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. Author manuscript; available in PMC: 2015 Aug 22.
Published in final edited form as: J Expo Sci Environ Epidemiol. 2008 Mar 26;19(1):79–89. doi: 10.1038/jes.2008.18

Community-Based Intervention to Reduce Pesticide Exposure to Farmworkers and Potential Take-Home Exposure to their Families

Asa Bradman 1, Alicia L Salvatore 1, Mark Boeniger 2, Rosemary Castorina 1, John Snyder 3, Dana B Barr 4, Nicholas P Jewell 1, Geri Kavanagh-Baird 5, Cynthia Striley 2, Brenda Eskenazi 1
PMCID: PMC4545293  NIHMSID: NIHMS681415  PMID: 18368011

Abstract

The U.S. EPA Worker Protection Standard requires pesticide safety training for farmworkers. Combined with re-entry intervals, these regulations are designed to reduce pesticide exposure. Little research has been conducted on whether additional steps may reduce farmworker exposure and the potential for take-home exposure to their families. We conducted an intervention with 44 strawberry harvesters (15 control and 29 intervention group members) to determine whether education, encouragement of handwashing, and the use of gloves and removable coveralls reduced exposure. Post-intervention, we collected foliage and urine samples, as well as hand rinse, lower-leg skin patch, and clothing patch samples. Post-intervention loading of malathion on hands was lower among workers who wore gloves compared to those who did not (median = 8.2 vs 777.2 μg/pair, respectively (p<0.001)); similarly, median MDA levels in urine were lower among workers who wore gloves (45.3 vs 131.2 μg/g creatinine, p<0.05). Malathion was detected on clothing (median = 0.13 μg/cm2), but not on skin. Workers who ate strawberries had higher MDA levels in urine (median=114.5 vs 39.4 μg/g creatinine, p<0.01). These findings suggest that wearing gloves reduces pesticide exposure to workers contacting strawberry foliage containing dislodgeable residues. Additionally, wearing gloves and removing work clothes before returning home could reduce transport of pesticides to worker homes. Behavioral interventions are needed to reduce consumption of strawberries in the field.

Keywords: pesticides, organophosphate, malathion, farmworkers, exposure, agriculture, strawberries, transfer coefficients, biomarkers

INTRODUCTION

The U.S. EPA Worker Protection Standard (WPS) provides pesticide safety information to farmworkers (U.S. EPA 1992; 2005). Combined with re-entry intervals after pesticide applications, these regulations are designed to reduce pesticide exposure and protect farmworker health. However, the WPS does not directly address the potential for occupational “take-home” exposure, where workers transport pesticide residues on their skin or clothing to their homes, potentially exposing family members. Agricultural pesticides have been detected on farmworkers’ skin, clothing, and work shoes, and also in dust from farmworkers’ vehicles and homes, suggesting the potential for take-home exposure (McCurdy et al., 1994; Bradman et al., 1997; Krieger and Dinoff, 2000; Lu et al., 2000; Curl et al., 2002; Coronado et al., 2006).

The use of protective clothing, gloves, and handwashing are known to reduce pesticide exposure to workers. For example, studies of pesticide mixers, loaders, and applicators (MLAs) demonstrate the effectiveness of protective clothing in reducing worker dermal loading by 50-95% (Keifer, 2000). Factors influencing exposure included the type of weave, fabric (e.g. synthetics vs. cotton), and fabric finishes (water repellants and starch) (Nigg et al., 1986; Fenske, 1988; Obendorf et al., 1991; Lander and Hinke, 1992; Csiszar et al., 1998; Racz et al., 1998; Saleh et al., 1998; Gomes et al., 1999; Boeniger and Lushniak, 2000; Kissel and Fenske, 2000; Krieger and Dinoff, 2000). Gloves have also been found to reduce pesticide dermal loading among MLAs (Putnam et al., 1983; Nigg et al., 1986; Lander and Hinke, 1992). A few studies of field farmworkers have also confirmed that protective clothing (McCurdy et al., 1994; Krieger and Dinoff, 2000) and glove use (Gomes et al., 1999) can reduce the amount of pesticides reaching farmworkers’ skin. Several studies also demonstrate that handwashing can reduce worker exposure to chemicals, including pesticides (Boeniger and Lushniak, 2000; Marquart et al., 2002; Curwin et al., 2003). Overall, the literature on worker pesticide exposure prevention has several implications on the potential for take-home exposure: (1) if dislodgeable pesticides are present in fields it has been shown that they accumulate on farmworkers’ hands, other skin, clothing, and shoes; and, (2) if not removed either by changing clothing or washing, pesticides may be carried home where they can contaminate homes and/or directly expose family members.

In the present study, we conducted an intervention to reduce malathion exposure to strawberry harvesters and the potential for take-home exposure to their families. The intervention components included provision of: 1) warm water and hand cleansers to encourage handwashing; 2) disposable gloves; 3) removable outer clothing (coveralls) to prevent contamination of clothes and skin; 4) laundering of coveralls; 5) laundry bags and shoe bins for storage of work clothing and shoes in their car and home, and 6) six weekly in-field health and pesticide education presentations. In this paper, we compare levels of malathion urinary metabolite measurements in the control and intervention groups; and evaluate the efficacy of the intervention in preventing worker exposure and reducing the potential for take-home exposure to their families.

METHODS

Study Population and Design

This community-based research study was conducted between July and October 2003 by the Center for Children's Environmental Health Research, a community-university partnership including the University of California Berkeley, state and federal agencies, other educational and research institutions, and several community-agencies in Monterey County (Israel et al., 2005). Community partners for this study include: Clínica de Salud del Valle de Salinas, California Rural Legal Assistance, and the Grower-Shipper Association of Central California. The Center's Community Advisory Board (CAB) and Farmworker Council (FC) participated in the development of this intervention study. The CAB is composed of representatives from the agricultural industry, county environmental and health agencies, local elected representatives, advocacy groups, farmworkers, and other community members. The FC is composed of 8 long-term farmworkers from the Salinas Valley.

We focused on strawberry harvesters for two reasons: 1) to standardize the tasks performed by study participants, and 2) because malathion, which is measurable in environmental and personal samples and in urine (as a metabolite), is used on this crop. We enrolled workers from a farm where malathion was expected to be used for insect control. Workers were eligible to participate if they were 18 years or older, spoke English or Spanish, and were planning to work at the farm until the season ended. In October 2003, 44 strawberry workers provided samples for this study, of which 29 were randomly assigned to the intervention group (intervention group=29; control group n=15). More information on study methods is presented elsewhere (Salvatore et al., submitted for publication). Written informed consent was obtained from all participants. All procedures were approved by the University of California, Berkeley Committee for the Protection of Human Subjects.

Strawberry Production

California produces 80% of the fresh market and processed strawberries grown in the United States, with harvests exceeding 1.5 billion pounds annually (USDA, 2006). The Watsonville/Salinas region includes over 13,000 planted acres, about one third of the state's total (USDA, 2006). Raised-bed plasticulture is the primary production method. In this system, individual beds (~38 cm high x 61cm wide with 25 cm between rows) with subsurface drip irrigation tapes are covered with plastic and planted annually with day-neutral berries. Day neutral berries produce continuously, so harvests continue the entire growing season until cold weather sets in. Approximately 200 compounds are approved to control pests on strawberries; however, relatively few are used in large volume. Several million pounds of pesticides are used annually on strawberries in the Watsonville/Salinas region, primarily pre-planting soil fumigants (DPR, 2003). Common post-planting applications include fungicides (sulfur, captan, thiram) and insecticides (malathion, naled, methomyl). Pest pressures determine the frequency of post-planting insecticide applications and vary widely depending on insect lifecycles, weather, natural enemies, etc. Malathion was applied for lygus bug (Lygus hesperus) control twice during the growing season on the farm we studied, once in July, and once in October, just before the post intervention sampling. Berries are harvested by hand with most workers paid on a piece-rate basis. The crews working in the fields we studied were hired directly by the farmer and only worked in fields managed by him. They worked exclusively in strawberries for the entire summer.

Personal and Demographic Data Collection

Two weeks prior to implementation of the intervention, farmworkers were interviewed in Spanish by bilingual interviewers using a standardized questionnaire. Interviews were conducted during breaks in a private location away from other workers. Information obtained focused on personal and demographic information, use of protective clothing and gloves, personal and dietary behaviors related to pesticide exposure, number of household members, household members’ occupations and home pesticide use. This instrument was reviewed by staff (many of whom had been farmworkers) and community partners, and pre-tested with the FC.

Intervention Components

Each workday for six weeks prior to completion of the post-intervention sampling, intervention group participants were provided with lightweight removable coveralls (cotton/polyester) to wear over their normal work clothing (Sweet Manufacturing, Dallas, Texas). Used coveralls were picked up every seven days and washed by a professional laundry service. Farmworkers in the intervention group were also provided with disposable nonlatex gloves and containers for storing work shoes and clothes separately from family clothes. Warm water and soaps were provided to encourage handwashing. Five field-based educational sessions were conducted with intervention group participants throughout the study period. These sessions aimed to educate workers about pesticide exposure and protective behaviors and to troubleshoot barriers to adopting the behaviors promoted in the intervention. Information about the participatory research methods used to select coveralls, soaps, gloves, and the provision of warm water will be presented in a separate manuscript.

At the end of the intervention study, control group members were provided with two pairs of coveralls, storage containers, a supply of disposable nonlatex gloves, and an educational workshop that covered the information given to intervention participants.

Evaluation of the Intervention: Biological, Environmental, and Personal Sample Collection

To confirm the potential for workers’ exposure to malathion, we measured dislodgeable foliar residues and levels of malathion metabolites in workers’ urine specimens, as well as malathion in hand rinse, clothing patch, and skin patch samples. The goal of this cross-sectional sampling campaign was to assess the efficacy of specific components of the intervention. Details of collection and measurements are provided below.

Dislodgeable foliar residue (DFR)

To determine the potential for dermal exposure and accumulation on clothes, we measured malathion in DFR samples collected from the fields that participants worked in. To collect the DFR samples, we first divided the fields into five equal sized plots. We recorded the lengths and number of rows in each plot. Plants from each plot were randomly selected and three to four leaf punches were taken from each plant. A total of 40 punches 1 inch in diameter were collected for each DFR sample. The DFR samples were immediately placed in a cooler on ice packs and transferred to the field laboratory, where the samples were placed in a beaker with 300 mL of 0.1% Sur-Ten solution and mixed on a shaker table for 20 minutes. The rinseate was decanted into sample jars and frozen at −80°C until shipment on dry ice to the University of Kentucky Department of Horticulture research laboratory in Lexington, KY for analysis. Quality control procedures included the use of field spikes and blanks.

Prior to analysis, samples were thawed at room temperature and sample weight was determined gravimetrically. The sample was transferred to a separatory funnel, and then partitioned against ethyl acetate. The organic fractions were pooled, the volume reduced to 1-2 mL, transferred to a small vial and blown to just dryness with nitrogen. The sample was then vortex mixed with 2 mL of ethyl acetate. One μL aliquots were injected into a gas chromatograph (GC; Hewlett-Packard 5890) at 250°C with a nitrogen phosphorous detector at 280°C. A DB-5 column was used with helium as the carrier gas (6 ml/min), with a retention time of 2.4 min. Determinations were made in triplicate. The limit of detection (LOD) was 0.03 ng. To determine the concentration of malathion in the samples, external standards were used (0.00033, 0.00333 or 0.0333 g/μL). An external standard was shot twice per day (in triplicate). Concentrations of malathion in the experimental samples were calculated by use of proportions, based on the external standard. After calculation of malathion concentration in the experimental sample, mean concentration and its standard deviation were then calculated and expressed as μg malathion/g of original sample. No malathion residue was measured in field blank samples (n=3) indicating that no contamination occurred in the field during processing or handling. The low (n=2 at 2 μg), medium (n=2 at 20 μg), and high (n=3 at 200 μg) spike recoveries were on average 223%, 121% and 117% of the spiked concentrations, respectively.

Urine collection and malathion metabolite measurements

We evaluated workers’ exposure to malathion post-intervention by measuring metabolites in spot urine samples. Urine sampling was timed to occur on the same day harvesters entered fields after expiration of the 72 hour pre-harvest interval for malathion on strawberries. The post-intervention sampling was conducted with one intervention crew (n=29) and one control crew (n=15). Spot urine samples were collected following procedures outlined by CDC (CDC, 2003). Participants voided into a sterile collection cup in a field restroom. Samples were kept on ice packs until transport to the field office for aliquotting and storage at −80°C. For quality control purposes, frozen field blanks and spikes, prepared earlier by CDC, were defrosted, re-packaged in the field in a manner identical to collection procedures for actual samples, and then shipped blinded to CDC.

Urine samples were analyzed by the Division of Laboratory Services, Centers for Disease Control and Prevention, Atlanta, Georgia. Malathion dicarboxylic acid (MDA) and six non-specific dialkyl phosphate (DAP) OP metabolites were measured: dimethylphosphate (DMP); dimethyldithiophosphate (DMDTP); dimethylthiophosphate (DMTP); diethylphosphate (DEP); diethyldithiophosphate (DEDTP); and diethylthiophosphate (DETP). Urine specimens were lyophilized to remove water then the residue was redissolved in acetonitrile:diethyl ether (1:1). The DAPs were derivatized to their chloropropyl phosphate esters and then analyzed by isotope dilution gas chromatography-tandem mass spectrometry (GC-MS/MS) (Bravo et al., 2004). The specific metabolite of malathion, MDA, was measured as the intact metabolite using high performance liquid chromatography (HPLC)-atmospheric pressure chemical ionization-tandem mass spectrometry (Olsson et al., 2004). Creatinine concentrations in urine were determined using a commercially available diagnostic enzyme method (Vitros CREA slides, Ortho Clinical Diagnostics, Raritan, NJ). We adjusted metabolite levels for creatinine, which is standard practice in occupational studies of adult populations (Lauwerys and Hoet, 1993). We calculated the molar sum of the MDA and total dimethyl alkylphosphate (DMAP) urinary metabolites to estimate the fraction of total DMAP urinary metabolites that could be attributed to malathion exposure. Given that there are many potential sources of DAP exposure (e.g., diet (Lu et al., 2005)) and our focus on malathion in field and personal measurements, we report data analyses results for MDA only.

Laboratory quality control included repeat analysis of three in-house urine pools enriched with known amounts of pesticide residues whose target values and confidence limits were previously determined. The validity of each analytical run was determined using the Westgard rules for quality control (Westgard, 2003). The LOD for the MDA analysis was 0.3 μg/L. We assigned an imputed value of the LOD/√2 to field sample results that were below the detection limit (Hornung and Reed, 1990; Barr et al., 2004). Of 14 field blanks, no MDA metabolites were detected indicating that virtually no contamination occurred in the field during processing and handling. The MDA recoveries for 10 low (20 μg/L) and 10 high (60 μg/L) field spikes averaged 96% and 105%, respectively.

Clothing and skin patch samples

To assess the potential for dermal exposure under worker clothing and the accumulation of malathion on clothing, we collected skin patch (n=38) and clothing (n=41) samples post-intervention from both intervention and control groups. To collect the samples, we placed square first-aid cotton gauze into a cardboard frame with a 10 cm diameter circle cut out. This ensured that all patch samples had the same area of exposed material. Clothing patches were applied externally to the lower leg of either coveralls worn by participants in the intervention group or to the regular work pants worn by participants in the control group. For skin patch samples, the cardboard frame was taped directly to the skin on the lower leg. The patches were randomly offset so the skin and clothing patch samples were never aligned directly on top of each other.

Malathion on clothing and skin patch samples was measured by DataChem Laboratories, Inc. in Salt Lake City, UT. Samples were extracted in 30 mL of isopropanol for one hour. GC-mass spectrometry analyses were performed in single ion monitoring mode. A volume of 1 μl was injected, splitless for 0.5 minutes, at 280°C, on a DB-5 column with 30 m × 0.32 mm i.d. × 0.5 μm. The LOD and limit of quantification (LOQ) for malathion were 0.02 and 0.06 μg/cm2, respectively. Field quality control samples included 25 blank, spike, and duplicate patch samples. Malathion residues were not detected in blank samples indicating no measurable contamination occurred in the field during processing or handling. Levels of malathion in five field blanks spiked at 0.02 μg/cm2 were below the LOD. Recoveries for 5 medium (0.13 μg/cm2) and 5 high (1.3 μg/cm2) field spikes averaged 88% and 72%, respectively.

Hand rinse samples

To assess dermal exposure on hands, we collected hand rinse samples from 42 workers post-intervention from both intervention and control groups. To collect the samples, 500 mLs of water with 0.1% Sur-Ten (dioctyl sulfosuccinate sodium salt (CAS# 577-11-7)) was placed in a clean plastic bag supported by a plastic cylinder. Participants washed their hands in the solution for 60 seconds. Nailbrushes pre-cleaned in isopropanol were provided to remove surface dirt and brush fingernails. After washing, the solution was transferred to collection jars, transported on ice to the field office, and transferred to a −80°C freezer for storage until shipment on dry ice to the laboratory for analysis.

Hand rinse samples were analyzed by the National Institute of Occupational Safety and Health in Cincinnati, Ohio. A 10-mL aliquot was vortex mixed followed by cleanup with a solid phase extraction C18 cartridge. The sample was then washed with 5 mL water and eluted with 3 mL of acetonitrile. Water was added to bring the aliquot to 65% acetonitrile:35% water. Chromatographic analysis using an Agilent 1100 HPLC System (Palo Alto, CA, USA) with UV detection at 210 nm was achieved by the injection of a 100 μl sample onto a Zorbax C18-RX column followed by elution at a rate of 1.0 ml/min. An LOD of 10 ppb and LOQ of 30 ppb were based on average values of instrumental baseline noise and the lowest standard that could be reliably measured. We assigned an imputed value of the LOD/√2 to levels below the detection limit. We conducted a recovery study of spiked handwash samples (six each) at 10, 50, 250, 500, 1000, 5100 and 10200. Average recovery for each spiking level ranged from 96-109%, with overall recovery of 100.4% ± 4.2%.

We conducted two additional quality assurance studies to evaluate the reliability of the hand rinse measurements. First, because the hand rinse solutions were likely to contain bacteria, fungi, soil, plant extracts, and other materials, we conducted an experiment to determine whether malathion degrades in the rinseate during the time, typically 1-2 hours, between sample collection and freezing. To accomplish this, we prepared three sets of hand rinse solutions. Group 1 consisted of the clean Sur-Ten rinsing solution. For Group 2, a staff member wore nitrile gloves for 3 hours (creating the conditions for a bacterial bloom, mimicking conditions for workers who wore gloves); he then removed the gloves and followed the hand rinse protocol. For Group 3, a staff member went to an (unsprayed) strawberry field and rubbed his hands over the plants and soil to mimic the material that accumulates on worker hands during harvesting. He then followed the hand rinse protocol. For each group, the solution was aliquoted to nine samples, each spiked with 200 ng of malathion. Within each set of nine samples, we froze three samples immediately, let three samples sit at room temperature for one hour before freezing, and, finally, let three samples sit at room temperature for two hours before freezing. Recoveries for all groups ranged from 82-108%. The results of this quality assurance evaluation suggest that recoveries for field samples were acceptable. Final results were not adjusted for any degradation during transit.

Second, to assess the efficacy of the hand rinse protocol, a subset of 14 intervention participants provided three sequential hand rinses that were analyzed separately. Rinse #1 removed an average of 59% (range, SD=31-74%, 13%) of the total malathion measured in 3 sequential rinses. Combining Rinse # 1 and Rinse # 2 increased the recovery to 85% (range, SD=72-92%, 7%), indicating that the initial rinse removed most, but not all, of the recoverable pesticide on the hands of field workers (see Table 1).

Table 1.

Proportion of total malathion removed from hands by sequential rinses (n=14 participants, three rinses each).

Rinse no. Portion (%) of total malathion detected in sequential rinses
Mean SD Range
1 59 13 (31 – 74)
1 and 2 85 7 (72 – 92)
1 – 3 100 - -
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a Wash time = 60 s; a brush was used to remove surface dirt.

Data Analysis

Data analysis focused on evaluating the association of specific field intervention components (glove use and coveralls) and worker exposure among the 29 intervention participants and 15 controls. To accomplish this objective, we compared post-intervention median urinary metabolite levels of the intervention (n=29) and control (n=15) groups using either the Mann-Whitney U test or Kruskal-Wallis test. We also compared these urinary metabolite levels between the intervention and control groups stratified by potential exposure risk factors, including age and number of strawberry boxes picked as continuous variables and gender, smoking status, glove use, and strawberry consumption while working as categorical yes/no variables.

We evaluated these exposure risk factors to assess additional confounding that was not accounted for in the study design, and the significance of compliance with intervention protocols. Among the 29 intervention group participants, all wore coveralls and 27 complied with the glove use requirement; among the 15 controls, 6 wore gloves on a routine basis, including on the day of sampling. Because glove use appeared to be a primary determinant of exposure, we conducted additional analyses based on reported glove use independently of intervention or control group assignment. Finally, to assess the independent contribution of not wearing gloves to worker exposure, we constructed a multivariate regression model with log (base e) transformed MDA metabolite levels as the dependent variable and glove use, home pesticide use, and eating strawberries in the field (all coded as yes/no) and time worked in the field on the day of sample collection (continuous), as independent variables.

We also compared malathion levels on lower-leg skin with clothing patches and residues on worker hands stratified by reported glove use. Based on the DFR data and hand rinse malathion levels, we calculated agricultural transfer coefficients (TCs) for these workers (Korpalski et al., 2005; U.S. EPA, 2000). Finally, we used quantile regression to compare MDA levels among the post-intervention strawberry workers with levels reported for Mexican American adults participating in the National Health and Nutrition Examination Survey (NHANES) (CDC, 2003). All data analyses were performed with Stata Version 10 (StataCorp LP, College Station, TX).

RESULTS

Demographic Characteristics

All 44 study participants were born in Mexico; 80% were male, 80% spoke only Spanish, and 82% had attended school through the 6th grade or less. The mean age of study participants was 31 (SD=10) years and 68% lived at or below the federal poverty level (U.S. Census Bureau, 2003). Fifty-five percent of participants lived with six or more people in their home and 93% percent lived with at least one other agricultural worker. Twenty-five percent of farmworkers lived with their children, 11% had children under six years old; 23% smoked.

Urinary Metabolites

Table 2 presents the farmworkers’ creatinine-adjusted post-intervention urinary MDA metabolite levels. After implementation of the intervention, MDA metabolite levels were essentially the same among workers in the intervention group compared to the control group (median=56.3 versus 56.9 μg/g). However, six of the control group members wore gloves, a key component of the intervention. Among controls, median MDA metabolite levels were significantly lower among the 6 workers who wore gloves (26.9 μg/g) compared to the 9 who did not (154.0 μg/g) (Mann-Whitney U test p=0.003). Thus, in the analyses presented below, we report data by exposure factors independent of post-intervention group assignment.

Table 2.

MDA metabolite and creatinine levels in farmworkers’ urine samples post-intervention.

Farmworker subgroups MDA levels (μg/g)
DF (%) Median IQR (25th, 75th) Range
Post-intervention a, b
Control group (n=15) 100 56.9 27.1, 321.0 16.1 – 444.6
Intervention group (n=29) 97 56.3 34.7, 90.0 0.2 – 921.3
Total (n=44) 98 56.8 33.6, 142.6 0.2 – 921.3
Total creatinine (mg/dl) (n=44) 100 105.6 85.2, 144.2 45.0 – 274.2
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Abbreviations: DF, detection frequency; IQR, interquartile range; MDA, malathion dicarboxylic acid

a

Intervention group members were provided with gloves and coveralls (100% of workers complied with the coverall use protocol, and 93% complied with the glove use protocol).

b

Urine samples were collected post-intervention from workers on the same day they picked strawberries from the sprayed fields.

Table 3 presents post-intervention MDA metabolite levels by potential exposure factors. Wearing gloves was associated with lower metabolite levels (median MDA metabolite levels were 131.2 μg/g among the 11 workers who did not wear gloves versus 45.3 μg/g among the 33 workers who did; Mann Whitney U test p<0.05). The 14 workers who reported working more than 6 hours in the field on the day of sample collection had significantly higher median MDA metabolite levels (71.7 μg/g) compared to workers who worked less than 6 hours (43.6 μg/g)(p<0.05). Workers’ age, sex, smoking status, wearing coveralls over work clothes, and number of strawberry boxes picked were not associated with metabolite levels.

Table 3.

Farmworker MDA (μg/g) levels by potential exposure factors (Control and Intervention groups, n=44).

n (%) 50th IQR (25th, 75th)
Age (mean=30 years)
    18-24 15 34 56.6 32.5, 163.1
    25-34 14 32 46.2 35.7, 89.1
    35-58 15 34 77.3 31.8, 321.0
Gender
    Male 35 80 56.9 35.7, 157.9
    Female 9 20 34.7 26.8, 64.2
Smoking status
    No 34 77 62.7 35.7, 154.0
    Yes 10 23 37.6 31.8, 77.3
Number of hours worked in the field today
    <=6 30 68 43.6 27.1, 90.0
    >6 14 32 71.7* 53.0, 321.0
Eat unwashed strawberries in the field today
    No 23 52 39.4 31.5, 66.1
    Yes 21 48 114.5* 45.3, 217.7
Number of strawberries eaten today
    None 23 52 39.4 31.5, 66.1
    1-10 18 41 85.3 41.6, 321.0
    >10 3 7 207.4* 163.1, 217.7
Wear gloves today
    No 11 25 131.2* 56.9, 359.4
    Yes 33 75 45.3 31.5, 81.4
Wear coveralls today
    No 15 34 56.9 27.1, 321.0
    Yes 29 66 56.3 34.7, 90.0
Boxes strawberries picked
    2-15 9 21 64.2 31.5, 114.5
    16-19 17 40 56.3 39.1, 163.1
    20-25 17 40 61.2 35.7, 131.2
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*

Mann-Whitney U test or Kruskal-Wallis p-value <0.05

Many workers (48%) reported eating strawberries, which may have contained malathion residues, while working in the field. Median MDA metabolite levels were significantly higher among workers who ate strawberries in the field (114.5 μg/g) versus those workers who did not (39.4 μg/g) (Mann Whitney U test p<0.01). Among workers who did eat strawberries, the intervention (n=12) and control (n=9) groups’ MDA metabolite levels were very similar (123.5 and 114.5 μg/g, respectively). Among workers who did not eat strawberries, the intervention group's (n=17) median MDA metabolite levels (39.1 μg/g) were lower than the control group's (n=6) median MDA metabolite levels (56.8 μg/g); however, this difference was not statistically significant (p=0.5).

We constructed a multivariate regression model with log transformed MDA metabolite levels as the dependent variable and glove use, eating strawberries in the field and hours worked in the field as independent variables. Results indicate that glove use was inversely associated with MDA metabolites levels (β = −1.2 (95% CI: −2.0, −0.5)) even when controlling for strawberry consumption and hours worked in the field (R2 (for model) = 0.43). Strawberry consumption also remained significantly associated with MDA metabolite levels (β = 1.1 (95% CI: 0.4, 1.7)). Converted to an arithmetic scale, the adjusted coefficients indicate that predicted MDA metabolite levels are 3.4 times higher among workers not wearing gloves compared to workers who wore gloves, and 2.9 times higher among workers eating unwashed strawberries in the field compared to workers who did not.

Finally, we calculated the molar sum of the MDA and total DMAP urinary metabolites— to estimate the fraction of total DMAP urinary metabolites that could be attributable to malathion exposure. Based on this comparison, we found that on average approximately 36% of total farmworker DMAP metabolites likely came from malathion versus other sources.

Post-intervention urinary MDA concentrations were also significantly higher by two orders of magnitude among the strawberry harvesters (n=44) (median = 56.8 μg/g) compared to Mexican American adults sampled by NHANES (n=680) (median = 0.2 μg/g (<LOD))(p<0.001) (CDC, 2003; Barr et al., 2005). Because our study took place in October, which is late in the strawberry harvest season when yields are lower, workers spent an average of 5.6 hours (SD=0.5) in the field on the day of sample collection. If these harvesters had been in the field 8 or more hours we would expect malathion exposure levels to have been higher.

Clothing and Skin Patch Samples

Of the 44 post-intervention study participants, 41 provided clothing patch samples and 38 provided skin patch samples. Table 4 presents malathion levels found on the post-intervention farmworkers’ clothing patch and skin patch samples. Participants wore the patch samples for an average of 5.5 hours (SD = 0.8 hours). Malathion was detected on 76% (31 of 41) of clothing patch samples, with a median of 0.13 μg/cm2. Levels among control (n=12) and intervention (n=29) group participants were similar (median=0.10 and 0.14 μg/cm2 respectively, p=0.23). Malathion was detected on only 3% (1 of 38) of skin patch samples, indicating that even a single layer of clothing protects the skin from dermal exposure.

Table 4.

Malathion levels on farmworker clothing patch, skin patch, hand rinse (glove wearers vs. non-glove wearers) and dislodgeable foliage samples.

Malathion concentrations
n DF (%) Mean (SD) p50 IQR (25th,75th) Min-Max
Clothing patch (μg/cm2) 41 76 0.17 (0.15) 0.13 0.07, 0.22 0 – 0.70
Skin patch (μg/cm2) 38 3 0.006 (0.03) 0 0, 0 0 – 0.21
Hand rinse total (μg per pair of hands) 42 83 206.0 (422.6) 12.1 5.8, 102.1 3.5 – 1,936.4
Hand rinse (gloves worn) 32 78 25.0 (77.6) 8.2a 5.5, 13.8 3.5 – 445.7
Hand rinse (gloves not worn) 10 100 785.2 (551.0) 777.2a 382.2, 1,037.6 102.1 – 1,1936.4
Dislodgeable foliage (μg/cm2) 8 100 1.0 (0.2) 1.0 0.9, 1.1 0.6 – 1.4
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Abbreviations: DF, detection frequency; SD, standard deviation.

a

Glove use vs. non-glove use (P-value (Mann-Whitney U-test)) = 0.0001.

To assess the potential for bias due to behavioral differences between the participants that could affect exposure, we performed additional analyses of our clothing patch data. Malathion levels measured on the workers’ clothing samples were essentially the same between the workers who wore gloves and those who did not (medians= 10.1 μg/cm2 versus 10.0 μg/cm2, respectively) -- suggesting that the exposure potential (contact with malathion) was similar between the two groups. In addition, the malathion levels measured on the clothes of intervention group members were slightly, but not statistically, higher than the control group (medians = 11.0 μg/cm2 versus 7.7 μg/cm2, respectively). Given these two findings, it is unlikely that there were any substantial behavioral differences between the groups that would affect exposure.

Hand Rinse Samples

Of the 44 post-intervention study participants, 42 provided hand rinse samples. Table 4 indicates that median malathion levels were significantly lower on the hands of farmworkers who wore gloves versus those who did not (8.2 vs 777.2 μg/pair of hands, Mann Whitney U test p<0.001), independent of post-intervention group participation. Based on the quality assurance evaluation of the hand rinse sample collection efficacy described in the Methods section, it is likely that our measurements of dermal loading, which are based on the first rinse, underestimate total dermal exposure.

DFR

During the post-intervention sampling period, we collected DFR samples from a recently sprayed field and analyzed them for malathion. Mean ± SD malathion levels for the eight DFR samples were M ± SD = 1.0 ± 0.2 μg/cm2 (See Table 4).

Transfer Coefficient (TC)

Based on the DFR data and the workers’ hand rinse malathion levels, we calculated agricultural transfer coefficients (TCs) for malathion accumulation on worker hands using the following equation (Korpalski et al., 2005):

TC (cm2/h) = Exposure (μg of malathion)/h) / DFR (μg malathion/cm2 leaf surface) Eq 1 TCs are the ratio of the exposure, determined usually by passive dosimetry on workers that reenter fields treated with pesticides, to the concurrent DFR samples collected in that crop. For these calculations we used the geometric mean of the DFR malathion levels (0.97 μg/cm2; n=8). Table 5 presents the TC results for 42 harvesters. The TC levels for all harvesters ranged from 0.6 to 413.3 cm2/hr. The TC levels for workers who wore gloves ranged from 0.6 to 73.4 cm2/hr, and from 20.7 to 413.3 cm2/h for those who did not wear gloves. Median TC levels among workers who wore gloves (1.6 cm2/hr) were nearly an order of magnitude lower than workers who did not wear gloves (148.9 cm2/hr). For workers that provided three sequential hand rinses and who did not wear gloves, the TCs ranged from 35.3 to 584.4 cm2/h (median TC = 242.9 cm2/h)(Table 5). When the arithmetic mean of the DFR malathion levels is used instead of the geometric mean for these calculations, the resulting TC values are approximately 3% lower.

Table 5.

Calculated transfer coefficients (TC) for accumulation of malathion on the hands of strawberry harvesters.

n Mean hours in the field (SD) Transfer coefficient (cm2/h)a
GM (95% CI) 50th Min-Max
All workers 42 5.6 (0.5) 4.7 (2.5, 8.9) 2.2 0.6 – 413.3
Workers who wore gloves 32 5.7 (0.5) 1.8 (1.2, 2.5) 1.6 0.6 – 73.4
Workers who did not wear gloves 10 5.5 (0.5) 110.1 (55.9, 216.8) 148.9 20.7 – 413.3
Provided three hand rinse samples and did not wear gloves 7 5.3 (0.3) 195.7 (86.2, 444.6) 242.9 35.3 – 584.4
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a

For TC calculations, workers’ malathion exposure are based on hand rinse samples (see Eq.(1)).

b TC calculations were based on first hand rinse sample only (see Results and Discussion for additional information).

c TC calculations were based on three sequential hand rinse samples.

DISCUSSION

To assess farmworker exposure and the efficacy of this intervention, we measured malathion in DFR, hand rinse, and clothing patch samples, and MDA metabolites in urine from strawberry harvesters working in fields recently sprayed with malathion. We found that post-intervention loading of malathion on hands was lower among workers who wore gloves compared to those who did not (median = 8.2 vs 777.2 μg/pair, respectively (p<0.001)); similarly, median MDA levels in urine were lower among workers who wore gloves (45.3 vs 131.2 μg/g, p<0.05). Additionally, we detected malathion on clothing (median = 0.13 μg/cm2), but not on skin. Overall, these findings suggest that the intervention was successful in reducing pesticide exposure among strawberry harvesters.

On a cross-sectional basis, the post-intervention median MDA metabolite levels were similar in the intervention (56.3 μg/g) and control groups (56.9 μg/g). However, six (of 15) workers in the control group wore gloves on a routine basis while three workers in the intervention group did not wear gloves; thus, we also analyzed the data stratified by glove use. Malathion levels on worker hands and MDA urinary metabolites were significantly lower in workers wearing gloves compared to workers who did not wear gloves. We detected negligible levels of malathion on skin samples whether or not workers were wearing one pant layer or an extra coverall as part of the intervention. We found much higher levels on clothing patch samples, with similar levels among control and intervention group participants. These findings indicate that wearing gloves resulted in lower levels of malathion on worker hands, and absorbed dose, and that, while malathion accumulated on worker clothing, it was not breaking through to the skin. These findings are consistent with studies reporting reduced pesticide exposure among farmworkers wearing protective clothing and gloves (Gomes et al., 1999; Keifer, 2000; Krieger and Dinoff, 2000). The data also suggest that wearing protective gloves and removing work clothes before returning home could reduce pesticide loading on skin and worker clothing, and, therefore, reduce transport of pesticides to worker homes.

Urinary MDA metabolite levels were significantly higher among workers who reported eating strawberries in the field on the day of sample collection compared to those who did not, suggesting that strawberry consumption could confound the association between glove use and exposure; however, strawberry consumption was evenly distributed between glove wearers and non-glove wearers. Furthermore, the significant inverse association of glove use and MDA metabolite levels remained consistent in multivariate models when controlling for strawberry consumption. Based on the coefficients resulting from this regression model, urinary MDA metabolite levels were 3.4 times higher among workers not wearing gloves compared to workers who wore gloves, and 2.9 times higher among workers eating unwashed strawberries in the field compared to those who did not. In this model, glove use, strawberry consumption and time worked in the field on the day of sample collection explained over 40% of the variation in MDA urinary metabolite levels (R2 (for model) = 0.43).

Given our result that strawberry consumption among harvesters working in a recently sprayed field was associated with significantly higher urinary MDA levels, U.S. EPA should further evaluate the potential for malathion exposure to pregnant women, other adults, and children at U-Pick farms.

The malathion DFR levels we observed (mean = 1.0 ± 0.24 μg/cm2) on samples collected within 72 hours of treatment were slightly higher than those reported in the literature. For example, Hernandez et al. (2002) reported mean malathion DFR levels of 0.39 ± 0.16 μg/cm2 on 10 strawberry samples collected after expiration of the restricted entry interval of 12 hours (Hernandez et al., 2002). Zhang et al. (2005), found mean leaf punch DFR levels ranging from 0.22 ± 0.047 μg/cm2 to 0.014 ± 0.007 μg/cm2 in samples collected 1-10 days after application (Zhang, 2005). Hernandez et al. (1997) reported mean malathion DFR from 28 samples during the strawberry harvest of 0.074 ± 0.088 μg/cm2, although they did not report how long after the application the samples were collected.

The maximum TC (584.4 cm2/h) we calculated is comparable to the assigned TC values (400 and 1500 cm2/h) published for strawberry harvesting by the U.S. EPA (U.S. EPA, 2000). If we assume that transfer to bare hands is rapidly saturable, and equilibrium is reached over a 1- to 2-hour exposure, as has been suggested in other studies (Spencer et al., 1995; Brouwer et al., 1999), the workers’ maximum TC would increase 2.5-fold to1,417.2 cm2/h. The dermal exposure measure (hand rinse sampling data) we used to calculate the TCs, however, reflects exposure only to the workers’ hands, not the whole body. Because these harvesters likely experienced dermal exposure via their faces and necks as well as their hands, we expect that our TC calculations are an underestimate. We found no other published agricultural TC data for comparison.

This study has several limitations. The cross-sectional relationship we found between glove use and decreased urinary metabolite levels must be interpreted with caution due to the inherent differences in individuals who, for example, would tend to comply with the intervention components and those who would not. However, malathion levels on clothing patch samples did not vary by intervention group or glove use, suggesting no substantial differences in exposure-prone behavior between the groups that would bias this finding. Also, as a cross-sectional study, we had no means of measuring changes in longitudinal exposure. Additionally, our study design did not allow us to directly test the efficacy of handwashing on pesticide loading on worker hands or metabolite levels. Because intervention participants wore gloves, leaving minimal residues on the hands to be washed away, we could not assess whether additional handwashing would have reduced dermal loading. However, these results should not be interpreted to suggest that handwashing is not important for reducing farmworker exposure. The hand rinse data confirm that dislodgeable malathion residues were present on worker hands, indicating that efforts to increase handwashing among farmworkers should be encouraged. In a separate paper, we will analyze the efficacy of the intervention in changing knowledge, attitudes, and exposure-related behaviors, including handwashing frequency.

Our quality assurance evaluation of the efficacy of the hand rinse sample collection procedures indicate that, on average, only 59% of the material removed by three sequential rinses was recovered in the first rinse, suggesting that our hand loading estimates significantly underestimate total hand pesticide loading and dermal exposure. This finding is consistent with studies showing that hand rinse methods may underestimate total dermal pesticide exposure by two to five-fold (Fenske and Lu, 1994). In our study, poor efficacy of the sampling method did not affect our ability to observe significant differences in hand pesticide loading related to glove use. However, as noted by Fenske and Lu (1994), for studies focusing on dermal exposure as the primary outcome, “...efficiency studies should be conducted prior to field investigations.”

Finally, we focused on malathion exposure and strawberry harvesters; thus the measured pesticide exposure may not be generalizable to other farmworker populations. However, a strength of the study is that our findings are the result of both biomonitoring and environmental sampling and are potentially relevant to strawberry harvesters elsewhere in the country. For intervention participants, we did not collect clothing patch samples from the pants that workers wore under their coveralls. For this reason, we did not directly measure potential breakthrough through the coveralls onto intervention participants’ clothing. However, the very low levels of malathion measured on participants’ skin, whether they were wearing a single pant layer (controls) or coveralls and pants (intervention), suggests that very little malathion broke through the coveralls and accumulated on the intervention participants’ clothing. Thus, the coveralls were likely effective in preventing accumulation of malathion on their regular work clothing. Additionally, given that very little malathion broke through worker clothing onto the skin, and the strong association we found between glove use and MDA urinary metabolite levels, our findings indicate that, excluding strawberry consumption, most worker exposure was due to dermal absorption through the hands.

CONCLUSION

Glove use among strawberry harvesters significantly reduced malathion exposure as measured by malathion loading on hands, and MDA urinary metabolite levels. Clothing prevented virtually all accumulation of malathion on skin elsewhere on workers’ bodies. The use of coveralls by intervention participants likely prevented malathion accumulation on the workers’ regular clothing. The use of gloves and possibly handwashing to minimize pesticide levels on worker hands, and coveralls to prevent pesticide accumulation on worker clothing, is likely to reduce the potential for occupational take-home pesticide exposure to families and children among strawberry harvesters entering fields after expiration of the malathion post-harvest interval (72 hours). The benefits of these intervention components may be more significant for farmworkers entering fields after expiration of the 12 hour malathion re-entry interval, when malathion dislodgeable foliar residues are likely to be higher than the levels we observed. In follow-up to this study, we are working with community partners, agricultural officials, and grower organizations to disseminate the study findings and identify workable strategies to prevent take-home pesticide exposure. Future research should focus on the specific benefits of handwashing and also evaluate whether these approaches can reduce farmworker and family exposure to other pesticides with different physical-chemical characteristics. Finally, behavioral interventions are needed to reduce consumption of strawberries in the field and the U.S. EPA should evaluate the feasibility of encouraging harvesters to wear gloves when dislodgeable foliar pesticide residues are present in fields.

Acknowledgments

This research was funded by NIEHS CBPR Grant RO1ES11352. Additional support was provided by U.S. EPA grant RD 83171001 and NIEHS grant PO1 ES009605. Analysis for hand rinse, clothing patch, and dislodgeable foliar residue samples was supported by NIOSH. Additional support for environmental sample collection analysis and M. Boeniger's time was provided by NIOSH. The contents of this manuscript are the responsibility of the authors and do not necessarily represent the official views of the funding agencies. The authors declare they have no competing financial interests. We thank the field staff and the farmworkers and farmers who participated in this study for their valuable time and commitment. We also thank Natividad Medical Center for hosting our field office in Salinas, CA. Finally, we thank J. Ferber and R. Krieger for their technical assistance.

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