Christensen et al., 2016
Christensen, C. H., Barry, K. H., Andreotti, G., Alavanja, M. C., Cook, M. B., Kelly, S. P., Burdett, L. A., Yeager, M., Beane Freeman, L. E., Berndt, S. I., & Koutros, S.; “Sex Steroid Hormone Single-Nucleotide Polymorphisms, Pesticide Use, and the Risk of Prostate Cancer: A Nested Case-Control Study within the Agricultural Health Study;” Frontiers in Oncology, 2016, 6, 237; DOI: 10.3389/fonc.2016.00237.
ABSTRACT:
Experimental and epidemiologic investigations suggest that certain pesticides may alter sex steroid hormone synthesis, metabolism or regulation, and the risk of hormone-related cancers. Here, we evaluated whether single-nucleotide polymorphisms (SNPs) involved in hormone homeostasis alter the effect of pesticide exposure on prostate cancer risk. We evaluated pesticide-SNP interactions between 39 pesticides and SNPs with respect to prostate cancer among 776 cases and 1,444 controls nested in the Agricultural Health Study cohort. In these interactions, we included candidate SNPs involved in hormone synthesis, metabolism or regulation (N = 1,100), as well as SNPs associated with circulating sex steroid concentrations, as identified by genome-wide association studies (N = 17). Unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Multiplicative SNP-pesticide interactions were calculated using a likelihood ratio test. We translated p-values for interaction into q-values, which reflected the false discovery rate, to account for multiple comparisons. We observed a significant interaction, which was robust to multiple comparison testing, between the herbicide dicamba and rs8192166 in the testosterone metabolizing gene SRD5A1 (p-interaction = 4.0 x 10(-5); q-value = 0.03), such that men with two copies of the wild-type genotype CC had a reduced risk of prostate cancer associated with low use of dicamba (OR = 0.62 95% CI: 0.41, 0.93) and high use of dicamba (OR = 0.44, 95% CI: 0.29, 0.68), compared to those who reported no use of dicamba; in contrast, there was no significant association between dicamba and prostate cancer among those carrying one or two copies of the variant T allele at rs8192166. In addition, interactions between two organophosphate insecticides and SNPs related to estradiol metabolism were observed to result in an increased risk of prostate cancer. While replication is needed, these data suggest both agonistic and antagonistic effects on circulating hormones, due to the combination of exposure to pesticides and genetic susceptibility, may impact prostate cancer risk. FULL TEXT
Thomas et al., 2010b
Thomas, K. W., Dosemeci, M., Coble, J. B., Hoppin, J. A., Sheldon, L. S., Chapa, G., Croghan, C. W., Jones, P. A., Knott, C. E., Lynch, C. F., Sandler, D. P., Blair, A. E., & Alavanja, M. C.; “Assessment of a pesticide exposure intensity algorithm in the agricultural health study;” Journal of Exposure Analysis and Environmental Epidemiology, 2010, 20(6), 559-569; DOI: 10.1038/jes.2009.54.
ABSTRACT:
The accuracy of the exposure assessment is a critical factor in epidemiological investigations of pesticide exposures and health in agricultural populations. However, few studies have been conducted to evaluate questionnaire-based exposure metrics. The Agricultural Health Study (AHS) is a prospective cohort study of pesticide applicators who provided detailed questionnaire information on their use of specific pesticides. A field study was conducted for a subset of the applicators enrolled in the AHS to assess a pesticide exposure algorithm through comparison of algorithm intensity scores with measured exposures. Pre- and post-application urinary biomarker measurements were made for 2,4-D (n=69) and chlorpyrifos (n=17) applicators. Dermal patch, hand wipe, and personal air samples were also collected. Intensity scores were calculated using information from technician observations and an interviewer-administered questionnaire. Correlations between observer and questionnaire intensity scores were high (Spearman’s r=0.92 and 0.84 for 2,4-D and chlorpyrifos, respectively). Intensity scores from questionnaires for individual applications were significantly correlated with post-application urinary concentrations for both 2,4-D (r=0.42, P<0.001) and chlorpyrifos (r=0.53, P=0.035) applicators. Significant correlations were also found between intensity scores and estimated hand loading, estimated body loading, and air concentrations for 2,4-D applicators (r-values 0.28-0.50, P-values<0.025). Correlations between intensity scores and dermal and air measures were generally lower for chlorpyrifos applicators using granular products. A linear regression model indicated that the algorithm factors for individual applications explained 24% of the variability in post-application urinary 2,4-D concentration, which increased to 60% when the pre-application urine concentration was included. The results of the measurements support the use of the algorithm for estimating questionnaire-based exposure intensities in the AHS for liquid pesticide products. Refinement of the algorithm may be possible using the results from this and other measurement studies. FULL TEXT
Hoppin et al., 2002
Hoppin, J. A., Yucel, F., Dosemeci, M., & Sandler, D. P.; “Accuracy of self-reported pesticide use duration information from licensed pesticide applicators in the Agricultural Health Study;” Journal of Exposure Analysis and Environmental Epidemiology, 2002, 12(5), 313-318; DOI: 10.1038/sj.jea.7500232.
ABSTRACT:
Epidemiologists frequently rely on self-reported information regarding a variety of exposures including smoking history, medication use, and occupational exposure because other sources of information are either unavailable or difficult to obtain. One way to evaluate the accuracy of self-reported information is through logic checks using other sources. To assess the quality of the self-reported pesticide product use history of 57,311 licensed pesticide applicators in the Agricultural Health Study (AHS), we compared the self-reported decade of first use and total years of use to the year the pesticide active ingredient was first registered for use. We obtained pesticide active ingredient registration information from the United States Environmental Protection Agency (USEPA) and other publicly available sources for the 52 pesticides on the AHS initial questionnaires administered from 1994 to 1997. Based on the registration year, we assessed 19 pesticides for potential inaccuracies regarding duration of use or decade of first use. When calculating potential total years of use, we did not consider the impact of chemicals being removed from the market, since the possibility for continued use existed. The majority of respondents provided plausible responses for both decade of first use and total duration of use. On average, 1% of the subjects overestimated total possible duration of use, ranging from less than 1% for carbofuran and chlorpyrifos to 5% for imazethapyr. Decade of first use was also reasonably reported, although more subjects did not report decade of first use than duration of use, with an average of 6% of subjects missing decade information for an individual chemical. For subjects who reported a decade of first use, 98% gave plausible responses on average, with overestimates highest for cyanazine, introduced in 1971 (6% reported earlier use), and chlorimuron ethyl, introduced in 1985 (7% reported earlier use). This analysis provided the opportunity to consider only one source of potential overreporting of exposure, and while underreporting may have also occurred, we cannot evaluate its role nor the balance between these potential inaccuracies. While we are unable to validate directly the accuracy of a respondent’s use of pesticides, this analysis suggests that participants provide plausible information regarding their pesticide use. FULL TEXT
Saldana et al., 2007
Saldana, T. M., Basso, O., Hoppin, J. A., Baird, D. D., Knott, C., Blair, A., Alavanja, M. C., & Sandler, D. P.; “Pesticide exposure and self-reported gestational diabetes mellitus in the Agricultural Health Study;” Diabetes Care, 2007, 30(3), 529-534; DOI: 10.2337/dc06-1832.
ABSTRACT:
OBJECTIVE: To examine the association between pesticide use during pregnancy and gestational diabetes mellitus (GDM) among wives of licensed pesticide applicators.
RESEARCH DESIGN AND METHODS: Using data from the Agricultural Health Study (AHS), we estimated the association between self-reported pesticide-related activities during the first trimester of the most recent pregnancy and GDM among 11,273 women whose pregnancy occurred within 25 years of enrollment.
RESULTS: A total of 506 (4.5%) women reported having had GDM. Women who reported agricultural pesticide exposure (mixing or applying pesticides to crops or repairing pesticide application equipment) during pregnancy were more likely to report GDM (odds ratio [OR] 2.2 [95% CI 1.5-3.3]). We saw no association between residential pesticide exposure (applying pesticides in the home and garden during pregnancy) and GDM (1.0 [0.8-1.3]). Among women who reported agricultural exposure during pregnancy, risk of GDM was associated with ever-use of four herbicides (2,4,5-T; 2,4,5-TP; atrazine; or butylate) and three insecticides (diazinon, phorate, or carbofuran).
CONCLUSIONS: These findings suggest that activities involving exposure to agricultural pesticides during the first trimester of pregnancy may increase the risk of GDM.
Curwin et al., 2007
Curwin, Brian D., Hein, Misty J., Sanderson, Wayne T., Striley, Cynthia, Heederik, Dick, Kromhout, Hans, Reynolds, Stephen J., & Alavanja, Michael C.; “Pesticide dose estimates for children of Iowa farmers and non-farmers;” Environmental Research, 2007, 105, 307-315; DOI: 10.1016/j.envres.2007.06.001.
ABSTRACT:
Farm children have the potential to be exposed to pesticides. Biological monitoring is often employed to assess this exposure; however, the significance of the exposure is uncertain unless doses are estimated. In the spring and summer of 2001, 118 children (66 farm, 52 non-farm) of Iowa farm and non-farm households were recruited to participate in a study investigating potential take-home pesticide exposure. Each child provided an evening and morning urine sample at two visits spaced approximately 1 month apart, with the first sample collection taken within a few days after pesticide application. Estimated doses were calculated for atrazine, metolachlor, chlorpyrifos, and glyphosate from urinary metabolite concentrations derived from the spot urine samples and compared to EPA reference doses. For all pesticides except glyphosate, the doses from farm children were higher than doses from the non-farm children. The difference was statistically significant for atrazine (p<0.0001) but only marginally significant for chlorpyrifos and metolachlor (p=0.07 and 0.1, respectively). Among farm children, geometric mean doses were higher for children on farms where a particular pesticide was applied compared to farms where that pesticide was not applied for all pesticides except glyphosate; results were significant for atrazine (p=0.030) and metolachlor (p=0.042), and marginally significant for chlorpyrifos (p=0.057). The highest estimated doses for atrazine, chlorpyrifos, metolachlor, and glyphosate were 0.085, 1.96, 3.16, and 0.34 μg/kg/day, respectively. None of the doses exceeded any of the EPA reference values for atrazine, metolachlor, and glyphosate; however, all of the doses for chlorpyrifos exceeded the EPA chronic population adjusted reference value. Doses were similar for male and female children. A trend of decreasing dose with increasing age was observed for chlorpyrifos. FULL TEXT
Curwin et al., 2002
Curwin, B., Sanderson, W., Reynolds, S., Hein, M., & Alavanja, M.; “Pesticide use and practices in an Iowa farm family pesticide exposure study;” Journal of Agricultural Safety and Health, 2002, 8(4), 423-433; DOI: 10.13031/2013.10222.
ABSTRACT:
Residents of Iowa were enrolled in a study investigating differences in pesticide contamination and exposure factors between 25 farm homes and 25 non-farm homes. The target pesticides investigated were atrazine, metolachlor, acetochlor, alachlor, 2,4-D, glyphosate, and chlorpyrifos; all were applied to either corn or soybean crops. A questionnaire was administered to all participants to determine residential pesticide use in and around the home. In addition, a questionnaire was administered to the farmers to determine the agricultural pesticides they used on the farm and their application practices. Non-agricultural pesticides were used more in and around farm homes than non-farm homes. Atrazine was the agricultural pesticide used most by farmers. Most farmers applied pesticides themselves but only 10 (59%) used tractors with enclosed cabs, and they typically wore little personal protective equipment (PPE). On almost every farm, more than one agricultural pesticide was applied. Corn was grown by 23 (92%) farmers and soybeans by 12 (48%) farmers. Of these, 10 (40%) grew both soybeans and corn, with only 2 (8%) growing only soybeans and 13 (52%) growing only corn. The majority of farmers changed from their work clothes and shoes in the home, and when they changed outside or in the garage, they usually brought their clothes and shoes inside. Applying pesticides using tractors with open cabs, not wearing PPE, and changing from work clothes in the home may increase pesticide exposure and contamination. Almost half of the 66 farm children less than 16 years of age were engaged in some form of farm chores, with 6 (9%) potentially directly exposed to pesticides, while only 2 (4%) of the 52 non-farm children less than 16 years of age had farm chores, and none were directly exposed to pesticides. Farm homes may be contaminated with pesticides in several ways, resulting in potentially more contamination than non-farm homes, and farm children may be directly exposed to pesticides through farm chores involving pesticides. In addition to providing a description of pesticide use, the data presented here will be useful in evaluating potential contributing factors to household pesticide contamination and family exposure. FULL TEXT
Coble et al., 2011
Coble, J., Thomas, K. W., Hines, C. J., Hoppin, J. A., Dosemeci, M., Curwin, B., Lubin, J. H., Beane Freeman, L. E., Blair, A., Sandler, D. P., & Alavanja, M. C.; “An updated algorithm for estimation of pesticide exposure intensity in the agricultural health study;” International Journal of Environmental Research and Public Health, 2011, 8(12), 4608-4622; DOI: 10.3390/ijerph8124608.
ABSTRACT:
An algorithm developed to estimate pesticide exposure intensity for use in epidemiologic analyses was revised based on data from two exposure monitoring studies. In the first study, we estimated relative exposure intensity based on the results of measurements taken during the application of the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) (n = 88) and the insecticide chlorpyrifos (n = 17). Modifications to the algorithm weighting factors were based on geometric means (GM) of post-application urine concentrations for applicators grouped by application method and use of chemically-resistant (CR) gloves. Measurement data from a second study were also used to evaluate relative exposure levels associated with airblast as compared to hand spray application methods. Algorithm modifications included an increase in the exposure reduction factor for use of CR gloves from 40% to 60%, an increase in the application method weight for boom spray relative to in-furrow and for air blast relative to hand spray, and a decrease in the weight for mixing relative to the new weights assigned for application methods. The weighting factors for the revised algorithm now incorporate exposure measurements taken on Agricultural Health Study (AHS) participants for the application methods and personal protective equipment (PPE) commonly reported by study participants. FULL TEXT
Blair et al., 2011
Blair, A., Thomas, K., Coble, J., Sandler, D. P., Hines, C. J., Lynch, C. F., Knott, C., Purdue, M. P., Zahm, S. H., Alavanja, M. C., Dosemeci, M., Kamel, F., Hoppin, J. A., Freeman, L. B., & Lubin, J. H.; “Impact of pesticide exposure misclassification on estimates of relative risks in the Agricultural Health Study;” Occupational and Environmental Medicine, 2011, 68(7), 537-541; DOI: 10.1136/oem.2010.059469.
ABSTRACT:
BACKGROUND: The Agricultural Health Study (AHS) is a prospective study of licensed pesticide applicators and their spouses in Iowa and North Carolina. We evaluate the impact of occupational pesticide exposure misclassification on relative risks using data from the cohort and the AHS Pesticide Exposure Study (AHS/PES).
METHODS: We assessed the impact of exposure misclassification on relative risks using the range of correlation coefficients observed between measured post-application urinary levels of 2,4-dichlorophenoxyacetic acid (2,4-D) and a chlorpyrifos metabolite and exposure estimates based on an algorithm from 83 AHS pesticide applications.
RESULTS: Correlations between urinary levels of 2,4-D and a chlorpyrifos metabolite and algorithm estimated intensity scores were about 0.4 for 2,4-D (n=64), 0.8 for liquid chlorpyrifos (n=4) and 0.6 for granular chlorpyrifos (n=12). Correlations of urinary levels with kilograms of active ingredient used, duration of application, or number of acres treated were lower and ranged from -0.36 to 0.19. These findings indicate that a priori expert-derived algorithm scores were more closely related to measured urinary levels than individual exposure determinants evaluated here. Estimates of potential bias in relative risks based on the correlations from the AHS/PES indicate that non-differential misclassification of exposure using the algorithm would bias estimates towards the null, but less than that from individual exposure determinants.
CONCLUSIONS: Although correlations between algorithm scores and urinary levels were quite good (ie, correlations between 0.4 and 0.8), exposure misclassification would still bias relative risk estimates in the AHS towards the null and diminish study power.
Curwin et al., 2005
Curwin, B. D., Hein, M. J., Sanderson, W. T., Nishioka, M. G., Reynolds, S. J., Ward, E. M., & Alavanja, M. C.; “Pesticide contamination inside farm and nonfarm homes;” Journal of Occupational and Environmental Hygiene, 2005, 2(7), 357-367; DOI: 10.1080/15459620591001606.
ABSTRACT:
Twenty-five farm (F) households and 25 nonfarm (NF) households in Iowa were enrolled in a study investigating agricultural pesticide contamination inside homes. Air, surface wipe, and dust samples were collected. Samples from 39 homes (20 F and 19 NF) were analyzed for atrazine, metolachlor, acetochlor, alachlor, and chlorpyrifos. Samples from 11 homes (5 F and 6 NF) were analyzed for glyphosate and 2,4-Dichlorophenoxyac etic acid (2,4-D). Greater than 88% of the air and greater than 74% of the wipe samples were below the limit of detection (LOD). Among the air and wipe samples, chlorpyrifos was detected most frequently in homes. In the dust samples, all the pesticides were detected in greater than 50% of the samples except acetochlor and alachlor, which were detected in less than 30% of the samples. Pesticides in dust samples were detected more often in farm homes except 2,4-D, which was detected in 100% of the farm and nonfarm home samples. The average concentration in dust was higher in farm homes versus nonfarm homes for each pesticide. Further analysis of the data was limited to those pesticides with at least 50% of the dust samples above the LOD. All farms that sprayed a pesticide had higher levels of that pesticide in dust than both farms that did not spray that pesticide and nonfarms; however, only atrazine and metolachlor were significantly higher. The adjusted geometric mean pesticide concentration in dust for farms that sprayed a particular pesticide ranged from 94 to 1300 ng/g compared with 12 to 1000 ng/g for farms that did not spray a particular pesticide, and 2.4 to 320 ng/g for nonfarms. The distributions of the pesticides throughout the various rooms sampled suggest that the strictly agricultural herbicides atrazine and metolachlor are potentially being brought into the home on the farmer’s shoes and clothing. These herbicides are not applied in or around the home but they appear to be getting into the home para-occupationally. For agricultural pesticides, take-home exposure may be an important source of home contamination. FULL TEXT
Coble et al., 2005
Coble, J., Arbuckle, T., Lee, W., Alavanja, M., & Dosemeci, M.; “The validation of a pesticide exposure algorithm using biological monitoring results;” Journal of Occupational and Environmental Hygiene, 2005, 2(3), 194-201; DOI: 10.1080/15459620590923343.
ABSTRACT:
A pesticide exposure algorithm was developed to calculate pesticide exposure intensity scores based on responses to questions about pesticide handling procedures and application methods in a self-administered questionnaire. The validity of the algorithm was evaluated through comparison of the algorithm scores with biological monitoring data from a study of 126 pesticide applicators who applied the herbicides MCPA or 2,4-D. The variability in the algorithm scores calculated for these applicators was due primarily to differences in their use of personal protective equipment (PPE). Rubber gloves were worn by 75% of applicators when mixing and 22% when applying pesticides, rubber boots were worn by 33% when mixing and 23% when applying, and goggles were worn by 33% and 17% of applicators when mixing and when applying, respectively. Only 2% of applicators wore all three types of PPE when both mixing and applying, and 15% wore none of these three types of PPE when either mixing or applying. Substantial variability was also observed in the concentrations of pesticides detected in the post application urine samples. The concentration of MCPA detected in urine samples collected on the second day after the application ranged from less than < 1.0 to 610 microg/L among 84 of the applicators who applied MCPA. The concentrations of 2,4-D detected in the urine samples ranged from less than < 1.0 to 514 microg/L among 41 of the applicators who applied 2,4-D. When categorized into three groups based on the algorithm scores, the geometric mean in the highest exposure group was 20 microg/L compared with 5 microg/L in the lowest exposure group for the MCPA applicators, and 29 microg/L in highest exposure group compared with 2 microg/L in the low exposure group for the 2,4-D applicators. A regression analysis detected statistically significant trends in the geometric mean of the urine concentrations across the exposure categories for both the 2,4-D and the MCPA applicators. The algorithm scores, based primarily on the use of PPE, appear to provide a reasonably valid measure of exposure intensity for these applicators, however, further studies are needed to generalize these results to other types of pesticides and application methods. FULL TEXT