1254 VOLUME 115 | NUMBER 7 | July 2007 • Environmental Health Perspectives

Research | Children’s Health

Farmworker children, like their parents, are

exposed to pesticides (Arcury et al. 2005,

2006; Coronado et al. 2004; Lambert et al.

2005; Quandt 2004; Strong et al. 2004;

Thompson et al. 2003). Pesticide exposure has

health consequences for all persons exposed

(Reigart and Roberts 1999). Immediate consequences of limited pesticide exposure

include rash, nausea, vomiting, and blurry

vision. Immediate effects of significant

exposure include disorientation, loss of continence, coma, and death. Delayed consequences of limited or significant exposure

may include sterility, birth defects, neurodegenerative disease, and cancer (Reigart and

Roberts 1999). 

The potential consequences of pesticide

exposure are greater for children than for

adults (Eskenazi et al. 1999; Faustman et al.

2000; Weiss et al. 2004). Because of their

short stature and characteristic behaviors,

children have greater exposure to pesticides in

the environment than do adults. Children

have a greater surface to volume ratio than do

adults; therefore, they receive a greater dose

from the pesticides to which they are exposed.

They metabolize toxicants slower than do

adults, so the pesticide dose they receive

remains with them longer.

The research reporting farmworker

children’s exposure to pesticides has considered only the concentrations of dialkylphosphate metabolites of organophosphorus (OP)

pesticides (Barr et al. 2004), with one exception (Fenske et al. 2002). Analyses from

Washington (Curl et al. 2002; Fenske et al.

2000a, 2000b; Koch et al. 2002; Loewenherz

et al. 1997; Thompson et al. 2003), Oregon

(Lambert et al. 2005), California (Bradman

et al. 2005; Mills and Zahm 2001), the Rio

Grande Valley of Texas (Shalat et al. 2003),

and North Carolina (Arcury et al. 2005,

2006) indicate that farmworker children are

exposed to OP pesticides, and that the concentrations of dialkylphosphate metabolites

in their urine are high. However, measurement of the dialkylphosphate metabolites

does not provide information on the specific

OP pesticides to which these children are

exposed (Needham et al. 2005). Further,

these analyses do not provide information on

the exposure of these children to the nondialkylphosphate OP pesticides (e.g.,

acephate) or to other non-OP pesticides.

Although knowledge of the general levels of

dialkylphosphate OP pesticides in farmworker children is valuable, knowing specific

pesticides to which farmworker children are

exposed is important because it will indicate

the sources of this exposure. For example,

knowing that the metabolite specific to

parathion is present in a large percentage of

children’s urine samples will direct efforts to

identify and then eliminate the sources of

parathion exposure.

Only Fenske and colleagues (2002) have

reported pesticide-specific metabolite levels in

urine samples collected from farmworker

children. Focusing on the major metabolites

of the OP pesticides chlorpyrifos (TCPy) and

parathion (PNP) among 75 children of farmworkers and pesticide applicators, they found

TCPy in urine samples from 18 (24%) of the

children, and PNP in urine samples from

5 (7%) of the children. PNP had no statistically significant predictors, whereas the only

statistically significant predictor of TCPy was

living in a household that used an OP pesticide in a garden.

Investigators have proposed a model of

farmworker child pesticide exposure that

includes paraoccupational, residential, and

environmental factors (Fenske et al. 2005;

Quandt et al. 2006). Paraoccupational exposure results from contact with persons (e.g.,

parents) doing farm work. Residential

exposure results from contact with agricultural

pesticides in the home as well as from the residential application of pesticides. Agricultural

Address correspondence to T.A. Arcury, Department

of Family and Community Medicine, Wake Forest

University School of Medicine, Medical Center

Boulevard, Winston-Salem, NC 27157-1084 USA.

Telephone: (336) 716-9438. Fax: (336) 716-3206.

E-mail: tarcury@wfubmc.edu

The research reported in this paper was supported

by grant R25 OH07611 from the National Institute

for Occupational Safety and Health. 

T.A.A. has received funding for the development

of pesticide safety educational materials from

Syngenta Crop Protection, Inc. The other authors

declare they have no competing financial interests.

Received 12 December 2006; accepted 28 March

2007.

Pesticide Urinary Metabolite Levels of Children in Eastern North Carolina

Farmworker Households

Thomas A. Arcury,1 Joseph G. Grzywacz,1 Dana B. Barr,2 Janeth Tapia,3 Haiying Chen,4 and Sara A. Quandt5

1Department of Family and Community Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA;

2National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA; 3North Carolina

Farmworkers Project, Benson, North Carolina, USA; 4Department of Biostatistical Sciences, Division of Public Health Sciences, and 5Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine,

Winston-Salem, North Carolina, USA

BACKGROUND: In this investigation we documented the pesticide urinary metabolite levels of

farmworker children in North Carolina, determined the number of different metabolites detected

for each child, and delineated risk factors associated with the number of metabolites.

METHODS: Urine samples were collected from 60 Latino farmworker children 1–6 years of age

(34 female, 26 male). Interviews were completed by their mothers in Spanish. We analyzed urine

samples for 14 pesticide metabolites, including the organophosphate pesticides chlorpyrifos,

coumaphos, diazinon, isazaphos, malathion, pirimiphos, and parathion and its methyl counterpart;

a common metabolite of at least 18 pyrethroid insecticides; the repellent DEET; and the herbicides

2,4,5-trichlorphenoxyacetic acid, 2,4-dichlorophenoxyacetic acid, acetochlor, atrazine, and metolachlor. Predictors included measures of paraoccupational, residential, and environmental exposure,

child characteristics, and mother characteristics.

RESULTS: Thirteen metabolites were present in the urine samples. Organophosphate pesticide

metabolites were detected in a substantial proportion of children, particularly metabolites of

parathion/methyl parathion (90.0%; geometric mean 1.00 μg/L), chlorpyrifos/chlorpyrifos methyl

(83.3%; geometric mean 1.92 μg/L), and diazinon (55.0%; geometric mean 10.56 μg/L). The

number of metabolites detected ranged from 0 to 7, with a mode of 4 detected (28.3%). Boys, children living in rented housing, and children with mothers working part-time had more metabolites

detected.

CONCLUSIONS: Children in farmworker homes experience multiple sources of pesticide exposure.

Pesticides may remain in their environments for long periods. Environmental and occupational

health changes are needed to address these exposures. Research is needed with more precise measures of exposure and on the health effects of concurrent exposure to multiple pesticides.

KEY WORDS: agriculture, biomonitoring, child health, farmworker, health disparities,

Latino/Hispanic, occupational health, pesticide exposure, pesticide metabolites. Environ Health

Perspect 115:1254–1260 (2007). doi:10.1289/ehp.9975 available via http://dx.doi.org/ [Online

28 March 2007]
pesticides may be brought into the dwelling by

workers on clothing, boots, or containers, or

directly applied to the dwelling. Characteristics

of the dwelling, such as amount of carpeting

and general repair, can affect the amount of

pesticides that enter and accumulate there.

Control of the dwelling and cleaning the

dwelling will reduce the amount of agricultural

pesticides that accumulate. Environmental

exposure results from pesticides that are

applied in the larger environment in which the

child lives, such as drift during application.

Each form of exposure is moderated by safety

behaviors exercised by household residents and

by characteristics of the child and parents. The

implementation of safety behaviors is more

likely if parents have received safety training.

Safety behaviors include showering immediately after work as well as storage and laundering of soiled work clothes separate from the

child’s clothing. Child characteristics, such as

sex and age, will modify exposure, because children of different ages and sex have different

exposure behaviors. Children of different ages

metabolize pesticides differently. Parental characteristics that could modify exposure behaviors include educational attainment and level

of employment.

This analysis has two objectives. The first

objective is to describe specific urinary pesticide metabolite concentrations for young children living in farmworker households located

in eastern North Carolina and the number of

metabolites detected for each child. The second objective is to delineate the paraoccupational, residential, and environment risk

factors associated with the number of pesticide metabolites present. We used data collected from 60 Latino children, 1–6 years of

age, living in eastern North Carolina farmworker households during the 2004 agricultural production season.

Materials and Methods

Data for this analysis were collected as part of

Casa y Campo, a 4-year community-based participatory environmental justice project in

which environmental health scientists, health

care providers, and farmworker advocates collaborated to reduce pesticide exposure among

farmworkers and their families. Casa y Campo

was implemented in a six-county area of eastern North Carolina, including Duplin,

Harnett, Johnston, Sampson, Wake, and

Wayne counties. For 2004, the North

Carolina Employment Security Commission

(Raleigh, NC) estimated that 21,614 migrant

and seasonal farmworkers (not counting

dependents) worked in these counties during

peak harvest, accounting for one-quarter of the

86,040 migrant and seasonal farmworkers in

the state. Agriculture in these six counties is

diverse (Table 1). Most of the farms in these

counties producing tobacco (1,329 farms),

sweet potatoes (188 farms), and vegetables

(435 farms) would employ migrant and seasonal farmworkers. All of these farms, as well as

the large number of farms producing grains,

soybeans, and cotton, would use pesticides to

which farmworkers and their families could be

exposed. Results describing the dialkylphosphate urinary metabolites of OP pesticides for

participants in this analysis have been published previously (Arcury et al. 2006).

Sample and data collection. Sampling,

recruitment, and data collection have been

described in detail (Arcury et al. 2006).

Briefly, from July through August 2004, as

part of a larger survey, we recruited 60 households with an adult resident who was

employed in farm work and at least one resident child 1–6 years of age. We used a sitebased sampling approach to locate and recruit

eligible participants (Arcury and Quandt

1999). All 60 eligible households agreed to

participate. The mother of each child completed an interview questionnaire, and she

was asked to collect a first morning urine void

from the resident child between 1 and 6 years

of age who was closest to age 5. Respondents

were told they would receive a small gift, a

bag of food, at the end of the study for completing the interview, and $10 for collecting

the urine sample. All participants gave written

informed consent. Study procedures were

approved by the Wake Forest University

School of Medicine and the Centers for

Disease Control and Prevention (CDC)

Institutional Review Boards.

The interviewer-administered questionnaires were completed in the respondents’

homes in their preferred language, which was

Spanish in all cases. Data were collected on

respondent and child characteristics, household characteristics, and dwelling quality.

Interviews took approximately 25 minutes to

complete. At the end of the interview, participants were asked to collect a first morning

void from the selected child the next morning. Urine collection materials were left with

participants. Each child’s urine sample was

picked up from the home by a project staff

member the morning the sample was collected, and transported in a cooler with blue

ice to our field laboratory, where it was frozen

to –20°C. In seven instances, first morning

voids were not collected, and the child participant provided a spot void.

Laboratory analysis. The frozen urine samples were shipped overnight on dry ice to the

CDC in Atlanta, Georgia, for analysis. Samples

were analyzed using a modification of the

method of Olsson et al. (2004). Briefly, 2-mL

urine samples were hydrolyzed by enzymes to

liberate the glucuronide- or sulfate-bound conjugated metabolites. Hydrolysates were

extracted using a mixed-mode solid-phase

extraction cartridge. Concentrated extracts

were analyzed using high-performance liquid

chromatography–tandem mass spectrometry.

Two precursor/product ion pairs were analyzed

per analyte, one for quantification and one for

confirmation. Analyte concentrations were

quantified using isotope dilution calibration.

Approximately 10% of the samples tested were

positive and negative quality control samples. 

Measures. The outcome variables were

based on pesticide metabolite concentrations

obtained from each child’s urine sample.

Metabolites included the OP insecticide

metabolites TCPy, 3-chloro-4-methyl-7-

hydroxycoumarin (CMHC), 2-isopropyl-4-

methyl-6-hydroxypyrimidinol (IMPY),

5-chloro-1,2-dihydro-1-isopropyl-[3H]-1,2,4-

triazol-3-one (CIT), malathion dicarboxylic

acid (MDA), PNP, and 2-diethylamino-6-

methyl pyrimidin-4-ol (DEAMPY), the

pyrethroid insecticide metabolite 3-phenoxybenzoic acid (3PBA), diethyl-m-toluamide

(DEET) repellent, and the herbicide metabolites 2,4,5-trichlorophenoxyacetic acid (2,4,5-

T), 2,4-dichlorophenoxyacetic acid (2,4-D),

acetochlor mercapturate (ACE), atrazine mercapturate (ATZ), and metolachlor mercapturate (MET). Measures included the

concentrations of each metabolite and the

total number of different pesticide metabolites

detected in each child’s urine sample.

We constructed measures for each domain

in the model of pesticide exposure. Measures

of paraoccupational exposure included the following variables: whether mother was currently employed doing farm work, whether

father was currently employed doing farm

work, and number of farmworkers in the

household (1, 2, ≥ 3). Residential exposure

Farmworker child pesticide metabolite levels

Environmental Health Perspectives • VOLUME 115 | NUMBER 8 | August 2007 1255

Table 1. Selected agricultural characteristics of counties from which participants were recruited, 2002

Census of Agriculture.a

No. of farms producing 

No. Acres in Sweet Corn for Wheat 

County of farms farmland Tobacco potatoes Vegetables grain for grain Soybeans Cotton

Duplin 1,190 234,658 213 9 74 341 137 401 58

Harnett 740 114,361 144 19 55 157 44 173 38

Johnston 1,144 194,211 360 91 76 152 108 431 56

Sampson 1,178 298,483 243 44 135 279 162 431 126

Wake 846 92,803 161 18 51 22 54 102 1

Wayne 722 171,449 208 7 44 187 134 296 80

aData from U.S. Department of Agriculture (2007).
measures included home ownership (rent,

own, other), ease of cleaning (easy, difficult),

number of bathrooms (1, 2, 3), and number

of rooms with carpet (0, 1–2, ≥ 3). Proximity

to nearest agricultural field was the environmental exposure variable; it had the values of

adjacent when agricultural fields directly

abutted or were across the road from the property on which the respondent’s dwelling was

located, and nonadjacent when agricultural

fields did not abut the property on which the

respondent’s dwelling was located.

Safety behavior measures included the following: parental pesticide safety training

(mother and father each coded no farm work,

farm work and training, farm work and no

training), whether any farmworker in the

household regularly delayed showering after

work for > 15 min, whether any farmworker in

the household regularly changed work clothes

inside the dwelling, storage of soiled farm work

clothes (everyone stores clothes outside, anyone

stores clothes inside, anyone stores work

clothes with other clothes), and whether everyone launders soiled farm clothes separately.

Child and parental characteristics included

child sex and age (1 or 2 years, 3 or 4 years,

5 or 6 years), mother’s education (≤ 6 years, 7

to 9 years, ≥ 10 years), and mother’s current

employment (none, part time, full time).

Data analysis. Medians and geometric

means (when the number of detects was

> 50%), unadjusted and adjusted for creatinine, were calculated for the individual 14 urinary pesticide metabolites. Concentrations

below the analytic limits of detection (LOD)

were substituted by the LOD divided by the

square root of 2 (Homung and Reed 1990).

To identify potential predictors for the number of detects, we examined bivariate associations between the outcome and the predictor

variables through analysis of variance

(ANOVA). Only the variables whose p-values

were < 0.20 were considered in further model

selection process. Finally, a model with three

variables (mother’s employment, child’s sex,

and ownership) was prescribed by a forward

selection procedure. Analyses were repeated

with creatinine-adjusted measures to control

for the seven children who gave spot voids.

The results did not differ, and the results for

non-creatinine–adjusted measures are presented. All analyses were performed using SAS

version 9.0 (SAS Institute Inc., Cary, NC).

Results

Exposure, safety behavior, and personal characteristics. The 60 children considered in this

analysis varied in paraoccupational, residential, and environmental sources of pesticide

exposure, as well as safety behaviors and personal characteristics (Table 2). Most of their

mothers (68.3%) and fathers (56.7%) were

employed as farmworkers at the time of data

collection. More than two-thirds (36.7%)

lived in households with two farmworkers,

and 25.0% lived in households with three or

more farmworkers. Most (53.3%) of the children lived in rented homes, whereas 33.4%

lived in farmworker-owned homes and 13.3%

lived in homes of other tenure. More than

half (58.3%) lived in homes that their mothers described as easy to clean, and more than

half (53.3%) of these children lived in

dwellings with one bathroom. Most (91.7%)

lived in dwellings with at least one carpeted

room, and almost 70% lived in dwellings

with three or more carpeted rooms. The

dwellings in which over half (56.7%) of the

children lived were not adjacent to agricultural fields.

Twenty-three of 41 mothers (38.3% of

total sample) and 17 of the 34 fathers (28.3%

of total sample) employed in farm work had

not received pesticide safety training. Threefifths of the children lived in households in

which individuals employed in farm work did

not shower immediately after work, and

83.3% lived in households in which individuals changed out of their soiled farm work

clothes inside the dwelling. However, 15%

lived in households in which farm work

clothes were stored with other laundry, and

20% lived in households in which farm work

clothes were laundered with other work

clothes.

More of these children were girls (56.7%)

than boys (43.3%). Most were 3 or 4 years of

age (60.0%), with one-third 5 or 6 years of

age, and 8.3% 1 or 2 years of age. More than

half of their mothers had less than a secondary education. About one-quarter of their

mothers did not work outside the home, with

one-quarter working part-time and one-half

working full-time.

Pesticide metabolite levels. The metabolites

for 13 of the 14 pesticides were present in the

urine samples of the 60 children (Table 3).

Among these metabolites were those of seven

OP insecticides. PNP was the most common

OP insecticide present; PNP was present in

90% of the samples, and had a geometric mean

(GM) of 1.0 μg/L unadjusted for creatinine,

and 1.25 μg/g adjusted for creatinine. TCPy

was present in 83.3% of the samples, IMPY

was present in 55.0% of the samples, and

MDA was present in 28.3% of the samples.

The GM for TCPy was 1.92 μg/L unadjusted

for creatinine, and 2.38 μg/g adjusted for creatinine; and the GM for IMPY was 0.56 μg/L

unadjusted for creatinine, and 0.70 μg/g

adjusted for creatinine. The median for MDA

was 0.21 μg/L unadjusted for creatinine, and

0.33 μg/g adjusted for creatinine. CMHC was

detected in seven (11.7%) of the samples,

DEAMPY in three (5.0%) of the samples, and

CIT in one (1.7%) of the samples.

The pyrethroid insecticide metabolite

3PBA was present in 40.0% of the farmworker child urine samples. The metabolite

for DEET repellent was present in 10.0% of

the farmworker child urine samples. The herbicide 2,4,5-T was present in one (1.7%) of

the samples. The herbicide 2,4-D was present

in 41.7% of the samples, ACE was present in

Arcury et al.

1256 VOLUME 115 | NUMBER 8 | August 2007 • Environmental Health Perspectives

Table 2. Exposure characteristics of children 1 to

6 years of age living in farmworker families, eastern North Carolina, 2004.

Exposure characteristics No. (%)

Paraoccupational exposure

Mother does farm work 41 (68.3)

Father does farm work 34 (56.7)

No. of farmworkers in house

1 23 (38.3)

2 22 (36.7)

3 or more 15 (25.0)

Residential exposure

Home ownership

Own 20 (33.3)

Rent 32 (53.3)

Other 8 (13.3)

Ease of cleaning

Easy 35 (58.3)

Difficult 25 (41.7)

No. of bathrooms

1 32 (53.3)

2 or 3 28 (46.7)

No. of rooms with carpet

0 5 (8.3)

1 or 2 14 (23.4)

3 or more 41 (68.3)

Environmental exposure: proximity to 

nearest agricultural field

Adjacent 26 (43.3)

Nonadjacent 34 (56.7)

Safety behaviors

Mother’s pesticide safety training

No farm work 19 (31.7)

Farm work and training 18 (30.0)

Farm work and no training 23 (38.3)

Father’s pesticide safety training

No farm work 26 (43.3)

Farm work and training 11 (18.3)

Farm work and no training 17 (28.3)

Anyone in house delays shower after 36 (60.0)

farm work

Anyone in house changes farm clothes 50 (83.3)

inside

Farm work clothes storage 

Everyone stores clothes outside 11 (18.3)

Anyone stores clothes inside 40 (66.7)

Anyone stores clothes with other clothes 9 (15.0)

Everyone launders farm clothes separately 48 (80.0)

Child and mother characteristics

Child sex

Female 34 (56.7)

Male 26 (43.3)

Child age (years)

1 or 2 5 (8.3)

3 or 4 36 (60.0)

5 or 6 19 (31.7)

Mother’s education (years)

1 to 6 34 (56.7)

7 to 9 15 (25.0)

≥ 10 11 (18.3)

Mother’s employment

None 14 (23.3)

Part-time 16 (26.7)

Full-time 30 (50.0)
21.7% of the samples, and ATZ was present

in 6.7% of the samples.

Pesticides detected per child: number and

predictors. The number of metabolites

detected in the children’s urine samples varied

from zero to seven. One child (1.7%) had no

detects, five children (8.3%) had one detect,

one child (1.7%) had two detects, 16 children

(26.7%) had three detects, 17 children

(28.3%) had four detects, eight children

(13.3%) had five detects, nine children

(15.0%) had six detects, and three children

(5.0%) had seven detects. 

Three of the predictors had statistically

significant associations with the mean number

of pesticides detected in the bivariate analysis

(Table 4). Children residing in rented homes

had 4.41 pesticides detected in their urine

samples, compared with 3.65 detects among

those living in an owned home and 3.00

detects among those living in other homes.

Boys had 4.46 pesticides detects, compared

with 3.59 for girls. Finally, children of mothers working part-time had 5.06 detects, compared with 3.63 detects among children whose

mothers worked full-time and 3.43 detects

among children whose mothers did not work.

In the multivariate analysis, mother’s employment remained significantly associated with

number of detects, whereas the association of

child sex trended toward significance and

home ownership was not significant. 

Discussion

Urine samples from most study children living in farmworker homes have a variety of

pesticide metabolites. At least one specific

pesticide metabolite was found in the urine

samples for 59 of these 60 children; the urine

samples for 88.3% of these children had three

or more specific pesticide metabolites present.

Analysis of the general dialkylphosphate OP

pesticide metabolites for these same children

found at least one of these six metabolites in

every sample (Arcury et al. 2006).

The specific pesticide metabolites present

indicate multiple sources and pathways of

potential exposure among children in farmworker homes, and illustrate the length of

time these pesticides remain in the environments of farmworker children. The pesticide

metabolites present in these children’s urine

indicate at least four pathways of exposure:

a) a paraoccupational take-home pathway in

which workers bring pesticides into their

homes on their person or on their clothing;

b) an environmental pathway in which pesticides applied to nearby fields drift into the

residential environment; c) a residential pathway of pesticides applied in the home; and

d) a residual pathway in which pesticides

deposited inside the home from any of the

pathways at an earlier time remain active.

Earlier research in North Carolina showed

that farmworkers had little knowledge of the

pesticides used where they work, and that

they did not have information about pesticides applied to dwellings that were rented or

grower-provided (Arcury et al. 2001; Early

et al. 2006). Drift has been shown to result in

the dispersal of agricultural pesticides to

dwellings on surrounding land (Fenske et al.

2000b; Ward et al. 2006; Weppner et al.

2006). Finally, agricultural communities have

generally higher levels of environmental pesticides compared with nonagricultural communities, with residues from discontinued

pesticides found in contemporary rural environments and homes (Lu et al. 2004; Quandt

et al. 2004; Wolz et al. 2003). However, the

possible sources and pathways of exposure for

several pesticide metabolites found in the

samples from these children remain unclear.

In discussing the sources and pathways of

potential exposure, it is important to remember

that the urine samples used in this analysis

were collected in June and July 2004.

Exposure of these children to pesticides in

Mexico should be discounted as the source of

the metabolites; these are the children of seasonal rather than migrant farmworkers, who

have been settled in North Carolina for several years. Many of these children were born

in the United States, and have limited direct

or indirect contact with sources of exposure

from Mexico.

Chlorpyrifos and diazinon have been used

for residential as well as agricultural applications. However, chlorpyrifos was banned for

residential use by the end of 2001, and diazinon was banned for residential use at the end

of 2004. Fifty of the 60 children had TCPy,

the chlorpyrifos metabolite, in their urine.

This would indicate that chlorpyrifos exposure for these children did not result from

recent residential application, but could result

Farmworker child pesticide metabolite levels

Environmental Health Perspectives • VOLUME 115 | NUMBER 8 | August 2007 1257

Table 3. Pesticide urinary metabolites for children 1–6 years of age (n = 60) in eastern North Carolina farmworker households, 2004. 

Creatine unadjusted Creatinine adjusted

LOD Detects (μg/L) (μg/g creatinine) 

Pesticide urinary metabolites (ng/mL) no. (%) Median GM Median GM

Organophosphorus insecticides

TCPy (chlorpyrifos) 0.2 50 (83.3) 2.47 1.92 3.38 2.38

CMHC (coumaphos) 0.2 7 (11.7) 0.14 — 0.17 —

IMPY (diazinon ) 0.7 33 (55.0) 0.49 0.56 0.60 0.70

CIT (isazophos) 1.5 1 (1.7) 1.06 — 1.19 —

MDA (Malathion) 0.3 17 (28.3) 0.21 — 0.33 —

PNP (parathion) 0.1 54 (90.0) 1.55 1.00 1.53 1.25

DEAMPY (pirimiphos methyl) 0.2 3 (5.0) 0.14 — 0.17 —

Pyrethroid insecticides

3PBA 0.1 24 (40.0) 0.07 — 0.15 —

DEET repellent 0.1 6 (10.0) 0.07 — 0.08 —

Herbicides

2,4,5-T 0.1 1 (1.7) 0.07 — 0.08 —

2,4-D 0.2 25 (41.7) 0.14 — 0.23 —

ACE (acetochlor) 0.1 13 (21.7) 0.07 — 0.09 —

ATZ (atrazine) 0.3 4 (6.7) 0.09 — 0.10 —

MET (metolachlor) 0.2 0 (0.0) ND ND ND ND

Abbreviations: —, the small number of detects (< 50%) make the GM an unreliable measure for this metabolite; ND, no

detects. 

Table 4. Predictors with a significant relationship to number of pesticide urinary metabolite detects, children 1 to 6 years of age (n = 60) in eastern North Carolina farmworker households, 2004.

No. of detects Significance

Characteristic (mean ± SE) Bivariate analysisa Multivariate analysisa

Home ownership 0.045 —

Own 3.65 ± 0.35

Rent 4.41 ± 0.27

Other 3.00 ± 0.54

Child sex 0.035 0.078

Male 4.46 ± 0.31

Female 3.59 ± 0.27

Mother’s employment 0.004 0.008

None 3.43 ± 0.39

Part-time 5.06 ± 0.37

Full-time 3.63 ± 0.27

—, not significant.

aOther variables considered in the bivariate analysis and included in the multivariate analysis were mother does farm

work, father does farm work, number of farmworkers in house, ease of cleaning house, number of bathrooms, number of

rooms with carpet, proximity to nearest agricultural field, mother’s pesticide safety training, father’s pesticide safety

training, anyone in house delays shower after farm work, anyone in house changes farm clothes inside, farm work

clothes storage, everyone launders farm clothes separately, child age, and mother’s education.
from the take-home and drift pathways as

well as from residual deposition. The diazinon metabolite IMPY was present in 33 of

the 60 samples. Because it was still available

for residential use at the time these data were

collected, it is possible that diazinon exposure

could result from any of the pathways.

Malathion has outdoor residential use,

whereas parathion has no residential use. Yet

54 of the 60 children had the parathion

metabolite PNP and 17 had the malathion

metabolite MDA. Farmworkers may be

exposed to malathion at work (e.g., picking

peppers), but it is unlikely that they would

work in fields to which parathion has been

applied (e.g., cotton). Therefore, exposure

resulting in the presence of PNP is most likely

the result of the drift and residual deposition

pathways, whereas MDA could result from

the take-home, drift, or residual pathways.

Pyrethroid insecticides are widely used

for residential and agricultural applications.

The 24 children with 3PBA, the pyrethorid

metabolite, in their urine sample could have

been exposed through any of the pathways.

The herbicides 2,4-D and acetochor are

widely used for residential and agricultural

applications, and their metabolites could

result from exposure through any of the

pathways.

Atrazine is used on corn (85% of all use),

sorghum (10%), and sugar cane (3%). Threequarters of all corn has atrazine applied. Corn

and sorghum are grown in North Carolina.

However, field corn and sorghum are

machine cultivated and harvested. Sweet corn

is hand picked, but few farmworkers are

employed for this task. The most plausible

explanation for four farmworker children having the metabolite for atrazine in their urine

can be limited to drift or contaminated water

supplies.

The sources and pathways of exposure

among farmworker children to the OP pesticides coumaphas, pirimiphos methyl, and

isazophos, and to the herbicide 2,4,5-T are

not apparent. Coumaphas is used to control

insects on livestock, and farmworkers in

North Carolina seldom work with livestock.

However, CMHC, the metabolite of coumaphas, was detected in samples from seven

of the children. Pirimiphos methyl is used

postharvest to treat stored corn and sorghum

grain, activities with limited farmworker participation. Yet DEAMPY, the metabolite of

pirimiphos methyl, was detected in samples

from three children. Isazophos was used to

treat lawns and turf; use of isazophos was cancelled in North Carolina at the end of 1998,

and cancelled by the U.S. Environmental

Protection Agency (EPA) in mid-1999. Yet

CIT, the metabolite of isazophos, was found

in one child. Finally, the herbicide 2,4,5-T

has been banned for use in the United States

since 1986, years before most Latino farmworkers had immigrated to North Carolina.

The metabolite for 2,4,5-T was detected in

the sample of one child. The metabolites for

isazophos and 2,4,5-T were found in samples

from different children.

Few data exist with which our results can

be compared. The 1999–2000 National

Health and Nutrition Examination Survey

(NHANES) data provide results for the proportion of detects and levels detected for the

OP pesticide metabolites TCPy, IMPY,

MDA, and PNP (Barr et al. 2005). Caution

must be taken in the comparison of our results

with those reported for the NHANES sample.

Development is a major factor affecting pesticide metabolism; and our participants were

1–6 years of age, whereas the youngest age

group included in the NHANES sample was

6–11 years of age (Table 5). For TCPy, the

percent of children with pesticide metabolite

detected in the entire 1999–2000 NHANES

sample (91%), among the Mexican-American

sample (87%), and among children 6 to 11

years of age (97%) was somewhat greater than

for the children in our sample (83%), with the

GMs, 50th percentile, and 95th percentile

(creatinine unadjusted and adjusted) being

lower for the entire and Mexican-American

samples, but higher among children 6 to 11

years of age. The percent detects for IMPY

and PNP was higher for our sample (55% and

90%, respectively) compared with the

NHANES samples (total sample, 29%, 22%;

Mexican-American sample, 24%, 34%; children 6 to 11 years of age sample, 26%, 26%).

The 95th percentile of IMPY for our sample

(3.94 μg/L unadjusted for creatinine, 3.01

μg/g adjusted for creatinine) approximates

those for the NHANES samples. The 95th

percentile for PNP is greater among the children in our sample (6.32 μg/L unadjusted for

creatinine, 7.99 μg/g adjusted for creatinine)

than for the entire NHANES sample (5.0,

4.2) and among children 6 to 11 years of age

(4.2, 4.2). However, the 95th percentile is

much higher in the NHANES MexicanAmerican sample (21, 17). The percent

detects for MDA for the children in our sample (28%) is lower than the percent detects in

the entire 1999–2000 NHANES sample

(52%), among the Mexican-American sample

(61%), and among children 6 to 11 years of

age (56%). However, the 95th percentile

(6.87 μg/L unadjusted for creatinine, 8.64

μg/g adjusted for creatinine) for the children

in our sample is greater than that for the entire

NHANES sample (1.6, 1.8), the NHANES

Mexican-American sample (1.6, 1.7), and

among children 6 to 11 years of age (2.8, 3.7).

No comparison data are available for the

pyrethroid insecticide metabolite 3PBA,

DEET repellent, or any of the herbicide

metabolites.

Fenske and colleagues (2002) reported on

the presence and levels of two OP pesticide

metabolites for children residing in Washington

State agricultural households. Not all of the

children lived in farmworker households. They

found TCPy in urine samples from 24% (18 of

Arcury et al.

1258 VOLUME 115 | NUMBER 8 | August 2007 • Environmental Health Perspectives

Table 5. Comparison of percent detects, creatinine unadjusted and adjusted geometric mean, 50th percentile, and 95th percentile, for children 1–6 years of age (n = 60) in eastern North Carolina farmworker

households, 2004, and 1999–2000 NHANESa data for total, Mexican-American, and children 6–11 years

of age.

Creatinine unadjusted Creatinine adjusted

Metabolites and samples Percent detects GM 50th 95th GM 50th 95th

TCPy

Eastern North Carolina farmworker children 83.3 1.92 2.47 16.91 2.38 3.4 17.46

1999–2000 NHANESa

Total 91 1.77 1.7 9.9 1.58 1.47 8.4

Mexican Americans 87 1.61 1.67 7.4 1.46 1.44 5.8

Children 6–11 97 2.88 2.7 16 3.11 3.2 14

IMPY

Eastern North Carolina farmworker children 55.0 0.56 0.49 3.94 0.70 0.60 3.01

1999–2000 NHANESa

Total 29 — — 3.7 — — 3.4

Mexican Americans 24 — — 4.2 — — 3.9

Children 6–11 26 — — 3.6 — — 5.1

MDA

Eastern North Carolina farmworker children 28.3 — 0.21 6.87 0.57 0.34 8.64

1999–2000 NHANESa

Total 52 — < LOD 1.6 — < LOD 1.8

Mexican Americans 61 — < LOD 1.6 — < LOD 1.7

Children 6–11 56 — 0.49 2.8 — 0.44 3.7

PNP

Eastern North Carolina farmworker children 90.0 1.00 1.55 6.32 1.25 1.52 7.99

1999–2000 NHANESa

Total 22 — — 5 — — 4.2

Mexican Americans 34 — — 21 — — 17

Children 6–11 26 — — 4.2 — — 4.2

aData from Barr et al. 2005. 
75) children, with level of 6.0 μg/L (SD = 17)

among children residing within 200 feet of an

agricultural field and 1.3 μg/L (SD = 4.9) for

those living more than 200 feet from a field.

This metabolite was present in 83.3% of our

samples, and had a GM of 1.92 μg/L. They

reported PNP in urine samples for 7% (5 of

75) of the children. The comparable mean

PNP levels were 33 μg/L (SD = 210) and 0.

This metabolite was present in 90.0% of our

samples, and had a GM of 1.00 μg/L. These

differences reflect regional differences in

agricultural uses.

Earlier studies have not considered the

number of different pesticide metabolites present in individual children. Combining doses

from several pesticides in a single child may

result in interactions beyond the effects of a

single toxicant (Eskenazi et al. 1999). Among

these 60 children, 54 had two or more pesticide metabolites, with nine children having

six pesticide metabolites and three children

having seven pesticide metabolites. There was

no discernable pattern in the combinations of

pesticide metabolites present in the samples.

Boys versus girls, and those living in rented

housing versus owned or other housing had a

greater number of pesticide metabolites in

bivariate analysis. This may reflect sex differences in behavior. More important, it reflects

how control of a dwelling may decrease exposure. Children whose mothers worked parttime versus full-time or who did not work

had a greater number of pesticide metabolites

in the bivariate and multivariate analysis. The

interpretation of this association is not clear.

We conducted further analysis to examine

whether mothers working part-time were

more likely to be employed doing farm work;

this was not the case. It is possible that mothers working part-time are less able then

unemployed women to attend to household

hygiene, whereas mothers working full-time

could count on more help in home care.

Research is needed that has greater precision

in measuring of pesticide exposure risk factors

(Arcury et al. 2006; Quandt et al. 2006).

The results of this study should be considered in light of its limitations. The cross-sectional design limits analysis to association

rather than causation. The lack of environmental pesticide samples does not allow for

consideration of the location of exposure.

Data on residential pesticide application were

not collected. The metabolites present in the

urine may also include exposure to the less

toxic environmental degradates of the target

pesticides. The determination of whether a

metabolite was detected in a child’s urine

sample, and therefore the total number of

metabolites detected in a child, may be limited by the LOD of current analytic procedures; therefore, the number of metabolites

detected is a conservative estimate. Finally,

the measurement of risk factors may lack

precision. This study also has several

strengths. It is based on a relatively large sample of children. It is one of the few studies of

farmworker child exposure on the East Coast.

Finally, it is one of only a few studies to

examine a large number of specific pesticide

metabolites for farmworker children.

Several implications for environmental

and occupational health practice and research

can be drawn from our results. Pesticide

exposure pathways for farmworkers and their

children are multiple and complex. Pathways

include take-home, drift, residential application, and residues. The sources of exposure to

several of the pesticide metabolites found in

the urine samples are not clear. Farmworkers

must be educated about the take-home pathway. Current U.S. EPA Worker Protection

Standard training (U.S. EPA 1992), when

workers receive it (Arcury et al. 2001; U.S.

Government Accountability Office 2000),

provides little information about pesticides

that can be taken home on the clothes, boots,

and skin of workers, and about how to reduce

or eliminate the amount taken home.

Regulations on pesticide application to reduce

drift must be reviewed. Farmworker and all

rural families must be educated about drift

and how to reduce exposure. Farmworker

housing regulations must be improved to

reduce the need for residential pesticide application (Early et al. 2006; Housing Assistance

Council 2001). Farmworkers need to be educated about alternatives to using pesticides,

such as residential integrated pest management. Farmworker and other rural homes

need to be deep cleaned to remove pesticides

from indoor environments. Procedures for

this deep cleaning need to be developed and

tested (McCauley et al. 2006a).

Research is needed that includes more

precise measures of exposure pathways.

Laboratory techniques measuring pesticide

metabolites in urine and other matrices as well

as environmental monitoring have improved

substantially (Barr et al. 2006; Hoppin et al.

2006). However, measurement of exposure

pathways in epidemiologic research has not

improved greatly. For example, questions

about recent employment as a farmworker

must be changed to questions about the

amount of farm work conducted in the 3 days

before samples for biomonitoring are collected.

Research on the health effects of concurrent exposure to multiple individual pesticides

and classes of pesticides is also needed.

Current knowledge of health effects of pesticides in general is limited, but documentation

is beginning (McCauley et al. 2006b).

Examining health effects in light of the interactions of several different pesticides will provide greater insight into the actual risks to

those exposed.

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