382 VOLUME 112 | NUMBER 3 | March 2004 • Environmental Health Perspectives

Children’s Health Article

The potential health effects of pesticide exposure for children are highlighted in several

recent publications (Eskenazi et al. 1999;

National Research Council 1993; Zahm and

Ward 1998). Pesticides pose a greater health

risk for children than for adults because of their

small body size and rapid development. Recent

research has demonstrated that multiple types

of pesticides are present in many dwellings in

the United States, particularly in low-income,

urban neighborhoods (Berkowitz et al. 2003;

Pang et al. 2002; Whyatt et al. 2002).

Children in agricultural communities

may be exposed to pesticides in the home

because of pathways common to all children

(diet, drinking water, residential pest control), as well as parental take-home pesticides

and factors related to farm proximity (drift,

playing in pesticide-treated fields; Camann

et al. 1995; Fenske 1997; Fenske et al. 1999).

Pesticides can remain stable indoors for

extended periods of time, creating a significant exposure for children, especially those

young enough to play on floors or to place

toys and household objects in the mouth

(Lewis et al. 2001). Simcox et al. (1995) have

shown that pesticide concentrations of

organophosphate (OP) insecticides in house

dust and soil samples were greater for agricultural homes than for nonagricultural homes

in the same communities. McCauley et al.

(2001) found that OP residues in farmworker

housing were associated with distance from

fields and with number of farmworkers in the

home.

These exposure routes are particularly

relevant to children of migrant and seasonal

farmworkers. Adults in their households often

apply chemicals and perform hand labor in

fields treated with pesticides. In addition,

these children often live in close proximity to

the fields. Farmworker housing is frequently

in poor repair and therefore subject to considerable pesticide application for pest control

(Harrison 1995; National Advisory Council

on Migrant Health 1995).

The research reported here focuses on

exposure of farmworker children to a broad

range of pesticides. Although previous studies

have examined pesticide residues in the homes

of such children, they have concentrated on

agricultural chemicals and primarily on OPs

(e.g., Simcox et al. 1995).

The present study is part of a project

designed to develop and evaluate a culturally

appropriate pesticide education intervention

for farmworker families. During formative

research, we used dust wipes to collect samples

that were analyzed for a variety of pesticides.

We present results of these analyses, describing the types and amounts of pesticides found

in farmworker dwellings and their distribution

on floor, toy, and hand surfaces in the home.

We also examine possible predictors of pesticide presence in these homes to identify the

probable pathways of exposure.

Materials and Methods

Recruitment. Data were collected between

June and December 2001 from 41 households

located in four western North Carolina counties and two southern Virginia counties. To be

eligible for the study, the household had to

contain a family consisting of at least two

related persons, one of whom was a seasonal,

migrant, or year-round farmworker employed

in agriculture by someone outside of his or her

family. At least one adult in the family had to

have a child between 12 and 84 months of age

residing in the house. The primary interviewee

in all households was the mother.

The lack of a census and the dispersed

nature of farmworker residences in the mountains precluded the use of a random sample or

a block cluster design. Therefore, we developed a strategy to obtain a sample representative of the variability in the local farmworker

population. Potential participants were initially identified using a site-based sampling

approach (Arcury and Quandt 1999). This

approach first identified sites or locations

where members of farmworker families were

likely to be found. These locations included

women’s groups sponsored by Cooperative

Extension agents and Partnerships for

Children, classes for English as a second language and high school equivalency (GED),

and social and health services organizations,

such as migrant health programs, county

health departments, churches, and the Special

Supplemental Nutrition Program for Women,

Infants, and Children offices. Project research

staff visited these sites during the months preceding data collection to introduce themselves

and the project, and to determine when and

how many eligible families were expected to

be in residence during the agricultural season.

Address correspondence to S.A. Quandt, Department

of Public Health Sciences, Medical Center Blvd.,

Piedmont Plaza II, Suite 512, Wake Forest University

School of Medicine, Winston-Salem, NC 27157-

1063 USA. Telephone: (336) 716-6015. Fax: (336)

713-4157. E-mail: squandt@wfubmc.edu

We acknowledge the laboratory assistance of M.M.

Zuniga, J.M. Clothier, and B. Walker.

This work was supported by a grant from the

National Institute of Environmental Health Sciences

(R01 ES08739).

The authors declare they have no competing

financial interests.

Received 30 June 2003; accepted 12 November

2003.

Agricultural and Residential Pesticides in Wipe Samples from Farmworker

Family Residences in North Carolina and Virginia

Sara A. Quandt,1 Thomas A. Arcury,2 Pamela Rao,2 Beverly M. Snively,1 David E. Camann,3 Alicia M. Doran,2

Alice Y. Yau,3 Jane A. Hoppin,4 and David S. Jackson2

1Department of Public Health Sciences and 2Department of Family and Community Medicine, Wake Forest University School of Medicine,

Winston-Salem, North Carolina, USA; 3Southwest Research Institute, San Antonio, Texas, USA; 4National Institute of Environmental

Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA

Children of farmworkers can be exposed to pesticides through multiple pathways, including

agricultural take-home and drift as well as residential applications. Because farmworker families

often live in poor-quality housing, the exposure from residential pesticide use may be substantial.

We measured eight locally reported agricultural pesticides and 13 pesticides commonly found in

U.S. houses in residences of 41 farmworker families with at least one child < 7 years of age in western North Carolina and Virginia. Wipe samples were taken from floor surfaces, toys, and children’s

hands. We also collected interview data on possible predictors of pesticide presence, including characteristics of the household residents, cleaning practices, and characteristics of the home. All families were Spanish-speaking, primarily from Mexico. Results indicate that six agricultural and

11 residential pesticides were found in the homes, with agricultural, residential, or both present in

95% of homes sampled. In general, residential pesticides were more commonly found. Presence of

both types of pesticides on the floor was positively associated with detection on toys or hands.

Agricultural pesticide detection was associated with housing adjacent to agricultural fields.

Residential pesticide detection was associated with houses judged difficult to clean. Although the

likelihood of agricultural pesticide exposure has been considered high for farmworker families, these

results indicate that residential pesticide use and exposure in this population merit further study.

Key words: agriculture, children, exposure, house dust, Latino. Environ Health Perspect 112:382–387

(2004). doi:10.1289/ehp.6554 available via http://dx.doi.org/ [Online 12 November 2003]
The researchers returned to the sites at the

appropriate time and were introduced to the

group or to individual families by agency staff.

Being introduced to the researchers by someone the family member already knew and

trusted helped allay concerns about the risks

of participating in the study. As the project

progressed, observation in the community

demonstrated that some families were quite

isolated and not accessible in the recruitment

sites. Therefore, additional participants were

located using snowball sampling from the

already enrolled families.

Data collection. Data collection took place

in the participant’s home. It consisted of an

interviewer-administered questionnaire, collection of wipe samples, and observations of the

residence and neighborhood. The questionnaires and interviews were conducted in the

participant’s preferred language, which was

Spanish in all cases. Interviewers were bilingual

females. Informed consent was obtained in

accordance with procedures approved by the

Wake Forest University School of Medicine

Institutional Review Board.

Wipe samples from three types of surfaces

(floors, toys, and children’s hands) were

collected in each home, following protocols

described elsewhere (Geno et al. 1996;

Harding et al. 1993). Floor samples were collected from two to four 18-inch-square sections (0.42–0.84 m2) of uncarpeted flooring

in living areas of the house. The number of

squares depended upon the amount of suitable floor space available. Most of the homes

had a high percentage of carpeted flooring, so

most of the samples were taken from uncarpeted kitchens and hallways. The sections

were measured and marked using masking

tape. Toy samples were taken from two or

three suitable toys identified by the mother or

child as most frequently handled favorites.

Suitable toys were nonplush and made of

materials that would not be damaged by the

alcohol. Wipe samples were taken from the

child’s hands by wiping all surfaces of both

hands, including between the fingers, with

two sponges. Older children wiped their own

hands. The child was instructed to pick up

and unfold the sponge, and then to wipe all

surfaces of both hands. In cases where the

child was too young or needed assistance, the

mother took the samples while wearing a pair

of nitrile gloves.

In 11 households, field blanks were also

collected. Two sponges were wetted with

15 mL of isopropanol and placed directly into

the jar with tweezers. An additional 50 mL of

isopropanol was added, and the jar was sealed

with Teflon tape and placed in the cooler.

Duplicates of the floor samples were collected

in six homes. The area directly adjacent

to the original sample was measured and

marked, and the sample was collected using

the complete floor wipe protocol. The floor

dust samples were quite repeatable. Of the

34 pairs of collocated duplicate-floor measurements with detectable loadings, 59% of

the pairs had differences < 20%, and 94%

had differences < 50%.

In addition to the physical samples and

direct interview data, observations were

made regarding the general environment of

the dwelling. A map was drawn of the yard

and immediate neighborhood, including

estimated distances to cultivated fields and

neighboring houses. These data were used to

classify dwellings as adjacent or nonadjacent

to agricultural fields. The difficulty of cleaning the house was also rated on a scale of

1 (most difficult to clean) to 10 (easiest to

clean), based on age and type of dwelling,

general state of repair, and crowding due to

number of occupants and amount of furniture and other material possessions.

Laboratory analysis. Samples were analyzed for two groups of pesticides. Eight were

pesticides [atrazine, disulfoton (total), esfenvalerate, lindane, metolachlor, oxyfluorfen,

pendimethalin, simazine] used frequently in

agriculture in the study area and detectable by

a single analytic method. Thirteen pesticides

[carbaryl, α-chlordane, γ-chlordane, chlorpyrifos, 4,4 ́-dichlorodiphenyldichloroethylene

(DDE), 4,4 ́-dichlorodiphenyl-trichloroethane

(DDT), diazinon, heptachlor, methoxychlor,

cis-permethrin, trans-permethrin, orthophenylphenol, propoxur] are commonly

detected in house dust throughout the United

States (Camann et al. 2000). The two groups

are labeled “agricultural” and “residential,”

acknowledging that the composition of

the agricultural list reflects local farming

practices. Once each month, wipe samples

were shipped on dry ice to the laboratory

(Southwest Research Institute, San Antonio,

TX), where they were stored at –12°C. Within

10 days after arrival, each wipe sample was

shake-extracted as previously described (Geno

et al. 1996). Each wipe sample was spiked with

chlorfenvinphos as an extraction surrogate and

then shake-extracted, first with isopropanol,

and then twice with 1:1 diethyl ether:hexanes.

The combined extract was concentrated

with an N-Evap evaporator (Organomation

Associates, Berlin, MA) to 5 mL (hand wipe

and blank samples) or 20 mL (floor and toy

wipes). One milliliter was passed through a carbograph cartridge to remove interferences from

coeluting compounds and concentrated to a

final volume of 1.0 mL in 10% diethyl ether in

hexanes. Amounts of the target pesticides in

samples were determined using an Agilent

6890/5973 gas chromatograph/mass spectrometer (Agilent Technologies, Palo Alto, CA)

as previously described (Geno et al. 1995).

Quantification was performed using a mixture

of deuterated polyaromatic hydrocarbons,

deuterated pesticides, and 13C-labeled pesticides

as internal standards. The nominal analyte

detection limit was the analyte level in the lowest standard of the initial five-point calibration

curve. Detection limits ranged from 0.05 to

0.3 μg for individual pesticides and 0.8 μg for

total disulfoton in hand wipes and from 0.25

to 1.5 μg for individual pesticides and 4 μg for

total disulfoton in floor and toy wipes.

No target analytes were detected in the

matrix blanks run with each batch of about

10 samples, indicating that there was no laboratory-introduced contamination. Recovery of

chlorfenvinphos generally ranged from 75 to

150%, suggesting that each wipe sample was

extracted well. A matrix blank wipe was

fortified with all target analytes at a low level

(1–6 μg), allowed to equilibrate for 30 min,

and extracted and analyzed as a regular sample with each batch of samples. All pesticides

except carbaryl were extracted efficiently,

with the spike recovery mean ± SD ranging

from 77 ± 11% for lindane to 137 ± 42% for

4,4 ́-DDT. Reported wipe amounts are not

adjusted for spike recoveries. Carbaryl recovery declined over laboratory batches, suggesting degradation of the laboratory spiking

solution or declining extraction efficiency.

The use of new spiking solution in the final

batch of samples resulted in a 62% recovery

from the matrix spike. Although somewhat

lower than the other chemicals, this suggests

that the low spike recoveries were caused by

degrading laboratory spiking solution and

that the results presented here are reliable.

Two agricultural pesticide analytes (atrazine

and metolachlor) and one residential pesticide

analyte (4,4 ́-DDE) were not detected in any

household. One residential pesticide analyte

(ortho-phenylphenol) was detected in all

samples and in field blanks, but not in laboratory blanks, and is excluded from all analyses

reported here.

Data analysis. Several measures were

created from the questionnaire and observation data collected. A nuclear family household was defined as a respondent (with or

without a spouse) and biologic offspring.

Nonrelatives in the household were defined as

individuals not related to the respondent.

Individuals ≥ 18 years of age were defined as

adults. Hand surface area was estimated using

the child’s age, based on results of a simple linear regression model to predict estimated hand

surface area from age in seven children with an

age range similar to that of the focal children.

Houses were rated as adjacent to agricultural

fields if they were next to, across the road

from, or within a short walk from fields.

Those rated as nonadjacent were located in

areas with no fields (e.g., in town or across the

valley from the nearest agricultural field).

All samples were successfully analyzed

for pesticides. Pesticide amounts removed

Children’s Health | Pesticides in farmworker homes

Environmental Health Perspectives • VOLUME 112 | NUMBER 3 | March 2004 383
(micrograms) were measured separately on the

three surfaces: hand, floor, and toy. Pesticide

loadings (micrograms per square meter) were

calculated by dividing amounts by corresponding surface areas. Means and SDs of pesticide

amounts and loadings were calculated from

samples having detected pesticide only. The

four primary outcome measures defined a priori

were the presence of at least one agricultural pesticide detected in wipes from floor, toys, hands,

and any surface sampled. Secondary outcome

measures were defined after data collection to

divide households into groups according to

number of residential pesticides detected on surfaces sampled (low, 0–2 pesticides; medium,

3–5 pesticides; high, 6–8 pesticides).

Odds ratios (ORs) and 95% confidence

intervals (CIs) were calculated to evaluate associations between categorical variables—for

example, the presence of agricultural pesticides

on floors and toys. Multiple logistic regression

models were used to predict binary outcome

measures; proportional odds models were used

to predict ordinal outcome measures. Potential

predictors were observed sample characteristics

and were included in multiple regression models if p < 0.2 in corresponding simple regression analyses. If two predictors in a model were

highly related, then only the one judged to

have greater predictive value was included. No

interaction terms were included. ORs and

95% CIs from the multiple regression analyses

were used to evaluate associations between

outcome measures and potential predictors,

adjusting for other predictors in the model.

Statistical significance was defined as p < 0.05

(two-sided). SAS software (SAS Institute Inc.,

Cary, NC) was used for all statistical analyses.

Results

Table 1 presents descriptive analysis of the

households and their residents. The agricultural

and residential pesticides detected in floor, toy,

and hand wipes are presented in Table 2. The

most commonly detected agricultural pesticides

were oxyfluorfen and simazine, and the most

commonly detected residential pesticide was

trans-permethrin. At least one agricultural pesticide was detected in 18 floor samples (44%),

12 toy samples (29%), and nine hand samples

(22%). Twenty households (49%) had at least

one agricultural pesticide detected in any

location. At least one residential pesticide was

detected in 39 households (95%), 39 floor

samples (95%), 24 toy samples (58%), and

19 hand samples (46%). Eight homes had a

low (0–2) number of pesticides detected, 21 a

medium (3–5) number, and 12 a high (6–8)

number. No home had more than 8 of a possible 13 residential pesticides detected.

When agricultural pesticide detection was

compared among sources within households,

there was a strong positive association between

detecting agricultural pesticides in toy wipes

and detecting them in floor wipes (OR = 13;

95% CI, 2.3–74). There was a similar strong

positive association between floor wipe and

hand detections (OR = 6.7; 95% CI, 1.2–38).

This association was even greater between agricultural pesticides found on floors and in toy

or hand wipes combined (OR = 17; 95% CI,

2.9–93). In each case, this estimated OR was

> 1 (p < 0.05), which indicates elevated risk,

given presence of agricultural pesticide(s) on

the floor.

When household pesticide detection was

compared among the three sources within

households, there was a strong positive association between detecting > 0 household pesticide in toy wipes and detecting > 3 pesticides

in floor wipes (OR = 7.0; 95% CI, 1.7–28).

There was a similar strong positive association

between > 3 pesticides in floor wipes and ≥ 1

pesticides in hand wipes (OR = 7.7; 95% CI,

1.7–34). The association was even greater

between residential pesticides found on floors

and in toy or hand wipes combined (OR = 12;

CI, 2.5–59). Similarly, in each case, the estimated OR was > 1 (p < 0.05), which indicates

greater likelihood of pesticides on toys or

hands, given presence of > 3 residential pesticides on the floor.

A summary of pesticide loadings is given

in Table 2, with values for floors and hands

expressed as loadings (micrograms per square

meter), and toy wipes expressed as weights

(micrograms). There is considerable variability in the loadings or weights of pesticides

found on the same surface between different

chemicals and within the same chemical.

Among possible predictors of at least one

agricultural pesticide detected on any surface

sampled (Table 3), two reached statistical significance. An elevated risk of agricultural pesticides was observed given agricultural fields

adjacent to the house (unadjusted OR = 18;

95% CI, 3.7–88). The association persisted

for location adjacent to agricultural fields,

after adjusting for other possible predictors

(adjusted OR = 20; 95% CI, 3.2–126.2). No

other predictors were statistically significant

in the multiple logistic regression model.

Two of the possible predictors of level of

residential pesticides detected on any surface

Children’s Health | Quandt et al.

384 VOLUME 112 | NUMBER 3 | March 2004 • Environmental Health Perspectives

Table 1. Description of 41 farmworker households, North Carolina and Virginia, 2002.

Characteristics No. (%) Characteristics No. (%)

Respondent characteristics

Sex

Female 41 (100)

Male 0 (0)

Age (years)

< 25 11 (27)

25–29 15 (37)

30–34 8 (20)

≥ 35 7 (17)

Work

Agricultural 16 (39)

Nonemployed/houseworker 21 (51)

Other 4 (10)

Focal child characteristics

Sex

Female 25 (61)

Male 16 (39)

Age (months)

12–24 11 (27)

25–48 13 (32)

≥ 49 17 (42)

Qualifying farmworker characteristics

Sex

Female 5 (12)

Male 36 (88)

Age (years)

< 25 7 (17)

25–29 14 (34)

30–34 7 (17)

≥ 35 13 (32)

Relationship to respondent

Self 4 (10)

Spouse 32 (78)

Other 5 (12)

Proportion of year in agricultural work (%)

< 50 6 (15)

50–75 8 (20)

≥ 76 27 (66)

Household characteristics

Household structure

Nuclear family 23 (56)

Nuclear family + other relatives 12 (29)

Nuclear family + others 6 (15)

(including relatives)

Household size (no. of persons)

3 4 (10)

4 12 (29)

5 10 (24)

≥ 6 15 (20)

No. of adults in household ≥ 18 years of age

2 20 (49)

3 10 (24)

≥ 4 11 (27)

No. of children in household < 18 years of age

1 12 (29)

2 16 (39)

3 or 4 13 (32)

No. of farmworkers in household

1 18 (44)

2 14 (34)

≥ 3 9 (22)

Housing characteristics

Housing type

Mobile home or trailer 22 (54)

One-family house or other 14 (34)

Building with ≥ 2 units 5 (12)

Ease of cleaning

Easy 21 (51)

Difficult 20 (49)

Air conditioning

Does not have 34 (83)

Has and uses 4 (10)

Has and does not use 3 (7)

Owns a vacuum cleaner

Yes 28 (68)

No 13 (32)

Housing adjacent to fields

Yes 18 (44)

No 23 (56)
sampled (Table 4) were statistically significant

(p < 0.05). Increased odds of a higher number

of residential pesticides was associated with

houses judged difficult to clean (unadjusted

OR = 4.2; 95% CI, 1.2–14.9) and rented

housing (unadjusted OR = 4.6; 95% CI,

1.0–21). These two predictors, plus pesticide

application work, non-nuclear family, and

mobile home were tested in the multiple

regression model based on p < 0.2 in the simple regression. An increased likelihood of residential pesticides was observed, given that

houses were judged difficult to clean (adjusted

OR = 5.1; 95% CI, 1.2–22.4).

Discussion

This study of North Carolina and Virginia

farmworker residences demonstrates that pesticide residues can be detected in wipe samples

for both agricultural chemicals and those commonly found in dwellings. As the first study to

screen for a large number of both agricultural

and residential pesticides in farmworker

dwellings, this extends previous research on

exposure of this population. Unlike previous

studies that have focused on OP pesticides

(e.g., Curl et al. 2002; Fenske et al. 2002;

Simcox et al. 1995), this study includes OPs,

pyrethroids, organochlorines, carbamate, and

herbicides. Our findings indicate that all categories of pesticides examined, not just OPs,

are present in farmworker homes with young

children in residence.

The presence of common residential pesticides detected in this study is similar to that

obtained from house dust samples in seven

areas across the United States (Camann et al.

2000). As in the present study, Camann et al.

(2000) found that organochlorines, OPs, carbamates, and pyrethroids were present in most

homes. The data in the present study suggest

that housing quality predicts household

pesticide levels. Houses that are harder to

clean may provide better habitats for pests

(resulting in greater use of pesticides) as well

as prevent the removal of pesticide-containing

dust. Others have found that pesticides

remain embedded in carpets and that carpets

have a higher number of pesticides and higher

concentrations than indoor air or outdoor

soil (Lewis et al. 1994). Because of the wide

variety of residential pesticides detected and

differences in their concentrations, the composite measure used here of low, medium,

and high numbers of pesticides may be too

crude to identify the strongest predictors of

pesticide presence.

The findings from this study confirm

that the floor may be a reservoir of pesticide

residues in the home. The greatest number of

detections was in floor wipe samples. For only

one pesticide was the number of detections

on the floor less than that on hands or toys.

Children’s Health | Pesticides in farmworker homes

Environmental Health Perspectives • VOLUME 112 | NUMBER 3 | March 2004 385

Table 2. Measured chemical loadings on floors (μg/m2), toys (μg),a and children’s hands (μg/m2), 41 farmworker homes, North Carolina and Virginia, 2002.

Floor Toys Hands

Pesticide Category No. (%)b Mean ± SD Min–Max No. (%)b Mean ± SD Min–Max No. (%)b Mean ± SD Min–Max

Agricultural

Disulfoton OP 3 (7) 6.5 ± 2.3 4.3–8.9 0 — — 0 — —

Esfenvalerate PY 5 (12) 29.9 ± 37.9 4.8–94.0 2 (5) 3.7 ± 4.0 0.9–6.5 2 (5) 49.8 ± 15.8 38.6–60.9

Lindane OC 2 (5) 2.2 ± 0.2 2.0–2.3 1 (2) 0.4 — 1 (2) 11.0 —

Oxyfluorfen HE 10 (24) 15.7 ± 21.7 1.9–71.0 3 (7) 0.6 ± 0.1 0.5–0.7 2 (5) 31.0 ± 39.0 3.4–58.6

Pendimethalin HE 3 (7) 20.2 ± 34.3 0.4–59.8 2 (5) 1.9 ± 1.0 1.2–2.5 1 (2) 17.9 —

Simazine HE 9 (22) 20.4 ± 22.5 1.2–70.0 6 (15) 5.9 ± 10.9 0.4–27.9 5 (12) 22.5 ± 34.0 2.7–82.1

Residential

Carbaryl CA 8 (20) 6.2 ± 6.1 1.2–18.5 0 — — 0 — —

α-Chlordane OC 14 (34) 1.9 ± 2.4 0.2–8.8 3 (7) 0.5 ± 0.1 0.3–0.6 1 (2) 2.2 —

γ-Chlordane OC 17 (41) 2.8 ± 3.5 0.3–12.5 6 (15) 0.6 ± 0.4 0.2–1.2 1 (2) 4.4 —

Chlorpyrifos OP 32 (78) 8.9 ± 18.4 0.2–87.9 11 (27) 2.1 ± 3.9 0.2–13.6 6 (15) 6.1 ± 6.1 1.9–18.0

4,4 ́-DDT OC 2 (5) 10.0 ± 0.5 9.6–10.3 1 (2) 0.5 — 0 — —

Diazinon OP 14 (34) 2.6 ± 3.1 0.4–9.4 2 (5) 0.5 ± 0.3 0.3–0.7 0 — —

Heptachlor OC 4 (10) 2.0 ± 0.8 1.0–2.9 0 — — 0 — —

Methoxychlor OC 1 (2) 19.4 — 0 — — 2 (5) 8.0 ± 5.3 4.3–11.7

cis-Permethrin PY 27 (66) 30.6 ± 66.4 1.4–317.4 7 (17) 2.6 ± 1.3 1.5–5.1 4 (10) 13.5 ± 7.5 8.7–24.7

trans-Permethrin PY 38 (93) 33.5 ± 85.7 0.8–488.1 21 (51) 1.8 ± 1.6 0.3–4.9 16 (39) 10.0 ± 8.3 3.4–33.6

Propoxur CA 15 (37) 4.0 ± 7.8 0.5–30.5 1 (2) 0.5 — 0 — —

Abbreviations: CA, carbamate; HE, herbicide; Max, maximum; Min, minimum; OC, organochlorine; PY, pyrethroid. In some cases, there was only one (mean only) or no (—) observation. aPesticide weights are presented because toy surface area could not be measured. bNumber of samples above detection limit (percentage of total samples) (n = 41).

Table 3. Analysis of associations between presence of any agricultural pesticide and potential risk factors,

using logistic regression modeling, farmworker homes, North Carolina and Virginia, 2002.

Potential risk factor Unadjusted Adjusteda

(reference group) No. OR (95% CI) p-Value OR (95% CI) p-Value

House next to agriculture 41 18.0 (3.7–88.0) < 0.0001 20.0 (3.2–126.2) 0.03

(not next to)

Non-nuclear family 41 2.4 (0.7–8.6) 0.16 4.9 (0.8–30.6) 0.09

(nuclear family)

≥ 3 agricultural workers in 41 2.6 (0.5–12.1) 0.23 — —

household (vs. ≤ 2)

October–December interview 41 1.1 (0.3–3.7) 0.69 — —

(vs. June–September)

Pesticide application 41 4.2 (0.9–19.1) 0.05 2.5 (0.4–17.0) 0.34

work (no such work)

Multifamily building 19 0.7 (0.1–5.3) 0.70 — —

(single-family home)

Mobile home 36 1.0 (0.3–3.8) 1.0 — —

(single-family home)

House difficult to clean 41 1.6 (0.5–5.6) 0.43 — —

(not difficult)

Rent house 41 2.3 (0.5–10.7) 0.29 — —

(own house)

Windows always closed 41 1.7 (0.2–11.3) 0.59 — —

(sometimes open)

Vacuum < once/week 41 1.3 (0.4–4.6) 0.65 — —

(at least once)

Age of farmworker in 41 0.9 (0.4–2.2) 0.82 — —

units of 10 years

Age of respondent in 41 0.6 (0.2–1.6) 0.28 — —

units of 10 years

—, Other variables were not included in multiple regression model.

aAdjusted ORs and corresponding 95% CIs and p-values are calculated using a multiple regression model with “house

next to agriculture,” “non-nuclear family,” and “pesticide application work” as independent variables. 
For both agricultural and residential pesticides, presence of pesticides on the floor predicted pesticides on hands or toys. This is

consistent with the ideas that pesticides are frequently tracked into the home on feet and that,

once inside the home, they are resuspended

into the air and redeposited as they fall on surfaces (Lewis et al. 2001; U.S. EPA 1999).

Relatively fewer homes had detectable

agricultural pesticides than residential pesticides, perhaps because specific agricultural pesticides are used on an as-needed basis in most

of the common crops (e.g., Christmas trees,

ornamental plants, and tobacco). Furthermore,

the data collection period spanned 7 months,

so exposure peaks may have been missed.

Dwellings adjacent to fields can be contaminated by drift during application and by

subsequent wind circulation of dust from

fields. Previous studies (Lewis et al. 2001)

have demonstrated that pesticides applied

outside dwellings are redeposited inside the

dwelling within hours. Fenske et al. (2002)

showed a gradient in indoor detection of agricultural pesticides based on proximity to

orchards where such pesticides are sprayed.

Koch et al. (2002) used analysis of biomarkers

in children’s urine to demonstrate temporal

associations of spraying and exposure. This

supports the possibility of drift from agricultural fields being a source of agricultural

pesticides in the present study. Repeated measurements across the agricultural cycle could

better substantiate drift as the mechanism of

household contamination.

The presence of agricultural pesticides may

be due to safety and hygiene practices of the

workers (not measured here). Those applying

pesticides can bring residues into dwellings on

clothing and skin, as well as track it in on

boots. Further investigation is needed of farmworker behaviors such as hand washing before

entering the dwelling, storage of soiled work

clothes, and showering practices after work.

Work environment characteristics such as

availability of personal protective equipment

and washing facilities should also be investigated. Prior research in eastern North Carolina

found that < 40% of workers had access to

wash water in the fields (Arcury et al. 2001). A

large number reported never having received

the pesticide safety training mandated by the

U.S. EPA Worker Protection Standard (Arcury

et al. 1999). Health beliefs of many migrant

workers lead them to delay showering after

work (Arcury et al. 2001). Because they believe

that their bodies must cool down before washing after work or risk rheumatism or other

health effects, they may fail to follow pesticide

safety measures that would remove pesticide

residues from the skin and decrease transfer to

children and household surfaces. Similar practices and training deficiencies have been

reported in Oregon (McCauley et al. 2001) and

Washington (Thompson et al. 2003). If these

same health beliefs and practices characterize

the western North Carolina and Virginia study

area, they may account for some of the indoor

contamination with agricultural pesticides.

We presume that hygiene practices that

would reduce the introduction of agricultural

pesticides into the home are deficient, resulting in agricultural pesticides in households.

However, the greater recoveries of residential

than agricultural pesticides may indicate that,

in fact, work-related hygiene practices are

more effective than those related to the use

of residential pesticides. Qualitative data collected from worker families at the same time

wipe samples were collected (data not reported

here) showed a high degree of misinformation

about the safety of residential pesticides.

Further analyses of these data may reveal the

particular practices introducing such pesticides

into the home.

It is difficult to interpret the health significance of the amounts of pesticides measured

in the wipe samples. The ranges are large, even

for floor samples standardized for area. Other

studies measuring indoor pesticides have used a

variety of different sampling methods—air

concentrations, foundation soil samples, and

carpet vacuuming, as well as the surface, toy,

and hand wipes used here (Gordon et al. 1999;

Lewis et al. 2001; Simcox et al. 1995; Whyatt

et al. 2002). However, there is no standard for

presenting such data, making it impossible to

compare one study with another. The wide

ranges suggest that examination of behavioral

predictors in a larger sample is warranted.

The findings of this study need to be

considered in light of several limitations. The

sample size is small, particularly given the

wide variability of pesticide loadings detected.

Because a random sample was not possible,

there could be bias introduced by the sampling method. These findings are also limited

by the lack of data on several important

predictors of concentrations in the home—

number of days since last application of residential pesticides and since last application in

adjacent fields. Farmworkers rarely have

access to such data. They generally reside in

rented or grower-supplied housing for which

they have no records of pesticide application.

They do not have records of when or what

pesticides were last applied in adjacent fields.

These fields may or may not belong to their

employer. North Carolina and Virginia, like

many states, have no statewide system for

reporting pesticide application.

This study extends previous research that

has detected dislodgeable residues of OP pesticides in rural dwellings occupied by migrant

and seasonal farmworkers (Lu et al. 2000;

Simcox et al. 1995; Thompson et al. 2003).

We demonstrate that pesticides used in the

home as well as those brought from agricultural settings into the indoor environment

contribute to the overall exposure of residents

Children’s Health | Quandt et al.

386 VOLUME 112 | NUMBER 3 | March 2004 • Environmental Health Perspectives

Table 4. Analysis of associations between high, medium, and low residential pesticides and potential risk

factors using the proportional odds model, farmworker homes, North Carolina and Virginia, 2002.

Potential risk factor Unadjusted Adjusteda

(reference group) No. OR (95% CI) p-Value OR (95% CI) p-Value

House next to agriculture 41 0.6 (0.2–2.0) 0.42 — —

(not next to)

Non-nuclear family 41 2.5 (0.7–8.3) 0.14 1.4 (0.4–5.6) 0.60

(nuclear family)

≥ 3 agricultural workers in 41 0.7 (0.2–2.9) 0.63 — —

household (vs. ≤ 2)

October–December interview 41 1.1 (0.3–3.5) 0.88 — —

(vs. June–September)

Pesticide application 41 0.3 (0.1–1.0) 0.06 0.3 (0.1–1.1) 0.07

work (no such work)

Multifamily building 19 1.2 (0.2–8.7) 0.84 2.9 (0.3–29.2) 0.38

(single-family home)

Mobile home 36 0.3 (0.1–1.3) 0.11 0.4 (0.1–1.6) 0.18

(single-family home)

House difficult to clean 41 4.2 (1.2–14.9) 0.03 5.1 (1.2–22.4) 0.03

(not difficult)

Rent house 41 4.6 (1.0–21.0) 0.05 2.3 (0.4–13.6) 0.34

(own house)

Windows always closed 41 2.4 (0.4–14.3) 0.35 — —

(sometimes open)

Vacuum < once/week 41 1.8 (0.6–5.9) 0.32 — —

(at least once)

Age of farmworker in 41 0.9 (0.4–2.1) 0.85 — —

units of 10 years

Age of respondent in 41 1.0 (0.4–2.4) 0.93 — —

units of 10 years

—, Other variables were not included in multiple regression model.

aAdjusted ORs and corresponding CIs and p-values are calculated using a multiple regression model with “non-nuclear

family,” “pesticide application work,” “multifamily” and “mobile home,” “house difficult to clean,” and “rent house” as

independent variables. 
Children’s Health | Pesticides in farmworker homes

Environmental Health Perspectives • VOLUME 112 | NUMBER 3 | March 2004 387

to pesticides. In this study, residents included

young children, whose susceptibility to pesticide-related health problems is, in general,

greater than that of adults. This study also

extends research on farmworker pesticide

exposure to the eastern United States. Further

investigation of pesticide exposure of farmworkers and their families is needed in other

areas to document the extent of the risk experienced by farmworker families and possible

health effects of such exposure.

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