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 Agency
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Table of Contents

I. INTRODUCTION 3

II. OVERVIEW OF THE AGENCY 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 Calculations 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 CONSIDERATIONS 26

V. REFERENCES 28
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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 and 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 studies to inform 

risk assessments, risk characterizations, and in 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 information 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 investigators 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 because 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 analysis focuses 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/sap/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 approaches used in the AHS, and in epidemiological analyses 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 exposure assessment in 

observational epidemiologic investigations 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 

differences are reflected in the inputs and algorithms used in the two occupational handler exposure 

assessment approaches. 
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In brief, the primary goal of the Agency„s occupational 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 handler 

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 amount 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 questionnaires completed by individual cohort members. The AHS 

has documented the quality of self-reported information and has performed field measurement substudies to assess the efficacy of the exposure intensity algorithm approach (Dosemeci et al., 2002 and 

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 important for appropriately

characterizing the results of Agency risk 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 explore 

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: Complete an evaluation of the exposure determinants used in the AHS and Agency 

approaches for pesticide applicator exposure assessment to illustrate potential differences. 

Step 2: Building on the findings from Step 1, Step 2 will use 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. 
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Step 3: This step may involve a large-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 will be evaluated by the Agency and investigators from the AHS. 

This case study is presented in terms of a problem formulation exercise, as discussed in the draft 

framework and the 2009 National Research Council document on advancing risk assessment science 

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 methodologies used by AHS 

investigators and the Agency. The 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 future 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 references cited in this case study. 
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II. Overview of the Agency and AHS Exposure Assessment Methods

Over the last several decades the Agency has actively engaged in refining the methodologies and data 

used for completing occupational exposure and risk assessments. These exposure assessment methods

for pesticide applicators were most recently presented to the SAP in 2007. The information from the 2007 

meeting is available at (http://www.epa.gov/scipoly/SAP/meetings/2007/010907_mtg.htm). The Agency 

evaluates the potential risks to pesticide applicators in order to determine labeling requirements for 

protective clothing use, requirements for engineering controls, or use criteria such as 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. 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. These parameters are then included in an algorithm which 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 applicators 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 risks, 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 classification 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, application methods, use of personal 

protective equipment, and pesticide equipment repair (Dosemeci et al. 2002). 

Both the AHS and the Agency approaches consider the temporal aspects of pesticide exposures which 

define cumulative overall exposures and, as 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 associates what is known about the temporal aspects of 

the use patterns 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 anticipated durations of exposure (e.g., short-term up to 30 days exposure). Within the AHS 

exposure classification approach, the exposure intensity score is multiplied by the cumulative lifetime 
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days of use to develop the intensity-weighted metric. Cumulative lifetime days 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 parameters 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. Office of Pesticide Programs: Background 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 section, 

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)1contains information on unit exposures, 

which define exposures based on the amount of pesticide used to the aforementioned exposure 

conditions. 

Exposure Rates For Specific Job Tasks

Unit exposures are exposure rates which relate the amount of exposure individuals receive relative to the 

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. Each 

job task/exposure permutation is referred to as an “exposure scenario”. These scenarios for handlers 

include mixing pesticide chemicals, loading the pesticides into application equipment, or applying 

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 that 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).
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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 because 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 categorized 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 environment. 

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 methodology 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:

Baseline: 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 

tractor 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 reflect 
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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 surrogate 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 accomplish 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. Pesticide 

labels, which are licenses that dictate the legal use parameters for a pesticide, are 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. This information aids in providing a broader basis for well informed regulatory 

decision making processes. Such information is collected and defined from a variety 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 associations, and land grant university extension agents. 

Use 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 risk assessment results are used to 

define regulatory requirements they are completed using the most current, broadly applicable information 

available. The 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 exposure 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 about 
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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 acreages 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. Pesticide labels describe specific legal limits for the amount that 

can legally be used on a crop for a given pest pressure. Maximum legally allowable application rates are

always assessed in Agency risk assessments to ensure the validity of label conditions. Additional 

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 whether uses were at maximum use rates or not.

Exposure Durations & 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 to 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 interest. The Agency always includes different 

patterns 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. This approach ensures risk assessment are 

health protective and that uncertainty from limited longitudinal 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 pesticide 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 Alavanja et al. (1996) and Alavanja et al. (1999). This study explores 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 commercial 

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 testing 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 as 
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a private, commercial applicators, or spouse, and/or crop type. At enrollment, all participants (applicators 

and spouses) reported on their personal 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 for the remaining 28

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 information regarding work practices and equipment was obtained from the subset 

of applicators who completed the second questionnaire at enrollment (Alavanja et al., 1999; 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) described, 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 determinants (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 parameters is provided in Section III.A. 
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III. Case Study 

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 problem 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 

characterization. 

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. Applicators 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 

considerations pertaining to risk assessment. The 2009 National Research Council document on 

advancing risk assessment science and decision making 

(http://dels.nas.edu/dels/rpt_briefs/IRA_brief_final.pdf) outlines the importance of problem formulation in 

exposure and risk assessment. The Agency plans to develop this case study as a problem formulation 

exercise. Successful problem formulation in this regard will serve as the foundation for the incorporation 

of epidemiology in risk assessment in a more rigorous, systematic fashion than is currently employed.. 
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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 understanding of these differences deserves consideration when

incorporating epidemiologic information into the pesticide regulatory process. An introduction to 

the comparison of the algorithms used to complete exposure assessments by the 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 – The analyses for Step 2 will be 

completed 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 assessment methods used by 

the Agency compared with the methods used in the AHS. Understanding the comparative results

will provide for more improved risk characterization. This aspect of the analysis will use 

biomonitoring data for a subset of pesticide applicators 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/meetings/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 case 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 

future analyses. 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 submitted to the 

AHS.
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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 approach for illustrating these 

differences is provided in Section 2. 

1. Algorithms Used for Risk Assessment Calculations

The algorithms used by the Agency and the AHS for the purposes of exposure calculations are described 

in this 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 description 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 generally used (lb a.i./A); and,

Daily Area Treated = Application 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 calculated acreage estimates. Values selected as part of the standard 
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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 commodity 

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 toxicological 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 large 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 (greater 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, corresponding toxicological endpoints are then 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 in Dosemeci et al. (2002) and presented below:

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

Where:

Mix = [Mix] the mixing 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. 
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The 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 period 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 which

included more product-specific exposure information 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, this more detailed 

pesticide hygiene practice information has not been used in the published epidemiological AHS studies. 

For those interested in further details, the corresponding input parameters are provided (Appendix Figure 

B-2 and Appendix Table B-3). 

As described by Dosemeci 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 exposure determinants. The intensity score is 

used in epidemiologic analysis to modify the cumulative exposure measures, i.e., total lifetime days of 

exposure multiplied by the intensity of exposure. For most health outcomes, particularly chronic diseases, 

the relevant 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 intensityweighted cumulative exposure is used in the analysis depends on the chemical and/or health outcome of 

interest. The intensity-weighted cumulative exposure algorithm is provided below:
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Cumulative exposure = Intensity Level * Duration * Frequency

Where

Intensity Level = quantitative value for exposure (as described above)

Duration = number of years applied (years)

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

Preliminary Algorithm Review

There 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 and 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 algorithms 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 exposure 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 an 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. 
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Table 2: Algorithm Input Parameter Table

AHS Input Factors AHS EPA Input factors EPA

Mix Never (0); < 50% (3) ; > or = 50% (9) UE MLOD UE for application type

Apply Rank 0, 1- 9 UE APPL UE for application type

Repair No (0) ; Yes (2) NA (embedded in UE)

NA Label Rate Based on label (product specific)

NA Max acres treated Based on application method and crop

Duration Number of years applied (midpoints of 

the category ranges)

30 (default)* Potential working lifetime (years)

Frequency Days of applications per year (midpoints 

of the category ranges)

30 (default)* Potential days of application per year 

PPE Range: 0.1- 1 PPE and/or UE Either embedded in UE or protection factors 

are applied

MLOD = mixer/loader; APPL= Applicator; UE= Unit Exposure; PPE= Personal Protective Equipment; NA= Not applicable

* The working lifetime and days of application per year are parameters that are considered only when assessing cancer risks. Noncancer risks are not assessed on a time-basis but 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. In this example, the unit exposure is the driving factor for the 

Agency‟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 the 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 exposures and exposure weights is not a 

comprehensive analysis of the parameters. A more intensive analysis is anticipated in the future with 

collaboration 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 2007 

(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 unit 
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exposure can be illustrated 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 similar. 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

There 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 regulatory 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 

pesticide 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 chemicals 

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 specific 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 not 

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 exposure is dependent on intensity, 

frequency, and duration of contact. When calculating or considering the dose 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 incorporated 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 Pesticide 
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Regulation, and companies 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 Total Dermal Exposure (mg/lb a.i. handled)*

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 algorithm developed by Dosemeci et al. (2002) for 

use in the Agricultural Health Study.

The 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 exposure 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 explains, the assignment of the exposure weights for dermal exposure was based on the 
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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 acceptable 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 terms. 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 unit 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 as 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 ranking 

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 groundboom 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 application 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 fruit 

growers in a study published by De Cock 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 filters is suggested 

by the Carman et al. (1982) study. The value of intensity scores and related factors are presented 

Dosemeci et al. (2002) and illustrated in the following table: 
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Table 4: Table of General Algorithm Parameters 

Categories AHS Categories AHS

Rankings Rankings

Application Method Mix

Does not apply 0 Never 0

Herbicides < 50% of the time mixed 3

Aerial- aircraft 1 > or = 50% of the time mixed 9

Distribute tablets 1

In furrow/ Banded 2 Repair

Boom on tractor 3 Does not repair 0

Backpack 8 Repair 2

Hand Spray 9

Crop Insecticides PPE

Aerial- aircraft 1 PPE-0 (0% protection) 1

Seed treatment 1 Never used PPE

Distribute tablets 1 PPE-1 (20% protection) 0.8

In furrow/ banded 2 Face sheild or goggles

Boom on tractor 3 Fabric/ leather gloves

Backpack 8 Other protective clothing, such as boot

Hand spray 9 PPE-2 (30% protection) 0.7

Airblast 9 Cartridge respirator or gas mask

mist blower/fogger 9 Disposable outer clothing

Animal Insecticides PPE-3 (40% protection) 0.6

Ear Tags 1 Chemically resistant rubber gloves

Inject animal 2 PPE-1 & PPE-2 0.5

Dip animal 5 PPE-1 & PPE-3 0.4

Spray animal 6 PPE-2 & PPE-3 0.3

Pour on animal 7 PPE-1 & PPE-2 & PPE-3 0.1

Powder duster 9

Fungicides

Seed treatment 1

Distribute tablets 1

In furrown/ banded 2

Boom on tractor 3

Backpack 8

Hand Spray 9

Airblast 9

mist blower/fogger 9

Fumigants

Gas canister 2

Row fumigation 4

Pour fumigation 9

Preliminary Review of Input Parameters

The Agency intends to perform analyses 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, the 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 differences in algorithms may or may not influence the interpretation of

exposure assessments will be further evaluated in subsequent analyses. 
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The AHS cohort participates in a wide range of agricultural practices which are defined by the crops that 

they produce 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 pertinent 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 analysis 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 identified the Agency will focus 

on these in subsequent analysis 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 subset 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 to 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 was 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 in 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 diaries. 

The basis for using the Bakke et al. (2008) study as part of the Agency‟s case study is that it involved

collection of biomonitoring measures concurrent with daily and weekly activity diaries. Information 
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collected in the diaries includes a number of potential predictors of pesticide exposure (e.g., the total 

amount of product applied, 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 allows 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) provides 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-diethyl-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) aimed 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 their 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-trichloro-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. The 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 participants 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 apply what is understood from the methods 

analysis to actual exposure information. Given the variability in the parameters collected and measured,

Step 2 aims to investigate the contextual differences between the Agency and AHS exposure estimations 

approaches in the context of the Agency‟s extensive analysis of worker biomonitoring data. Welldeveloped 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.
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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 the AHS. If feasible a large-scale comparative analysis will follow the 

side-by-side and exposure and biomonitoring analyses. Elements 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 data 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 support 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 includes data for the North Carolina private pesticides applicators 

and the Iowa private and commercial applicators 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 repair, 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 completion 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 data 

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).]
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IV. Future Considerations

As part of the Agency‟s longer-term 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:

Determining 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 efforts 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 used 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 prevent 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 to 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 for 
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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 information 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 

Commission 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 applicable 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.farmfamilyexposure.org/) using the lessons learned from this exercise.
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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 Health 

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 Pesticide 

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. Exposure 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. Iwata, 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. Hart, 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 Approach for Estimating Exposure to 

Pesticides in the Agricultural Health Study. The Annals of Occupational Hygiene, 46(2): 245-260 
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Hackathorn, D.R. and D.C. Eberhart. 1985. Data base proposal for use in predicting 

mixer/loader/applicator exposure. American 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 

Contamination 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. Sheldon, 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 applicators 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 Environmental Assessment. United States Environmental Protection Agency. 

Available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=15263

U.S. EPA. 1995a. PHED: Pesticide Handlers 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. FIFRA 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, Speijers 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.
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APPENDIX 
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Appendix Table B-1: PHED surrogate exposure guide –applicator scenarios list

The scenarios 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: Closed 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) Groundboom: 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) Rights-of-Way Sprayer

Flagger

(25) Liquid Formulations

(26) Granular Formulations
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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 Pour, 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, open pour

(35) High Pressure Hand Wand: liquid, open pour

(36) Garden Hose-End Sprayer: liquid, open pour

(37) Termiticide Injection : liquid, open pour
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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 application method will range from 0 to 9 in exposure weights. The specific 

categories for the detailed algorithm are presented in Appendix Table B-3

Cab = [Cab] whether the individual reported using an enclosed cab with or without a charcoal filter. 

(weighting 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 washing 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 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 

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 (weighting 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)
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Appendix Table B-3: Table of Detailed Algorithm Parameters

Application Methods Goggles

Herbicides, Crop Insecticides, Fungicides Fabric/leather gloves Hyg-2 (60% protection) 0.4

Does not apply 0 Apron

Change clothing= right away or always use 

disposable clothing

Aerial- aircraft 1 Cloth overall

In furrow/ Banded 2 PPE-2 (30% protection) 0.7

Wash/shower= Bath/shower at the end of the 

day 

Boom on Tractor 3 Cartridge respirator or gas mask

Backpack 8 OR

Hand Spray 9 Chemically resistant boots Change clothing= at lunch or end of day

Mist blower/Fogger 9

Disposable outer clothing Wash/shower= hands/arms right away; 

wash/shower right away; or at lunch

Ariblast 9 PPE-3 (40% protection) 0.6

Livestock Insecticides Chemically resistant rubber gloves Hyg-3 (40% protection) 0.6

Does not apply 0 PPE-1 & PPE-2 0.5

Change clothing= right away or always use 

disposable clothing

Ear Tags 1 PPE-1 & PPE-3 0.4

Hang pest strips 2 PPE-2 & PPE-3 0.3 Wash/shower= Hands/arms at end of day

Rope Wick 2 PPE-1 & PPE-2 & PPE-3 0.1 OR

Dip animal 5 Spill Change clothing= at lunch or end of day

Spray animal 6 Changing clothes after a spill

Wash/shower= Bath/shower at the end of the 

day 

Spray buildings 6 Right away 1

Dust animals 7 Always use disposable clothing 1 OR

Pour on animals 7 At lunch 1.1

Change clothing= at end of next day; or later in 

week

Fog/mist animal 9 At the end of the day 1.2

Fumigants At the end of the next day 1.4 Wash/shower= hands/arms right away; 

Does not apply 0 Later in the week 1.8

Gas Canister 2 Repl Hyg-4 (20% protection) 0.8

Row fumigation 4 Replacing Old Fabric and Leather Gloves Change clothing= at lunch or end of day

Cab Change after each use 1 Wash/shower= Hands/arms at end of day

 Change once a month or 1-4 times a 

season 

1.1 OR

Change clothing= at end of next day; or later in 

Yes Cab; Yes Filter 0.1 Change when worn out 1.2

Yes Cab; No filter 0.5

Wash/shower= Bath/shower at the end of the 

day 

No Cab 1

Repair Hyg-5 (No protection factor) 1

No 0

Yes 2

Wash

Don't wash 0

Hose down sprayer 0.5

Hose down tractor 0.5

Clean nozzle 3

Rinse tank 1

Change clothing= at end of next day; or later in week

Wash/shower= Hands/arms only at end of day

Used with Boom, in furrow, hand spray, mist blower, or 

airblast on tractor