# DRAFT

Office of Pesticide Programs

& The Agricultural Health Study Comparison of Exposure Assessment

Approaches ( Addendum to Case Study B)

January 19 , 2010

Office of Pesticide Programs US Environmental Protection AgencyDraft, Do Not Cite

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# Table of Conten ts

I. INTRODUCTION 3

II. OVERVIEW OF THE AGEN CY AND AHS EXPOSURE ASSESSMENT METHODS 6

A. Office of Pesticide Programs: Background and Description 7

B. The Agricultural Health Study: Background and Description 10



### III. CASE STUDY 12

A. Step 1 Illustrate Method Differences 14 1. Algorithms Used for Risk Assessment Calculation s 14 2. Input Parameters for Exposure Algorithms 18

B. Step 2 Consideration of Overall Differences 23

C. Step 3 Example Case Evaluations 25



### IV. FUTURE CONSIDERATION S 26

V. REFERENCES 28Draft, Do Not Cite

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### I. Introduction

EPA is convening a meeting of the FIFRA Scientific Advisory Panel (SAP) in February, 2010 to provide peer review on the document, Draft Framework for Incorporating Human Epidemiologic an d Incident Data in Health Risk Assessment, along with three case studies. The draft framework, Case Study A (ecologic and retrospective epidemiology studies with atrazine) and an overview of Case Study B (exposure assessment in a prospective epidemiology study) were transmitted to the SAP on January 7, 2010. Case study C (human incident data on diazinon) was transmitted on January 12, 2010. This document contains the addendum to Case study B and provides additional details on the project plan described in the overview provided in the January 7, 2010 submission. The intent of this case study is to begin to develop an understanding of how to better use results from prospective epidemiologic stud ies to inform
risk assessment s , risk characterizations, and i n problem formulation . This case study is based on information from the Agricultural Health Study (AHS) ( http://aghealth.nci.nih.gov/ ) The AHS is an extensive, high quality epidemiologic study which collected i nformation on the patterns of pesticide exposures of its cohort. The AHS cohort includes approximately 57, 000 pesticide applicators and 32,000
of their spouses from states that are major agricultural production areas (i.e., Iowa and North Carolina).
AHS in vestigators have authored over 100 articles in the peer reviewed literature to date on many topics including ground

- breaking work on cancer and non

- cancer disease outcomes and exposure metrics. The AHS is therefore an excellent basis for a case study becau se of the robust methods used, the size of the selected cohort, the quality of the data, and the direct applicability of those studied in the cohort to routine risk assessments for pesticide users developed by the Agency.

This specific case study analysi s focus e s on individuals who mix, load, or apply pesticides ( i.e., commonly
referred to as handlers). The Agency has well

- developed, transparent, and peer reviewed methods for estimating occupational handler exposures ( http://www.epa.gov/scipoly/sa p/meetings/2007/010907_mtg.htm )

. Similarly, the AHS approach for exposure classification is well

- developed and has undergone significant peer

- review. The Agency
methods differ from the a pproaches used in the AHS
, and in epidemiological analys e s in general
, for considering the exposures of participants in a cohort because the goals of the two types of analyses are decidedly different

. The general differences in scope and purpose between ex posure assessment in observational epidemiologic investigation s and quantitative risk assessment have been well articulated in the scientific literature ( van den Brandt et al ., 2002 ). The types and methods of information collected for regulatory exposure assessment and that of prospective epidemiology are quite different. These
difference s are reflected in the inputs and algorithms used in the two occupational handler exposure assessment approaches.Draft, Do Not Cite

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In brief, the primary goal of the Agency„s occupationa l handler assessments is to evaluate the high end of
exposure estimates associated with specific application scenarios for a particular pesticide. EPA methods result in a single point estimate of anticipated exposure, based upon certain occupational handle r activities. In these evaluations, the Agency assesses the impact on exposure of different variables including different degrees of personal protective equipment (PPE) and/or engineering controls and
altered use criteria such as application rates or the a mount which could be treated in a day (e.g., acres )

. The goal of the AHS epidemiologic exposure assessment is to develop a relative exposure ranking of individuals who are actual pesticide users within a cohort to better understand the factors which could contribute to a specific observed outcome. It is not feasible to directly measure actual exposure directly in broad investigations such as the AHS because of many factors (e.g., cost and timing) . The AHS exposure
information was primarily derived from ques tionnaires completed by individual cohort members. The AHS has documented the quality of self

- reported information and has performed field measurement sub

studies to assess the efficacy of the exposure intensity algorithm approach ( Dosemeci et al ., 2002 an d
Coble et al ., 2005).

Being able to explain the differences between how the Agency completes exposure assessments and how exposures are considered in epidemiologic research (e.g., AHS ) is importan t for appropriately characterizing the results of Agency r isk assessments relative to epidemiological findings. This paper describes the formative stages of a collaborative effort between the Agency ( Office of Pesticide Programs, and the Office of Research and Development) and the investigators from the AHS to e xplore
these differences

. To complete this case study, the Agency has submitted a request for data from the
AHS. The AHS has agreed to support the Agency‟s current effort by facilitating the Agency‟s data request through the AHS Study Tracking and Review Systems (STaRS). Additionally, the AHS has provided background information about the study design , analytical methods, and overall study goals.
This multi

- agency collaborative effort is necessary to facilitate the Agency ‟s comprehensive understanding of the AHS exposure classification approach. The following outlines the proposed three steps for this process:

Step 1 : Com plete a n evaluation of the exposure determinant s used in the AHS and Agency approaches for pesticide applicator exposure assessment to illustrate potential differences

.

Step 2 : Build ing on the findings from Step 1 , Step 2 will u se exposure and biomonitoring data applicable to a subset of pesticide applicators in the AHS cohort to compare exposure metrics for
those individuals calculated using the Agency and AHS approaches.Draft, Do Not Cite

5 of 34 Step 3 : This step may involve a larg e
- scale comparative analysis using information pertaining to
the atrazine and alachlor users in the AHS cohort. Prior to the conduct of this step, a feasibility of this analysis w ill be evaluated by the Agency and investigators from the AHS

.

T his case study is presented in terms of a problem formulation exercise, as discussed in the draft
f ramework and the 2009 National Research Council document on advancing risk assessment scien ce and decision making ( http://dels.nas.edu/dels/rpt_briefs/IRA_brief_final.pdf ). Specifically, the problem formulation will identify key differences in the exposure assessment method ologies used by AHS investigators and the Agency . Th e information will be used to illustrate how such methodological
differences may influence conclusions drawn from these respective efforts

.

This document is organized as the following : Section II provides detailed information about the Agency and AHS exposure assessment methods. Section III provides the specific case study elements. Section
IV illustrates possible f uture considerations to expand on integrating epidemiology in risk assessment
decision

- making with the use of data from AHS and other possible sources of information (e.g., Farm Family Exposure Study)

. Section V contains the r eferences cited in this case study.Draft, Do Not Cite

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### II. Overview of the Agency and AHS Exposure Assessment M ethods

Over the last several decades the Agency has actively engaged in refining the methodologies and data used for completing occupational exposure and risk assess ments. The se exposure assessment method s for pesticide applicators w ere most recently presented to the SAP in 2007 . The information from the 2007 meeting is available at ( http://w ww.epa.gov/scipoly/SAP/meetings/2007/010907_mtg.htm ). The Agency
evaluat es the potential risks to pesticide applicators in order to determine labeling requirements for protective clothing use, requirements for engineering controls, or use criteria such a s maximum application rates . These assessments are based on information from pesticide labels, information on cultural production practices, and exposure conditions (referred to as unit exposures) for each type of job
task evaluated in an assessment. Exp osure conditions are defined by the types of application equipment that can be used to apply a pesticide and the amount of personal protective equipment or engineering controls used in an application. These parameters are then included in an algorithm whi ch is used to calculate quantitative estimates of exposure that in turn are used to estimate risks for each applicable toxicological endpoint that has been identified for a given pesticide

.

The AHS is a comprehensive epidemiological study of pesticide app licators and their families living in
Iowa and North Carolina . A major strength of the AHS is the collection of detailed information on pesticide use. In contrast to the Agency‟s approach of calculating quantitative estimates of exposure to
estimate ris ks , the AHS develops pesticide exposure classification metrics for exposure/outcome analyses (e.g., binning groups of applicators based on their relative anticipated exposures

– low to high for example) . Several approaches have been used for exposure class ification in the AHS for health outcome analyses, including ever/never use of a specific pesticide or groups of pesticides, cumulative lifetime days of use of a specific pesticide or groups of pesticides, and intensity

- weighted cumulative lifetime days of use. In some cases, exposure classification for non

- cancer outcomes has been based on reported use in the previous year. An exposure intensity algorithm was developed to generate numerical intensity scores using information on pesticide mixing/loading, a pplication methods, use of personal protective equipment, and pesticide equipment repair (Dosem e ci et al . 2002).

Both the AHS and the Agency approaches consider the temporal aspects of pesticide exposures which define cumulative overall exposures and, a s importantly, shorter

- term exposure patterns which could reflect annual seasons of intense use due to crop cultivation requirements (e.g., herbicide applications for spring corn planting). The Agency ‟s approach associate s what is know n about the tempora l aspects of
the use pattern s of a pesticide
, i.e. , the frequency and duration of pesticide application within a growing

season, as the basis for calculating risks for differing time constructs that were developed to represent commonly an ticipated durati ons of exposure (e.g., short

- term up to 30 days exposure). W ithin the AHS exposure classification approach, the exposure intensity score is multiplied by the cumulative lifetimeDraft, Do Not Cite

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days of use to develop the intensity

- weighted metric. Cumulative lifetime day s are determined through individual questionnaire information which specifically asks about years of use of specific pesticides, days per year of use, and the first decade of use. This information is combined to determine the duration and frequency paramet ers of the AHS exposure algorithm.

More details illustrating the methods used in both the Agency and AHS exposure assessment approaches are presented , respectively in Sections A and B below.



### A. Offi ce of Pesticide Programs: B ackground and Description

Risk assessments for occupational handlers serve as the basis for defining how best to mitigate potential risks posed by involvement in pesticide application practices. As mentioned in the above sectio n, pesticide application practices are defined by pesticide labels, information on cultural production practices, and exposure conditions (referred to as unit exposures) for each type of job task evaluated in an assessment. Exposure conditions are defined by the types of application equipment that can be used to apply a pesticide and the amount of personal protective equipment or engineering controls used in an application. The Pesticide Handler Exposure Database (PHED) 1 contains information on unit expos ures, which define exposures based on the amount of pesticid e used to the aforementioned exposure
conditions.

Exposure Rates For Specific Job Tasks

Unit exposures are e xposure rates which relate the amount of exposure individuals receive relative to th e amount of pesticide used or handled, equipment used, and levels of personal protection used serve as the basis for Agency risk assessments. The Agency has developed a series of these values to represent various anticipated job functions (e.g., pesticide applicator or handler) under a variety of conditions. E ach job task/exposure permutation is referred to as a n “exposure scenario ” . These scenarios for handlers include mixing pesticide chemicals, loading the pesticides into application equipment, or app lying pesticides with various types of application equipment. Exposure levels for handlers vary based on factors such as the amount of chemical to be used in an application (e.g., the amount of pesticide applied per acre and number of acres to be treated) , the kinds of equipment used (e.g., groundboom sprayer, airblast sprayer, or backpack sprayer), the crop or site treated (e.g., apples, cotton, or livestock), and the

1 It should be noted t hat the Agency is currently involved with the development of updated pesticide handler exposure data with the Agricultural Handlers Exposure Task Force (AHETF). As appropriate, these data will be used in lieu of PHED exposure estimates once completed (see www.exposuretf.com for additional information).Draft, Do Not Cite

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circumstances of the user (e.g., the level of personal protective equipment worn during handling activities).

The Agency defines a pesticide handler as an individual who is involved in the pesticide application
process and b ecause many factors or parameters are involved in characterizing such an exposure, it is important to fully understand the range of exposures that may occur. The use of scenarios is very common in exposure assessment as described in the U.S. EPA Guidelines for Exposure Assessment (U.S. EPA; FR Vol. 57, No. 104; May 29, 1992; http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=15263 ). The first step is to identify the workers that are likely to be exposed during that application process. Tasks associated with occupational pesticide use can be categ orized as follows:

Mixer/loaders: these individuals perform tasks using concentrated end

- use products in preparation for an application. Applicators: these individuals operate application equipment during the release of a pesticide product into the envir onment. Mixer/loader/applicators: these individuals are involved in the entire pesticide handling process. Flaggers: these individuals guide aerial applicators during the release of a pesticide product onto an intended target.

The Agency‟s exposure met hodology also takes into account varying levels of personal protective equipment (PPE) or the use of engineering controls. The levels of protection described below form the basis for calculations completed in most Agency assessments and they include:

Bas eline: Represents typical work clothing of a long

- sleeved shirt and long pants. No respiratory protection or dermal hand protection (i.e., chemical

- resistant gloves) are included in this scenario. Minimum PPE: Represents the baseline scenario with the use of chemical

- resistant gloves and a dust/mist respirator which is commonly used for exposure assessment purposes in different settings. Maximum PPE: Represents the baseline scenario with the use of an additional layer of clothing (e.g., a pair of coveralls) , chemical

- resistant gloves, and a cartridge type air

- purifying respirator which is commonly used for exposure assessment purposes in different settings. Engineering Controls: Represents the use of an appropriate engineering control such as a closed
tract or cab or closed loading systems for granular or liquids.

Given that many combinations of the above factors can be produced for any given application pattern, EPA has identified 37 major exposure scenarios for occupational workers. These scenarios reflec tDraft, Do Not Cite

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different handler activities, different equipment types, and various mitigation techniques (i.e., engineering controls) . The scenario numbers listed in Appendix Table B

- 1 PHED Surrogate Exposure Guide

– Applicator Scenarios List refer to the PHED surrogat e exposure guide (U.S. EPA, 1998b PHED Surrogate Exposure Guide

- Estimates of Worker Exposure from the Pesticide Handlers Exposure Database Version 1.1).

Exposure Factors Used To Describe Pesticide Use Patterns The unit exposure values described above are used in conjunction with what is known about exposure patterns for the populations of interest (e.g., applicators) to calculate quantitative overall estimates of exposure. The Agency uses measures of how much pesticide is used by applicators to accomplis h this which is defined by the area to be treated and application rate used relative to the use pattern(s) being
evaluated. Information about exposure patterns based on the cultural requirements for specific pesticides or types of pesticides and knowledge pertaining to the general production of crop(s) of interest are also
used to define these values. The Agency uses a variety of sources to define these factors. P esticide
label s , which are license s that dictate the legal use parameters for a pesticide, a re always considered
because they allow for an evaluation of the risks associated with the regulatory limits imposed on the use
of a pesticide. Additionally, information about common use practices which are dictated by the cultural requirements associated with the production of the crops to which a pesticide can be applied are also always considered. Th is information aid s in provid ing a broader basis for well informed regulatory decision making processes. Such information is collected and defined from a v ariety of sources that could include exposure monitoring data, sales and use surveys, agronomic profiles/information for crops
of interest , Agency resources (e.g., agricultural economists and agronomists), grower groups, professional applicators and associ ations, and land grant university extension agents.

U se information is then used to predict risks for each scenario or task associated with a pesticide and such results are applied to the overall user population. Because r isk assessment result s are use d to define regulatory requirements they are completed using the most current , broadly applicable information
available

. T he intended goals differ from those in prospective epidemiologic research such as the AHS where actual use information from specific cohort members is reported and summarized . Such information may be used in studies like AHS for analytical purposes such as grouping disease outcomes with relative exposure potential within a cohort.

A brief description of each factor used to define expo sure patterns by the Agency is described below.

Area Treated: The values used for area treated typically represent the acres which could be applied over the course of a workday for most applicators. These values are specific to the general category of application equipment used (e.g., ground sprayers) and crops treated. Much information is known aboutDraft, Do Not Cite

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the efficiency of various types of agricultural application equipment but this information is often attenuated by what is known about production acreage s for the crops of concern or agricultural census information
about factors such as farm size.

Application Rate : Application rates represent the amount of a pesticide administered to a particular crop required for control of a particular pest. Pestici de labels describe specific legal limits for the amount that can legally be used on a crop for a given pest pressure. M aximum legally allowable application rate s are always assessed in Agency risk assessment s to ensure the validity of label conditions. A dditional information about actual use patterns representing typical cultural practices, which probably do not represent maximum use rates given what is known from sales and pesticide use reporting data, are also
considered to allow for more informed risk management decisions. The basis for both of these approaches differs from prospective epidemiologic research where the intent is to reconstruct the exposures for the specific cohort regardless of whe ther uses were at maximum use rates or not.

Exposure Du rations & Patterns: The Agency typically knows little more than generalities about the longitudinal patterns of exposure that can occur over seasons of use for a pesticide. These patterns can also differ over time (e.g., within a growing season or year t o year) because pest pressures can vary due to weather and many other factors. Prospective epidemiologic research collects information on the specific patterns which have occurred within the cohort of in terest. T he Agency always includes different patter ns of exposure as a matter of course that ensure risks associated with high use periods and more seasonal uses of pesticides by applicators are considered. Th is approach ensures risk assessment are health protective and that uncertainty from limited longit udinal exposure information is addressed.



### B. The Agricultural Health Study: Background and Description

The Agricultural Health Study (AHS) is widely considered to be an extensive, high quality epidemiological research study focused on populations of pestici de applicators and their spouses in two major pesticide use areas (i.e., Iowa and North Carolina). The study design of the AHS along with detail descriptions of
the cohort can be found in Al a vanja et al. (1996) and Alavanja et al. (1999). This study explo res potential causes of cancer and other diseases among farmers and their families and among commercial pesticide applicators ( http://aghealth.nci.nih.gov/ ). The cohort for the AHS comes from private and commer cial pesticide applicators who applied for a pesticide license from the state Cooperative Extension Services or Departments of Agriculture between 1993 and 1997. In these states, one must obtain a pesticide applicators license and undergo training or test ing for safe handling of pesticides. Licensed applicators can be either a private farmer applicator or a commercial applicator (Alavanja et al ., 1996).

Participants in the AHS may have varying background and knowledge of pesticide application and their exposure characteristics may differ based on the farming characteristics prevalent to their state, status asDraft, Do Not Cite

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a private
, commercial applicators , or spouse , and/or crop type. At enrollment, all participants (applicators and spouses) reported on their persona l use of 50 common pesticides . Information on 22 pesticides was collected as part of the enrollment questionnaire. A subset of applicators completed a second take

- home questionnaire at enrollment that collected duration and frequency of use information fo r the remaining 2 8 pesticides. All applicators provided information regarding their pesticide mixing/loading habits, pesticide application methods, use of personal protective equipment, and whether they repaired their pesticide equipment. Additional info rmation regarding work practices and equipment was obtained from the subset of applicators who completed the second questionnaire at enrollment (Alavanja et al
., 199 9 ; Alavanja et
al
., 1996)

.

Information collected from the AHS questionnaires is used to develop pesticide exposure classification metrics for exposure/outcome analyses. Several approaches have been used in the AHS to reflect exposure classification for cancer analyses, including ever/never use of a specific pesticide or groups of pesticides (e.g., all insecticides or a subset of organochlorine insecticides), cumulative lifetime days of use, and intensity

- weighted cumulative lifetime days of use. In some cases, exposure classification for

non
- cancer outcomes has been based on reported use in the previous year. AHS developed exposure intensity algorithms to generate numerical intensity scores using information on pesticide mixing/loading, application methods, use of personal protective equipment, and pesticide equipment repair. As
Dosemeci et al

. (2002) describe d , the AHS intensity score algorithms use additive measures to capture mixing, application, repair, and PPE . These are independent factors to overall exposure. The multiplicative approach is taken for the dependent exposure determinant s (i.e., PPE). When used for exposure classification, the exposure intensity score is multiplied by the cumulative lifetime days of use to develop the intensity

- weighted metric. Additional description and discussion of the AHS algorithm and input paramete rs is provided in Section III.A

.Draft, Do Not Cite

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### III. C ase S tudy

As discussed in both the draft framework ( Draft Framework for Incorporating Human Epidemiologic and
Incident Data in Health Risk Assessment , 2010) and in the introduction to this paper, the main goal of all the case studies developed for the February, 2010 SAP is to illustrate examples of approaches for incorporating the results of epidemiology studies and human incident data into Agency risk assessment/characterizations and probl em formulations. The objective of the potential steps outlined within this case study is to explore whether any differences in exposure assessment approach used by the Agency and AHS would lead to an overall difference in risk assessment outcomes and
char acterization.

This specific case study focuses on the information pertaining to pesticide applicators available with in the AHS. As indicated above, the AHS is one of the most comprehensive sources for epidemiological data for pesticide applicators . A p plicators were selected primarily because they are likely to be the highest exposed group in the population and the Agency considers them in a wide array of risk assessments therefore providing a broad set of information to complete the analyses described below

. As noted above, this case study is being developed as a collaborative effort between the EPA‟s OPP and the investigators from the AHS

.

The goal of the Agency is to integrate into its processes the most recent methods and scientific
consideration s pertaining to risk assessment. T he 2009 National Research Council document on advancing risk assessment science and decision making ( http://dels.nas.edu/dels/rpt_briefs/IRA_brief_fi nal.pdf ) out lines the importance of problem formulation in exposure and risk assessment. The Agency plans to develop this case study as a problem formulation
exercise

. S uccessful problem formulation in this regard will serve as the foundation for the in corporation
of epidemiology in risk assessment in a more rigorous, systematic fashion than is currently employed.

.Draft, Do Not Cite

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The three potential phases of the envisioned case study approach include:

Section A Step 1 Illustrate Method Differences

– There are inherent differences in scope and purpose of exposure assessment in an observational epidemiologic investigation and a quantitative risk assessment which have been well articulated in the scientific literature ( van den
Brandt et al ., 2002)

. A solid u nderstand ing of these differences deserves consideration when incorporating epidemiologic information in to the pesticide regulatory process

. A n introduction to
the comparison of the algorithms used to complete exposure assessments by t he Agency and the AHS is included below. To the extent possible
, a discussion of the origins of these factors has
also been included.

Section B Step 2 Consideration Of Overall Differences

– Th e analyses for Step 2 will be

com pleted in the coming months . The intended approach is outlined below

. Step 2 builds on the knowledge gained in Step 1 of this analysis. The focus will be to begin to understand what performance differences exist between the quantitative exposure assessm ent methods used by the Agency compared with the methods used in the AHS. U nderstanding the comparative results will provide for more improved risk characterization. This aspect of the analysis will use biomonitoring data for a subset of pesticide applic ators from the AHS cohort as its basis (Bakke
et al , 2008; Thomas et al , 2009) and the Agency methodologies as presented in the 2007 SAP meeting ( http://www.epa.gov/scipoly/sap/me etings/2007/010907_mtg.htm ).

Section C Step 3 Example Case Evaluations

– This aspect of the analysis will be completed at
a later date and may involve a large

- scale comparative analysis of atrazine and alachlor users
from the AHS cohort based on the lessons learned in Steps 1 and 2. Prior to the conduct of this step, a feasibility of this analysis will be evaluated by EPA and the investigators from the AHS.

The information provided below expands on the three phases of the cas e study problem formulation and analysis. Section A provides a background of both the Agency and the AHS exposure approaches focusing on the unit exposure and exposure weight input parameters and serves as the foundation for
fu ture analys e s. Section B provides a summary of the studies intended to inform the biomonitoring data in the comparative analysis. Section C provides details on the data request that has been submit ted to the
AHS.Draft, Do Not Cite

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### A. Step 1 Illustrate Method Differences

This section provides a description of the algorithms used in Agency occupational handler exposure
assessments and in the AHS (Section 1 ). In addition, one possible ap proach for illustrating these differences is provided in Section 2

.



### 1. Algorithms Used for Risk Assessment Calculations

The algorithm s used by the Agency and the AHS for the purposes of exposure calculations are described
in th is section.

The Agency’s Scenario Based Approach: The Agency‟s occupational handler daily exposure algorithm is presented below:

Daily Exposure (mg a.i./day) = Unit Exposure (mg a.i./lb a.i.) * Application Rate (lb a.i./A) * Daily Area Treated (A/day)

Where:

Daily Exposure = Amount deposited on the surface of the skin that is available for dermal absorption or amount that is available to be inhaled (mg a.i. /day)

Unit Exposure = Normalized exposure value derived from August 1998 PHED Surrogate Guide Exposure Guide and various referenced studies (mg a.i./lb a.i. handled )

. The unit exposure reflects the application method, formulation of pesticide, and a certain degree of personal protective equipment (i.e., gloves and/or coveralls). The concept and de scription of unit exposures is provided in more detail in Section 2 and within Table 3

.

Application Rate = Normalized application rate based on logical unit treatment such as acres or gallons, maximum and typical values are ge nerally used (lb a.i./A); and
,

Daily Area Treated = A pplication area (A/day)

.

The maximum allowable Application Rate based on the label is always considered for the particular product that is being assessed. In some cases, a typical application rate may also be used when assessing lifetime use, if EPA has that information. For the Daily Area Treated parameter, the Agency uses generic standard values that are based on published scientific literature, surveys, knowledge of agricultural practices, and calcul ated acreage estimates. Values selected as part of the standardDraft, Do Not Cite

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operating procedures and policies are considered to be “typical

- to

- high

- end”
, however
, refinements can be modified by pesticide

- and crop

- specific knowledge, including information from commod ity organizations, professional applicators, and agricultural extension agents.

The Agency relies on use and usage information to inform frequency and duration of exposure

. The pattern of occurrence is important because it can result in different toxicolo gical effects based on the mode of action of the particular pesticide

. Frequency of application may or may not be stated on the pesticide product labeling information. If that information is not available, the Agency can assume that occupational exposures for most chemicals can occur over a single day or up to weeks at a time
, although each crop or application target is generally treated only a few times per season. Some applicators may apply pesticides over a period of weeks because they need to cover lar ge acreages and professional applicators perform applications at various properties across a region over time . Time

- based categories of short

- term (up to 30 days), intermediate

- term (greater than 30 days and up to six months), and long

- term exposures (gre ater than six months) have been developed to reflect a health protective exposure assessment for occupational pesticide applicators through the different exposure patterns

. Based on anticipated exposure duration, c orresponding toxicological endpoints are t hen selected to estimate risk to those engaged in particular occupational handler activities

.

Cancer risk calculations build on the above daily dose estimates to calculate a lifetime average daily dose (LADD). The LADD values are calculated by amortizing the Average Daily Dose estimates based on the
number of years worked over a lifetime (i.e., typically 30 years out of an assumed 70 year life span ) and the annual frequency of use (e.g., typically 30 working days per year with a particular chemical).

AHS Exposure Level Approach: The AHS general intensity and intensity

- weighted cumulative lifetime days exposure algorithms are described i n Dosemeci et al

. (2002) and presented below:

Intensity Level = (Mix + Apply + Repair) * PPE

Where:

Mix = [Mix] the m ixing status of the individual. The mixing score is weighted as 0 if the individual has never performed the job task of mixing, 3 if they mix less than 50% of the time, and 9 if they
mix 50% or more of the time.

Apply = [Appl] the application score for the individual is determined by the type of application method that the individual uses. If an individual selects more than one type of application method, the mean scores of the selected methods are used to define the [Appl] variable for the individual.Draft, Do Not Cite

16 of 34 T he scores for the specific application methods range from 0 to 9 and are described in more
detail in Section 2 and within Table 4

.

Repair = [Repair] the repair status of the individual is based on whether the individual does personally repair equipment (score = 2) or does not personally repair equipment (score = 0).

PPE = [PPE] the scores used to represent PPE range from 0.1 to 1 in values. They are based on the type of PPE that the individual reports using over the time peri od reviewed by the questionnaire. This information was collected as an overview of PPE usage not information directly correlated to any specific pesticide. A more detailed description of the types of PPE and the weighting factors is provided in Table 4

.

Information used to populate the general algorithm was collected with the enrollment questionnaire. An additional take home questionnaire was provided to the participants . This take

- home questionnaire w hich included more product

- specific exposure informat ion based on groups of pesticides or specific active ingredients. The take

- home questionnaire also asked about detailed work practices such as washing of equipment, replacement of gloves, and personal hygiene practices. At this time, th is more detailed
pe sticide hygiene practice information ha s not been used in the published epidemiological AHS studies

. For those interested in further details, the corresponding input parameters are provided (A ppendix Figure
B

- 2 and Appendix Table B

- 3 ).

As described by D osemeci et al . (2002 ), the intensity algorithms use additive measures to capture mixing, application, and repair. These are independent factors to overall exposure. The multiplicative approach is taken for the PPE factors because they are dependent exposu re determinants. The intensity score is used in epidemiologic analysis to modify the cumulative exposure measure s , i.e., total lifetime days of exposure multiplied by the intensity of exposure. For most health outcomes, particularly chronic diseases,
the r elevant exposure is based on the activity over a lifetime or a similar longer term period of time for that individual. Whether cumulative exposure (e.g., duration and frequency of pesticide use) or an intensity

weighted cumulative exposure is used in the a nalysis depends on the chemical and/or health outcome of
interest

. The intensity

- weighted cumulative exposure algorithm is provided below:Draft, Do Not Cite

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Cumulative exposure = Intensity Level * Duration * Frequency

Where

Intensity Level = quantitative value for expo sure (as described above)

Duration = number of years applied (years)

Frequency = number in days of applications per year (days per year)

Preliminary Algorithm Review

T here are intrinsic differences in the exposure estimation used in regulatory risk assessment and the data collection methods used in prospective epidemiologic studies. Exposure measurements and estimation typically rely on single participant data over a short period of time whereas the AHS data collection
method is a means to collect a nd characterize pesticide exposure for a group of participants over a working lifetime. The difference in exposure metrics is attributed to the nature of exposure estimations versus the AHS epidemiological data collection.

The use of each of the algorith ms described Section 1 can provide insight into examining the potential
differences between the two approaches. Table 2 presents the AHS input parameters as they have been discussed as part of the intensity and cumulative expo sure scores alongside the corresponding Agency algorithm input parameters. There are few occasions where the collected AHS information directly relates
to the Agency input parameters. These parameters are mainly illustrated as part of the intensity scores. The exposure weights used in that algorithm are best equated to the unit exposures used by the Agency. In other cases, the questionnaire information does not provide values for aspects of the exposure scenario that the Agency considers when calculating a n exposure in mass active ingredient per day (mg a.i./day). For example, application rate and acres treated per day are not explicit questions in the AHS questionnaires

.Draft, Do Not Cite

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Table 2: Algorithm Input Parameter Table

MLOD = mixer/loader; APPL= Applica tor; UE= Unit Exposure; PPE= Personal Protective Equipment; NA= Not applicable * The working lifetime and days of application per year are parameters that are co nsidered only when assessing cancer risks. Non

cancer risks are not assessed on a time

- basis b ut rather correlated to anticipated exposure duration



### 2. Input Parameters for Exposure Algorithms

This formative example of how to possibly illustrate differences between the Agency‟s predictive exposure approach and the method used in the AHS was made using the parameter which essentially defines the exposure rate within each of the approaches. Both parameters attempt to capture a similar aspect in the overall exposure assessment process

. I n this example, the unit exposure is the driving factor for the Agenc y‟s exposure algorithm and the lifetime exposure days (i.e., total number of days per year by total number of years used) are the driving factor in the AHS exposure assessment approach. Section 2 only
focuses on one aspect of t he exposure approach, however, similar understanding will be necessary for the other parameters in the algorithms (i.e., area treated, application rate, repair activities, etc). In addition, it is important to note that the discussion on the unit exposure s and exposure weights is not a comprehensive analysis of the parameters

. A more intensive analysis is anticipated in the future with
collaborati on and input from ORD and the investigators from the AHS

.

The Agency Unit Exposure Parameter

The Agency uses a concept known as “unit exposure” as the basis for assessing handler exposures to pesticides (Hackathorn and Eberhart, 1985). This concept has been discussed in the past (FIFRA SAP Review of Subdivision U, 1986) and most recently as part of the SAP in 200 7 ( http://www.epa.gov/scipoly/SAP/meetings/2007/010907_mtg.htm ). Unit exposures, as currently used by the Agency , represent exposure normalized by the amount of active ingredient handled based on the assumption that the two variables are proportional (i.e., if one doubles the amount of pesticide handled or applied, the resultant exposure will be doubled as well). The concept of the generic nature of the unitDraft, Do Not Cite

19 of 34

exposure can be illust rated by the following example: If an individual makes an application using a groundboom sprayer with either 10 pounds of chemical A or 10 pounds of chemical B and the same personal protective measures, the exposures to chemical A and chemical B would be s imilar. The generic nature of pesticide handler exposure refers to the fact that it is not necessarily properties of the active ingredient that is a major determinant of deposition on the body, but rather the formulation (e.g., liquid) and the application equipment. One just needs to know the amount of the active ingredient in the formulation. The PHED unit exposures are considered generic within a range of physical properties of the formulation, including formulation type, density and vapour pressure

The re exist unique unit exposures for each scenario that is typically assessed (each of the 37 scenarios
listed in Appendix Table B

- 1 ). The unit exposure concept is described in various guidelines published by the Agency and recognized by other pesticide regu latory agencies worldwide (e.g., Health Canada). There are different unit exposures for combinations of application equipment; formulation type; job function; and level of clothing/gloves. Data from the exposure studies are normalized by the amount of pest icide handled by the test subject and then adjusted by surface area of the individual. The generic nature of the unit exposures allows for standardized treatment across chemicals in terms of anticipated handler exposure by combining a generic unit exposure with chemical specific usage information [ e.g., application rates (lb a.i./acre) and frequency ] . In addition, the generic unit exposures are utilized only for
those chemicals considered non

- volatile or semi

- volatile based on physical chemistry. Volatile c hemicals like soil fumigants are typically assessed with chemical

- specific data or other appropriate measures of exposure. [Note: PHED is used as the basis for the vast majority of the Agency‟s pesticide exposure assessments, but in some cases, more spec ific monitoring data for a scenario can be used in lieu of PHED estimates. This is done because the data are more representative of the scenario of concern or the research upon which it is based may have been conducted under specific conditions which are n ot accounted for by PHED.]

As described in the Guideline for Exposure Assessment (U.S. EPA, 1992), exposure differs from dose. While exposure is generally defined as contact of visible external boundaries (i.e., mouth, nostrils, and skin) with a chemical agent, dose refers to the amount of chemical to which individuals are exposed that crosses the external boundary (U.S. EPA, 1992). The amount of e xposure is dependent on intensity, frequency, and duration of contact. When calculating or considering the do se and the risk associated with the exposure, chemical specific toxicological parameters (i.e., points of departure) as well as physiological parameters such as rate of intake (i.e., inhalation or ingestion) or uptake (i.e., dermal absorption) are incorpor ated into the exposure algorithm.

The database of unit exposures (i.e., PHED) has been reviewed in the past and was designed by a task force of representatives from U.S. EPA, Health Canada, the California Department of PesticideDraft, Do Not Cite

20 of 34

Regulation, and companie s which are members of companies of Crop Life America. A complete description of the software system is included in the PHED Reference Manual (USEPA, 1995a) which contains all the algorithms and analysis approaches included in PHED. An example of the PHED Surrogate Guide entry information for one application method is provided in Table 3 below:

Table 3 : Unit Exposure Information Provided for the Aerosol Can Application Scenario

Dermal Exposure

Clothing Scenario T otal Dermal Exposure (mg/lb a.i. han dled)*

No Clothing 480

Single Layer, No Gloves 190

Single Layer, Gloves 81

Inhalation Exposure

N/A 1.3

* Total dermal exposure is a compilation of unit exposures from the head, neck, upper and lower arm, chest, back, thigh, lower leg, and hand
[ Note: Although the unit exposure appears high , the values are normalized by lb a.i. applied. The amount of a.i. applied per replicate used to calculate the unit exposures were only 0.01 lb a.i. (one aerosol can). This scenario represents an insecticide crack/crevice treatment. ]

The Agricultural Health Study Exposure Weight Parameter

This section provides a summary of the development of exposure intensity weights used to generate pesticide applicator exposure intensity scores in an algorith m developed by Dosemeci et al

. (2002) for use in the Agricultural Health Study.

T he exposure weights used by the AHS were compiled from a variety of sources including PHED. The AHS used over 100 available published articles that evaluated pesticide expos ure in relation to mixing, application, or work practices in agriculture (Dosemeci et al ., 2002). PHED was used in conjunction with other peer reviewed literature sources to inform the development of the exposure weight values. As Dosemeci explain s , the a ssignment of the exposure weights for dermal exposure was based on theDraft, Do Not Cite

21 of 34

reliance of results obtained by passive dermal exposure methods (e.g., pads or patches, special clothing, coveralls caps and gloves) and fluorescent tracer techniques. Studies with acc eptable measures of inhalation exposure included personal air samplers at the breathing zone of the applicators.

The AHS intensity score differs from the Agency‟s exposure algorithm in that it does not attempt to quantify exposure or dose in absolute te rms . The exposure weight factors used in the AHS algorithm are unitless values that represent the relative differences between the various application methods and situations of use (e.g., amount of protective clothing worn /used ). Rather than a generic uni t exposure (mg/lb a.i. handled) as with PHED, the AHS exposure weights range from 0 to 9. Results from the acceptable exposure and biomonitoring studies were compared between individual job characterizations (e.g. such as mixing versus applying) as well a s within various application methods (i.e., backpack vs. groundboom or open cab vs. closed cab) (Dosemeci et al ., 2002). The exposure weights are calculated as the ratio between one set of application methods versus another and their relative exposure ran king

were developed. In Dosemeci‟s study, the following example is provided:

“mixer/ loaders have ~9 fold higher exposure than aerial applicators (Knarr et al ., 1985; Chester
et al ., 1987), hence the score „9‟, and have 3

- fold higher exposures than groun dboom applicators (Rutz and Krieger, 1992; Brouwer et al ., 1994), who were assigned a score of „3‟.”

Dosemeci et al (2002) provides a summary of examples with corresponding studies to illustrate the relative differences between the exposures of varying ap plication methods. In addition, this process is also used to determine the relative difference in exposures based on use of PPE. Dosemeci et al (2002) concluded that a 50 percent level of protection was provided due to the use of rubber gloves worn by frui t growers in a study published by De C ock et al . (1995). Whereas a 50 percent protection factor for closed cabs on tractors and a 90 percent protection factor for the use of closed cabs with air filte rs i s suggested by the Carman et al

. (1982) study. The v alue of intensity scores and related factors are presented
Dosemeci et al

. (2002) and illustrated in the following table:Draft, Do Not Cite

22 of 34

Table 4: Table of General Algorithm Parameters

Preliminary Review of Input Parameters

The Agency intends to perform an alyses which may inform the issue of variability in exposure metrics. One of the important aspects of this analysis will be to consider what scientific factors influence the interpretation of differences in the AHS and the Agency algorithms. For example, th e Agency approach adjusts unit weights by the amount of pesticide handled
, In contrast, the AHS approach applies the intensity score to frequency and duration metrics which, account for the "amount" used but in a slightly
different manner . How these di fferences in algorithms may or may not influence the interpretation of exposure assessments will be further evaluated in subsequent analys e s

.Draft, Do Not Cite

23 of 34

The AHS cohort participates in a wide range of agricultural practices which are defined by the crops that they p roduce and the farming methods used to produce them. The diversity inherent with such practices in Iowa and North Carolina, can lead to the use of many different types of pesticide products which may be sold as liquids, granulars, or dust. Also, a variety of application equipment and levels of protective equipment can also be used because of this diversity. Potential analyses may account for other critical production practices which reflect the range of possible application practices described and per tinen t use information commonly considered in risk assessment. These could include parameters such as the amount treated on a daily basis which is related to the crop, application rate and the type of equipment
used.

The overall objective of this kind of an alysis is to evaluate whether or not there are differences in the predictions for exposure regardless of approach. There are likely many possible analytical approaches for defining these differences and a definitive approach has not yet been established. To reiterate, the criteria used for defining these differences will be determined based on the collaborative effort between the Agency and the investigators from the AHS. If major differences are identifie d the Agency will focus on these in subsequent an alysis to ensure that appropriate interpretation of epidemiologic data was considered

.



### B. Step 2 Consideration of Overall Differences

The second step in the case study will build on the initial analysis by evaluating exposure and biomonitoring data for a s ubset of pesticide applicators from the AHS cohort ( i.e., Bakke et al

. , 2008;
Thomas et al

. , 2009). The Bakke et al

. (2008) and Thomas et al

. (2009) studies are unique in that they provide exposure information on individual applicators that can be used t o compare estimates of exposure using both the Agency and AHS approaches with actual biomonitoring data collected from the
same individuals. This three

- way comparison will then be considered in context with the Agency‟s previous analysis of biomonitoring data (mostly from agricultural workers) which was presented to FIFRA SAP in 2007 ( http://www.epa.gov/scipoly/sap/meetings/2007/010907_mtg.htm ).

The Bakke et al

. (2008) study wa s designed to develop quantitative estimates of pesticide exposure to
atrazine and also provides background levels of selected non

- persistent pesticides. The study population consisted of thirty farmers from Iowa and ten control individuals not involved i n farming. The thirty farmers
are also participants in the AHS. The exposure metrics in this study are described as application

– specific exposure levels of atrazine as measured by atrazine mercapture (AZM) in urine and corresponding exposure activity diari es.

The basis for using the Bakke et al

. (2008) study as part of the Agency‟s case study is that i t involved collection of biomonitoring measures concurren t with daily and weekly activity diaries. InformationDraft, Do Not Cite

24 of 34

collected in the diaries includes a number of potential predictors of pesticide exposure (e.g., the total amount of product applie d , acreage treated, duration of application , application method, personal protective equipment, and personal hygiene practices). These exposure parameters can be used as inputs in the Agency‟s occupational handler exposure algorithm. In addition, because the farmers are also participants in the AHS, information related to the epidemiological and questionnaire information for these individuals may also be available. This extensive compilation of data allow s for a three

- way comparison between actual individual biomonitoring exposure measures, the Agency approach

- based exposure estimates, and the AHS intensity

- weighted cumulative exposure scores.

Thomas et al

. (2009) provi des a similar study to perform the analysis in Step 2

. It was designed to include a subset of AHS private pesticide applicators and to assess levels of exposure and potential determinants of exposure to 2,4

- dichlorophenoxyacetic acid (2,4

- D) and O,O

- dieth yl

- O

- 3,5,6

- trichloro

- 2

- pyridyl phosphorothioate (chlorpyrifos). 242 applicators agreed to participate in the study and 83 applicators actually participated in one or more monitoring visits during which products containing 2,4

- D or chlorpyrifos were used. Thomas et al . (2009) aim ed to provide measurement data that can describe the relationship between patterns of chemical use, exposures, and questionnaire

- based exposure surrogates. 2,4

- D and chlorpyrifos were selected by Thomas et al . (2009) based on thei r wide

- spread use within the AHS cohort in both Iowa and North Carolina. Exposure measurements were collected by dermal patch sampling, personal air sampling, hand wipe sampling, and biomonitoring through urinary samples analyzed for 2,4

- D or TCP (3,5,6

- tr ichloro

- 2

- pyridinol) concentrations. Information on exposure determinants (i.e., amounts of active ingredient mixed/loaded, time of pesticide handling and application, application type, and area treated) was also collected and report within the study. Th e analysis with the
Thomas et al . (2009) study will utilize the exposure metrics (i.e., dermal, inhalation, and biomonitoring) in conjunction with the collected exposure activity data. In addition, as with the Bakke et al

. (2008) study, because the partic ipants are a subset of the AHS, the epidemiological information that can be obtained on each of the individuals will also be compared with the Agency‟s occupational handler exposure
estimates

.

The objective of this second step in the case study is to ap pl y what i s understood from the methods analysis to actual exposure information

. G iven the variability in the parameters collected and measured, Step 2 aims to investigate the contextual difference s between the Agency and AHS exposure estimations approaches in the context of the Agency‟s extensive analysis of worker biomonitoring data. Well

developed and peer reviewed methods for estimating occupational handler exposures was discussed at the 2007 SAP meeting ( http://www.epa.gov/scipoly/sap/meetings/2007/010907_mtg.htm )

. The Agency plans to consult with the AHS investigators to ensure that relevant analyses are completed and interpreted appropriately.Draft, Do Not Cite

25 of 34



### C. Step 3 Example Case Evaluations

The third step in the case study will involve a large

- scale comparative analysis of atrazine and alachlor
users from the AHS cohort. Prior to the conduct of this step, a feasibility of this analysis will be evaluated by EPA and the investigators from th e AHS. If feasible a large

- scale comparative analysis will follow the
side

- by

- side and exposure and biomonitoring analyses

. E lements of the Steps 1 and 2 will be necessary in the analysis of data from the full cohort. In anticipation of the third step, a d ata request has been submitted and approved by the AHS Executive Committee (See below)

. The overall goal of this analysis would be to provide information on how chemical specific information from epidemiologic studies (e.g., AHS) could be used to help sup port the regulatory process. For example, if causal relationships are identified for a health effect of concern related to the use of certain types of application equipment or product type and that effect is associated with routinely high exposures, then perhaps additional requirements would be mandated for enhanced levels of personal protective equipment.

The Agency‟s data request to the AHS include s data for the North Carolina private pesticides applicators and the Iowa private and commercial applicator s that reported use of atrazine and/or alachlor at
enrollment . Specific information of interest includes frequency and duration of use; crop information; farm size; pesticide use factor information including mixing/loading, application method, equipment r epair, PPE use, tractor cab information, hygiene information; derived values for cumulative days and/or hours of use, algorithm intensity scores, and intensity

- weighted cumulative days and/or hours of use; and exposure categorization values (i.e., tertile, quartile, or quintile) if derived values exist. The analytical and statistical elements of this third step will be determined following the comple t ion of the previous two steps in the case study.

[Note: The AHS has several inherent mechanisms in place to protect the quality of the data from this study and the privacy/rights of the participants. The Agency will abide by all of these criteria to ensure full compliance with these policies . There are also possible limitations that may apply to the use of d ata collected in certain studies because of manner in which informed consent was granted and the criteria under which the Agency must consider research with human subjects as outlined in (40CFR26)

. ]Draft, Do Not Cite

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### IV. Future Considerations

As part of the Agency‟s longer

- te rm plans for using epidemiologic data like that in the AHS, following the completion of all phases of this case study, the Agency has identified several areas for further investigation. These are still in a very formative stage and the Agency will discuss the feasibility of these analyses with AHS scientists and others following completion of Steps 1

- 3 outlined above to further refine and investigate these areas of interest.

Examples of potential types of analyses that are under consideration include :

D etermining the degree to which associations/risk identified in epidemiologic research (e.g., health outcomes) remain a concern given current regulatory standards for pesticide

use. Temporality (i.e., change in exposure over time) is an important issue when interpreting epidemiology findings with pesticide chemicals. Pesticide exposures can vary significantly over time. Many pesticides have undergone significant changes in their use pattern for both agricultural and residential use due to risk mitigation e fforts by the Agency (e.g., reduction in use rates and limitations on the use pattern) and to significant changes in agronomic practices. As a result, many of the pesticides historically used in agriculture or in residential settings are rarely or

never us ed today. Instead, these pesticides have been replaced with newer active ingredients. In contrast, the use of some chemicals has increased over time. For example, as more genetically engineered corn and soybeans are planted in the U.S., the use of a few herbicides, notably glyphosate, have increased substantially. Moreover, pest pressures vary year to year leading to differences in pesticide use. To control some pests, the active ingredients used to treat a particular crop are rotated in an effort to p revent the development of resistant pests.

Considering the amount of historical change in US agriculture that has occurred, it is important to
understand whether risks/associations identified in epidemiologic investigations reflecting past pesticide usage , remain a risk of concern today, given changes in permitted use . Many current epidemiological studies focus on a time period where the Agency was concurrently implementing a number of pesticide regulatory actions. These regulatory actions were designed t o reduce risk to pesticide handlers (e.g., mandating engineering controls for many pesticides, classification of some pesticides as restricted use materials). A thorough examination and characterization of this will help the Agency better incorporate the results of epidemiological studies for use within its weight of evidence framework.

Data from AHS could be used to inform the Agency risk assessment process (e.g., for refining exposure factors). Often epidemiological research collects a rich dataset to account forDraft, Do Not Cite

27 of 34 potential biases and confounders. Many of these data could be valuable input parameters to the Agency‟s exposure and risk assessment. For example, AHS collected information on basic use patterns and years of use from the survey respondents. This information is potentially useful information in the Agency‟s calculation of lifetime exposure in cancer risk assessment or for informing durations of exposure considered in non

- cancer Agency risk assessments. The AHS is particularly rich in informat ion about pesticide application equipment, the use of personal protective equipment, and use practices. How to best utilize this information, within the ethical principles governing research involving humans as set forth in the report of the National
Comm ission for the Protection of Human Subjects of Biomedical and Behavioral Research, will be investigated in the future.

This document and the analyses presented above have been focused on data collected through the AHS. There are many other sources of app licable epidemiologic research focused on pesticide exposure. Ultimately, the Agency plans to consider other sources of epidemiological information (e.g., Farm Family Exposure Study

- http://www.farmfamily exposure.org/ ) using the lessons learned from this exercise

.Draft, Do Not Cite

28 of 34



### V. References

Alavanja, M.C.R., D.P. Sandler, S.B. McMaster, S.H. Zahm, C.J. McDonnell, C.F. Lynch, M. Pennybacker, N. Rothman, M. Dosemeci, A.E. Bond, and A. Blair. 1996. The Agricultural Heal th Study. Environmental Health Perspectives, 104(4): 362

- 369.

Alavanja, M.C.R., D.P Sandler., C.J. McDonnell, C.F. Lynch, M. Pennybacker, S.H. Zahm, D.T. Mage, W.C. Steen, W. Wintersteen, and Blair, A. 1999. Characteristics of Pesticide Use in a Pesti cide Applicator Cohort: The Agricultural Health Study. Environmental Research Section A, 80: 172

- 179.

Bakke, B., A.J. De Roos, D.B. Barr, P.A. Steward, A. Blair, L. Beane

- Freeman, C.F. Lynch, R.H. Allen, M.C.R. Alavanja, and R. Vemeulen. 2008. Exposur e to atrazine and selected non

- persistent pesticides among corn farmers during

Brouwer, D.H., E.J. Brouwer, and J.J. van Hemmem. 1996. Estimation of long

- term exposures to pesticides. American Journal Industrial Medicine; 25: 573

- 88.

Carmen, G.E., Y. Iw ata, J.L. Pappas, et al . 1982. Pesticide applicator exposure to insecticides during treatment of citrus trees with oscillating boom and airblast units. Archives of Environmental Contamination and Toxicology; 11: 651

- 9.

Chester, G., L.D. Hatfield, T.B. Ha rt, et al . 1987. Worker exposure to, and absorption of, cypermethrin during aerial application of an „ultra low volume‟ formulation of cotton. Archives of Environmental Contamination and Toxicology; 16: 69

- 78.

Coble, J., T. Arbuckle, W. Lee, M. Alavanja , and M. Dosemeci. 2005. The validation of a pesticide exposure algorithm using biological monitoring results. Journal of Occupational and Environmental Hygiene, 2: 194

- 201.

DeCock, J., D. Heederick, F. Hoek, et al . 1995. Urinary excretion of THPI in fruit growers with dermal exposure to captan. American Journal Industrial Medicine; 28: 245

- 56

Dosemeci, M., M.C.R. Alavanja,, A.S. Rowland, D. Mage, S.H. Zahm, N. Rothman, J.H. Lubin, J.A. Hoppin, D.P. Sandler, and A. Blair. 2002. A Quantitative Appro ach for Estimating Exposure to Pesticides in the Agricultural Health Study. The Annals of Occupational Hygiene, 46(2): 245

- 260Draft, Do Not Cite

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Hackathorn, D.R. and D.C. Eberhart. 1985. Data base proposal for use in predicting mixer/loader/applicator exposure. Americ an Chemical Society Symposium Series 273: 341

- 355
Knarr, R.D., G.L. Cooper, A. E. Brian, et al . 1985. Worker exposure during aerial application of a liquid and a granular formulation of Ordram selective herbicide to rice. Archives of Environmental
Cont amination and Toxicology; 14: 523

- 7.

Rutz, R., and R.I. Krieger. 1992. Exposure to pesticide mixer/loaders and applicators in California. Reviews of Environmental Contamination and Toxicology; 129: 121

- 39.

Thomas K.W., M. Dosemeci, J.A. Hoppin, L.S. Shel don, C.W. Croghan, S.M. Gordon, M.L. Jones, S.J. Reynolds, J.H. Raymer, G.G. Akland, C.F. Lynch, C.E. Knott, D.P. Sandler, A.E. Blair, and M.C.R. Alavanja. 2009. Urinary biomarker, dermal, and air measurement results for 2,4

- D and chlorpyrifos farm applica tors in the Agricultural Health Study. Journal of Exposure Science and Environmental Epidemiology, epub ahead of print, doi:10.1038/jes.2009.6

U.S. EPA. 1992. Guidelines for Exposure Assessment. EPA/600Z

- 92/001. Washington, DC: Office of
Health and Envi ronmental Assessment. United States Environmental Protection Agency.
Available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=15263

U.S. EPA. 1995a. PHED: Pesticide Handler s Exposure Database Reference Manual V1.1

U.S. EPA, 1998b PHED Surrogate Exposure Guide

- Estimates of Worker Exposure From The Pesticide Handlers Exposure Database Version 1.1 (August 1998)

US EPA. 2007. Review of Worker Exposure Assessment Methods. FI FRA Scientific Advisory Panel.
Available at http://www.epa.gov/scipoly/SAP/meetings/2007/010907_mtg.htm

van den Brandt P, Voorrips L, Hertz

- Picciotto I, Shuker D, Boeing H, Spe ijers G, Guittard C, Kleiner J, Knowles M, Wolk A, Goldbohm A. 2002 The contribution of epidemiology Food Chem Toxicol.
2002 Feb

- Mar;40(2

- 3):387

- 424.Draft, Do Not Cite

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# APPENDIXDraft, Do Not Cite

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Appendix Table B

- 1: PHED surrogate exposure guide

– applicator scenarios list

The scen arios in the guide include:

Mixer/Loader

(1) Dry Flowable: Open Mixing (2) Granular: Open Mixing (3) All Liquid Formulations: Open Mixing (4) Wettable Powders: Open Bag (5) Wettable Powders: Water Soluble Packets (6) All Liquid Formulations: Clo sed Mixing

Applicator

(7) Aerial

- Fixed Wing: Liquid Formulations; Enclosed Cockpits (8) Aerial

- Fixed Wing: Granular Formulations; Enclosed Cockpits (9) Helicopter: Enclosed Cockpit (10) Aerosol Can (11) Airblast: Open Cab (12) Airblast: Enclosed Cab (13) Groundboom: Open Cab (14) Groundb oom: Enclosed Cab (15) Solid Broadcast Spreader, Open Cab, Ag Uses (16) Solid Broadcast Spreader, Enclosed Cab, Ag Uses (17) Granular Bait Dispersed by Hand (18) Low Pressure Handwand (19) High Pressure Handwand (20) Backpack (21) Hand Gun (Lawn) (22) Paintbrush (23) Airless Sprayer (House Stain) (24) Ri ghts

- of

- Way Sprayer

Flagger

(25) Liquid Formulations (26) Granular FormulationsDraft, Do Not Cite

32 of 34 Mixer/Loader/Applicator

(27) Airblast: Wettable Powder or Liquid, Open Pour, Open Cab Groundboom (28) Groundboom

- Liquid, Open Pour and Open Cab (29) Groundboom

- Liquid or Dry flowable, Open Pou r, Enclosed Cab (30) Belly Grinder (31) Push

- type Granular Spreader (Granular, Open Pour) Handheld Spray Equipment (32) Low Pressure Handwand: Liquid, Open Pour (33) Low Pressure Handwand: Wettable Powder, Open Pour (also for Crack and Crevice) (34) Backpack Sprayer: liquid, o pen pour (35) High Pressure Hand Wand: liquid, open pour (36) Garden Hose

- End Sprayer: liquid, open pour (37) Termiticide Injection : liquid, open pourDraft, Do Not Cite

33 of 34

Appendix Figure B

- 2: The AHS Detailed Algorithm of Intensity
Intensity Level = [(Mix * Enclosed) + (Appl * Cab) + Repair + Wash] * PPE * Repl * Hyg * Spill

Where:

Mix = [Mix] never mixed (score = 0) or Mixed (score = 9)

Enclosed = [Enclosed] whether an individual uses an enclosed system (score = 0.5) or not (score = 1.0) for mixing

Appl = [Appl] the reported ap plication method will range from 0 to 9 in exposure weights. The specific categories for the d etailed algorithm are presented in Appendix Table B

- 3

Cab = [Cab] whether the individual reported using an enclosed cab with or without a charcoal filter. (weigh ting factors range from 0.1 to 1)

Repair = [Repair] the repair status of the individual is based on whether the individual does personally repair equipment (score = 2) or does not personally repair equipment (score = 0).

Wash = [Wash] the status of washi ng the pesticide equipment after an application (weighting factors range from 0 to 3)

PPE = [PPE] the scores used to represent PPE range from 0.1 to 1 in values. They are based on the type of PPE that the individual reports using over the time period revi ewed by the questionnaire. This information was collected as an overview of PPE usage not information directly correlated to any specific pesticide. A more detailed description of the types of PPE and the weighting factors is presented in Appendix Table B

- 3

Repl = [Repl] captures the replacement of old gloves (weighting factors range from 1.0 to 1.2)

Hyg = [Hyg] provides information on personal hygiene and the individual‟s trend in changing into clean clothes and washing hands or taking a bath/shower (weig hting factors range from 0.2 to 1.0); and

Spill = [Spill] whether or not the individual reports changing clothes after a spill (weighting factors range from 1.0 to 1.8)Draft, Do Not Cite

34 of 34

Appendix Table B

- 3: Table of Detailed Algorithm Parameters