### 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 significantexposure include disorientation, loss of continence, coma, and death. Delayed consequences of limited or significant exposuremay 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 farmworkerchildren’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 areexposed to OP pesticides, and that the concentrations of dialkylphosphate metabolitesin 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 onthe exposure of these children to the nondialkylphosphate OP pesticides (e.g., acephate) or to other non-OP pesticides. Although knowledge of the general levels ofdialkylphosphate 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) andparathion (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 from5 (7%) of the children. PNP had no statistically significant predictors, whereas the only statistically significant predictor of TCPy wasliving 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 agriculturalpesticides 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. Quandt 5

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

**2 National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA;**  **3 North Carolina**  **Farmworkers Project, Benson, North Carolina, USA; 4** **Department of Biostatistical Sciences, Division of Public Health Sciences, and**

**5 Department 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 herbicides2,4,5-trichlorphenoxyacetic acid, 2,4-dichlorophenoxyacetic acid, acetochlor, atrazine, and metolachlor. Predictors included measures of paraoccupational, residential, and environmental exposure, child characteristics, and mother characteristics.

R

ESULTS: 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). Thenumber 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 occupationalhealth 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 assex and age, will modify exposure, because children of different ages and sex have different exposure behaviors. Children of different agesmetabolize pesticides differently. Parental characteristics that could modify exposure behaviors include educational attainment and level of employment. This analysis has two objectives. The firstobjective 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 riskfactors associated with the number of pesticide metabolites present. We used data collected from 60 Latino children, 1–6 years ofage, 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, healthcare 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 beexposed. Results describing the dialkylphosphate urinary metabolites of OP pesticides forparticipants 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, aspart of a larger survey, we recruited 60 households with an adult resident who wasemployed 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 toparticipate. 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, abag 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 onrespondent and child characteristics, household characteristics, and dwelling quality. Interviews took approximately 25 minutes tocomplete. 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 staffmember 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 morningvoids 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 toliberate 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), thepyrethroid 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. Measuresof 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). Proximityto 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 workclothes 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 fornon-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 thisanalysis varied in paraoccupational, residential, and environmental sources of pesticideexposure, 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 ormore 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 thechildren 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 hadnot received pesticide safety training. Threefifths of the children lived in households in which individuals employed in farm work did not shower immediately after work, and83.3% 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 unadjustedfor 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 metabolite3PBA was present in 40.0% of the farmworker child urine samples. The metabolite for DEET repellent was present in 10.0% ofthe 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 to6 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, comparedwith 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 ofpotential exposure among children in farmworker homes, and illustrate the length oftime 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 thatthey 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 havegenerally higher levels of environmental pesticides compared with nonagricultural communities, with residues from discontinuedpesticides 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 ofthe 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 usedfor residential as well as agricultural applications. However, chlorpyrifos was banned forresidential 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 aswell 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 plausibleexplanation 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 exposureamong 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 sorghumgrain, activities with limited farmworker participation. Yet DEAMPY, the metabolite of pirimiphos methyl, was detected in samples from three children. Isazophos was used totreat 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 Statessince 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 95thpercentile 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 ismuch 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 NHANES a 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 NHANES a
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 NHANES a
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 NHANES a 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 NHANES a
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 thenumber 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). Amongthese 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 inbivariate analysis. This may reflect sex differences in behavior. More important, it reflectshow 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 ofmetabolites 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 severalstrengths. 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. Pathwaysinclude 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. Farmworkersmust 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 toreduce the need for residential pesticide application (Early et al. 2006; Housing AssistanceCouncil 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. REFERENCES

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