii ) is generally cooked (e.g., pumpkin), and (iii) is established for feed purposes (e.g., field corn vs. cornmeal).

The USDA Pesticide Data Program (PDP; http://www.ams.usda.gov/AMSv1.0/pdp ) tests commodities in U.S. food supply for pesticide resid ues. It has tested over 85 different commodities: fresh/frozen/canned fruit and vegetabl es, fruit juices, dairy products, grains, corn syrup, nuts, peanut butter, honey, poultry, beef, p ork, catfish. PDP has tested for more than 440 different pesticides. Samples are collected by 12 participating States, representing about 50 percent of the Nation's population and all regions.

FDA’s Total Dietary Survey (TDS; FDA 1991-2004) (no t used for pesticides) is a market basket study program that collects and analyzes ~280 foods for levels of pesticide residues, industrial chemicals, and toxic and nutrient elements. Foods i n TDS are prepared as they would be consumed (table-ready) prior to analysis.22

2.3.6 Drinking Water Concentration Data As with the food residue data, SHEDS-Dietary can us e point estimates or (empirical) distributions of drinking water concentrations usin g any source, modeled or measured (Field Trials, PDP, FDA; PRZM-EXAMS, etc.).

Environmental fate models that can be used to predi ct drinking water concentrations are PRZMEXAMS and SCIGROW ( http://www.epa.gov/oppefed1/models/water/ ). The Agency generally uses environmental fate models (e.g., PRZM-EXAMS) t o generate predicted drinking water concentrations for pesticides.



## 2.4 Methods Issues

2.4.1 Issues Regarding Selection of Consumption Database Since NHANES has not yet focused special attention on children, the CSFII survey continues to have many more food diaries for children. For exam ple, CSFII has 2,972 infant diaries versus 1,971 diaries in NHANES. For children aged 1-2 ye ars old, a population of concern due to potentially high exposures, CSFII has 4,287 diaries vs. 2,460 diaries in NHANES. Another issue is whether or not to use all food diaries, or simpl y the two day diaries. In contrast to CSFII data, only one day of food intake was collected during th e first four years (1999-2002) of the NHANES survey. Therefore, NHANES has a slightly la rger total number of one day (only) diaries (N=22,035 subjects) as it does two day diar ies (N=16627 subjects, or 33254 person-days).

Some alternative approaches for imputing values for missing data in CSFII have been explored. The two fields of interest are: (i) direct drinking water, and (ii) time of eating occasion. The modeled results appear to be relatively robust with respect to data imputations on these two variables. Approximately 738 diaries, or 1.8% of t he total 41,214 CSFII food diaries did not report any information regarding direct drinking wa ter consumption. SHEDS-Dietary (as well as Calendex-FCID and the other models) assume that the se diaries did not consume any direct drinking water. DEEM-FCID uses only the 40,476 of the 41,214 CSFII food diaries that responded to this question (may have included peopl e that did not consume any direct drinking water) when conducting drinking water risk assessme nts; but this subset generally has not affected any of the comparisons with SHEDS-Dietary (drinking water alone, or food+drinking water). For eating occasions, approximately 3,948 records, or 0.6% of the 598,829 food records in the CSFII database had missing values for the ti me of day question. SHEDS-Dietary replaced those missing values with 12:00 noon since it was t he most reported frequency reported, and assumes zero consumption for non-reports in drinkin g water intake.

2.4.2 Issues Regarding Sampling Food Residues SHEDS-Dietary allows Monte-Carlo simulations to be based on specific food items, as well as raw agricultural commodities (RAC-FF). Figures 2-1 and 2-2 illustrates this process. If residues are specified for particular food(s) (e.g., cheeseb urger), then SHEDS-Dietary randomly draws a residue from that corresponding distribution and ig nores any residue data assigned to the23

ingredient RAC-FF (e.g., beef, tomatoes, wheat, etc .). If residues are not specified for any food item, then the model randomly draws residues for ea ch of the RAC-FF ingredients. The Agency generally requires information at the commodity lev el, and so this option may be applied to a certain category of food items, such as milk (versu s other dairy products), and meats (‘steak’).

The current version of SHEDS-Dietary randomly draws 1 residue value for each commodity (RAC-FF) and applies that commodity residue to all foods, on all eating occasions. However, the Monte Carlo simulation can draw a new food resi due for each eating occasion. The Agency asked the 1999 SAP, “Under what circumstances shoul d the EPA consider using the (DEEM) Eating Occasion approach?” The Panel (1999) noted:

“Dietary exposure analysis is an extremely complex process. It utilizes many pieces of data from different sources, each carrying its own limitations and deficiencies for the purpose. Therefore, a careful documentation of the database limitations and the uncertainties associated with the estimated exposur e is essential for a proper interpretation of the exposure estimates.” 1

The qualifying comments reflect a complexity in acc ounting for differences in eating habits across the population. To illustrate this point, t he food consumption diary presented above (Table 2-1) indicates that the 1 yr old consumed th e same food (‘home fries’) on two different eating occasions. It is likely that the child had ‘leftovers’ in the evening meal. If that is the ca se (or more home fries were prepared from the same bag of potatoes), then it would be appropriate to assume that the same composite residue was prese nt on both eating occasions. On the other hand, if the child consumed two servings of ‘home f ries’ from different fast food restaurants on two different eating occasions, then it may be more appropriate to randomly draw separate residues for each eating occasion. Such conditiona l modeling decisions can better made after a closer inspection of the food consumption data. A decision rule based on a few more variables (e.g., food item and primary source of food) may be helpful to determine if different residues should be drawn for subsequent eating occasions, an d/or the same residue can be applied to all eating occasions. We have found that this modeling assumption often does not have a significant effect on the 99.9th for food-only exposure assessm ents – since most people consume various foods on only a single eating occasion as noted in the NMC CRA (USEPA FIFRA SAP 2005, 2007); (Nako, et. al, 2007 ISEA cited earlier).

One of ORD’s/OPP’s planned activities is to conduct a more thorough systematic review of the food consumption diaries. We anticipate that such analyses may help towards developing decision rules for selecting or not selecting a new residue. For example, if the commodity comes from the same food items, then the model uses the s ame residue. If not, then the model compares the foods’ sources, time of eating occasions, and i f the foods were eaten at home. If those factors differ, then the model selects different residues, else retains the same residue. The potential for different decision rules also suggests the developm ent of some type of uncertainty analyses.

1 FIFRA SAP (2000) Report No. 2000-01B, May 25, 2 000, Pages 33-35.24

2.4.3 Modeling Longitudinal Food Consumption The approach for constructing longitudinal consumpt ion profiles is currently being reevaluated, since the National Health and Nutrition Examination Survey (NHANES) food consumption data does not provide data on calendar dates (season), n or locations (region). Available food consumption data are cross-sectional (2days for an individual). When randomly drawing multiple one-day diaries from multiple individuals that are intended to represent a single individual’s behavior over time, the modeler faces a dilemma wit h optimizing inter- and intra-person variability (see Figure 2-3). If a small number of diaries are drawn for each individual to cover a long simulation period, then each diary must be re- used many times; that is, each diary must be used on many different dates in the simulation to r epresent the individual’s behavior (for dietary exposure, the key variable is total caloric consump tion). While this creates repetitive or habitual behavior patterns, it also narrows the behavioral s pace and lessens the within-person consumptionvariability. Using many different one-da y diaries would address these last two concerns by broadening the simulated individual’s b ehavioral space and increasing the withinperson variability. However, this approach would ex acerbate other problems. In particular, any two persons belonging to the same cohort will draw their diaries from the same diary pools, and the samples will tend to converge to the same overa ll average behavior of the cohort.

Figure Figure 2 2 - 3 . Different methods for . Different methods for . Different methods for assembling longitudinal dia ries from cross assembling longitudinal diaries from cross assembling longitudinal diaries from cross-

sectional data sectional data 1



## Longitudinal Diary Assembly – Overview

0 0 . 2 0 . 4 0 . 6 0 . 8 1113253 74961738 5971 0 91 211 3 31 45 X

X

Time 0 0.2 0.4 0.6 0.8 11019283 7465564738 29 11 0010 91181271 361 4515 4 Always use same diary

Option 2 method (before autocorrelation between days)

Option 2 method (diaries reordered to target autocorrelation) Option 1 method 0 0.2 0.4 0.6 0.8 1

X Pick new diary every day

0 0.2 0.4 0.6 0.8 1142 7405 36679921 051 181 3 11 441 571 7 01 831 9 62 092 22 X

0 0 .2 0 .4 0 .6 0 .8 111 01 92 83 74 65 56 47 38 29 11 0 01 0 91 1 81 2 71 3 61 4 51 5 4 X

0

0 . 1 0 . 2 0 . 3 0 . 4 0 . 5 0 . 6 0 . 7 0 . 8 0 . 9 1

1 2 4 4 7 7 0 9 3 1 1 6 1 3 9 1 6 2 1 8 5 2 0 8 2 3 1 2 5 4 2 7 7 3 0 0 3 2 3 3 4 6 0 0 .2 0 .4 0 .6 0 .8 111 01 92 83 74 65 56 47 38 29 11 0 01 0 91 1 81 2 71 3 61 4 51 5 4 X

0

0 . 1 0 . 2 0 . 3 0 . 4 0 . 5 0 . 6 0 . 7 0 . 8 0 . 9 1

1 2 4 4 7 7 0 9 3 1 1 6 1 3 9 1 6 2 1 8 5 2 0 8 2 3 1 2 5 4 2 7 7 3 0 0 3 2 3 3 4 6

Weekends

Weekdays

0 0 .2 0 .4 0 .6 0 .8 111 01 92 83 74 65 56 47 38 29 11 0 01 0 91 1 81 2 71 3 61 4 51 5 4 X

0

0 . 1 0 . 2 0 . 3 0 . 4 0 . 5 0 . 6 0 . 7 0 . 8 0 . 9 1

1 2 4 4 7 7 0 9 3 1 1 6 1 3 9 1 6 2 1 8 5 2 0 8 2 3 1 2 5 4 2 7 7 3 0 0 3 2 3 3 4 6 0 0 .2 0 .4 0 .6 0 .8 111 01 92 83 74 65 56 47 38 29 11 0 01 0 91 1 81 2 71 3 61 4 51 5 4 X

0

0 . 1 0 . 2 0 . 3 0 . 4 0 . 5 0 . 6 0 . 7 0 . 8 0 . 9 1

1 2 4 4 7 7 0 9 3 1 1 6 1 3 9 1 6 2 1 8 5 2 0 8 2 3 1 2 5 4 2 7 7 3 0 0 3 2 3 3 4 6

Weekends

Weekdays25

Most of the existing random-draw methods of diary s election assume that all diaries that are suitable (meaning they are from the correct age-gen der cohort and match the chosen daytype) are equally likely to be chosen, and that any subsequen t draws are independent of prior draws. As detailed in the SHEDS-Multimedia 2007 SAP documents ( http://www.epa.gov/scipoly/sap/meetings/2007/081407 _mtg.htm , Glen et.al. (2007) proposed a new method for developing longitudinal activity pro files. This “D & A” (Diversity & Autocorrelation) method presented above drops both these assumptions by assigning each simulated person a “target behavior,” and then pref erentially sampling diaries to produce the target behavior. The method assigns target behavior s and executes the preferential sampling based on the value of D (diversity) specified by th e modeler. If not executed carefully, preferential sampling can result in behavioral bias es, where some diaries are consistently drawn more often than others. The method contains interna l rules for this sampling that ensure that over a large number of simulated persons, all available diaries in each diary pool will be sampled nearly uniformly.

In this method, a new random draw is made for every day in the simulation. Thus, a one-year longitudinal diary would be comprised of potentiall y 365 different diaries. The D statistic affects the width of the diary selection probability peak a round the target behavior, with a low D giving a broad peak and a high D giving a sharp, narrow pe ak. Depending on the width of this peak and the number of diaries in the pool, some diaries may be selected multiple times, but others may be selected just once or not at all.

The longitudinal data from Lu et al., 2006a,b (Alex Lu, personal communication) were used to develop the D and A statistics needed to apply the Glen et al., 2007 approach for SHEDS-Dietary longitudinal diary construction. We need to model longitudinal food consumption in order to account for chemical half-lives and seasonal patter ns in exposures across three primary sources: food, drinking water, and non-dietary exposures fro m residential uses. The longitudinal dimension does not appear to be critical for obtain ing estimates of a single total daily exposure at per capita upper percentiles (see US EPA (2004) for some comparisons). A focus on longitudinal exposures may expand as the Agency continues to dev elop physiologically-based pharmacokinetic models (PBPK) for pyrethroids and o ther pesticides. The 2005 SAP noted that one-day simulation models may underestimate risks i f carry-over effects from consecutive days of exposures are of concern.2 All three sources (f ood, drinking water and residential) have a potential for seasonal exposure patterns (positive autocorrelation). We can anticipate strong patterns in drinking water exposures since most peo ple consume water daily, and both the surface water and ground water models generally produce dri nking water concentrations that exhibit positive autocorrelation. Similarly, non-dietary e xposures from residential uses will reflect seasonal patterns in product usage, as well as corr elations in daily activities for a particular

2 FIFRA SAP (2005), Minutes, p.10, “In particular, if one applies a 4.1-fold inter-species scaling fa ctor to the 5.4 hr half-time for reversal of brain AChE inhibition in rats, one obtains a predicted half-time of 22 hr in the 70 kg human adult. Such a long half-time would force the risk assessment model to address carryover of inhibition from one day to the next. In considering this issue, the Agency should take into account cases where there is a do se dependency for inhibition reversal half-lives.” p.56.26

person. For food, one can conceive an individual p urchasing a bag of treated apples, and consuming one or a few apples from that bag over co nsecutive days.

The relative importance of these three sources (foo d, drinking water, and residential) vary by chemical, as well as across individuals within a su bpopulation. In two companion papers, Lu et. al. (2006a, 2006b) reported that residential uses a ppear to be more important for exposures to some synthetic pyrethroids, while dietary exposures appear to be relatively more important for some organophosphate pesticides. Their assessment is based on a longitudinal study of 23 elementary school-age children, using urinary metab olites as exposure biomarkers. The researchers collected two spot daily urine samples, first-morning and before-bedtime voids, throughout a consecutive 15-day study period, which consisted of three phases. Children consumed their conventional diets during phase 1 (d ays 1-3) and phase 3 (days 9-15). During phase 2 (days 4-8), organic food items were substit uted for most of children’s conventional diet, including fresh fruits and vegetables, juices, proc essed fruit or vegetables (e.g., salsa), and wheator corn-based items (e.g., pasta, cereal, popcorn, or chips) for 5 days. Meats and dairy products were not substituted. A description is provided in the paper: “Parents were asked to request organic foods for their children in phase 2 with th e goal of exactly replacing the items the children would have normally eaten as part of their conventional diet. This method ensured that any detectable change in dietary pesticide exposure would be attributable to the organic food rather than a change in the diet.”3 The researcher s found lower levels of two organophosphate pesticides during phase 2 when organic foods were p rovided, but no observable change in levels of pyrethroid insecticides. However, they did find a significant correlation between the homeowners self-reported use of pyrethroid products (N=7 household users) and concentration levels of two pyrethroid metabolites (Lu, C. et.al. , 2006b).

The literature also contains alternative methods fo r developing longitudinal consumption profiles; we will consider these in future versions of SHEDS-Dietary. For example, promising effort is described in three papers, authored by a team of researchers from government (NCI), academic and other private institutions. Dodd et.a l. (2006) provide a comprehensive review of existing methods used to estimate long-term dietary intake using cross-sectional data. Tooze et.al. (2006) present a new method for estimating l ong-term intake of episodically consumed foods using food frequency questions (FFQ). A food frequency question (FFQ) is: ‘How often have you (respondent) consumed fish during the past 30 days?’ Tooze et.al. (2006) present a two stage model, with the first part (logistic regressi on) predicting the probability of consuming a particular food, and the second part (regression on log transformed consumption amount) predicting the amount of food consumed (>0). In th e third paper, Subar et.al. (2006) apply this method to the Eating at America’s Table Study (EATS ) data. The researchers noted that people that consume foods more frequently (FFQ) also tend to consume greater amounts of that food per occasion. Subar et.al. (2006) also provide a brief review of the development of the Food Propensity Questionnaire, a set of FFQ that was int roduced in the 2003-2006 NHANES.

Findings in Givens et al., 2007 suggest a longitudi nal dietary survey with minimum 6 consecutive days’ dietary consumption in each of 4 seasons would be adequate to represent an

3 Lu et.al., 2006a, p.260.27

individual 1 year dietary consumption pattern, and improve cross-sectional approach. “The majority of the Panel is convinced that given the d ata and analysis presented by the Agency, it is not sufficient to construct the longitudinal dietar y consumption pattern based on the 8-day eating occasions.” (p. 33 of 2007 SAP). Based on comments by the 8/07 SAP, we prefer using the D&A methodology rather than the 8-diary approach us ed in the SHEDS-Multimedia residential module.

We will use real data as available to evaluate thes e different approaches and provide the basis to decide which to use in future versions of SHEDS-Die tary. We anticipate that these and other research activities may help us to improve the curr ent approach for modeling longitudinal consumption, and to develop appropriate uncertainty analyses to characterize the pesticide dietary exposure assessments. In the meantime, SHEDS-Dieta ry uses the D&A approach for modeling longitudinal food consumption. There are many pote ntial covariates and measures of diversity across many subpopulations. The diet, health and n utrition literature contains a rich volume of research, indicating that food consumption patterns may vary by race, ethnicity, lifestyle (activities and energy requirements) and socio-econ omic factors.

2.4.4 Considering Persisting Effects The SHEDS-Dietary Eating Occasion analysis uses the Maximum Persisting Dose (PD) in addition to Total Daily Exposure (for each person-d ay) to calculate exposure per capita various percentiles e.g. 99.9th. For the hypothetical case below, the Total Daily Exposure is 2 ug/kg (=sum of 2 exposure events), the Max PD is 1.25 μ g/kg (Max point on green line) based on a 2.5 hr half-life. The max PD reflects an approximate s ingle bolus dose that produce same level of peak inhibition as the two exposures.

Figure 2-4 depicts a hypothetical scenario in which a person obtains dietary exposures on two eating occasions (e.g., ate 1 slice of watermelon a t noon and another at 5 pm). The red triangles depict the amount of exposure (1 μ g ai/kg bw) obtained on each eating occasion, while the green line following the first exposure event depicts the Persisting Effect on cholinesterase inhibition. For this example, the recovery half-life is assumed to be 2.5 hours (150 minutes). Therefore, the persisting effect from the first exposure event (1 μ g ai/kg bw) is approximately 25% of the peak effect (0.25=(1/2)^(300/150)) when the second expos ure event occurs 5 hours (300 minutes) later.

The Persisting Dose reflects the combined effect fr om the current exposure and the persisting effects from recent exposures. The persisting dose at the second eating occasion amounts to 1.25
μ g ai/kg bw (1.25=1+0.25), which is also the Maximum Persisting Dose over this person-day. The Maximum Persisting Dose is interpreted as an eq uivalent (single) bolus dose that produces the same peak level of inhibition as the exposure p atterns from the simulated person-day. This refinement has different effects for different expo sure profiles. For the hypothetical scenario above, it reduces TDE from 2 μ g /kg bw to 1.25 μ g /kg bw. If those exposures occurred on a single eating occasion (e.g., 2 slices at either no on or 5 pm), then this refinement would not affect that particular outcome (Max Persisting Dose=2 μ g /kg bw=TDE). This analysis is based on several important assumptions: (i) the time to peak effect is instantaneous (for convenience), (ii)28

direct drinking water consumption is allocated over 6 fixed events at fixed times, and (iii) the subject (person) is healthy (no carry over effects)

.

Figure Figure 2 2 - 4 . Hypothetical Figure . Hypothetical Figure . Hypothetical Figure to Illustrate Persisting Effe ct for an Individual to Illustrate Persisting Effect for an Individual to Illustrate Persisting Effect for an Individual29

Figure Figure 2 2 - 5 . Example SHEDS . Example SHEDS . Example SHEDS-

Dietary modeled longitudinal dietary exposure prof le for Dietary modeled longitudinal dietary exposure profi 1 individual 1 individual

Figure 2-5 shows an example longitudinal dietary ex posure profile for one individual using SHEDS-Dietary. The persisting dose (green line) wa s modeled from a cumulative data analysis using the chemical half lives.

2.4.5 Algorithm for Matching (Behavioral) Diaries: Food C onsumption and Activity
Patterns It is a challenge to merge dietary and residential exposures because the behavioral data are from different sources. It is more complicated for the l ongitudinal data. Bins by important variables are used to merge the data; a balance between number of key variables and randomization has to be controlled. Too many variables used to form bins w ill reduce the randomization and too few variables will increase randomization but increase misclassification between the dietary and residential exposures. An average of 50 to 100 data points in each bin is used as a criterion to select the key variables to make sure that we have enough sample size in each bin for the randomization. Key variables are age, gender, body weight, total caloric intake/METS, race, season, weekday and region. The D & A method descri bed above (see bottom of figure 2-3) uses total calories to turn the cross-sectional dietary exposure into longitudinal food consumption patterns, and uses waking time at home to turn the residential cross-sectional activity patterns30

into longitudinal patterns. Then age, gender, body weight, total caloric intake/METS, race, season, weekday and region can be used to form the bin to match dietary and residential exposures. A proposed methodology for matching foo d consumption and activity diaries, to merge SHEDS-Multimedia dietary and residential modu les, will be presented to the EPA FIFRA SAP in July, 2010. This methodology, described late r in this manual in the section entitled, “Algorithm for Matching (Behavioral) Diaries: Food Consumption and Activity Patterns.” has been tested through “soft linking” the two modules with a permethrin pesticide case study.

2.4.6 Considering Timing and Amounts of Drinking Water Co nsumption SHEDS-Dietary utilizes the CSFII or NHANES data to assess the timing and amounts of indirect drinking water intake (i.e., through foods, infant formula, ‘kool aid’, coffee, tea, etc.) within a simulated person-day. The model contains two options for allocating direct drinking water consumption (i.e., through tap or bottled wat er) throughout the day: (1) fixed approach, and (2) empirical using the recent NHANES data or the B ayer DWCS data described above. In the fixed approach, SHEDS allocates the CSFII responden ts’ total direct drinking water consumption (mL/day) over 6 fixed occasions (6:00 am, 9:00 am, 12:00 noon, 3:00 pm, 6:00 pm, and 9:00 pm. Preliminary analyses revealed that there is no sign ificant difference between these two approaches.

The second option uses the Bayer DWCS data to alloc ate the total amount of direct drinking water consumed throughout the simulated person-day. 4 This procedure involves the following steps:5

1. Generate cohort (‘bins’) by gender, age, season 2. For each DWCS diary, calculate the percent of Total Direct DW, by Occasion 3. For each CSFII or NHANES diary, randomly select a B ayer DW diary from appropriate ‘bin’ 4. Use Total Direct DW from CSFII or NHANES and percen tage of DW from DWCS data to calculate direct DW amount for each Eating Occas ion (time of occasions also from DWCS)

This second option cannot be applied for the infant subpopulation since the DWCS data did not include infants. Although the DWCS study did not a ppear to have the same level of sophistication as the CSFII in its sampling design, our expert view is that these data are useful to model the timing of direct drinking water intake fo r several reasons, including: (1) the marketing firm, the NPD group, has extensive experience at mo nitoring eating and drinking trends in the US and Canada, (2) the design of the data collectio n instrument, i.e. recording consumed amount of water for each drinking event, led to better 24 hour recall, (3) reasonable response rates

4 The Panel noted: “In further development of thi s approach, EPA should make use of any reliable sou rce of relevant empirical data on daily patterns of drinki ng water consumption; ideally adapted to the likely consumption behavior in specific regions or smaller areas of th e country.” P.59 of 63, SAP 2005b. 5 This algorithm can be modified for longitudinal models, ‘binning’ respondents (persons), rather tha n diaries (person-days) to retain the intrapersonal informati on contained in these 7-day drinking water diaries.31

(>30%), and the relatively high percent of responde nts that completed 7 day diaries (82%), and (4) the 7 day study period reduces the need to mode l intrapersonal variability over this duration.

An alternative method being considered for using th e drinking water data is a two step approach for each simulated person: (1) randomly draw one of the 30 years, and (2) apply the predicted concentration for January 1 st of that selected year to calculate exposure for Da y 1, and so on, with predicted concentration on December 31 st being applied to calculate exposure for Day 365. This general approach, available in other aggregate mode ls, has the advantage of retaining autocorrelation present in the predicted drinking w ater concentration data.

Figures 2-6 and 2-7, taken from DWCS report (Figure s 5 and 6, respectively), indicate that many respondents consume direct drinking water on multip le occasions, and at all times throughout the day. This provides some support for using a simple modeling assumption (e.g., equal amounts allocated across 5 or 6 occasions). Those distribu tions do not reflect variations in drinking water intake across individuals. The report suggests tha t these data may be used to model drinking water exposures, by eating occasion:

“It may be possible, using the information collecte d by the DWCS to “allocate” the total daily water consumption amount reported in the CSFI I into various drinking occasions. Specifically, if each subject in the CSFII survey w as randomly matched to subjects in the DWCS, based on survey season, region, age, gender, and total amount of drinking water consumed per day, then the total amount reported by that CSFII participant can be allocated to the same number of drinking occasions as those r eported by the matching DWCS participant. Similarly, the proportion of the tota l daily water consumption allocated to each of these drinking occasions can be assumed to be si milar to that reported by the matching DWCS participant. This approach would then allow a less than 24-hour assessment of both food and drinking water (aggregate assessment) for a pesticide.” 6

6 Barraj, L.M. et.al. (2004), E x ponent®, Inc.; National Product Database (NPD) Grou p., p.17.32

Figure Figure 2 2 - 6 . Total Number of Occasions of Direct Drinking Wat r Consumption . Total Number of Occasions of Direct Drinking Wate r Consumption r Consumption

Figure Figure 2 2 - 7 . Distribution of Direct Drinking Water Consumptio , By Time of Day . Distribution of Direct Drinking Water Consumption , By Time of Day , By Time of Day

Table 2-5 provides the total number of drinking wat er diaries in the DWCS by gender, age and season. Infants less than one year old were not in cluded in this survey. The two adult bins (20- 49 yrs, 50+ yrs) contain a large number of diaries since they encompass a greater range of years. The DWCS contains a relatively large number of drin king water diaries for most of the children’s ‘bins’; the one-year old female, summer bin has the fewest number of diaries (N=29).33

Table Table 2 2 - 5 . Total Number of DWCS Diaries, By Age Group, Gende and Season Total Number of DWCS Diaries, By Age Group, Gender and Season and Season

Season Subtotal Subtotal Age Group Gender Winter Summer Age-Season Age Group

M 98 128 226 1 yr F 136 29 165 391

M 167 97 264 2 yrs F 125 73 198 462

M 132 81 213 3 yrs F 151 89 240 453

M 128 63 191 4 yrs F 149 98 247 438

M 141 109 250 5 yrs F 67 63 130 380

M 663 404 1,067 6-12 yrs F 624 457 1,081 2,148

M 491 322 813 13-19 yrs F 577 368 945 1,758

M 2,871 1,999 4,870 20-49 yrs F 4,036 2,544 6,580 11,450

M 1,975 1,688 3,663 50+ yrs F 3,332 2,717 6,049 9,712

M 6,666 4,891 11,557 Total F 9,197 6,438 15,635 27,192

The DWCS raw data files did not include sampling we ights to make projections at the per capita level. The report noted that the estimated direct drinking water intakes reported by the DWCS respondents were slightly higher than the 1994-1998 CSFII respondents. For example, the overall mean intake of DWCS respondents was 37.8 oz /day (40.6 oz/day =summer, 35.7 oz/day =winter) 7 , while the CSFII respondents reported 29.6 oz/day (32.4 oz/day =summer, 27.8 oz/day =winter). 8,9 While no formal statistical tests were presented, the report noted that this difference may be due to the fact that “the DWCS provided part icipants with a time grid to report their water consumption, thus potentially helping them re member all their water consumption occasions, in contrast to the CSFII general 24 hour total consumption recall question.” 10

In future versions, we plan to utilize the reported time of water consumption data from recent NHANES.

7 1117.9 ml/day, 1200.7 ml/day, 1055.8 ml/day respec tively
8 785.4 ml/day, 958.2 ml/day, 822.1 ml/day respecti vely

9 Barraj, L.M. et.al. (2004), Table 7, p. 26. Figure 5 provides some estimates, by age groups. (p.31)

10 Barraj, L.M. et.al. (2004), p.16

.34

2.4.7 Considering Number of Person-Days (or Person-Years) to Simulate Agency risk assessors typically specify 1,000 itera tions per diary during a DEEM-FCID simulation, providing for about 41 million person-d ay simulations (=41,214 person-day diaries x 1000 iterations/diary). Except for extremely unusu al circumstances, this number of iterations has provided very stable results at the per capita 99.9 th percentile for all subpopulations (i.e., not much ‘simulation’ or ‘random seed’ uncertainty). S imilarly, users can specify any number of iterations per diary using SHEDS-Dietary (cross-sec tional). The sensitivity analyses presented in this section were based on only 150 iterations whic h appeared to be sufficient to verify results with DEEM-FCID. We specified fewer iterations sinc e SHEDS-Dietary (for cross-sectional analyses) retains all of the output from each simul ated person-day (creating 4 GB in output with 150 iterations), allowing sensitivity analyses to b e conducted much more efficiently. ORD/NERL has not developed recommended number of pe rson-years for SHEDS-Dietary.

2.4.8 Sensitivity Analysis Issues The most difficult part of conducting sensitivity a nalyses is in the problem formulation: defining a particular issue of concern, evaluating the avail able data inputs, developing method(s) to assess how sensitive the results are to that concern, and characterizing the degree to which that analysis addresses that concern. More details are provided b elow in the section, “Sensitivity Analyses Methods.”

2.4.9 Uncertainty Analysis Issues Uncertainty analyses may help ascertain the relativ e importance of the data inputs. There is uncertainty in estimates of a total single-day expo sure from various factors, including: limited food consumption data (CSFII), food recipes (FCID), available residue data (e.g., PDP monitoring, crop translations), and processing fact ors. This preliminary list of factors expands with longitudinal measures and the use of PBPK mode ls. To date, OPP has not utilized formal uncertainty analyses in its pesticide dietary expos ure assessments.



## 2.5 Output Capabilities SHEDS-Dietary can be used to estimate population di stributions (and select percentiles of interest such as 95 th , 99 th , 99.9 th ) of aggregate or cumulative dietary exposures, and generate the following output results:

 CDFs of dietary exposures for populations of intere sts, including food and water separate or combined, as well as by eating occasion ;

 Bar charts, pie charts, and summary tables showing contribution to total exposure (e.g., 99.9-100th), by food, commodity, or commodit y-chemical (for multichemicals);

 Summary statistics of dietary (food, drinking water , or sum) exposure by age group and/or gender (including females 13-49 yrs ol d); and

 Eating occasion, sensitivity, and uncertainty analy ses to identify key factors.35

A summary of various SHEDS-Dietary outputs and anal yses is given in Table 2-6.

Table Table 2 2 - 6

. Potential Applications of the SHEDS Potential Applications of the SHEDS-

- Dietary model Dietary model Dietary model

Variable/Modeling Description User-only Analysis
Eaters-only Report Deterministic calculation of exposures among people that consume a treated commodity or food Contribution Analysis Shares of Total Exposure, by Commodity, Food or Diaries Current reports provide shares of total exposures ( 99.9 th – 100 th percentiles), by commodity or by food Shares of Total Exposure, by Chemical – Commodity, Food or Diaries For cumulative exposure assessments, SHEDS keeps tr ack of residues, by chemical (i.e., not used RPF combined residue)

Shares of Total Person-days, by Commodity, Food or Diaries (i) ‘Exceeders’ or shares of total person-days (99. 9 th – 100 th percentiles), by commodity. (ii) focus on diaries: percent of simulations exceeding target Sensitivity Analysis Consumption ‘Outliers’ Effect of Diaries with Repo rted High Amounts Consumed
Percent Samples Treated Effect of the Estimated Percent of Samples Treated (Half Level Of Detection (Half-LOD) used for monitoring data)
Percent Crop Treated Effect of Annual Fluctuations in Percent Crop Treat ed (assuming all other factors constant) Processing Factors Effect of Estimated Processing Factors Uncertainty Analysis
Uncertainty - Cohorts Effect of Different Factors for Developing ‘Cohorts ’ or ‘Bins’ for Food Diaries
Uncertainty – Subsamples Effect of using a Subsample of the Food diaries and Residue data on per capita estimates (200 person-years) Uncertainty – Subsamples of residues, by commodity Residue by commodity
Uncertainty – Models Comparing Results Across All M odels

The SHEDS-Dietary module retains detailed person-le vel outputs for each dietary exposure assessment. Presently, the SHEDS-Dietary Cross-Sec tional simulations retain more detailed information than the SHEDS-Dietary Longitudinal sim ulations. For the Cross-Sectional simulation, the SHEDS-Dietary module retains detail ed information for each exposure event, including food diary ID, time of eating occasion, f ood item, FCID commodity (RAC-FF), amount consumed (g), residue (ppm), and exposure (ug ai). Since the CSFII and NHANES\WWEIA surveys contain numerous food diaries, a Cross-Sect ional simulation specifying 200 iterations for each of the 40,214 CSFII diaries will generate 8,04 2,800 person-day outcomes (=40214x200), which may take up to 10 GB of hard disk space. To facilitate data processing, SHEDS-Dietary splits the outputs into multiple data tables. In t he example above, the outputs from a SHEDSDietary cross-sectional simulation would be stored 200 separate tables in the SAS library ‘Output’, and the filenames for each table will sta rt with the prefix as provided by the User.

The outputs from a SHEDS-Dietary longitudinal simul ation are currently stored in a single table in the SAS library ‘OLONG’, with the filename start ing with the prefix as provided by the User36

followed by the suffix (long) (e.g., SAS data table for case Study #3: Olong.Cperm_long). The results retained in the longitudinal simulation inc lude: person ID, date, food diary ID, total calorie, time of eating occasion and exposure (ug a i). The data fields and formats for the crosssectional and longitudinal simulations are provided in the SHEDS-Dietary User Guide, Appendix 3.1 (cross-sectional simulations), and Appendix 3.2 (longitudinal simulations). Since commodity level details are not retained in the longitudinal simulation, the contribution analyses is limited compared to ‘cross-sectional’ simulations. {We will consider outputting detailed results in future versions; perhaps saving the results for each model ed person in a separate table (file) to ensure that the data tables are not too large for data pro cessing.}

There are several advantages and disadvantages of r etaining all of the detailed data. The primary disadvantage is that a single simulation may take u p a considerable amount of hard disk space (8+ GB); if users need to perform simulations for m ultiple chemicals, then this may pose a significant issue (e.g., the hard disk on the Agenc y’s laptops have 80 GB capacity). Another disadvantage is that appending data to the output t ables may increase the processing time considerably as those tables become increasingly la rger. If the user is only interested in knowing aggregate daily exposure at some per capita percent ile (e.g., 99.9 th ), then processing time can be shortened since the model only needs to retain tota l daily exposure for each simulated day.

The advantages for retaining the detailed outputs f or each simulated person-day, include: (i) querying/viewing detailed outputs for select diarie s and/or persons, (ii) perform alternative methods for assessing contributions (exceeders), (i ii) facilitate sensitivity analyses (‘what-if’ scenarios), (iv) conduct sensitivity analyses (outl iers), (v) perform ‘eating occasions’ analyses for multi-chemical assessments, (vi) pass on dietary ex posures by time of day to PBPK model.

We describe two of these advantages below. First, it is important to recognize that the user can query these output tables to calculate aggregate di etary exposures at various per capita percentiles, as provided in the “Exposure and %APAD : Summary Table”. Suppose the user wants to know how aggregate exposure at the per cap ita 99.9 th percentile changes if we removed that pesticide’s use on lettuce. Then, the user co uld either zero out residues for lettuce and rerun the simulation, or zero out the exposures from the simulation and recalculate the 99.9 th percentile. The first option will take considerabl e processing time (especially in a cumulative setting) and add simulation uncertainty since a new set of residues is randomly selected for each person-day-food. The latter option is more efficie nt since the model simply recalculates the 99.9 th percentile with the existing results - zeroing out exposures from that particular commodity. The user can submit a batch job to perform such ‘w hat-if’ scenarios for various combinations of foods (e.g., removing only lettuce, or removing onl y apples, or removing apples+lettuce, etc.).

The second example, described in further below, is performing sensitivity analyses to assess potential uncertainties (measurement error) regardi ng reported food consumption. Nako and Xue (2006) identified several food consumption diaries that reported high drinking water intake, and wanted to assess the effects of those diaries upon the per capita 99.9 th percentile. The user needs to first identify which food consumption diaries ar e of concern and what adjustments are to be made (e.g., drop diary altogether or adjust the rep orted consumption). Once that determination is made, the simulated exposures can be adjusted accor dingly, and the per capita 99.9 th percentile37

recalculated from the adjusted outcomes. Since SHE DS-Dietary retains the outcomes, the user can perform such sensitivity analyses without ‘chan ging’ the underlying food consumption data base. The user needs to use supplemental SAS code (Macros) to conduct such analyses since the Agency had difficulty incorporating these options i nto the SHEDS-Dietary version 1.0 GUI.



## 2.6 Sensitivity Analysis Methods and Results

2.6.1 Sensitivity Analyses Methods

The following sensitivity analyses have been run us ing SHEDS-Dietary v1:

 key data to determine their impact, such as using C SFII vs. NHANES/WWEIA consumption data or PDP vs. TDS residue data;

 different algorithms to assess the impact such as a llocating drinking water consumption equally over 6 fixed eating occasions vs. using inf ormation from the Bayer Drinking Water Study (discussed below);

 different residue sampling algorithms such as filli ng in non-detects with zero, half detection limit, detection limit);

 outliers – impact of keeping or removing “outliers” on the key exposure output parameters (e.g., exposure); and

 mitigation -- assessing impact of removing one or a group of RACs (e.g. delete one commodity such as a particular fruit to see the imp act on the average and high exposure percentile).

The sections below show results of sensitivity anal yses on food and drinking water consumption outliers. Other analyses are illustrated in the ca se studies of Chapter 3.

2.6.2 Sensitivity Analyses on Food Consumption ‘Outliers’ A component of the Agency’s risk characterization i s to “Evaluate the tails of the food exposure distribution to verify that unusual consumption pat terns are not inappropriately impacting on the results of the assessment.” 11 Identifying ‘unusual’ consumption patterns require s inspection of the food diaries. If the amounts consumed are not unreasonably high, then no further analyses is required. As the panel noted,

“ The CSFII is designed to be representative of the p opulation as a whole. Hence the “tails” of the distribution are still part of the d istribution and, therefore, cannot be said to impact the results of the assessment inappropria tely. ” 12

If consumption values are so unusual so as to bring into question the accuracy of the data (e.g., measurement or data entry error), then quantitative ‘what-if’ analyses may be appropriate. A question for the exposure modeler is how sensitive are exposures at the upper per capita

11 EPA SAP, 2005, p.187.

12 SAP minutes, 2005, p.36.38

percentiles to one or a few such data records? The open source coding of SHEDS-Dietary enables the user to perform such analysis in a quic k and cost-effective manner.

Figure 2-8 presents a Box and Whisker plot of potat o consumption among children ages 1 and 2 years old. The amount of potatoes consumed by the CSFII diary highlighted earlier (Table 2-1; ID=28517-2-2) is about twice as high as the second highest eater in this age group. This amount appears to be an outlier when focusing on only ‘fri ed’ potato consumption, but not so much the case when considering potato consumption in other f ood forms (e.g., boiled). As absolute amount consumed, this amount does not appear to be implausible: a 1 yr old, 13 kg boy eating 300 grams of home fries on two occasions. But a co nsiderable amount of resources may be expended to defend that assessment, and using the S HEDS-Dietary model, we can determine that the per capita estimates are fairly robust to this one diary. In particular, if we either (i) removed this ‘outlier’ from the Monte-Carlo simulations, or (ii) adjusted the amount consumed to lower level (e.g., second highest amount), the per capita estimates at the 99.9 th percentile will not change considerably.

Figure Figure 2 2 - 8 . Box - and - Whisker Plo Whisker Plo Whisker Plot of Potato Consumption by Day for Child

t of Potato Consumption by Day for Children 1 t of Potato Consumption by Day for Children 1- 2 Yrs. Old Yrs. Old

2.6.3 Drinking Water Consumption Outliers As in the case of various food items, there are som e high reported drinking water consumption amounts in the CSFII. Figure 2-9 presents a Box-Co x transformation of drinking water consumption (ml/kg bw/day) for all infants in the C SFII data base. The two highest amounts are located in the upper right hand corner – deviating above the otherwise linear pattern established by the majority of the remaining reported consumpti ons . These two values are, respectively, 52% and 41% higher on a ml/kg bw basis than the nex t (third) highest reported consumption39

value. An inspection of the food diaries indicate that a set amount of formula was reportedly prepared and consumed by these two infants on multi ple occasions throughout the day. The first infant diary (28892-2-1) was for a newborn (0 mont h old) weighing 3.2 kg, that reportedly consumed a total of 1,997 ml that day (1819 mL indi rect, 117 direct), or about 624 ml/kg bw/day.

An inspection of the CSFII diary indicated that thi s infant consumed a total of 8 oz of formula (6 ounces consumed directly + 2 oz used to prepare 0.2 5 cup of dry rice cereal) at 8:00 am, 9:30, 11, 1:30, 4:30, 6:00, 10 and 11:30 pm; an additional 4 oz of formula alone was prepared/consumed at 1:00 am. The second infant-dairy (26837-3-2) was a one month old that weighed 3.6 kg, and consuming a total of 2,044 ml that day (1,926 ml in direct, 118 direct), or about 568 ml/kg bw/day. An inspection of this second diary indicat e that that infant consumed 8 oz of formula on nine different occasions throughout the day, at 4:0 0 am, 6:00 am, 8:00 am, 10:00 am, 12:00 pm, 2:00 pm, 6:00 pm, 8:00 pm and 10:00 pm. These two drinking water intake amounts appear to be ‘outliers’ based on the available references, an d a brief review of the pediatric literature (e.g., U.S. EPA, 2008).-200204060Box Cox Transformed Consumption, mL/kg bw

-4 -2 0 2 4 Z-score

lambda = 0.512

Figure Figure 2 2 - 9 . Box - Cox Plot of Infant Direct Water Consumption (mL/kg bw/day) Cox Plot of Infant Direct Water Consumption (mL/kg bw/day) bw/day)

Analyses of the SHEDS-Dietary simulated output for the infant subpopulation indicated that two food diaries constituted about 70% of all high simu lated outputs in the top 0.1% of simulated person-days. Again, the question of concern was ho w sensitive are the estimates at the upper percentiles to the drinking water intakes reported for these two respondents. Agency staff used40

(cross-sectional) SHEDS-dietary to conduct two ‘what-if’ scenarios: ( 1) drop these two diaries from the Monte Carlo simulation, and (2) reduce the reported amounts consumed by 50 percent. Neither of these resulted in marked changes in the estimated exposures at the per capita 99.9 th percentile. The sensitivity analyses for the potat o eater and these high infant water intake diaries,showed similar insensitivities to these ‘ou tliers.’ The Agency previously noted the robustness of the results to residue outliers:

“... it is often not the extreme upper tail of a residue distribution which is responsible for driving the 99.9 th or 99 th percentile exposure levels, but rather a combinati on of reasonable (but high end) consumption and reasonable (but high end) resi due levels of one or two frequently consumed agricultural commodities. ” US EPA (1999), pp. 21-22.

While that quote referred to residue ‘outliers’, the two case studies above suggests th at a similar level of robustness appears to hold for consumption ‘outliers’ as well. While such analyses cannot be performed if the consumption diaries are fixed in the code, the open source code of SHEDS-dietary provides agency modelers with complet e access to all of the underlying data and algorithms. This feature enables the Agency to qua ntitatively address other questions that risk managers may have as PBPK models are used to assess dietary risks to pesticides and other chemicals.

Examples of other sensitivity analyses are presente d in Chapter 3.



## 2.7 Uncertainty Analysis Methods and Results

2.7.1 Uncertainty Analysis Methods

SHEDS-Dietary has a simple bootstrapping method for conducting uncertainty analyses - utilizing only a subset of the consumption and resi due data inputs. This proposed method is designed to gain some insight about ‘How much bette r would our estimates be if we had more data?’, by conducting the uncertainty analyses in t he other direction ‘How far off will our estimates go if we used only a subset of the consum ption and/or residue data?’.

The SHEDS-Dietary bootstrap procedure to conduct un certainty analysis entails the following: 1) Randomly draw certain percentage (e.g., 50%) of per son-day from CSFII data or/and randomly draw certain percentage (e.g., 50%) of res idue data from pollutant residue files by raw commodity and food form. Run 100 times for variability. 2) Repeat step 1 many times, e.g., 200 times. 3) Quantify variability from each run. 4) Conduct uncertainty analyses from different runs (e .g. 200 times). 200 50 th , 95 th and 99 th values can be acquired respectively. The ratio of 9 5 th vs. 5 th percentile of a given percentile can be used to evaluate the uncertainty. The bigger the uncertainty ratio, the bigger uncertainty produced by subsets of dietary a nd residue data. 5) Obtain important sources contributing to the total uncertainty (e.g., structure, scenario).41

To check whether there are enough data for consumpt ion and residue data sources, and which data set was relatively more important for exposure , uncertainty analyses applying statistical bootstrapping of certain percentages of both data s ets, were conducted with SHEDS-Dietary for permethrin, as shown in the table and figure below.

2.7.2 Uncertainty Analyses on Selection of Food Consumpti on Diaries

This section describes SHEDS-Dietary uncertainty an alyses focusing on the selection of food consumption diaries. It includes uncertainty analy ses for the permethrin application (assessing impact of residues vs. consumption, and sample size s; assessing impact of number of exposure days before dose results stable).

Figure 2-10 shows the uncertainty for 3 CDFs for bo otstrap sampling of 50% of residues and 20%, 50%, 80% of food consumption data. The CDF of the 50% of residues and 20% of consumption data has the biggest uncertainty. It p resents uncertainty results for daily dietary cispermethrin exposure, based on bootstrapping 200 tim es. The ratio of 97.5 th percentile to the 2.5 th percentile (95% confidence interval) is 15.07/4.63= 3.3. In the same way, we can calculate those ratios for other schemes of bootstraps to evaluate what are the major factors contributing the overall uncertainty.

Figure Figure 2 2 - 10 . Uncertainty analysis profiles for daily dietar cis . Uncertainty analysis profiles for daily dietary - permethrin exposure. permethrin exposure. permethrin exposure.42

Table 2-7 shows results of uncertainty analyses. T he green highlighted cell (3.3) reflects the 95 th /5 th percentile ratio for an uncertainty run that used subsets: 50% cis-permethrin residue by RAC, and 20% of NHANES dietary consumption data for 3-5 year-olds. The NHANES food consumption data base contains a total of 6,204 foo d diaries, and only a subset of those diaries are selected for each simulation (6204*0.2=1241). W e ran this 100 times, for a sample size of 124,100 for variability for the same subset. We th en ran another 100 times for uncertainty for the different subsets with the same bootstrap sampling rate, yielding sample size of 12,410,000. The 99 th percentile was calculated from that simulation, an d the process was repeated 100 times, producing one hundred estimates of aggregate exposu re at the per capita 99th percentiles. From these 100 values we found the 97.5 th and 2.5 th percentiles; their ratios yield the uncertainty ra tio, 3.3 (15.07/4.63, see Figure 2-10). The bigger the u ncertainty ratio, the bigger uncertainty produced by subsets of dietary and residue data.

Table Table 2 2 - 7 . Bootstrap uncertainty analyses for cis . Bootstrap uncertainty analyses for cis . Bootstrap uncertainty analyses for cis- permethrin permethrin permethrin

10 20 30 40 50 60 70 80 90
10 5.3 3.2 3.6 3.4 4.7 3.5 4.3 4.6 3.3 20 2.8 2.5 3.4 2.5 3.3 3.1 3.5 2.5 2.6 30 2.5 2.3 2.3 2.8 2.5 2.5 2.7 2.3 2.6 40 1.9 2.1 2.2 2.1 2.1 2.0 2.0 2.2 2.0 50 2.3 2.4 1.7 2.2 2.3 2.3 2.0 1.9 1.9 60 2.0 2.1 2.0 1.8 1.9 1.8 1.6 2.2 2.2 70 1.8 1.9 1.8 2.1 1.9 2.2 1.9 2.0 2.0 80 2.1 1.9 1.8 1.8 1.8 1.7 1.8 1.7 1.9 90 1.7 2.0 1.8 1.8 1.7 2.0 2.0 1.9 1.8 dietary sampling percent Bootstrap sampling percentage for cis-permethrin re sidue data Ratio of 97.5th v.s. 2.5th percentile of uncertaint y 99th CDFs by various percentage of bootstrap



## 2.8 Quality Assurance

Three types of quality assurance have been conducte d with SHEDS-Dietary. First, ORD and contractor Alion followed the SHEDS QAPP (US EPA, 2 010) when developing the code and GUI. Second, ORD evaluated the model for Arsenic ( Xue et al., 2010). Third, OPP provided an independent review, and conducted a comparison of r esults to the DEEM-FCID model (modelto-model evaluation), as described below.43

2.8.1 Comparison SHEDS vs. DEEM (Model-to-Model Evaluatio n) This section compares SHEDS-Dietary and DEEM-FCID. Table 2-8 presents the per capita estimates for chemical ‘ABC’ at upper percentiles ( 95 th , 99 th , 99.9 th ) used by the Agency in acute dietary risk assessments. Figure 2-11 presents the per capita estimates for chemical ‘ABC’ at the 99.9 th percentile for nine subpopulations for 17 separate drinking water scenarios. The differences should reflect only simulation uncertai nty (i.e., differences due to different draws of random numbers) since the models both rely upon the CSFII sampling design.

The Panel previously noted the importance of respec ting differences due to model uncertainty.13 SHEDS-Dietary was developed to evaluate the increm ental effects of specific modeling assumptions. This tool can also help explore other issues, such as the sensitivity analyses as discussed above, as well as other types of analyses that may be requested as the Agency progresses toward using PBPK models.

We briefly describe the DEEM-FCID model since the A gency has generally relied upon this model to conduct dietary risk assessments under FQP A. For each food diary, DEEM-FCID applies a Monte-Carlo simulation to calculate total daily exposure, as depicted by Equation (1).14 DEEM-FCID conducts a fixed number of ‘itera tions’ to each food diary, allowing the user to specify the number of iterations per diary. Age ncy risk assessors typically run DEEM-FCID with 1,000 iterations per diary.15 DEEM-FCID keeps track of the total daily exposure for each simulated person-day, and applies the corresponding CSFII survey weights to project the simulated person-days to a per capita level. If th e user specifies only one iteration, then the per capita percentiles would reflect interpersonal vari ability – variation in exposures across the subpopulation due to differences in food consumptio n. If multiple iterations are specified, DEEM-FCID treats each modeled person-day as separat e (independent) simulation. The per capita estimates reflect both intrapersonal variabi lity and interpersonal variability. Note that the purpose of these Monte-Carlo simulations is to obta in an estimate of a high-end aggregate total daily exposure.

Table 2-8 presents DEEM-FCID and SHEDS-Dietary esti mates (cross-sectional) of total daily exposure at selected percentiles for chemical ABC, for 9 subpopulation groups. Table 2-8 suggests that these two models produce similar resu lts across these subpopulations for this particular set of anticipated (food) residues. Chi ldren often have higher exposures than adults

13 US EPA – FIFRA SAP Minutes 2004-04, p. 24. http://www.epa.gov/oscpmont/sap/meetings/2004/index .htm#april

. 14 The Monte Carlo procedure draws a residue for ea ch RAC-FF. While a particular commodity (Potato, t uber w/peel) may be used in multiple foods, the cooking method may differ, and thus, it will have a differe nt food form. The food form for potatoes used in ‘White potato, h ome fries w/Lard’ is ‘cooked-fresh-fried’ (ff=213, see legend in Table 1). This particular diary may have contained other foods with ‘Potato, tuber w/peel’ - some of which may have the same food forms, e.g., 71411000- 100701=‘W hite potato skins, with adhering flesh, fried, with cheese and bacon’, while others have different food forms, e.g ., 71603010=’Potato salad’, 71101110=’Baked potato ’. If the cooking method is the same (e.g., ‘‘Pork fat’ or ‘L ard’ used to fry eggs and home fries), then the sam e residue is applied to all those consumption amounts (‘home fri es’, ‘White potato skins’, etc.). But if the food forms are different (e.g., ‘Potato salad’ is boiled, ff=212; ‘Baked potato’, ff=211), then a different residue i s independently drawn and applied for those food forms in the total daily simulation. 15 Risk assessors may increase this to 5,000 or mor e iterations if the results are sensitive at this l evel.44

(mg ai/kg bw/day) at these upper per capita percent iles due to higher intakes of many foods as a percent of their bodyweight.45

Table Table 2 2 - 8 . A Comparison of DEEM . A Comparison of DEEM . A Comparison of DEEM- FCID and SHEDS FCID and SHEDS FCID and SHEDS-

Dietary Exposure Results for Dietary Exposure Results for Chemical ABC. Chemical ABC.

DEEM-FCID results (1 simulation w/1000 iterations)
95 th Pctile 99 th Pctile 99.9 Pctile Subpopulation (mg/kg/day) (mg/kg/day) (mg/kg/day)
U.S. General 0.00209 0.01076 0.04873 All Infants (< 1 yr) 0.00402 0.01661 0.05982 Children 1-2 yrs old 0.00931 0.03261 0.12403 Children 3-5 yrs old 0.00688 0.02717 0.10643 Children 6-12 yrs old 0.00328 0.01515 0.06653 Children 13-19 yrs old 0.00137 0.00762 0.03755 Adults 20-49 yrs old 0.00130 0.00714 0.03410 Adults 50+ yrs 0.00178 0.00879 0.03748 Females 13-49 yrs old 0.00139 0.00792 0.03780 SHEDS-Dietary results (150 iterations)

95 th Pctile 99 th Pctile 99.9 Pctile Subpopulation (mg/kg/day) (mg/kg/day) (mg/kg/day)
U.S. General 0.0021 0.0108 0.0476 All Infants (< 1 yr) 0.0037 0.0158 0.0556 Children 1-2 yrs old 0.0094 0.0326 0.1228 Children 3-5 yrs old 0.0070 0.0272 0.1041 Children 6-12 yrs old 0.0034 0.0154 0.0697 Children 13-19 yrs old 0.0014 0.0078 0.0361 Adults 20-49 yrs old 0.0013 0.0071 0.0323 Adults 50+ yrs 0.0018 0.0086 0.0365 Females 13-49 yrs old 0.0014 0.0079 0.0358 Ratio (DEEM-FCID/SHEDS)

95 th Pctile 99 th Pctile 99.9 Pctile Subpopulation (mg/kg/day) (mg/kg/day) (mg/kg/day)
U.S. General 0.99 1.00 1.02 All Infants (< 1 yr) 1.09 1.05 1.07 Children 1-2 yrs old 0.99 1.00 1.01 Children 3-5 yrs old 0.97 0.99 1.02 Children 6-12 yrs old 0.97 0.98 0.95 Children 13-19 yrs old 0.97 0.97 1.03 Adults 20-49 yrs old 0.99 1.00 1.05 Adults 50+ yrs 1.01 1.02 1.02 Females 13-49 yrs old 0.98 0.99 1.05

In addition to comparing exposures in Table 2-8, we compared SHEDS-Dietary and DEEM results for contribution of exposure from major com modities. Table 2-9 shows that these results are also very similar.46

Table Table 2 2 - 9 . Contribution to exposure by commodities for 1 . Contribution to exposure by commodities for 1 . Contribution to exposure by commodities for 1- 2 year - olds.

comcode Commodities DEEM SHEDS 95003590 Strawberry 34.5 40.3
12002600 Peach 13.0 8.9 95001750 Grape 10.8 13.1 11000070 Apple, fruit with peel 7.6 8.2 95003600 Strawberry, juice 7.2 6.5
12002300 Nectarine 3.7 4.5 11000100 Apple, juice 3.3 2.9
12002850 Plum 2.7 0.8 95001780 Grape, raisin 2.6 2.7 12002880 Plum, prune, juice 2.1 3.0 12002620 Peach juice 1.4 0.0 12000130 Apricot, dried 1.2 1.2 4013550 Spinach 1.1 0.3 95001760 Grape, juice 0.9 1.5
9013990 Watermelon 0.8 1.2 9023560 Squash, summer 0.8 0.1
9021350 Cucumber 0.7 0.0
11002660 Pear 0.6 0.4 Percentage

Figure 2-11 plots the DEEM-FCID and (cross-sectiona l) SHEDS-Dietary estimates of exposure at the per capita 99.9 th for 17 different drinking water scenarios, for 9 a ge groups. This plot suggests that these two models produce similar resu lts across many different drinking water scenarios, because the correlation is near 1 (line at near 45 degree angle). For any particular scenario, the infant subpopulation (pink) has the h ighest exposures (mg ai/kg bw/day) since infants generally have higher drinking water intake s as a percent of their bodyweight (mL/kg bw/day).47 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01

DEEMSSHEDS U. S. Population
All Infants 1-2 years 3-5 years 6-12 years 13-19 years 20-49 years 50+ year female 13-49 years

Figure Figure 2 2 - 11 . Comparison of SHEDS and DEEM on 99.9 . Comparison of SHEDS and DEEM on 99.9 th percentiles of drinking water. Chemical ABC exposure (mg/kg/day) of 17 different Chemical ABC exposure (mg/kg/day) of 17 different s cenarios. scenarios.48 

# 3 Applications/Case Studies to Date

ORD and OPP scientists collaborated to refine, eval uate (compare with DEEM model), and apply the SHEDS dietary module (food and drinking water) for a number of analyses (food and drinking water scenarios, half-life and eating occa sion sensitivity analyses, longitudinal simulations with half-life and eating occasion anal yses, examining various ways of sampling residues for sensitivity analyses, analyses for con tribution by crops and chemicals to identify key risk contributors and help assess risk mitigation s cenarios) to refine OPP's risk assessments and inform their risk management decisions for the foll owing:

Aldicarb RED (2006)

 development/testing of eating occasion analyses

 allowed comparison to DEEM-based analyses

 applied Bayer DWCS data (little difference) for dir ect water intake Carbaryl (2007)

 explored longitudinal (multi-day) eating occasion a nalyses (DW-infants, 5+ hrs) N-Methyl Carbamate CRA (2007)

 supported contention that not significantly overest imating risk by not accounting for recovery (food-only)

 maximum exposure, by eating occasion, provides best case scenario for recovery Organophosphates CRA (2009-2011)

 updating the 2006 OP Cumulative Risk Assessment

 SHEDS longitudinal eating occasion analysis used to consider persisting effects (carryover) on AChE inhibition using chemical-specific re covery (half-life) rates

 SHEDS contribution analyses allowed assessing effec ts of mitigation options on the population 99.9 th percentile

EPA/ORD scientists have also applied SHEDS-Dietary to As and MeHg case studies for research purposes, to answer questions about ranges of popul ation exposures, major food contributors, differences in exposures for vulnerable populations , and evaluation of modeled estimates against duplicate food and biomarker data.



## 3.1 Arsenic (As)

Dietary exposure from food to toxic inorganic arsen ic (iAs) in the general US population has not been well studied. This SHEDS-Dietary research quan tifies dietary As exposure, and analyzes the major contributors to total As and iAs. Anothe r objective was to compare model predictions to observed data using both duplicate diet data and (after linkage with a PBPK model) biomarker data.49

Probabilistic exposure modeling for dietary As was conducted with the SHEDS-Dietary model, using NHANES/WWEIA consumption data and TDS residue data. The dose modeling was conducted by combining the SHEDS-Dietary model with a physiologically-based pharmacokinetic (PBPK) model in EOHSI’s MENTOR-3P s ystem (Xue et al., 2010). Model evaluation was conducted via comparing exposure and dose modeling predictions against NHEXAS duplicate diet data and NHANES biomarker mea surements, respectively, for the same individuals.

The Xue et al., 2010 SHEDS-Dietary publication revealed that toxic inorg anic As (iAs) exposure from food is more important than drinking water for the U.S.. The major food contributors to iAs exposure were vegetables, fruit juices, and fruits; rice; beer and wine; and flour, corn, and wheat (Figure 3-1). The major food contributor for tAS e xposure is fish (contributing 60% of exposure; Figure 3-2). The mean modeled tAs exposure from fo od is 0.38 μ g /kg/day, ~14 times higher than the mean As exposures from the drinking water. The mean iAs exposure from food is 0.05
μ g /kg/day (1.96 μ g /day), ~2 times higher than the mean iAs exposure s from the drinking water. Approximately 10% of tAs exposure from foods is the toxic iAs form. SHEDS modeled exposure and dose estimates matched well with the d uplicate diet data and measured As biomarkers (Figures 3-3 and 3-4). This model evalu ation effort provides more confidence in the exposure assessment tools used, including SHEDS-Die tary.

Some key results are shown in Figures 3-1 to 3-4:50 1 12%
11%

18%

13% 5% 17% 24% Beer,Wine Flour,Corn,Wheat
Fruit,Fruit Juice
Others Poultry,pork,beef,egg

Rice Vegetables

Source: Xue et al., EHP 2010 Figure Figure 3 3 - 1 . Contribution of Inorganic Arsenic Intake by Food . Contribution of Inorganic Arsenic Intake by Foods

1 2%

60%

4% 5% 4% 5% 7% 9% 4% Beer,Wine

Fishes Flour,Corn,Wheat
Fruit,Fruit Juice
Others Poultry,pork,beef,egg

Rice
Shellfish Vegetables

Source: Xue et al., EHP 2010 Figure Figure 3 3 - 2 . Contribution of Total Arsenic Intake by Foods . Contribution of Total Arsenic Intake by Foods . Contribution of Total Arsenic Intake by Foods51 0 1 2 3

0 10 20 30 40 50 60 70 80 90 100

PercentileD ie ta ry in t a k e (u g /k g /d a y ) MODEL 5th
MODEL 50th
MODEL 95th
NHEXAS

* Fill in no-detected with 1/2 LOD when As residue mean > 0

Figure Figure 3 3 - 3 . SHEDS . SHEDS . SHEDS- Dietary Exposure Model Evaluation for Arsenic Agai st Duplicate Dietary Exposure Model Evaluation for Arsenic Again st Duplicate Food Data Food Data

1 0 20 40 60 80 100

0 100 200 300 400 500 Urine Conc (ug/L)P e rc e n tile

Mean model prediction NHANES urine data Comparison of total As in Urine (ug/L) from NHANES data and PBPK model

N Mean Std 50th 25th 75th 95th

PBPK model 2355 18.32 46.86 8.1 4.7 16.1 58.9

Measured conc. 2355 18.06 42.12 4.9 2.5 14.6 74.8

Source: Xue et al., EHP 2010

Figure Figure 3 3 - 4 . Total Arsenic Model Evaluation for SHEDS . Total Arsenic Model Evaluation for SHEDS . Total Arsenic Model Evaluation for SHEDS- Dietary linked with MENTOR Dietary linked with MENTOR PBPK Model and Compared Against NHAN PBPK Model and Compared Against NHANES Urinary Biom arker Data. ES Urinary Biomarker Data. ES Urinary Biomarker Data.52

3.1.1 Arsenic Uncertainty Analysis Results SHEDS-Dietary has a simple bootstrapping method for conducting uncertainty analyses - utilizing only a subset of the consumption and resi due data inputs. The SHEDS-Dietary bootstrap procedure applied to Arsenic involved the following steps: 1) Randomly draw certain percentage (1/20 or 5%) of pe rson-day from CSFII data or/and randomly draw certain percentage (1/4 or 25%) of re sidue data from pollutant residue files by raw commodity and food form, 2) Perform Monte Carlo simulations (e.g., 100 iteratio ns per diary using cross-sectional method) 3) Get population based statistic from each run (e.g., aggregate exposure at per capita 99 th percentile) 4) Repeat the steps 1-3 many times, say 200 times 5) Conduct uncertainty analyses from different runs (e .g. will have 200 estimates of the level of aggregate exposure at the per capita 50 th , 95 th and 99 th percentiles, respectively). For each population based statistics (e.g., per capita 99 th percentile), the ratio of 95 th vs. 5 th percentile can be used to evaluate the uncertainty. The bigger the uncertainty ratio, the bigger uncertainty produced by subsets of dietary a nd residue data.

Figure 3-5 shows the uncertainty for 3 selected per centiles (see Xue et al., 2006 for details on this type of uncertainty analysis), and that 99th percen tile has the biggest uncertainty. It presents uncertainty results for daily dietary arsenic expos ure, based on bootstrapping 1/30 of CSFII diaries 200 times and 1/8 of the residues. For eac h percentile, such as 50 th , 90 th or 95 th , there are 200 values, from which the 95 th and 5 th percentile were acquired and its ratio was calcula ted. The ratio of the 95 th to 5 th percentile is 1.19 for 50 th percentile; 1.93 and 3.28 for 95 th and 99 th percentile respectively. We can see that there is r elatively little uncertainty regarding the estimate of the 50 th percentile as compared to higher percentiles with respect to the amount of residue and consumption data used in the exposure assessment. For the arsenic case study, we performed such bootstrap procedure and calculate 95 th /5 th ratios for various subsets of consumption and residue data to evaluate the relative contributions to the overall uncertainty.53 Uncertainty of daily dietary exposure of As with 1 /8 for As residue and 1/30 CSFII bootstrap

0.1 1 10 100

0 10 20 30 40 50 60 70 80 90 100
Uncertainty PercentileAs dietary exposure (ug/kg/day)

P50

P95

P99

Figure Figure 3 3 - 5 . Example SHEDS . Example SHEDS . Example SHEDS-

Dietary Uncertainty Analyses Dietary Uncertainty Analyses Dietary Uncertainty Analyses



## 3.2 Methyl Mercury from Fish Consumption (MeHg)

The MeHg case study examines exposures for vulnerab le populations. Asians, Native Americans, and Pacific Islanders (A/N/P) have shown higher levels of MeHg in previous NHANES; reasons have not been well studied. The ob jectives of this research are to examine dietary exposures to MeHg through fish consumption in different racial/ethnic groups, and extend previous NHANES blood level analyses.

Probabilistic exposure modeling for dietary MeHg wa s conducted with SHEDS-Dietary, using NHANES/WWEIA fish consumption data and FDA TDS fish residue data. MeHg exposures by race/ethnicity, age group, and food type were analy zed. For Asians, Native Americans, and Pacific Islanders, major contributors for MeHg are tuna, fresh water fish–other, seawater fish– other. Statistical analyses of blood MeHg levels b y race/ethnicity from 1999-2006 are being compared against previous published results for 199 9-2002 data (6 times larger sample size). Exposure estimates for MeHg in fish can explain the high level of MeHg in blood for populations with higher fish consumption.54

Results are shown in Figures 3-6 and 3-7, and Table s 3-1 to 3-2. For all age groups, the A/N/P group has higher mean dietary MeHg exposures than t he general population (Figure 3-6 and Table 3-1). 1-2 year-olds and A/N/P have the highe st ratio of SHEDS modeled MeHg exposure and NHANES MeHg blood levels. For A/N/P, 5 major c ontributors for MeHg are tuna, fresh water fish–other, seawater fish–other, salmon, and catfish (Figure 3-7). SHEDS exposure predictions correlate well with NHANES blood biomar ker levels in terms of age, gender, and ethnicity. Percentage of MeHg blood levels higher t han critical health-based concentrations is higher (up to 8x) for A/N/P compared to other racia l/ethnic groups (Table 3-2).

0.001 0.01 0.1 1

70 75 80 85 90 95 100

PercentileExposure (ug/day/kg) Mexican American Non-Hispanic White Non-Hispnaic Black Other Hispanic
Tribal,Asian,Pacific

Figure Figure 3 3 - 6 . SHEDS . SHEDS . SHEDS-

Dietary Methyl Mercury Exposure by Ethnicity Using 1999 Dietary Methyl Mercury Exposure by Ethnicity Using - 2006 NHANES Data NHANES Data55 Contribution of MeHg exposure from different fish t ypes for Asians, Native Americans, Pacific Islanders

tuna 26%

catfish

7%

salmon 9% shell fish 6%

shrimp 5% sword,shark 0% Other seawater fish 22% other freshwater fish 25%

Figure Figure 3 3 - 7 . Contribution of Methyl Mercury Exposure from Di ferent Fish Types for . Contribution of Methyl Mercury Exposure from Dif ferent Fish Types for Asians, Native Americans, and Pacific Islanders Sel f- Reported in NHANES Reported in NHANES Reported in NHANES56

Table Table 3 3 - 1 . SHEDS . SHEDS . SHEDS- Dietary Methyl Mercury Exposure by Ethnicity and A e from 1999 Dietary Methyl Mercury Exposure by Ethnicity and Ag e from 1999 e from 1999-

- 2006 NHANES Data 2006 NHANES Data

Ethnicity Age N Mean fold Std P95 P99 Mean fold Std P95 P99
ANP 0 < 1 170 0.1 1.7 64 0.0 4.9 0.01 1.6 6 0.00 0.53

1 to <2 89 1.7 5.7 804 7.3 42.0 0.13 4.9 62 0.62 3.09
2 to <3 96 0.9 1.7 307 8.2 14.9 0.07 1.9 23 0.54 1.13
3 to <6 200 0.8 1.1 542 3.3 25.8 0.05 1.2 35 0.18 1.61
6 to <11 285 1.7 1.6 1279 9.6 32.2 0.05 1.5 39 0.20 1.09
11 to <16 316 0.7 0.7 459 6.5 13.0 0.01 0.8 9 0.11 0.27
16 to <21 296 2.1 1.8 1565 14.8 34.2 0.03 2.1 25 0.17 0.70
21 to <50 604 3.8 1.6 2843 20.7 55.3 0.05 1.8 39 0.29 0.78
50+ 366 4.6 1.8 3896 21.9 73.0 0.06 1.8 52 0.30 0.68

REST 0 < 1 2517 0.1 69 0.0 1.3 0.01 8 0.00 0.14

1 to <2 1704 0.3 228 1.0 8.7 0.03 21 0.10 0.77
2 to <3 1622 0.5 419 2.1 11.5 0.04 32 0.14 0.77
3 to <6 3153 0.7 592 3.9 21.3 0.04 31 0.22 1.08
6 to <11 4815 1.0 856 4.0 28.3 0.03 29 0.13 1.04
11 to <16 7305 1.0 744 4.6 25.9 0.02 14 0.10 0.49
16 to <21 6721 1.1 845 5.0 25.3 0.02 11 0.08 0.38
21 to <50 13211 2.3 2895 12.8 46.7 0.03 37 0.16 0.60
50+ 11530 2.6 2101 15.9 45.9 0.03 29 0.21 0.59 ug/day ug/kg/day57

Table Table 3 3 - 2 Summary statistics o Summary statistics o Summary statistics of organic blood level by age g roup and ethnicity f organic blood level by age group and ethnicity (μg/liter) from 1999 (μg/liter) from 1999-

- 2006 NHANES data 2006 NHANES data 2006 NHANES data

Ethnicity Age (yr) N Mean fold Std P95 P99
ANP 1 to <2 36 0.76 3.0 133 4.40 4.40

2 to <3 42 0.59 2.2 102 2.21 4.00 3 to <6 91 0.57 1.5 182 3.40 6.82 6 to <11 96 1.01 2.7 277 4.40 6.80 11 to <16 94 0.69 1.5 152 3.03 4.70 16 to <21 126 1.24 2.0 244 4.90 6.71 21 to <50 270 1.69 1.6 421 5.52 6.63 50+ 108 1.70 1.4 374 5.03 5.78

REST 1 to <2 716 0.25 56 0.92 2.46

2 to <3 726 0.27 57 1.10 2.42 3 to <6 1570 0.38 99 1.62 3.60 6 to <11 1336 0.38 93 1.40 3.63 11 to <16 2089 0.45 91 1.76 4.35 16 to <21 3117 0.62 116 2.32 5.20 21 to <50 6339 1.04 281 3.81 6.30 50+ 3510 1.18 253 4.00 6.00

This research extends and is consistent with findin gs from previous studies focusing on higher blood levels in A/N/P populations, by examining die tary exposures to MeHg from fish consumption. A/N/P populations are exposed to highe r levels of MeHg from fish consumption than the general US population and other ethnicity groups. SHEDS-Dietary modeling allows identification of Hg intakes by age, gender, ethnic ity, and type of fish. Correlations of modeled dietary exposure predictions with NHANES blood biom arker levels suggest that fish consumption is a key exposure pathway for these pop ulations.



## 3.3 Permethrin

ORD and OPP scientists have collaborated on applica tion of SHEDS-Dietary to estimate permethrin dietary exposure to support OPP’s pyreth roid cumulative risk assessment. The objectives of this SHEDS-Dietary application are to quantify dietary permethrin exposures in the U.S. population, analyze the major contributors, an d compare model predictions to observed data using duplicate diet data from the EPA’s Children’s Total Exposure to Persistent Pollutants study (CTEPP).58

CSFII 1994-1996,1998 consumption data and PDP data for residues were used. Model predictions were evaluated against CTEPP duplicate food data for cis- and trans-permethrin (matched SHEDS and CTEPP data by age and gender). A bootstrap approach was applied to assess uncertainty and relative importance of dieta ry consumption vs. residue data. SHEDSDietary was linked to PBPK models and results compa red against NHANES biomonitoring data.

Results are as follows and shown in the Tables 3-3 to 3-5 and Figures 3-8 to 3-10 below:

 exposure: 0.44 to 2.2 μ g /day; as age increases, exposure increases

 by body weight, young children and 50+yrs. have hig hest exposure

 3 most important contributors overall: spinach, let tuce, cabbage

 for 98.5 to 99.5 %ile, lettuce more important

 results similar for cis- and trans-permethrin

 results similar using NHANES vs. CSFII

 SHEDS model results and CTEPP measurement results m atched well.

Using the SHEDS-Dietary model, the mean cis-permeth rin exposure for the U.S. population from food and drinking water ranged from 0.44 to 2.2 μ g /day; 3.4E-05 to 9.9E-5 mg/kg/day. The 95 th percentiles ranged from 0.90 to 24.48 μ g /day; 8.3E-6 to 6.8E-5 mg/kg/day. As Table 3-3 a lso shows, as age increases, exposure increases; normal izing by body weight, young children and adults over 50 years have the highest exposures. R esults were similar for cis- and transpermethrin. The three most important contributors o verall were spinach (48% cis-), cabbage (28% cis-), and lettuce (10% cis-). For the upper tails of the exposed population (98.5%ile to 99.5 %ile), lettuce was more important (43% cis-; s ee Figure 3-8).

In comparing with CTEPP measurement results, SHEDS- Dietary exposure estimates (246 paired comparisons) for mean, 95 th , and 99 th percentiles matched well: the cloud of 100 yellow variability lines from the model contain the observ ed data in Figure 3-9, and the ratio of modeled to measured data is close to 1 in Table 3-4 for bot h for cis- and trans- permethrin.

Figure 3-10 and Table 3-5 show the sensitivity of r esults to which consumption database is used: CSFII or NHANES. Table 3-5 shows that with the NHA NES data, lettuce is the most consumed (39.7%) and greatest food contributor to dietary ci s-permethrin exposure (47.5% in the % exposure column); using CSFII spinach was the most consumed (37.9% in the % food column) and greatest food contributor to dietary cis-permet hrin exposure (46%). The list of key RAC is also different, e.g. apple juice appears in NHANES but not CSFII. Figure 3-10 illustrates the differences in exposure CDFs using the two differen t consumption databases.

The model evaluation effort with this case study pr ovides more confidence in SHEDS-Dietary. More research is needed with PBPK linkage and model evaluation.59

Table Table 3 3 - 3 . Cis - permethrin exposure by age groups from SHEDS permethrin exposure by age groups from SHEDS permethrin exposure by age groups from SHEDS- Die tary with CSFII tary with CSFII Data

age group n mean std p95 p99 mean std p95 p99

0 < 1 years 297200 6.7E-01 4.7E+02 7.7E-02 1.1E+01 7.9E-05 5.6E-02 8.3 E-06 1.3E-03 1-2 years 419200 4.4E-01 3.8E+02 5.1E-01 6.0E+00 3.9E-05 3.7E-02 4.0 E-05 4.7E-04 3-5 years 878200 5.2E-01 4.2E+02 7.8E-01 8.0E+00 3.0E-05 2.4E-02 4.4 E-05 4.4E-04 6-12 years 417800 7.0E-01 8.9E+02 1.3E+00 1.3E+01 2.1E-05 2.7E-02 4.1 E-05 3.9E-04 13-19 years 244400 1.1E+00 1.6E+03 2.4E+00 1.9E+01 1.6E-05 2.2E-02 3.8 E-05 3.0E-04 20-49 years 935400 1.8E+00 2.7E+03 4.2E+00 3.8E+01 2.6E-05 4.1E-02 5.8 E-05 5.3E-04 50+ years 929200 2.2E+00 2.3E+03 5.0E+00 4.7E+01 3.2E-05 3.4E-02 6.8 E-05 6.6E-04 ug/day mg/kg/day

All data

spinach 48%

cabbage 10% lettuce 28% parsley 2% celery 4% tomato 2%

other

6% 98.5 to 99.5 percentiles

spinach 31%

cabbage 16% lettuce 43% parsley 2% celery 1% tomato 0%

other

7% Contribution of foods to cis-permethrin exposure

Figure Figure 3 3 - 8 . Contribution of Foods to Cis . Contribution of Foods to Cis . Contribution of Foods to Cis- Permethrin Exposure Using SHEDS Permethrin Exposure Using SHEDS Permethrin Exposure Using SHEDS- Dietary Dietary Dietary60

Figure Figure 3 3 - 9 . Comparison of SHEDS . Comparison of SHEDS . Comparison of SHEDS- Dietary Es Dietary Es Dietary Estimates Against CTEPP Duplicate Diet

timates Against CTEPP Duplicate Diet Exposure Data for Cis Exposure Data for Cis-

- and Trans and Trans and Trans-

Permethrin. (Note: The cloud of yellow lines Permethrin. (Note: The cloud of yellow lines illustrating 100 modeled variability runs contains the observed data.) the observed data.)

Table Table 3 3 - 4 . SHEDS . SHEDS . SHEDS- Di etary Evaluation against CTEPP Duplicate Diet Dat for Cis etary Evaluation against CTEPP Duplicate Diet Data for Cis for Cis- and Trans- Permethrin with CSFII Data Permethrin with CSFII Data Permethrin with CSFII Data

perm mean std p5 p25 p50 p75 p95 p99

SHEDS cispermethrin 6.9E-02 6.6E-01 4.9E-07 2.6E-05 2.5E-04 9.7E-03 1.7E-01 1. 3E+00CTEPP cispermethrin 6.5E-02 3.8E-01 4.2E-04 6.8E-04 1.3E-03 5.8E-03 1.7E-01 2. 6E+00SHEDS transpermethrin 6.8E-02 7.2E-01 0.0E+00 1.8E-05 1.8E-04 8.0E-03 1.5E-01 1. 2E+00CTEPP transpermethrin 8.9E-02 3.8E-01 1.0E-03 2.2E-03 4.8E-03 2.3E-02 2.2E-01 2. 0E+0061 Exposure of cis-permethrin with NHANES and CSFII (3 -5 year-olds)

70 75 80 85 90 95 100

0 0.001 0.002 0.003 0.004 0.005 0.006 exposure (mg/kg/day)percentile

CSFII

NHANES

Figure Figure 3 3 - 10 . SHEDS . SHEDS . SHEDS-

Dietary Modeled Exposure of Cis Dietary Modeled Exposure of Cis Dietary Modeled Exposure of Cis- Permethrin with NHANES and Permethrin with NHANES and CSFII for 3 CSFII for 3-

- 5 year - olds62

T able 3 - 5 . SHEDS . SHEDS . SHEDS-

Dietary Modeled Contribution of RAC to Total Food onsumption Dietary Modeled Contribution of RAC to Total Food C onsumption for Cis for Cis-

- Permethrin and 3 Permethrin and 3 Permethrin and 3-

5 year - olds

Contribution of RAC to total food consumption and c is-permethrin exposure (3-5 year-olds)

RAC % food % exposure RAC % food % exposure Spinach 37.9 46.0 Lettuce, head 39.7 47.5 Lettuce, head 29.0 34.5 Spinach 12.9 24.0 Cabbage 14.6 11.1 Cabbage 14.0 11.2 Endive 0.7 2.7 Endive 1.7 6.8 Lettuce, leaf 1.7 1.7 Parsley, leaves 0.3 2.7 Parsley, leaves 0.1 0.8 Lettuce, leaf 2.7 2.6 Spinach-babyfood 0.3 0.7 Pear 2.8 0.9 Brussels sprouts 0.8 0.6 Cantaloupe 4.1 0.7 Cantaloupe 3.2 0.4 Tomato 4.4 0.7 Celery 2.2 0.3 Peach 1.7 0.5 Pear 0.8 0.2 Broccoli 2.2 0.5 Peach 0.5 0.2 Watermelon 5.0 0.4 Tomato 1.5 0.2 Pepper, bell 0.7 0.3 Watermelon 2.2 0.1 Brussels sprouts 0.2 0.3 Broccoli 0.8 0.1 Celery 1.5 0.3 Pepper, bell 0.3 0.1 Apple, juice 4.2 0.3 CSFII NHANES63 

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