Organophosphate (OP) pesticides are the

most widely used pesticides in the United

States, and farmworkers are at high risk for

exposure. The health effects of acute exposure

to pesticides are well characterized. Previous

investigations have examined the long-term

health effects among workers after an acute or

chronic low-level exposure. Studies have

reported deficits in verbal and visual attention, motor dexterity, confusion, and lapses

in memory, among others (Eskenazi and

Maizlish 1988; McConnell et al. 1994;

Rosenstock et al. 1991). Others have reported

elevated risks for leukemia (Beane Freeman

et al. 2005), non-Hodgkin lymphoma

(Fritschi et al. 2005), and lung cancer (Beane

Freeman et al. 2005), although some studies

have not found significant associations (Burns

2005; Reynolds et al. 2005).

The U.S. Environmental Protection

Agency (EPA) has published tables of transfer

coefficients that estimate the amount of treated

foliage that a farmworker contacts while performing occupational tasks on various crops

(Science Advisory Council for Exposure 2000).

The estimates are based on standard assumptions about protective clothing worn by workers and the absorption rates for pesticides

through the skin or inhalation. Higher transfer

rates are estimated for workers who thin than

for workers who harvest, prune, weed, irrigate,

or perform other farm tasks. The extent to

which farm task is related to levels of worker

pesticide exposure when cross-sectional data

are examined remains controversial (Coronado

et al. 2004a, 2004b, 2004c; Fenske et al. 2004;

Krieger and Zhang 2004).

Items to consider in assessing the relationship of farm tasks and pesticide exposure

include transfer coefficients, total amount of

pesticides applied, time of pesticide application, and the multiple agricultural crops that

farmworkers may work with. Equivalent

transfer coefficients are assigned to various

orchard crops, such as apples, pears, cherries,

and peaches. Data from the U.S. Department

of Agriculture (USDA) (2000) show that differing crops have varying amounts of pesticides applied. Farmworkers generally work in

a variety of crops during a given growing season, and it remains unclear how work in multiple agricultural crops influences overall

worker exposure.

Growing research interest is seen in the levels and patterns of pesticide exposure among

children of farmworkers. This emphasis was

driven by a report of the National Research

Council (1993) expressing concern about pesticide residues on food. Some studies have identified possible risks for the development of

cancers, birth defects, and abnormal reflexes

among children and neurologic impairments

among adults and children (Blain 2001;

Guillette et al. 1998; Kirkhorn and Schenker

2002; Mills and Yang 2003; Rohlman et al.

2005; U.S. General Accounting Offices 2000;

Young et al. 2005).

Pesticide exposure in children is of concern because of the way in which exposure

occurs. Exposure is not always direct; it is

generally believed to occur from pesticides

brought to the home through the take-home

pathway of farmworkers (Thompson et al.

2003). Such paraoccupational exposure is

important because of children’s unique

behaviors, such as greater amounts of time

spent on floors where pesticides accumulate,

increased likelihood of dermal exposure from

wearing minimal clothing during the summer

spray season, and increased likelihood of pesticide ingestion from hand-to-mouth behavior (Mills and Zahm 2001).

The importance of agricultural task was

shown by Fenske et al. (2000), who reported

that children of pesticide applicators had

higher urinary concentrations of dialkylphosphates than do children of nonagricultural

workers living in the same community (based

on creatinine-adjusted spray-season estimates).

Lambert et al. (2005) have reported cropspecific information showing that children

whose parents worked in pear orchards had

higher concentrations of pesticide metabolites

in their urine than did children whose parents

worked in berries or cherries. Other studies

have shown that children of agricultural workers have higher exposures than children of

Environmental Health Perspectives • VOLUME 114 | NUMBER 7 | July 2006 999

Research

Address correspondence to G.D. Coronado, Cancer

Prevention Research Program, Fred Hutchinson

Cancer Research Center, P.O. Box 19024, 1100

Fairview Ave. N, M3-B232, Seattle, WA 98109

USA. Telephone: (206) 667-4216. Fax: (206) 667-

5977. E-mail: gcoronad@fhcrc.org

We acknowledge R. Fenske, J. Kissel, and J. Shirai

for providing the sampling and quality assurance

protocols, training field staff, and developing the

analytical procedures for the samples. We acknowledge members of our community advisory board for

designing and carrying out the project.

Although the research described in this article has

been funded in part by the by the U.S. Environmental

Protection Agency (R826886) and the National

Institutes of Health (P01 ES09601), it has not been

subjected to either agency’s required peer and policy

review and therefore does not necessarily reflect the

views of either agency, and no official endorsement

should be inferred.

The authors declare they have no competing

financial interests.

Received 31 August 2005; accepted 13 March 2006.

Organophosphate Pesticide Exposure and Work in Pome Fruit: 

Evidence for the Take-Home Pesticide Pathway

Gloria D. Coronado,1 Eric M. Vigoren,2 Beti Thompson,1 William C. Griffith,2 and Elaine M. Faustman2

1Cancer Prevention Research Program, Seattle, Washington, USA; 2Department of Environmental and Occupational Health Sciences,

School of Public Health and Community Medicine, University of Washington, Seattle, Washington, USA

Organophosphate (OP) pesticides are commonly used in the United States, and farmworkers are at

risk for chronic exposure. Using a sample of 218 farmworkers in 24 communities and labor camps

in eastern Washington State, we examined the association between agricultural crop and OP pesticide metabolite concentrations in urine samples of adult farmworkers and their children and OP

pesticide residues in house and vehicle dust samples. Commonly reported crops were apples

(71.6%), cherries (59.6%), pears (37.2%), grapes (27.1%), hops (22.9%), and peaches (12.4%).

Crops were grouped into two main categories: pome fruits (apples and pears) and non-pome fruits.

Farmworkers who worked in the pome fruits had significantly higher concentrations of dimethyl

pesticide metabolites in their urine and elevated azinphos-methyl concentrations in their homes and

vehicles than workers who did not work in these crops. Among pome-fruit workers, those who

worked in both apples and pears had higher urinary metabolites concentrations and pesticide

residue concentrations in dust than did those who worked in a single pome fruit. Children living in

households with pome-fruit workers were found to have higher concentrations of urinary dimethyl

metabolites than did children of non-pome-fruit workers. Adult urinary concentrations showed significant correlations with both the vehicle and house-dust azinphos-methyl concentrations, and

child urinary concentrations were correlated significantly with adult urinary concentrations and

with the house-dust azinphos-methyl concentration. The results provide support for the take-home

pathway of pesticide exposure and show an association between measures of pesticide exposure

and the number of pome-fruit crops worked by farmworkers. Key words: children of farmworkers,

contamination, crops, farmworkers, house dust, occupational exposure, pesticides, take-home

pathway, urinary metabolites, vehicle dust, WinBUGS. Environ Health Perspect 114:999–1006

(2006). doi:10.1289/ehp.8620 available via http://dx.doi.org/ [Online 13 March 2006]
nonagricultural workers (Loewenherz et al.

1997; Lu et al. 2000) and that pesticide

metabolite levels in children’s urine correlate

with pesticide metabolite levels in adults’ urine

within the same household (Curl et al. 2002).

A limited number of previous investigations

have examined households for the presence of

pesticide residues in dust samples where children are thought to be at risk of exposure

(Coronado et al. 2004c; Curwin et al. 2005;

Fenske et al. 2002; Lu et al. 2000; McCauley

et al. 2003; Shalat et al. 2003).

Using a large sample of farmworkers from

several agricultural communities in eastern

Washington State, we examined the association between work in specific agricultural

crops and levels of OP pesticide exposure

among adult workers and children living in

the same household. We aimed to test the

hypothesis that the take-home pathway results

in children’s exposure to pesticides.

Materials and Methods

Setting. A study that tested a culturally appropriate intervention to interrupt the take-home

pathway of pesticide exposure provides the

data for this report. The setting, study design,

study participants, and survey procedures

have been described previously (Thompson

et al. 2003). Briefly, the study took place in

the Yakima Valley of Washington State. An

estimated 50,000 people in the region work

in agriculture; the primary crops are apples,

grapes, pears, cherries, hops, and peaches

(USDA 2000). Approximately 50% of the

area population is Hispanic, and most work

in agriculture. For most crops cultivated in

the Yakima Valley, fieldwork is done by hand.

In Washington State during 1999 (the

year in which these data were collected),

172,000 acres of farmland were dedicated to

apple production; substantial acreage was

dedicated to the production of pears (24,400

acres), cherries (18,000 acres), peaches (2,500

acres), hops (25,076 acres), and grapes

(41,000 acres) (USDA 2000). Yakima

County ranked first among Washington State

counties in number of acres dedicated to the

production of apples (75,264 acres), pears

(10,190 acres), cherries (6,129 acres), peaches

(1,438 acres), and hops (20,061 acres) and

second in the acres dedicated to grape production (15,529 acres) (USDA 2004).

OP pesticide use in Washington State. One

of the most commonly used pesticides in the

Yakima Valley is azinphos-methyl. This is a

broad-spectrum insecticide registered for use in

the control of many insect pests on a wide variety of fruit, vegetable, nut, and field crops as

well as on ornamental plants, tobacco, and forest and shade trees. Azinphos-methyl is classified by the U.S. EPA as having level I toxicity.

It is reported to be highly toxic through inhalation, dermal absorption, ingestion, and eye

contact (Extension Toxicology Network

1996). The current reentry interval for azinphos-methyl applied to apples, pears, and

peaches is 14 days and for cherries, 15 days

(USDA 2003), during which time, only workers wearing protective equipment are allowed

in the fields. In 1999, the reentry interval for

azinphos-methyl was extended from 48 hr to

14 days. It is unclear to what extent the shorter

reentry interval was in practice during the

1999 season. For each annual crop season,

applications are limited to 8 pounds per acre

for apples, 6 pounds per acre for pears, and 3

and 4.5 pounds per acre for cherries and

peaches, respectively (Bayer CropScience LP

2003). Data from the voluntary Washington

Agricultural Statistics survey show that in 1999

an estimated 309,300 pounds of azinphosmethyl were applied to Washington State

apple orchards and 33,000 pounds, 17,500

pounds, and 2,000 pounds were applied to

pear, cherry, and peach orchards, respectively

(USDA 2000). Azinphos-methyl is generally

not used in hop or grape production (USDA

2001; Washington Association of Wine

Growers 2004).

Other OP pesticides were also used on

Yakima Valley crops in 1999 (USDA 2000):

Phosmet was applied to Washington State

apples and pears in estimated quantities of

46,000 pounds and 20,600 pounds, respectively; malathion was applied to apples and

peaches in quantities of 22,300 pounds and

1,500 pounds, respectively; methyl-parathion

was applied to apples and pears in quantities

of 17,100 pounds and 1,400 pounds, respectively; and chlorpyrifos was applied to apples,

pears, peaches, cherries, and grapes in quantities of 250,900 pounds, 28,300 pounds,

1,300 pounds, 20,600 pounds, and 8,000

pounds, respectively. 

Questionnaire. An in-person interview

was conducted with the farmworkers. The

main questionnaire was a 73-item instrument

that included nine sections. Workers were

asked whether or not they had worked with

apples, pears, peaches, cherries, hops, or

grapes in the previous 3 months, and to name

any additional crops. Workers were also asked

whether in the previous 3 months they had

performed the following agricultural job

tasks: harvesting or picking; pruning; loading;

packing; sorting or grading plants, fruits, or

vegetables; planting or transplanting; weeding; thinning; irrigating; mixing or loading

farm chemicals; spraying or applying pesticides; or other tasks. Before implementation,

the questionnaire was translated into Spanish,

piloted among farmworkers, and reviewed

and edited by members of a community advisory board. The questionnaire and all study

procedures were reviewed and approved by

the Fred Hutchinson Cancer Research

Center. All adult participants and parents of

child participants gave informed consent to

participate in the study.

Survey procedures. Recruitment procedures have been described previously

(Thompson et al. 2003). Briefly, individuals

who worked in agriculture were recruited

using an in-person survey of randomly

selected households conducted for another

study. Additional workers were recruited

from labor camps and from areas in the community known to have a large concentration

of agricultural workers. A total of 571 households were surveyed; 218 of these households

had age-eligible children (2–6 years of age)

and agreed to enroll in the specimen collection aspect of the study. This subgroup forms

the sample basis for this report.

Among eligible households (those with a

farmworker and age-eligible child), an adult

respondent and study child were identified

(Thompson et al. 2003). We collected urine

samples from the adult farmworker and study

child as well as dust samples from selected

areas of the home and the vehicle used to

commute to and from work. Samples were

collected between June and October 1999.

Specimen collection and laboratory analysis. Procedures for the urine and dust collection and laboratory analysis have been

described in detail elsewhere (Curl et al.

2002). Two or three spot urine samples were

collected. Each collection was separated by a

minimum of 3 days and collected within a

2-week period, with the first collection occurring at the interview. For each individual,

equal volumes of each urine sample were

combined before specimen analysis. This provided an estimate of pesticide exposure within

the 2-week period of assessment.

Urine samples were analyzed using gas

chromatographic procedures for the presence

of five dialkylphosphate compounds produced

by the metabolism of most OP pesticides:

dimethylphosphate (DMP), dimethylthiophosphate (DMTP), dimethyldithiophosphate

(DMDTP), diethylphosphate, and diethylthiophosphate. The limits of detection for

these compounds were 7.2 μg/L, 1.1 μg/L,

0.65 μg/L, 2.9 μg/L, and 1.2 μg/L, respectively.

Dust samples were collected from homes

and commuter vehicles using a Nilfisk vacuum

cleaner unit (model GS-80; Nilfisk of

America, Malvern, PA), and sampling took

place within 4 weeks of the interview.

Selection of the area to be vacuumed was

determined by asking the parent or adult participant where the child played most frequently. The size of the area vacuumed

depended on the floor type and ranged from a

1 m × 1 m area for plush carpets to a 2 m × 2

m area for hard or smooth floors. Foot wells

from both the front and back of cars (and only

the front of trucks) were vacuumed. Mats

were not removed before vacuuming. Dust

Coronado et al.

1000 VOLUME 114 | NUMBER 7 | July 2006 • Environmental Health Perspectives
samples were analyzed using gas chromatographic procedures for the presence of four

dimethyl OP pesticides (azinphos-methyl,

malathion, methyl-parathion, and phosmet)

and two diethyl OP pesticides (chlorpyrifos and

diazinon). The limits of detection for these pesticides residues and the percentage of analyzed

samples that contained detectable levels are

shown in Table 1. For this report, we limit our

analyses of the dust samples to the azinphosmethyl residues because in a large number of

samples other pesticides were below detection

levels. Because azinphos-methyl is a dimethyl

OP pesticide, we limited our analyses of the

urine samples to the dimethyl metabolites.

Statistical and classification methods.

Apples and pears are classified as pome fruit: a

fleshy fruit having several seed chambers.

Peaches and cherries are stone fruits, containing a single seed or pit. Grapes and hops are

vine/trellis and bunch/bundle crops, respectively. We grouped workers into two categories based on crop type. Pome-fruit workers

were those who worked in apples and/or pears

and possibly peaches, cherries, grapes, hops,

and other crops. Non-pome-fruit workers

worked only in peaches, cherries, grapes, hops,

or other crops. We grouped pears with apples

because they have similar harvest seasons, and

both are chemically and hand-thinned during

the pesticide spray season (June through

August). The rates and types of pesticide

application are similar for both crops.

We present the frequencies of detection

and estimated geometric mean (GM) concentrations and geometric standard deviations of

adult and child urinary dimethyl metabolites

and azinphos-methyl residues in vehicle and

house-dust samples. Concentrations for the

urinary samples are presented in units of

micrograms per liter and are not creatinine

adjusted. Concentrations for the dust samples

are presented in units of micrograms per gram.

Quantile–quantile plots (not shown)

demonstrate that the concentrations of the

urinary metabolites and of pesticide residues

in dust were approximately normally distributed after a log-transformation. Because some

samples had values below the limits of

detection, we modeled the missing data in a

multivariate normal hierarchical Bayesian

simulation model with conjugate noninformative priors using WinBUGS (Windowsbased Bayesian inference Using Gibbs

Sampling) (Spiegelhalter et al. 2003 ). The

data below the limits of detection were

treated as left censored data with an upper

cutoff at the limit of detection (Griffith et al.

2002). The statistical model assumed a common variance/covariance structure while

allowing for a shift in the means for different

farmworker classifications. After a burn-in

simulation run, we performed 250,000 simulations. Confidence limits for the log-normal

parameters (the 95% posterior predictive probability intervals) were estimated. To examine

differences in the means of farmworker

groups, at each simulation we compared the

means to see which were larger. These comparisons were recorded and summed, and we

report the probability that one GM was

greater than another.

Results

In this study, 571 (89.6%) of the 627 farmworkers identified for the study were interviewed. A small percentage of households

(3.8%) could not be contacted after at least

five visits, and 6.6% of eligible farmworkers

refused to participate, giving an overall

response rate of 89.6%, or 93.1% of the

known eligibles. Of the 571 respondents, 231

households (40.5%) included children 2–6

years of age. Of these, 218 households were

available for sample collection. The total number agreeing to provide urine samples included

213 adult farmworkers (92.2% of those eligible) and 211 children (91.3% of those eligible). House-dust samples were collected from

210 homes, and of the households with vehicles (n = 207), 205 (99.0%) allowed us to collect vehicle dust. For 54 homes and 15

vehicles, insufficient masses of dust were collected for analysis; thus, pesticide residue

analysis was conducted on 156 house-dust

samples and 190 vehicle-dust samples.

Three-quarters of the farmworkers in our

study sample reported working in apples

within the 3 months before being interviewed

(Table 2). More than one-third worked in

pears, and nearly two-thirds worked in cherries. Approximately one-tenth of workers

worked in peaches and nearly one-quarter

worked in grapes or hops. Fewer than onethird of the respondents worked in other

crops, which included asparagus, apricots,

plums, peppermint, corn, and onions.

When farmworkers reported working in

multiple crops, apples were the crop with the

greatest overlap with other crops. All of the

peach workers and all but two pear workers

had also worked in apples. The percentages of

cherry, grape, hop, and other crop workers

who had also worked in apples were 80.8,

69.5, 62.0, and 61.9%, respectively.

Those who worked in pome fruits were

slightly older than those who worked in nonpome fruits (Table 3). Pome-fruit workers, on

average, had fewer years of education and had

lower household incomes than did non-pomefruit workers; those working in both apples

and pears on average had the lowest education

and income levels. The groups were similar in

marital status. Birthplace differed slightly by

crop category. Approximately one-fifth of

pome-fruit workers reported having worked in

agriculture for 20 or more years; the proportion was about one-fourth for non-pome-fruit

workers. Most adult participants were male

and completed the survey in Spanish.

Eight of the farmworkers had DMP concentrations that were orders of magnitude

higher than other study participants and

ranged from 3,780 to 12,000 μg/mL (the

remaining study participants’ concentrations

ranged from less than the limit of detection to

100 μg/mL). All eight farmworkers reported

working in apples, and half had also worked in

pears; seven had worked as thinners. We performed our analysis with and without the data

from these farmworker households. The two

analyses showed similar relationships between

urinary metabolite and dust concentrations

and crop category; however, those households

were influential in estimating the GMs and

the geometric standard deviations. Including

them in the analysis made it unclear whether

the crop effect was primarily a result from

these eight households, or whether there was a

crop effect among all who worked in pome

fruit. By excluding them, we could show that

the association between crop category and

metabolite and dust concentrations was present among the rest of the population.

Take-home pathway for OP pesticides

Environmental Health Perspectives • VOLUME 114 | NUMBER 7 | July 2006 1001

Table 2. Crops in which study participants (n = 218) reported performing agricultural job tasks within the

previous 3 months.

Apples Pears Peaches Cherries Grapes Hops Other

Apples 156a

Pears 79 81

Peaches 27 24 27

Cherries 105 62 17 130a

Grapes 41 18 6 32a 59

Hops 31a 16 7 24 20 50

Other 39 21 11 36 12 13 63a

aNo answer was recorded for three different farmworkers as to whether or not they worked in apples, cherries, or other

crops, respectively. For these cross-tabulations, n = 217. 

Table 1. Limits of detection of pesticide residues in dust (μg/g) and percentages of analyzed vehicle (n =

190) and house-dust samples (n = 156) containing detectable levels of pesticide residue. 

Azinphos-methyl Phosmet Malathion Methyl-parathion Chlorpyrifos Diazinon

Limit of detection (μg/g) 0.08 0.08 0.13 0.11 0.11 0.11

Vehicle dust (%) 87 16 12 22 18 2

House dust (%) 85 15 13 14 26 4
Therefore, measurements taken from these

eight households are not represented in Tables

4–8; the results reported in these tables are

based on 210 farmworker households.

Examining the dimethyl urinary metabolites among workers who did or did not work

in pome fruits, we observed differences in the

frequency of detection and in the metabolite

concentrations (Table 4). Workers who

reported working in pome fruit had higher

concentrations of dimethyl metabolites than

did non-pome-fruit workers; GM concentrations were 2.4-fold, 3.5-fold, and 2.9-fold

higher for DMP, DMTP, and DMDTP,

respectively. Among pome-fruit workers, those

who worked in both apples and pears had the

highest dimethyl metabolite concentrations.

Children in our study had patterns of

exposure that were similar to those in adults

(Table 4). Children who lived in households

with a farmworker who worked in pome fruit

had greater frequency of detection of dimethyl

metabolites than did children living in households with a non-pome-fruit worker. The frequency of detection for DMP, for example,

was 7.1% for children living in households

with non-pome-fruit workers and 22.5% for

children living with pome-fruit workers. GM

concentrations of DMP, DMTP, and

DMDTP were 2.6-fold, 1.7-fold, and 1.5-fold

higher, respectively, for children who lived in

households with a pome-fruit worker. Among

children living with a pome-fruit worker,

those who lived in households with an apple

and pear worker had higher dimethyl metabolite concentrations than those who lived in

households with an apple or pear worker only.

Differences between the crop worker

groups are seen in the concentrations of azinphos-methyl residue in the dust samples

(Table 5). More than 90% of those who

worked in pome fruit had detectable azinphosmethyl in their vehicles and homes, compared

with only slightly more than 60% for the nonpome-fruit workers. Among pome-fruit workers, those who worked in both apples and

pears had the greatest percentage detection

and higher concentrations of azinphos-methyl

in their house and vehicle dust. Those who

worked in pome fruit had GM concentrations

of azinphos-methyl in their vehicle and house

dust that were 6.8-fold and 4.6-fold greater,

respectively, than those for farmworkers who

did not work in pome fruit.

The estimated correlations for the dimethyl

urinary metabolite concentrations and the

azinphos-methyl concentrations in house and

vehicle dust that we observed are given in

Table 6. Within both the adult and the child

urine samples, there were statistically significant high positive correlations between the

dimethyl metabolite concentrations, particularly for DMTP and DMDTP concentrations.

The vehicle- and house-dust concentrations of

Coronado et al.

1002 VOLUME 114 | NUMBER 7 | July 2006 • Environmental Health Perspectives

Table 3. Demographic characteristics of study participants (%): selected adult farmworkers with a child

2–6 years of age in the household by pome/non-pome crop classification (n = 217a).

Characteristic Non-pome (n = 59) Pome (n = 158) One pome (n = 79) Two pome (n = 79)

Age (years)

18–24 15.3 8.9 7.6 10.1

25–34 47.5 41.1 46.8 35.4

35–49 23.7 28.5 24.1 32.9

≥ 50 5.1 5.1 6.3 3.8

Not reported 8.5 16.5 15.2 17.7

Education

< 4th grade 25.4 32.3 27.8 36.7

5th through 8th 35.6 41.1 41.8 40.5

9th through 12th 32.2 21.5 22.8 20.3

≥ High school graduate 6.8 5.1 7.6 2.5

Annual household income (US$)

< 10,000 18.6 21.5 19.0 24.1

10,000 < 15,000 22.0 29.1 29.1 29.1

15,000 < 25,000 49.2 37.3 38.0 36.7

≥ 25,000 10.2 10.1 11.4 8.9

Not reported 0.0 1.9 2.5 1.3

Marital status

Married or living as married 86.4 88.6 91.1 86.1

Separated or divorced 3.4 2.6 2.5 2.5

Never married 10.2 8.2 6.3 10.1

Other 0.0 0.6 0.0 1.3

Birthplace

Mexico 83.1 94.9 93.7 96.2

United States 15.3 3.8 5.1 2.5

No. of years working in agriculture

< 10 45.8 48.1 53.2 43.0

10– < 20 28.8 31.0 22.8 39.2

≥ 20 25.4 20.9 24.1 17.7

Male sex 57.6 67.7 64.6 70.9

Interview in Spanish 86.4 94.3 92.4 96.2

aTotal n = 217 because of one pome classification missing value.

Table 4. Frequency of detection and estimated GM concentrations of dimethyl urinary metabolites among

adult farmworkers and their children, by agricultural crop (n = 210).

Metabolite and crop Detectiona (%) Estimatedb GM (μg/L) Estimatedb GSD p(pomeGM ≤ non-pomeGM)

Adult DMP 5.96 (4.02–10.74)

Non-pome fruit 8.8 0.71 (0.20–1.68) 0.017

Pome fruit 20.4 1.72 (0.80–2.89)

Apples or pears 14.7 1.19 (0.42–2.45)

Apples and pears 26.4 2.22 (0.97–4.00)

Adult DMTP 4.48 (3.90–5.29)

Non-pome fruit 86.0 4.35 (2.92–6.47) 0.000

Pome fruit 96.6 15.34 (12.02–19.54)

Apples or pears 94.7 13.42 (9.55–18.93)

Apples and pears 98.6 17.52 (12.41–24.83)

Adult DMDTP 6.72 (5.34–8.91)

Non-pome fruit 36.8 0.47 (0.26–0.81) 0.001

Pome fruit 61.2 1.37 (0.97–1.90)

Apples or pears 60.0 1.19 (0.74–1.88)

Apples and pears 62.5 1.58 (0.98–2.49)

Child DMP 2.84 (2.28–3.91)

Non-pome fruit 7.1 1.34 (0.59–2.39) < 0.001

Pome fruit 22.5 3.53 (2.40–4.65)

Apples or pears 18.9 3.06 (1.87–4.38)

Apples and pears 26.0 3.96 (2.54–5.49)

Child DMTP 3.61 (3.19–4.20)

Non-pome fruit 78.6 3.54 (2.50–4.98) 0.003

Pome fruit 91.2 6.18 (5.00–7.61)

Apples or pears 93.2 5.76 (4.29–7.76)

Apples and pears 89.0 6.61 (4.89–8.90)

Child DMDTP 4.83 (3.90–6.30)

Non-pome fruit 41.1 0.65 (0.39–1.03) 0.061

Pome fruit 46.3 0.98 (0.71–1.30)

Apples or pears 40.5 0.88 (0.58–1.32)

Apples and pears 52.1 1.08 (0.71–1.59)

GSD, geometric SD. Ranges are posterior predictive probability intervals. 

aBased on the number of samples analyzed: non-pome, adult n = 57, child n = 56; apples or pears, adult n = 75, child n = 74;

apples and pears, adult n = 72, child n = 73. bBased on the total number of samples: non-pome, n = 59; apples or pears, n =

75; apples and pears, n = 75; missing fruit classification, n = 1.
azinphos-methyl were also highly correlated.

Urine samples of children and adults living in

homes with elevated levels of dust indicate

exposure to higher levels of pesticides; adult

urine DMP and DMTP concentrations

showed significant correlations with both the

vehicle- and house-dust azinphos-methyl concentrations, and child urine DMP and

DMTP concentrations were correlated significantly with the house-dust azinphos-methyl

concentration.

When we examined differences in urinary

metabolite concentrations among those who

did and did not perform thinning within

pome-fruit and non-pome-fruit worker categories, we found no remarkable differences

between the groups (Table 7). Similarly, there

were no notable differences in concentrations

of azinphos-methyl and vehicle and dust samples between thinners and nonthinners when

categorized by work in pome fruit (Table 8).

Discussion

We assessed OP pesticide exposure among

farmworkers based on whether on not they

worked in pome-fruit crops. Those who

worked in pome fruit had higher concentrations of OP pesticide metabolites in their

urine and higher concentrations of azinphosmethyl residues in dust collected from their

homes and vehicles. The increased presence of

pesticide-laden dust probably contributed to

the pesticide exposure of children in the

household.

Previously, we reported higher proportions of urine samples with detectable levels of

the OP pesticide urinary metabolite DMTP

from children of farmworkers who reported

thinning, compared with urine samples from

children of nonthinners. We also reported

higher proportions of detectable azinphosmethyl in vehicle and house dust among

workers who reported thinning (Coronado

et al. 2004c). The present findings suggest

that agricultural crop was an important factor

that was not considered in our previous study;

91.4% of thinners reported having worked in

pome fruits in the 3 months before the survey. After controlling for work in pome-fruit

crops, our data revealed no significant differences between workers who did or did not

report thinning, for both percent detection

and concentration measurements.

The higher urinary metabolite levels found

in pome-fruit workers matched pesticide use

patterns in Washington State. Data from the

Washington Agricultural Statistics survey show

that in 1999 the rate of application of azinphosmethyl was higher in apples (1.8 lb/acre) than

in pears (1.4 lb/acre), cherries (1.0 lb/acre), or

peaches (0.8 lb/acre) (USDA 2000); thus, our

data appear to show higher exposure levels

among those who worked in crops with the

highest pesticide applications.

Take-home pathway for OP pesticides

Environmental Health Perspectives • VOLUME 114 | NUMBER 7 | July 2006 1003

Table 5. Frequency of detection and estimated GM concentrations and geometric standard deviations

(GSDs) of azinphos-methyl residues in vehicle and house dust (n = 210).

Pesticide and crop Detectiona (%) Estimatedb GM (μg/g) Estimatedb GSD p (pomeGM ≤ non-pomeGM)

Vehicle azinphos-methyl 4.65 (3.97–5.61)

Non-pome fruit 63.5 0.17 (0.11–0.26) < 0.001

Pome fruit 95.4 1.16 (0.89–1.51)

Apples or pears 94.1 0.78 (0.54–1.11)

Apples and pears 96.8 1.79 (1.24–2.58)

House azinphos-methyl 3.55 (3.07–4.25)

Non-pome fruit 62.5 0.17 (0.11–0.25) < 0.001

Pome fruit 92.7 0.79 (0.63–1.00)

Apples or pears 90.7 0.59 (0.43–0.82)

Apples and pears 94.6 1.05 (0.76–1.45)

Ranges are posterior predictive probability intervals. 

aBased on the number of samples analyzed: non-pome, vehicle n = 52, house n = 40; apples or pears, vehicle n = 68, house

n = 54; apples and pears, vehicle n = 62, house n = 55. bBased on the total number of samples: non-pome, n = 59; apples or

pears, n = 75; apples and pears, n = 75; plus one sample with missing fruit classification.

Table 6. Correlation matrix of dimethyl phosphate urinary metabolite concentrations and azinphos-methyl

residue concentrations in vehicle and house dust (n = 210).

Metabolite Adult Child Azinphos-methyl

or pesticide DMP DMTP DMDTP DMP DMTP DMDTP Vehicle House

Adult DMP 1.00

Adult DMTP 0.51* 1.00

Adult DMDTP 0.35* 0.73* 1.00

Child DMP 0.20 0.12 0.12 1.00

Child DMTP 0.21* 0.34* 0.22* 0.53* 1.00

Child DMDTP 0.13 0.34* 0.37* 0.39* 0.81* 1.00

Vehicle azinphos-methyl 0.28* 0.22* 0.13 0.10 0.15 0.09 1.00

House azinphos-methyl 0.32* 0.25* 0.09 0.25* 0.24* 0.16 0.52* 1.00

*Statistically significant: 95% posterior predictive probability interval does not include 0.0.

Table 7. Frequency of detection and estimated GM concentrations of dimethyl urinary metabolites among

adult farmworkers and their children, by agricultural crop (n = 210).

Metabolite and pome versus thin Detectiona (%) Estimatedb GM (μg/L) p(thinGM ≤ non-thinGM)

Adult DMP

Non-pome/non-thin 8.9 0.70 (0.17–1.82) 0.641

Non-pome/thin 8.3 0.49 (0.05–2.56)

Pome/non-thin 20.7 1.93 (0.59–4.63) 0.669

Pome/thin 20.3 1.55 (0.67–2.76)

Adult DMTP

Non-pome/non-thin 84.4 3.84 (2.45–5.99) 0.111

Non-pome/thin 91.7 7.01 (2.96–16.50)

Pome/non-thin 96.6 15.07 (8.74–26.03) 0.470

Pome/thin 96.6 15.43 (11.76–20.22)

Adult DMDTP

Non-pome/non-thin 37.8 0.46 (0.23–0.85) 0.443

Non-pome/thin 33.3 0.50 (0.14–1.68)

Pome/non-thin 65.5 1.71 (0.81–3.53) 0.752

Pome/thin 60.2 1.30 (0.88–1.87)

Child DMP

Non-pome/non-thin 6.7 1.15 (0.44–2.26) 0.335

Non-pome/thin 9.1 1.53 (0.37–4.13)

Pome/non-thin 21.4 3.37 (1.73–5.65) 0.477

Pome/thin 22.7 3.42 (2.25–4.62)

Child DMTP

Non-pome/non-thin 77.8 3.62 (2.45–5.30) 0.602

Non-pome/thin 81.8 3.24 (1.49–6.95)

Pome/non-thin 85.7 7.52 (4.63–12.18) 0.813

Pome/thin 92.4 5.90 (4.67–7.45)

Child DMDTP

Non-pome/non-thin 40.0 0.60 (0.34–1.02) 0.336

Non-pome/thin 45.5 0.77 (0.26–2.11)

Pome/non-thin 53.6 1.18 (0.62–2.20) 0.751

Pome/thin 44.5 0.93 (0.66–1.28)

Ranges are posterior predictive probability intervals. 

aBased on the number of samples analyzed: non-pome/non-thin, adult n = 45, child n = 45; non-pome/thin, adult n = 12,

child n = 11; pome/non-thin, adult n = 29, child n = 28; pome/thin, adult n = 118, child n = 119. bBased on the total number of

samples: non-pome/non-thin, n = 47; non-pome/thin, n = 12; pome/non-thin, n = 29; pome/thin, n = 121; plus one sample

with missing fruit classification. 
Particularly noteworthy is the distinct

gradient we see in the azinphos-methyl concentrations in vehicle and house dust.

Samples collected from households of farmworkers who worked in both apples and pears

had the higher pesticide concentrations than

did samples collected from households of

farmworkers who worked in only one pome

fruit (Table 5). Samples collected from households of farmworkers who did not work with

pome fruit had the lowest concentrations.

The association between higher adult and

child urinary OP pesticide metabolite concentrations and work with increasing numbers of

pome fruit was also observed (Table 4). These

findings suggest the potential for cumulative

pesticide exposure among workers performing

tasks on multiple crops.

We found no association within worker

groups between pesticide and metabolite concentrations and self-reported information

about the recent application of pesticides in

the workplace. This suggests that pesticides

that accumulate in the home environment

may account in part for the urinary metabolite levels of adults and children, and that

pome-fruit workers are more likely to track

home pesticides. The high positive correlation

between azinphos-methyl levels in house and

vehicle dust that we observed along with the

statistically significant positive correlations

between the dust concentrations and urinary

metabolite concentrations provides perhaps

the strongest evidence for the take-home

pathway (Table 6).

The Centers for Disease Control and

Prevention (CDC 2005) reported that the

GM concentration of the OP pesticide urinary

metabolite DMTP for adults 20–59 years of

age in the general population is 1.47 μg/L

averaged across a year. We observed concentrations three times higher in the spray season

for farmworker adults of similar ages who did

not work in pome-fruit crops (GM = 4.4

μg/L) and concentrations > 10 times higher

for those who did (GM = 15.3 μg/L).

Although the CDC report provides no data

for children in the same age group as our

study, it does give a GM urinary metabolite

concentration of 2.95 μg/L for DMTP in children 6–11 years of age. Our findings for children 2–6 years of age are 1.2 times higher

than this for those in households of farmworkers who did not work in pome fruits (GM =

3.5 μg/L) and > 2.1 times higher for those in

households of farmworkers who did work in

pome fruits (GM = 6.2 μg/L). Because our

children were younger, they may have higher

concentrations of metabolites than do older

children, as has been reported in a limited

number of previous investigations on the topic

(Loewenherz et al. 1997; Shalat et al. 2003).

Few previous pesticide exposure investigations have examined household dust. We

found higher concentrations of azinphosmethyl in house and vehicle dust among workers who reported having worked in the pome

fruits versus farmworkers who did not report

working in these fruits. Our GM concentration of azinphos-methyl in house dust (0.79

μg/g) among pome-fruit workers is comparable

with that reported for agricultural workers by

Lu et al. (2000) (median concentration = 1.0

μg/g combined value for pesticide applicators

and farmworkers in pome-fruit–growing

region in Washington State) and Shalat et al.

(2003) (median concentration = 0.51 μg/g for

homes in an agricultural community near the

U.S.–Mexico border). 

Our analyses support the notion that children of farmworkers are exposed through pesticides that are tracked into homes. Exposures

among adult workers and children living in

the same household varied with the agricultural crop in which the adult worked. This

finding would not be expected if dietary ingestion of pesticides or home use of pesticides

were primary sources of exposure because

these factors are unlikely to be related to work

in given agricultural crops. Our analyses, however, indicate that pesticide residues found in

house and vehicle dust were significantly

greater for workers who worked in pome fruit

than for those who did not, providing evidence

for increased exposure to pesticides that are

subsequently tracked from the fields to worker

vehicles and homes. Moreover, among the

pome-fruit workers, we observed higher concentrations of azinphos-methyl in vehicle dust

than in house dust, consistent with expectations that pesticides are carried from workplaces to vehicles and eventually homes on

workers’ clothing, hats, and boots.

Analyses of urine from children of farmworkers reported by other studies also provide

support for a take-home pathway. Lu et al.

(2000) showed that median DMTP concentrations in agricultural children were four

times higher than those of nonagricultural

children. A study by Koch et al. (2002) collected urine samples from 44 children on a

biweekly basis over a period of 21 months.

Data from the study show that children had

higher GM concentrations of combined

dialkylphosphates during the spray months

compared with the nonspray months. These

findings support the take-home pathway but

do not discount the contributions of other

pathways. Our data set was collected shortly

after the spray season. In a previous analysis

of this data, Curl et al. (2002) demonstrated a

strong correlation between urinary dimethyl

metabolite concentrations of adult farmworkers and children living in the same household,

a finding that would be expected with the

take-home pathway. Studies that examined

pesticide residues in dust samples also provide

support for the take-home pathway. Separate

studies conducted by Lu et al. (2000) and

Fenske et al. (2000) report higher median

house-dust concentrations of azinphos-methyl

in homes of agricultural families compared

with nonagricultural families, and McCauley

et al. (2003) showed that house-dust concentrations rose with increasing numbers of agricultural workers in a household. 

Limitations. This study has some limitations. It is possible that some urine and dust

samples were collected relatively late in the

spray season. Although we report higher urinary metabolite concentrations for those who

worked in crops where higher amounts of pesticides were used, the late-season collection of

urine samples may have increased the likelihood of detecting metabolites from pesticides

used on crops that are harvested late in the

season (e.g., apples and pears). Nevertheless,

we relied on local knowledge and previous

research conducted in the area to determine

when to begin collection (Simcox et al. 1999).

Apart from the possibility that the late-season collection may have attenuated our

reported exposure estimates, it is likely that

our urinary metabolite concentrations underestimate levels during peak exposures.

Information from the Washington State Tree

Fruit Research Commission shows that temperature readings for the spring of 1999 were

Coronado et al.

1004 VOLUME 114 | NUMBER 7 | July 2006 • Environmental Health Perspectives

Table 8. Frequency of detection and estimated GM concentrations of azinphos-methyl residues in vehicle

and house dust (n = 210).

Pesticide and crop Detectiona (%) Estimatedb GM (μg/g) p(thinGM ≤ non-thinGM)

Vehicle azinphos-methyl

Non-pome/non-thin 63.4 0.18 (0.11–0.29) 0.677

Non-pome/thin 63.6 0.14 (0.05–0.36)

Pome/non-thin 96.0 0.96 (0.53–1.75) 0.242

Pome/thin 95.2 1.22 (0.91–1.63)

House azinphos-methyl

Non-pome/non-thin 58.1 0.14 (0.09–0.22) 0.087

Non-pome/thin 77.8 0.27 (0.12–0.61)

Pome/non-thin 91.3 0.65 (0.39–1.09) 0.201

Pome/thin 93.0 0.83 (0.64–1.08)

Ranges are posterior predictive probability intervals. 

aBased on the number of samples analyzed: non-pome/non-thin, vehicle n = 41, house n = 31; non-pome/thin, vehicle n =

11, house n = 9; pome/non-thin, vehicle n = 25, house n = 23; pome/thin, vehicle n = 105, house n = 86. bBased on the total

number of samples: non-pome/non-thin, n = 47; non-pome/thin, n = 12; pome/non-thin, n = 29; pome/thin, n = 121; plus one

sample with missing fruit classification.
Take-home pathway for OP pesticides

Environmental Health Perspectives • VOLUME 114 | NUMBER 7 | July 2006 1005

much lower than normal and resulted in the

recommendation to growers to cancel or delay

the first several sprayings of the season (Koch

et al. 2002). The first application of OP pesticide for the 1999 season is reported to have

been in April, and the number of applications

in that season was reduced; thus, the levels of

OP pesticide urinary metabolites in our samples may have been lower than for other years.

Our urinary metabolite estimates may have

been further attenuated because peak excretion

of metabolites occurs relatively quickly (24–48

hr after exposure) (Feldmann and Maibach

1974) and we did not time our collections

according to spray events. Despite these

potential limitations, we were able to find significant differences (based on crop category)

both in urinary OP metabolite concentrations

in adults and children and in pesticide concentrations in home and vehicle dust.

Questions asked about work in the past 3

months resulted in a high percentage of workers reporting work in multiple crops; thus,

our ability to discern differences in exposure

levels associated with given crops was limited.

An additional limitation of our assessment of

occupational pesticide exposure is that we did

not include number of hours worked per

week. This factor could account for a substantial fraction of pesticide exposures and could

explain differences in urinary pesticide

metabolite concentrations. Although we do

not believe this limits the conclusions we can

draw from our results, further investigations

on this topic would benefit by collection of

this variable and its inclusion in analysis.

Farmworkers as a group are difficult populations to assess (Kamel et al. 2001; Zahm and

Blair 2001). The strength of this study is the

large sample size and the substantial variation

in types of agricultural crops reported in our

sample. Moreover, our data reflect the real-life

experience of farmworkers in that most work

in multiple crops. Two unique features of our

statistical analyses techniques were strengths.

First, we were able to concurrently examine

the relationships between adult and child urinary OP pesticide concentrations and pesticide residues in house and vehicle dust based

on crop and job task categories, whereas previous investigations have generally considered

only two-way comparisons. Second, we used a

modeling technique to estimate values that

were below the limit of detection (Griffith

et al. 2002); this offered greater predictive

power compared with some previous investigations that have assigned a single value to

data points that are below the limit of detection or have considered only data points above

the limit of detection. Previous investigations

have recruited farmworkers from a select number of farms or community organizations

(Fenske et al. 2000; Koch et al. 2002;

Loewenherz et al. 1997; Lu et al. 2000; Mills

and Zahm 2001; Simcox et al. 1999).

Growers who volunteer their farms for participation in studies may promote more protective work practices or apply fewer applications

of pesticides, thus potentially biasing collected

data. Our household recruitment process

attempted to minimize this bias.

Conclusions

Two general conclusions can be drawn from

our findings. First, our findings demonstrate

the potential for increased pesticide exposure

among workers performing tasks on multiple

crops. This is shown by the elevated urinary

OP metabolite concentrations (in adults and

children) and pesticide concentrations in dust

samples in those who worked in both applies

and pears, compared with those who worked

in a single pome fruit. Second, our findings

support the notion that pesticides are tracked

into homes of workers, where children are

exposed. This is demonstrated by the correlation in the quantity and type of pesticides

found in the home and accompanying vehicle, the finding that adults had higher urinary

OP pesticide metabolites than did children,

and the direct correlation between the concentration of pesticides in house dust and the

concentrations of urinary OP metabolites in

both adults and children.

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