Journal of Exposure Science and Environmental Epidemiology (2009) 19, 544–554
r 2009 Nature Publishing Group All rights reserved 1559-0631/09/$32.00
www.nature.com/jes
Exposure to atrazine and selected non-persistent pesticides among corn
farmers during a growing season
BERIT BAKKEa,b, ANNECLAIRE J. DE ROOSc,d, DANA B. BARRe, PATRICIA A. STEWART a, AARON BLAIRa,
LAURA BEANE FREEMANa, CHARLES F. LYNCHf, RUTH H. ALLENg, MICHAEL C.R. ALAVANJAa
AND ROEL VERMEULENa,h,i
a
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Rockville,
Maryland, USA
b
National Institute of Occupational Health, Oslo, Norway
c
Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
d
Department of Epidemiology, University of Washington, Seattle, Washington, USA
e
National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
f
Department of Epidemiology, University of Iowa, Iowa City, Iowa, USA
g
US Environmental Protection Agency, Washington, District of Columbia, USA
h
Institute for Risk Assessment Sciences, University of Utrecht, Utrecht, The Netherlands
i
Julius Center, Utrecht Medical Center, Utrecht, The Netherlands
The aim was to develop quantitative estimates of farmers’ pesticide exposure to atrazine and to provide an overview of background levels of selected nonpersistent pesticides among corn farmers in a longitudinal molecular epidemiologic study. The study population consisted of 30 Agricultural Health Study
farmers from Iowa and 10 non-farming controls. Farmers completed daily and weekly diaries from March to November in 2002 and 2003 on pesticide
use and other exposure determinants. Urine samples were collected at 10 time points relative to atrazine application and other farming activities. Pesticide
exposure was assessed using urinary metabolites and diaries. The analytical limit of detection (LOD) ranged between 0.1 and 0.2 mg/l for all pesticide
analytes except for isazaphos (1.5 mg/l) and diazinon (0.7 mg/l). Farmers had higher geometric mean urinary atrazine mercapturate (AZM) values than
controls during planting (1.1 vs oLOD mg/g creatinine; Po0.05). AZM levels among farmers were significantly related to the amount of atrazine applied
(P ¼ 0.015). Interestingly, farmers had a larger proportion of samples above the LOD than controls even after exclusion of observations with an atrazine
application within 7 days before urine collection (38% vs 6%, Po0.0001). A similar pattern was observed for 2,4-D and acetochlor (92% vs 47%,
Po0.0001 and 45% vs 4%, Po0.0001, respectively). Urinary AZM levels in farmers were largely driven by recent application of atrazine. Therefore, the
amount of atrazine applied is likely to provide valid surrogates of atrazine exposure in epidemiologic studies. Elevated background levels of non-persistent
pesticides, especially 2,4-D, indicate importance in epidemiologic studies of capturing pesticide exposures that might not be directly related to the actual
application.
Journal of Exposure Science and Environmental Epidemiology (2009) 19, 544–554; doi:10.1038/jes.2008.53; published online 3 December 2008
Keywords: exposure assessment, pesticide exposure, urine, farmers, prospective studies.
Introduction
A wide variety of agricultural pesticides is used on farms,
including herbicides, crop and livestock insecticides, fungicides, and fumigants (Curwin et al., 2005a). Farmers may be
exposed to these pesticides when mixing, loading and
applying. After application they may be exposed if they
1. Address all correspondence to: Dr. Michael C.R. Alavanja, Occupational and Environmental Epidemiology Branch, Division of Cancer
Epidemiology and Genetics, National Cancer Institute, NIH, DHHS,
6120 Executive Boulevard, Building EPS 8000, Rockville, MD 208527240, USA. Tel.: þ 1 301 435 4720. Fax: þ 1 301 402 1819.
E-mail: [email protected]
Received 15 January 2008; accepted 9 June 2008; published online 3
December 2008
work in treated fields or if equipment, work clothing or the
home environment is contaminated with pesticides.
One of the major challenges in epidemiologic studies of
health effects from pesticide exposure is obtaining an accurate
assignment of exposure. The application of pesticides by
farmers is characterized by large seasonal differences in the
types, amounts, and frequency of pesticide application
(Kromhout and Heederik, 2005). This temporal variation
in pesticide use may give rise to large within-worker
variations in exposure over the course of a year. In such
cases, a small number of measurements may lead to imprecise
estimates of long-term average exposures. However, it is
often infeasible to measure individual exposure levels over an
entire year or across all workers when studying chronic
health effects. In epidemiologic studies, individual long-term
Pesticide exposure among corn farmers
Bakke et al.
average exposures can be estimated by using (empirical)
deterministic models based on known determinants of
exposure (Preller et al., 1995). Such models may reduce
non-differential misclassification by using detailed longitudinal information on important exposure determinants
over time (i.e., taking into the account the effect of variation
in the application pattern throughout the season) to predict
exposure levels.
In this paper we describe application-specific exposure
levels for atrazine, and background exposure levels for some
selected non-persistent pesticides among corn farmers and
controls over an entire growing season. The purpose of the
exposure survey was to develop daily quantitative estimates
of farmers’ pesticide exposure to atrazine during 1 year using
measurements of atrazine mercapturate (AZM) in urine and
detailed information on pesticide application from exposure
diaries. The models will be used in a molecular epidemiologic
study evaluating the relationship between exposure to
atrazine and various immune effects in corn farmers
(Vermeulen et al., 2005).
Methods
Study Population
Corn farmers from Iowa were identified from the Agricultural
Health Study (AHS; Alavanja et al., 1996). To be eligible for
this study, the farmers had to be male, age 40–60 years, nonsmoking, and planning to personally apply atrazine to at
least 300 acres of corn as part of their normal farm operation
during the upcoming season. Non-farmers serving as controls
in the study were agricultural extension agents from Iowa
State University in Ames, IA. The control inclusion criteria
were the same as for farmers except that they had not applied
pesticides occupationally within the past 5 years. To reduce
the complexity of the field logistics, farmers and controls
residing in counties closest to the Iowa Field Station in Iowa
City, IA were selected. The inclusion of farmers and controls
continued until the target population of 30 farmers and 10
controls was reached. Of the subjects who were eligible and
invited to participate, the participation rate was 90% and
100%, among farmers and controls, respectively. Study
participants received a compensation of $300 for completing
the study protocol. This study was approved by the Human
Subjects Office at the University of Iowa and the National
Cancer Institute Institutional Review Board.
Data Collection
The field study periods were March/April 2002 through
January 2003, and March/April 2003 through January 2004.
Ten farmers and five controls were followed during the first
year, and twenty additional farmers and five controls during
the second year of the study. Two farmers participated in
both years. Table 1 gives an overview of the data collection
activities. The study population was followed throughout an
entire farming year, which was divided into preplanting
(March/April), planting (April/May), growing (May–September), postharvest (October/November) and an off-season
period (January). In total, we collected 10 urine samples
from each farmer: nine spot samples and one postapplication
overnight urine sample (average collection period B12 h).
Urine samples at time point 2–5 were self-collected according
to a standard protocol and kept cold at 41C. Other samples
were collected during a field research visit. The two farmers
who participated in both years had 18 spot samples and two
postapplication samples. We collected only spot urine
samples from controls: four samples from controls in the
first year (urine collection time points 1, 7, 9, and 10; Table 1)
Table 1. Description of data collection in the Corn Farmer Study, Iowa 2002–2004.
Urine collection
time point
1
2
3
4
5
6
7
8
9
10
Month
Season
Type of urine
sample
March/April
April/May
Preplanting
Planting
Spota
First morning voidb,c
April/May
April/May
April/May
April/May
May/June
July/September
October/November
January
Planting
Planting
Planting
Planting
Growing
Growing
Postharvest
Off-season
Postapplicationb,c,e
First morning voidb,c
First morning voidb,c
Spota
Spota
Spota
Spota
Spota
No. of days after first atrazine
application (average)
25
Morning on day of atrazine
application
Atrazine application
1
2
2
30
100
195
250
Type of questionnaire
Baseline questionnaire, start daily diary
Daily diaryc,d
Daily diaryd
Daily diaryd
Daily diaryd
Start weekly diary
Weekly diary
Weekly diary
Stop weekly diary
Off-season questionnaire
a
Urine sample collected during field research visit.
Self-collected.
c
Farmers only.
d
Weekly diary for controls.
e
Urine collected from atrazine postapplication through first morning void the next day.
b
Journal of Exposure Science and Environmental Epidemiology (2009) 19(6)
545
Bakke et al.
and six samples from controls in the second year (urine
collection time points 1, 6–10). The timing of urine
collections was scheduled relative to atrazine application
and seasonal farming activities (preplanting, pre- and postatrazine application during planting, growing, postharvest,
and off-season). In farmers, we first collected a preplanting
spot urine sample in March/April before any pesticide
application. The second sample (preapplication) was collected the morning of the first atrazine application of the year
for each farmer. The third urine sample (postapplication)
was collected starting at the completion of the first atrazine
application through the first morning void the next day. The
next seven urine samples were then collected at specific time
points throughout the year after the first atrazine application
(i.e., average 1, 2, 30, 100, 195, and 250 days) and therefore
not linked to any specific pesticide application. Urine
sample collection among controls occurred within the date
range of farmers’ sample collection for any given time point
(Table 1).
Several questionnaires were used to collect information
about pesticide use throughout the study period (Table 1).
Each farmer and control was administered a baseline
questionnaire when the first urine sample was collected in
March/April. The farmers then completed daily diaries
during the planting season (roughly 4 weeks, April–May),
followed by weekly diaries for the remainder of the growing
and postharvest season (roughly 28 weeks, May–November).
Controls completed weekly diaries from April to November
(for roughly 32 weeks). Finally, each farmer and control was
administered an off-season questionnaire by a nurse in
January of the following year.
Sampling and Analysis of Pesticides in Urine
At each urine collection time point, approximately 50 ml of
urine was retained. The urine samples were stored in coolers
with icepacks and transported to the clinical laboratory in the
Department of Pathology at the University of Iowa
Hospitals and Clinics where they were aliquoted and stored
at 801C until analysis. External quality control was based
on splits of selected field urine samples submitted as blind
duplicates to the laboratory.
Urine samples were analyzed for a standard panel of 14
non-persistent pesticides (pesticide (measured compound)):
2,4,5-T (2,4,5-T), 2,4-D (2,4-D), pyrethroid (3-phenoxybenzoic acid), acetochlor (acetochlor mercapturate), atrazine
(AZM), isazaphos (5-chloro-1,2-dihydro-1-isopropyl-[3H]1,2,4-triazol-3-one), coumaphos (3-chloro-4-methyl-7-hydroxycoumarin), pirimiphos methyl (2-diethylamino-6-methyl
pyrimidin-4-ol), diethyl-m-toluamide (diethyl-m-toluamide),
diazinon
(2-isopropyl-4-methyl-6-hydroxypyrimidinol),
malathion (malathion dicarboxylic acid), metolachlor (metolachlor mercapturate), methyl parathion (para-nitrophenol), chlorpyrifos (3,5,6-trichloro-2-pyridinol (TCPY)).
The urine analysis panel covered six of the reported
546
Pesticide exposure among corn farmers
pesticides including four of the major pesticides used by the
farmers.
Pesticide metabolites (or parent compounds) were measured in urine using a modification of a high performance
liquid chromatography–tandem mass spectrometry method
with atmospheric pressure chemical ionization that included
confirmatory ions as well as quantification ions (Olsson et al.,
2004). The target analytes were quantified using isotope
dilution calibration. Creatinine in urine was determined using
a commercially available enzyme slide technology (Vitros 250
Chemistry System, Ortho-Clinical Diagnostics; Olsson et al.,
2004). The analytical limit of detection (LOD) ranged
between 0.1 and 0.2 mg/l for all pesticide analytes except for
isazaphos (1.5 mg/l) and diazinon (0.7 mg/l).
Calculation of Exposure Determinants
The daily and weekly diaries provided information on some
potential predictors of pesticide exposure (e.g., the total
amount of product applied, acreage treated, duration of
application, application method, personal protective equipment, personal hygiene practices). This information was used
to identify predictors of exposure. Reported product names
were linked to their respective EPA labels and various
databases (i.e., http://entweb.clemson.edu/pesticid/) to identify the amount of active ingredients (a.i.) in the product. The
amount of product used, as reported in the questionnaires,
was converted to total kg of a.i. based on the percentage or
amount of a.i. in each product. Summary variables were then
created for total kg of a.i. used the day before and 7 days
before the urine collection. These timeframes were selected
because they coincided with information we had on pesticide
use from daily diaries in the planting season and weekly
diaries for the remainder of the year. Furthermore, given the
relative short half-lives of non-persistent pesticides one would
not expect applications carried out more than 7 days before
urine collection to be directly related to the specific urine
sample. Similarly, we calculated a summary variable for
acreage treated and duration of application for the same time
periods.
Statistical Analysis and Exposure Modeling
The laboratory analytical precision for each pesticide
metabolite was estimated as the CV and the intraclass
correlation coefficient (ICC) from pairs of duplicate urine
samples (n ¼ 30; from both farmers and controls), which had
been aliquoted and assigned random identification numbers
before shipment to the laboratory. The CV was estimated as
CV(%) ¼ O(exp(s2ws)1)100 where s2ws is the estimated
within-sample error variance obtained from a one-way
analysis of variance of the ln-transformed levels of each
pesticide (PROC MIXED of SAS; Kim et al., 2006). The
ICC was estimated as the between-sample variation divided
by the sum of the between-sample variation and withinsample variation over all subjects.
Journal of Exposure Science and Environmental Epidemiology (2009) 19(6)
Pesticide exposure among corn farmers
Using cumulative probability plots, the urinary analytes
were found to be best described by lognormal distributions
and were ln-transformed for statistical analyses. Standard
measures of central tendency and distributions (i.e., arithmetic mean, median, geometric mean (GM), range, and
geometric standard deviation) were calculated. Urinary
concentrations reported as below the LOD were
replaced with values equal to the LOD divided by 2
(Hornung and Reed, 1990) and adjusted for creatinine
before analysis.
Correlation between urinary levels of AZM and possible
exposure determinants (i.e., the amount of atrazine a.i.
applied, acreage treated, and duration of application) was
evaluated using the Spearman’s correlation (RSpearman)
coefficient.
Mixed effect models (PROC MIXED) were used to
evaluate differences between farmers and controls and
between seasons within farmers and controls, and to analyze
the association between potential determinants of exposure
and the amount of AZM excreted in the urine of farmers.
Other pesticides were not modeled because the numbers of
observations with a pesticide application within 7 days before
the urine collections were limited. Seventeen farmers had
urine collections at time points 5 (first morning void) and 6
(spot sample during the day) on the same day and hence
these samples cannot be regarded as independent observations given the high level of autocorrelation. For these
farmers the urine collection at time point 6 was omitted from
the mixed model analysis. In mixed effect models subject was
introduced as a random effect to account for repeated
measures on the same person and to estimate the betweenand within-person variance components. The within- and
between-worker variability was also expressed as the ratio
between the 97.5th and 2.5th percentiles of the log-normal
within- and between-worker exposure distribution, and
computed as exp(3.92 variance component0.5; Rappaport,
1991). Several structures of the covariance matrix were
explored. A compound symmetric covariance matrix was
selected based on the Akaike’s Information Criterion (AIC).
An intercept-only model without any explanatory variables
was calculated to estimate baseline variance components
using restricted maximum likelihood estimation. Possible
demographic (e.g., age, body mass index (BMI)), lifestyle
(e.g., past smoking status, alcohol consumption) and
occupational exposure determinants (e.g., fixed effects) were
first investigated in univariate models. Occupational exposure
determinants evaluated were: amount of atrazine a.i. applied,
acreage treated, duration of application, type of mixing/
loading method (direct tank filling by a hose coupling to a
bulk container; premixed in a secondary container and
transferred by pouring into the primary spray tank; scooping
a solid directly into the tank), type of application method
(backpack; handheld spray gun; boom on tractor/truck open
or closed cab), use of personal protective equipment (dust
Journal of Exposure Science and Environmental Epidemiology (2009) 19(6)
Bakke et al.
mask; gloves; coveralls; long sleeved shirt; hat), personal
hygiene practices (washing hands; washing body), urine
collection time point per season, and type of urine collection
(e.g., spot sample, postapplication sample). Multivariate
models were then constructed using a forward stepwise
procedure (Kleinbaum et al., 1998). The model was built in
steps beginning with the variable with the lowest P-value
(variables with P-values 40.2 were not included) in the
univariate models and the largest decrease of the AIC in the
univariate analyses, and adding variables until further
additions did not result in a statistically significant P-value
for the added variable or earlier variables lost their
significance (P-value 40.1). AIC was used to compare
different models. Separate analyses were performed for
atrazine on the postapplication urine samples only. In these
analyses the second postapplication sample from the two
farmers, who participated in both years, was not included.
All models were evaluated for pesticide concentrations
adjusted and unadjusted for creatinine. Results did not
differ, however, and therefore we only present creatinineadjusted urinary pesticide levels. Median urinary creatinine
concentration was 1.36 g/l (range 0.1–4.2) in our study
samples.
The proportion of urine samples above the LOD of
farmers and controls were compared using Fisher’s exact test.
SAS version 9.1.3 (SAS Institute Inc., Cary, NC, USA)
was used for all statistical analyses.
Results
In total, 97 different products, containing 61 different a.i.,
were reported in the diaries. Table 2 identifies the 12 most
frequently reported pesticides (reported by at least 25% of
the farmers) and the number of farmers who had applied the
corresponding pesticides 1 day and within 7 days before urine
collections. All farmers (by design) and none of the controls
reported applying atrazine. Except for glyphosate (n ¼ 29)
and 2,4-D (n ¼ 25), other pesticides were applied by fewer
than 50% of the farmers. The four pesticides applied in the
highest quantities (kg of a.i.) were acetochlor, atrazine,
glyphosate, 2,4-D and chlorpyrifos.
Quality Control Urine Measurements
One farmer did not provide urine samples for time points
3–5 (planting). Laboratory results were not available for
four farmers at time point 10 (off-season) and one control
at time point 1 (preplanting). In total, 367 urine samples
were successfully analyzed resulting in 5034 analytical
results.
Of the pesticides analyzed, only atrazine, 2,4-D, chlorpyrifos and acetochlor were used regularly by the farmers;
thus, we report detailed results for only these four pesticides.
Results of other measured urinary analytes are reported in
547
548
Bakke et al.
Table 2. Overview of pesticides most frequently reported by study farmers (more than 25% of farmers), and frequency of farmers with pesticide application 1 day and within 7 days before
urine collection.
Pesticide
Na
Medianb cum. kg
applied per farmer
(percentiles 10–90)
Medianc cum. acres
applied per farmer
(percentiles 10–90)
Amount of active
ingredient measured
in urine?
Number of farmers applying pesticides 1 day (and within 7 days)
before urine collections, by urine collection time point
Preplanting
Planting
1
30
29
25
14
13
11
10
10
10
9
8
7
217
188
51 g
31
1.3
4.73
28
2.8
0.75
567
3.0
10.8i
(47–530)
(41–623)
(4.4–293)
(7.1–262)
(0.55–4.7)
(1.8–21)
(5.0–90)
(0.51–43)
(0.27–3.0)
(88–1495)
(0.74–28)
(0.63–27)
610
623f
357 h
70
200
150
220
103
155
636
218
425i
(278–2063)
(240–1840)
(9.5–1103)
(16–490)
(60–420)
(45–600)
(80–587)
(4.8–1198)
(50–463)
(90–1155)
(60–2005)
(125–1111)
Yes
No
Yes
Yes
No
No
No
No
No
Yes
No
No
0d
0
0
0
0
0
0
0
0
0
0
0
(0)e
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
0
0
2
0
0
0
0
0
0
1
0
0
(0)
(1)
(6)
(3)
(0)
(0)
(1)
(0)
(0)
(1)
(0)
(0)
3
30
1
6
1
2
4
2
2
2
6
2
6
(30)
(6)
(10)
(4)
(2)
(4)
(3)
(2)
(2)
(6)
(2)
(6)
4
16
5
3
1
2
3
1
0
2
4
0
4
(30)
(6)
(10)
(4)
(2)
(4)
(3)
(2)
(2)
(6)
(2)
(6)
5
13
2
4
2
0
1
1
0
0
4
0
3
(30)
(6)
(10)
(5)
(2)
(4)
(3)
(2)
(2)
(6)
(2)
(6)
6
11
1
2
2
0
1
2
0
0
4
0
2
(30)
(7)
(10)
(5)
(2)
(4)
(3)
(2)
(2)
(6)
(2)
(6)
Postharvest
Off-season
8
9
10
7
0
0
0
0
0
0
0
0
0
0
0
0
(3)
(13)
(3)
(0)
(2)
(0)
(2)
(2)
(2)
(0)
(1)
(0)
0
0
0
0
0
0
0
0
0
0
0
0
(0)
(2)
(1)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
0
0
0
0
0
0
0
0
0
0
0
0
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
0
0
0
0
0
0
0
0
0
0
0
0
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
a
Number of farmers reported applying the pesticide.
Median of cumulative kg applied per farmer over 1 year.
c
Median of cumulative acres treated per farmer over 1 year.
d
Number of farmers applying pesticide 1 day before urine collection.
e
Number of farmers applying pesticide within 7 days before urine collection.
f
One farmer did not report acreage. The median of cumulative acres treated per farmer therefore relates to 28 farmers.
g
Three farmers did not report the amount of pesticide applied. The median of cumulative kg applied per farmers therefore relates to 22 farmers.
h
Five farmers did not report acreage. The median of cumulative acres treated per farmers therefore relates to 20 farmers.
i
One farmer did not report the amount of pesticide applied. The median of cumulative kg applied and acres treated per farmers therefore relates to six farmers.
b
Pesticide exposure among corn farmers
Journal of Exposure Science and Environmental Epidemiology (2009) 19(6)
Atrazine
Glyphosate
2,4-D
Chlorpyrifos
Nicosulfuron
Mesotrione
Dicamba
Clopyralid
Rimsulfuron
Acetochlor
Flumetsulam
Isoxaflutole
2
Growing
Pesticide exposure among corn farmers
Bakke et al.
Table 3. Within- and between-worker variability of atrazine mercapturate for all farmers and controls and by season.
Season
Urine collection
time point
Na
Nb 4LOD (%) AMc
Farmers (n ¼ 30)
All
Preplanting/off-season
Planting
Growing/postharvest
1–10
1,10
2–6
7–9
313
60
157
96
181
8
133
40
Controls (n ¼ 10)
All
Preplanting/off-season
Planting
Growing/postharvest
1; 6–10
1,10
6
7–9
49
19
5
25
3
1
0
2
Percentiles
10
90
5.2
0.22
9.7
1.0
GMd
GSDe
0.30
0.06
1.0
0.10
8.0
2.7
6.5
5.0
(58)
(13)
(85)
(42)
2.2
0.12
3.5
1.4
oLOD
oLOD
oLOD
oLOD
(6)
(5)
(0)
(8)
0.11
0.08
NAj
0.13
oLOD
0.17
0.06
2.4
oLOD
0.13
0.05
22
oLOD oLOD oLOD oLOD
oLOD
0.25
0.07
2.6
GSDwwf GSDbwg R0.95wwh R0.95bwi
6.8
2.1
4.0
4.6
2.2
1.9
3.6
1.7
1881
20
223
397
21
13
156
7
a
Total number of samples.
Number of samples above the limit of detection (LOD).
c
Arithmetic mean (mg/g creatinine), urinary concentrations below the LOD were replaced with LOD/2.
d
Geometric mean (mg/g creatinine), urinary concentrations below the LOD were replaced with LOD/2. Owing to left censoring the GM may be biased.
e
Geometric standard deviation. Owing to left censoring the GSD may be biased.
f
Within worker geometric standard deviation.
g
Between worker geometric standard deviation.
h
R0.95ww: The ratio between the 97.5th and 2.5th percentiles of the log-normal within-worker exposure distribution, was computed as exp(3.92 variance
component0.5; Rappaport, 1991).
i
R0.95bw: The ratio between the 97.5th and 2.5th percentiles of the log-normal between-worker exposure distribution, was computed as exp(3.92 variance
component0.5; Rappaport, 1991).
j
Not applicable, all samples oLOD.
b
Appendix A. Estimates of CVs for the four pesticide analytes
based on duplicate samples from the study were as follows
(analyte (CV)): AZM (6.6%); 2,4-D (12.1%); acetochlor
mercapturate (8.3%; after exclusion of one outlier); and
TCPY (32.7%). The ICC was Z80% for all analytes.
Factors Related to Urinary Levels of Atrazine
Mercapturate
Table 3 gives an overview of urinary levels for AZM, before
and after stratification by season for both farmers and
controls. Farmers had significantly higher GM urinary levels
in the planting season compared to controls (Po0.05), but
there was no difference in urinary levels between farmers and
controls in the preplanting/off-season or in the growing
season (P40.05). Statistically significant increases in urinary
AZM were seen among farmers in the planting season versus
the preplanting/off-season (Po0.05). Among controls the
mean urinary pesticide levels of AZM did not differ by
season (P40.05). The proportion of samples above the LOD
was higher among farmers than controls even when
observations with an atrazine application within the last
week were not included.
Not unexpectedly, given the seasonality of pesticide
application, the within-worker variance was considerably
higher than the between-worker variance for AZM
(R0.95ww ¼ 1881 vs R0.95bw ¼ 21). After stratification by
season, the within-worker variance was reduced considerably
(range of R0.95ww: 20–397). The same change was seen for
Journal of Exposure Science and Environmental Epidemiology (2009) 19(6)
the between-worker variance except for the planting season,
where the R0.95bw increased (R0.95bw ¼ 156).
Most of the information on pesticide application practices
reported in the daily and weekly diaries did not show much
variation in application practices between farmers. Most
farmers used a spray boom with an enclosed cab for applying
pesticides (n ¼ 26) and direct tank filling as the mixing and
loading method (n ¼ 25). Farmers also were similar with
regard to hygiene and use of personal protective equipment
practices (i.e., most farmers washed their hands and body at
the end of the day and most farmers used gloves when mixing
and loading pesticides but not when spraying the pesticide).
However, there were large variations among farmers in the
amount of atrazine applied and acreage treated with atrazine
(Table 2). Because atrazine has a presumed relatively short
biological half-life (24–28 h; Gilman et al., 1998), only
applications occurring 1 day before urine collection were
included in the analysis (n ¼ 66). Correlation between the
duration of application and amount of atrazine applied
(rSpearman ¼ 0.55, Po0.0001); the duration of application
and acreage treated (rSpearman ¼ 0.69, Po0.0001); and acreage treated and amount of atrazine applied (rSpearman ¼ 0.72,
Po0.0001) was moderate to high.
Statistical modeling of the determinants indicated that the
amount of atrazine applied the day before urine collection
was the best predictor of urinary AZM levels when all
spot and the postapplication samples were included in the
model (Table 4). Separate analysis restricted to the post549
Pesticide exposure among corn farmers
Bakke et al.
Table 4. Final model for factors related to urinary atrazine mercapturate levels in farmers who had applied atrazine within 1 day of urine collection.
Modela
All urine samples
N
Atrazine
ln(atrazine mercapturate, mg/g creatinine)
Intercept
Amount applied 1 day before urine collection (kg)
Type of urine collection
Spote
Postapplication
Variance
2
bws (explained variance)
2
wws (explained variance)
b
b
SE
c
Only postapplication urine sample
P-value
66d
N
b
SE
P-value
0.29
0.093
0.38
0.0039
0.45
0.022
29i
0.48
0.0076
0.33
0.0030
0.16
0.016
0.26
0
0.29
0.38
Totalf
1.54
2.06
Modelg
1.1
1.31
%h
29
38
a
Compound symmetry covariance structure assumed. LOD/2 imputed for samples below the LOD.
Regression coefficient.
Standard error.
d
Only observations with an atrazine application the day before a urine collection are included. Seventeen farmers had urine collections at time points 5 and 6
on the same day. For these farmers the urine collection at time point 6 was omitted from the mixed model analysis. One farmer had no laboratory result for
the postapplication sample.
e
Spot samples include first morning voids.
f
Total variance from model with only random effects.
g
Variance from model with fixed and random effects.
h
Percent of variance explained by fixed effects.
i
Second postapplication urine sample from two farmers, who participated in both years, are not included in the model. One farmer had no laboratory result
for the postapplication sample.
b
c
application samples gave similar results. Identified exposure
determinants explained 29% and 38% of the between- and
within-worker variance, respectively (Table 4). There were no
significant effects of BMI, past smoking status, and current
alcohol consumption on AZM urine levels (P40.05,
not shown).
Urinary Levels of 2,4-D, Acetochlor Mercapturate, and
Chlorpyrofos by Season
Of the 12 most frequently reported pesticides (Table 2) only
biomarkers of 2,4-D, acetochlor and chlorpyrifos were
quantified in the urine in addition to atrazine. Table 5 gives
an overview of urinary levels for 2,4-D, acetochlor mercapturate and TCPY before and after stratification by season for
both farmers and controls. Farmers had significantly higher
GM urinary 2,4-D levels compared to controls in each
seasons (Po0.05). These differences remained significant
even after exclusion of urines collected within 7 days of a
2,4-D application.
No statistically significant difference between farmers and
controls in urinary levels for acetochlor mercapturate and
TCPY were found in any season except a borderline
significant difference for acetochlor mercapturate in the
planting season (P ¼ 0.09). Although, no difference was seen
in average levels of all samples analyzed across the entire
year, for acetochlor mercapturate a striking difference was
observed in the proportion of samples above the LOD
550
between farmers and controls (48% vs 4%; Fisher exact
test Po0.0001). This difference remained after exclusion of
urine samples collected within 7 days of an acetochlor
application (45% vs 4%; Fisher exact test Po0.0001).
A similar pattern (after exclusion of urine samples with
an application in the past 7 days) was observed for atrazine
(38% vs 6%; Po0.0001), and 2,4-D (92% vs 47%;
Po0.0001).
Statistically significant increases in 2,4-D, and acetochlor
mercapturate levels were seen among farmers in the planting
season versus the preplanting/off-season (Po0.05). These
differences remained significant for 2,4-D even after exclusion
of urines collected within 7 days of the corresponding
application. Interestingly, this pattern seemed to be independent of whether the farmer himself had applied 2,4-D during
the growing season (n ¼ 25) or not (n ¼ 5). No statistically
significant differences between seasons in GM urinary levels
for TCPY were seen for farmers (P40.05). GM urinary
pesticide levels of each agent among the controls did not
differ by season (P40.05).
Discussion
In this study we have investigated exposure patterns of
atrazine and selected non-persistent pesticides throughout a
whole growing season among a group of corn farmers from
Journal of Exposure Science and Environmental Epidemiology (2009) 19(6)
Pesticide exposure among corn farmers
Journal of Exposure Science and Environmental Epidemiology (2009) 19(6)
Table 5. Overview of 2,4-D, acetochlor (acetochlor mercapturate), and chlorpyrifos (3,5,6-trichloro-2-pyridinol (TCPY)) levels for all farmers and controls and by season.
Season
Farmers (N ¼ 30)
Pesticide
Controls (N ¼ 10)
Urine collection Na Nb 4LOD (%) AMc GMd GSDe
time point
Percentiles
10
All
Preplanting/off-season
Planting
Growing/postharvest
2,4-D
2,4-D
2,4-D
2,4-D
1–10
1,10
2–6
7–9
314
61
157
96
291
51
150
90
(93)
(84)
(96)
(94)
14.4
2.9
22.9
7.8
2.1
0.64
3.4
2.2
All
Preplanting/off-season
Planting
Growing/postharvest
Acetochlorf
Acetochlor
Acetochlor
Acetochlor
1–10
1,10
2–6
7–9
313
60
157
96
150
10
101
39
(48)
(17)
(64)
(41)
5.0
0.24
9.2
0.98
0.20
0.07
0.46
0.10
9.2
3.2
12.6
4.6
All
Preplanting/off-season
Planting
Growing/postharvest
Chlorpyrifosg
Chlorpyrifos
Chlorpyrifos
Chlorpyrifos
1–10
1,10
2–6
7–9
314
61
157
96
142
28
71
43
(45)
(46)
(45)
(45)
2.2
3.5
1.7
2.2
0.42
0.47
0.41
0.41
7.0
7.3
6.9
7.1
Urine collection Na Nb 4LOD (%)
time point
AMc
GMd
GSDe
90
7.5
0.17 32.6
5.2 oLOD 4.3
7.9
0.21 67.3
6.1
0.19 23.0
1; 6–10
1,10
6
7–10
49
19
5
25
23
13
3
7
(47)
(68)
(60)
(28)
oLOD 5.9
oLOD 0.20
oLOD 24.7
oLOD 0.68
1; 6–10
1,10
6
7–10
49
19
5
25
2
0
0
2
(4)
(0)
(0)
(8)
oLOD
oLOD
oLOD
oLOD
1; 6–10
1,10
6
7–10
49
19
5
25
18
7
2
9
(37)
(37)
(40)
(36)
5.0
5.1
4.7
5.0
0.52
0.50
1.35
0.37
0.16
0.24
0.25
0.11
0.17
0.06
oLOD oLOD
oLOD oLOD
0.27
0.07
1.4
1.2
1.8
1.5
0.41
0.37
0.54
0.43
Percentiles
10
90
4.1
3.6
7.8
3.9
oLOD
oLOD
oLOD
oLOD
1.8
1.8
6.0
1.6
2.6
3.2
oLOD 0.17
oLOD oLOD
oLOD oLOD
oLOD 0.25
5.3
5.1
7.2
5.4
oLOD
oLOD
oLOD
oLOD
4.7
4.6
4.7
5.1
a
Total number of samples.
Number of samples above the limit of detection (LOD).
c
Arithmetic mean (mg/g creatinine), urinary concentrations below the LOD were replaced with LOD/2.
d
Geometric mean (mg/g creatinine), urinary concentrations below the LOD were replaced with LOD/2.
e
Geometric standard deviation.
f
Acetochlor mercapturate.
g
3,5,6-Trichloro-2-pyridinol (TCPY).
b
Bakke et al.
551
Bakke et al.
the AHS cohort (Alavanja et al., 1996). Atrazine exposure
levels as measured by urinary AZM was significantly
associated with the amount of atrazine applied but explained
only part of the variability in metabolite levels. This
information is being used to assess daily pesticide exposure
throughout a year for an ongoing molecular epidemiologic
study on various immune effects in corn farmers (Vermeulen
et al., 2005).
The goal of the exposure assessment in an epidemiologic
study is to develop estimates of exposure for every agent of
interest. In this study, 61 a.i. were reported. A standard panel
analysis of the urine samples provided quantitative information on 14 a.i. (of which 6 overlapped with the 61 a.i.
reported). The panel was developed to measure widespread
exposures in non-occupationally exposed subjects (Olsson
et al., 2004) and was selected for this study for practical
reasons. The urine analysis panel covered four of the major
pesticides used by the farmers including two of the most
commonly used herbicides (atrazine and 2,4-D) and the most
commonly used insecticide (chlorpyrifos) and provided some
valuable data on background levels of other pesticides in a
farming population (Appendix A). Atrazine was the target
pesticide in our study; therefore, urine collection scheduling
was partially non-random with respect to atrazine applications. For other pesticides, the strategy was essentially
random as urine collection days were selected a priori and
independent of pesticide applications. Thus, except for
atrazine, the application of the reported pesticides was not
directly associated with urine collections. The data therefore
have limited use for estimating exposure levels from
application of other pesticides. However, the exposure data
provided information on pesticide levels during seasons
when the farmers are not applying pesticides, that is,
background levels. On the basis of the results of this study,
it appears difficult to capture the application of more
than one pesticide within a study if the sampling strategy is
not targeted to specific pesticides of interest. To overcome
this problem, and to limit practical and cost-related
constraints associated with scheduled farm visits, selfcollection of urine samples may be an alternative when
several pesticides are of interest. In this study all samples
collected as part of the self-collection protocol were obtained
successfully and stored and documented appropriately until
collected by the field staff.
For atrazine, we found that urinary metabolite levels were
directly related to recent application of the pesticide.
However, a significant part of the between- and withinfarmer variability in AZM levels could not be explained by
differences in the total amount of a.i. applied or by any of the
other recorded determinants. Differences in actual work
practices, behaviors, metabolism and/or meteorological
conditions might account for this. Under the assumption
that unexplained variance leads to random misclassification
our models can be used to predict exposure levels.
552
Pesticide exposure among corn farmers
However, the significant residual between-farmer variability
observed in our model might indicate that the prediction
error is not random and that this factor (although
unexplained) needs to be accounted for in the prediction of
the individual exposure levels. This can be carried out in our
study by obtaining the empirical best linear unbiased
predictors of the individual’s random effects parameter.
Furthermore, there was some indication that even if atrazine
was not recently applied, background levels (as assessed by
the proportion of samples above the LOD) were higher
among farmers than controls. However, these differences
seemed to be minor as compared to differences in exposure
levels during application. Therefore, it seems reasonable to
estimate long-term exposure levels based on application
information collected in diaries. It should, however, be noted
that AZM is only one of the urinary metabolites of atrazine
that can be measured in urine (Barr et al., 2007). Results
on background levels should therefore be interpreted with
some caution.
For 2,4-D, we found that farmers had consistently higher
urinary levels and larger proportions of samples above the
LOD as compared to controls throughout the year, including
when the pesticide was not recently applied. This suggests
that farmers may be exposed to 2,4-D through other
occupational, environmental or dietary sources that are not
strictly related to the actual application of 2,4-D. This also
seemed to hold true for the few farmers that did not apply
2,4-D themselves during this particular growing season. This
observation might hint towards a more general environmental factor leading to 2,4-D exposure. However, we
cannot exclude that applications were not reported accurately
in the diaries (e.g., applications actually performed by a
contractor and not the farmer). Nonetheless, the elevated
background levels of 2,4-D in farmers compared to controls
indicates that the exposure to 2,4-D during non-application
days needs to be investigated to develop an accurate estimate
of long-term exposure to 2,4-D.
For acetochlor mercapturate, background levels were
only slightly elevated among farmers as compared to
controls whereas for TCPY, no differences in background
levels were detected. For these compounds, it would
appear that farmers’ exposure is more directly related to
the application of these compounds as we observed for
atrazine.
The farmers in this study experienced large day-to-day
variability in exposure to atrazine because of differences in
the application pattern throughout the year. This causes a
challenge in epidemiologic studies because a small number
of measurements may lead to imprecise estimates of longterm average exposures. Determinants of exposure were
therefore identified for which information across the
whole year was available. Estimation of long-term exposure
based on empirical modeling of exposure using information
from exposure diaries offers an advantage over using
Journal of Exposure Science and Environmental Epidemiology (2009) 19(6)
Pesticide exposure among corn farmers
measurement data alone, as it will allow us to estimate
the exposure level for each farmer for every day/week
throughout the season. In a previous study among pig
farmers, it was found that applying empirical models instead
of the few exposure measurements available on each
individual can compensate for the loss of information
because of unmeasured factors affecting exposure (Preller
et al., 1995).
This exposure assessment study was designed to support a
study of seasonal differences in immune markers related to
atrazine applications and not for identifying all important
determinants of atrazine exposure. As a result, the farmers
were homogeneous with regard to many application characteristics. This likely limited our ability to identify other
significant determinants of exposure such as glove use,
washing hands, and so on, which have been found to be
important in other studies (Hines et al., 2001; Stewart et al.,
2001). The model for atrazine may therefore not be suitable
for predictions of urinary metabolite levels in other study
populations with more diverse farming activities. Furthermore, as the sampling did not follow a strict probability
sample procedure results might not necessarily reflect
exposure circumstances among corn farmers in Iowa at
large. However, the measured exposure levels among farmers
after an application in this study are comparable to levels
found in another study among Corn farmers in Iowa
(Curwin et al., 2005b).
In summary, the results of this study show that urinary
atrazine metabolite levels were largely driven by recent
application of the pesticide, and therefore the amount of
pesticide applied, duration of application or acreage treated
are likely to provide a valid surrogate of exposure in the
molecular epidemiologic study. We observed differences
between urinary levels of 2,4-D between farmers and controls
that were persistent throughout the active farming season.
These differences appeared regardless of whether the farmer
reported applying 2,4-D within the past 7 days before a urine
collection, and also appeared in farmers who had reported no
application of 2,4-D in that year. Furthermore, because we
were not able to identify sources of the increased background
level of these farmers, these sources should be investigated in
future studies of 2,4-D exposure.
Acknowledgements
This work was supported by the Intramural Research Program of the National Institutes of Health, National Cancer
Institute and funding from the Environmental Protection
Agency, USA. We thank Cynthia J Hines (National Institute
for Occupational Health and Safety), Jane Hoppin (National
Institute of Environmental Health Sciences), and Kent
Journal of Exposure Science and Environmental Epidemiology (2009) 19(6)
Bakke et al.
Thomas (US Environmental Protection Agency), for useful
comments on an earlier version of this article.
Disclaimer
The views expressed are those of the authors and do not
necessarily represent the official policy of US Environmental
Protection Agency and the Centers for Disease Control and
Prevention.
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Pesticide exposure among corn farmers
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Appendix A
Selected percentiles of urine concentrations (in mg/g creatinine) of 10 pesticides for 30 corn farmers from the Agricultural Health Study and 10
controls, 2002–2004.
Pesticidea
Nb
Percent 4LODc
Selected percentiles
10
50
75
90
95
Farmers
Diazinon
Malathiond
Metolachlord
2,4,5-Tf
Pyrethroidf
Isazaphosf
Coumaphosf
Pirimiphos methylf
Diethyl-m-toluamidef
Methyl parathionf
313
314
314
314
314
313
314
313
313
314
12
55
24
0.6
11
11
12
11
3.2
49
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
0.52
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
1.5
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
0.37
oLOD
2.4
0.71
oLOD
0.17
1.6
0.23
0.32
oLOD
0.96
2.7
3.5
1.3
oLOD
0.42
2.6
0.32
1.3
0.12
1.5
Controls
Diazinone
Malathione
Metolachlorf
2,4,5-Tf
Pyrethroidf
Isazaphosf
Coumaphosf
Pirimiphos methylf
Diethyl-m-toluamidef
Methyl parathionf
49
49
49
49
49
49
49
49
49
49
16
51
8.2
0
6.1
10
6.1
4.1
2.0
57
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
0.64
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
0.20
oLOD
1.3
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
oLOD
0.50
2.7
2.0
oLOD
oLOD
oLOD
2.8
oLOD
oLOD
oLOD
1.1
3.1
3.1
0.68
oLOD
0.88
3.8
0.50
oLOD
oLOD
1.4
d
a
Estimates of coefficient of variations (CV) for the urinary pesticides were as follows (pesticide (CV)): diazinon (66.4%); malathion (7.1%); metolachlor
(9.1%); pyrethroid (12.1%); isazaphos (19.2%); coumaphos (30.6%); diethyl-m-toluamide (3.7%); and methyl parathion (58.5%). None of the duplicate
samples analyzed for 2,4,5-T were above the limit of detection. The ICC was above 80% for all analytes except methyl parathion (36.9%), diazinon and
coumaphos (both 0%). The ICC for pirimiphos methyl could not be calculated.
b
Total number of samples.
c
Percentage of samples above the limit of detection (LOD).
d
Reported used during the study period by farmers.
e
Reported used by controls during the study period.
f
Not reported used during the study period.
554
Journal of Exposure Science and Environmental Epidemiology (2009) 19(6)