Inferring Past Pesticide Exposures: A Matrix of Individual Active Ingredients in Home and Garden Pesticides Used in Past Decades

BACKGROUND: In retrospective studies of the health effects of home and garden pesticides, self-reported information typically forms the basis for exposure assessment. Study participants generally find it easier to remember the types of pests treated than the specific pesticides used. However, if the goal of the study is to assess disease risk from specific chemicals, the investigator must be able to link the pest type treated with specific chemicals or products. OBJECTIVES: Our goal was to develop a “pesticide–exposure matrix” that would list active ingredients on the market for treating different types of pests in past years, and provide an estimate of the probability that each active ingredient was used. METHODS: We used several different methods for deriving the active ingredient lists and estimating the probabilities. These methods are described in this article, along with a sample calculation and data sources for each. RESULTS: The pesticide–exposure matrix lists active ingredients and their probabilities of use for 96 distinct scenarios defined by year (1976, 1980, 1990, 2000), applicator type (consumer, professional), and pest type (12 categories). Calculations and data sources for all 96 scenarios are provided online. CONCLUSIONS: Although we are confident that the active ingredient lists are reasonably accurate for most scenarios, we acknowledge possible sources of error in the probability estimates. Despite these limitations, the pesticide–exposure matrix should provide valuable information to researchers interested in the chronic health effects of residential pesticide exposure

Agricultural Pesticide Use and Risk of t(14;18)-Defined Subtypes of Non-Hodgkin Lymphoma

Pesticides have been specifically associated with the t(14;18)(q32;q21) chromosomal translocation. To investigate whether the association between pesticides and risk of non-Hodgkin lymphoma (NHL) differs for molecular subtypes of NHL defined by t(14; 18) status, we obtained 175 tumor blocks from case subjects in a population-based case-control study conducted in Nebraska between 1983 and 1986. The t(14;18) was determined by interphase fluorescence in situ hybridization in 172 of 175 tumor blocks. We compared exposures to insecticides, herbicides, fungicides, and fumigants in 65 t(14;18)-positive and 107 t(14;18)-negative case subjects with those among 1432 control subjects. Multivariate polytomous logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Compared with farmers who never used pesticides, the risk of t(14;18)-positive NHL was significantly elevated among farmers who used animal insecticides (OR = 2.6; 95%CI, 1.0-6.9), crop insecticides (OR = 3.0; 95% CI, 1.1-8.2), herbicides (OR = 2.9; 95% CI, 1.1-7.9), and fumigants (OR = 5.0; 95% CI, 1.7-14.5). None of these pesticides were associated with t(14;18)-negative NHL. The risk of t(14;18)-positive NHL associated with insecticides and herbicides increased with longer duration of use. We conclude that insecticides, herbicides, and fumigants were associated with risk of t(14;18)-positive NHL but not t(14;18)-negative NHL. These results suggest that defining subsets of NHL according to t(14;18) status is a useful approach for etiologic research. (Blood. 2006; 108:1363-1369

Atrazine and cancer incidence among pesticide applicators in the agricultural health study (1994-2007).

Atrazine is a triazine herbicide used widely in the United States. Although it is an animal carcinogen, the mechanism in rodents does not appear to operate in humans. Few epidemiologic studies have provided evidence for an association

AGRICOH: A Consortium of Agricultural Cohorts

AGRICOH is a recently formed consortium of agricultural cohort studies involving 22 cohorts from nine countries in five continents: South Africa (1), Canada (3), Costa Rica (2), USA (6), Republic of Korea (1), New Zealand (2), Denmark (1), France (3) and Norway (3). The aim of AGRICOH, initiated by the US National Cancer Institute (NCI) and coordinated by the International Agency for Research on Cancer (IARC), is to promote and sustain collaboration and pooling of data to investigate the association between a wide range of agricultural exposures and a wide range of health outcomes, with a particular focus on associations that cannot easily be addressed in individual studies because of rare exposures (e.g., use of infrequently applied chemicals) or relatively rare outcomes (e.g., certain types of cancer, neurologic and auto-immune diseases). To facilitate future projects the need for data harmonization of selected variables is required and is underway. Altogether, AGRICOH provides excellent opportunities for studying cancer, respiratory, neurologic, and auto-immune diseases as well as reproductive and allergic disorders, injuries and overall mortality in association with a wide array of exposures, prominent among these the application of pesticides

GWAS of Follicular Lymphoma Reveals Allelic Heterogeneity at 6p21.32 and Suggests Shared Genetic Susceptibility with Diffuse Large B-cell Lymphoma

Non-Hodgkin lymphoma (NHL) represents a diverse group of hematological malignancies, of which follicular lymphoma (FL) is a prevalent subtype. A previous genome-wide association study has established a marker, rs10484561 in the human leukocyte antigen (HLA) class II region on 6p21.32 associated with increased FL risk. Here, in a three-stage genome-wide association study, starting with a genome-wide scan of 379 FL cases and 791 controls followed by validation in 1,049 cases and 5,790 controls, we identified a second independent FL–associated locus on 6p21.32, rs2647012 (ORcombined = 0.64, Pcombined = 2×10−21) located 962 bp away from rs10484561 (r2<0.1 in controls). After mutual adjustment, the associations at the two SNPs remained genome-wide significant (rs2647012:ORadjusted = 0.70, Padjusted = 4×10−12; rs10484561:ORadjusted = 1.64, Padjusted = 5×10−15). Haplotype and coalescence analyses indicated that rs2647012 arose on an evolutionarily distinct haplotype from that of rs10484561 and tags a novel allele with an opposite (protective) effect on FL risk. Moreover, in a follow-up analysis of the top 6 FL–associated SNPs in 4,449 cases of other NHL subtypes, rs10484561 was associated with risk of diffuse large B-cell lymphoma (ORcombined = 1.36, Pcombined = 1.4×10−7). Our results reveal the presence of allelic heterogeneity within the HLA class II region influencing FL susceptibility and indicate a possible shared genetic etiology with diffuse large B-cell lymphoma. These findings suggest that the HLA class II region plays a complex yet important role in NHL

Agricultural pesticide use and risk of t(14;18)-defined subtypes of non-Hodgkin lymphoma

Pesticides have been specifically associated with the t(14;18)(q32;q21) chromosomal translocation. To investigate whether the association between pesticides and risk of non-Hodgkin lymphoma (NHL) differs for molecular subtypes of NHL defined by t(14; 18) status, we obtained 175 tumor blocks from case subjects in a population-based case-control study conducted in Nebraska between 1983 and 1986. The t(14;18) was determined by interphase fluorescence in situ hybridization in 172 of 175 tumor blocks. We compared exposures to insecticides, herbicides, fungicides, and fumigants in 65 t(14;18)-positive and 107 t(14;18)-negative case subjects with those among 1432 control subjects. Multivariate polytomous logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Compared with farmers who never used pesticides, the risk of t(14;18)-positive NHL was significantly elevated among farmers who used animal insecticides (OR = 2.6; 95% CI, 1.0-6.9), crop insecticides (OR = 3.0; 95% CI, 1.1-8.2), herbicides (OR = 2.9; 95% CI, 1.1-7.9), and fumigants (OR = 5.0; 95% CI, 1.7-14.5). None of these pesticides were associated with t(14;18)-negative NHL. The risk of t(14;18)-positive NHL associated with insecticides and herbicides increased with longer duration of use. We conclude that insecticides, herbicides, and fumigants were associated with risk of t(14;18)-positive NHL but not t(14;18)-negative NHL. These results suggest that defining subsets of NHL according to t(14;18) status is a useful approach for etiologic research

A Quantitative Approach for Estimating Exposure to Pesticides in the Agricultural Health Study

We developed a quantitative method to estimate long-term chemical-specific pesticide exposures in a large prospective cohort study of more than 58000 pesticide applicators in North Carolina and Iowa. An enrollment questionnaire was administered to applicators to collect basic time- and intensity-related information on pesticide exposure such as mixing condition, duration and frequency of application, application methods and personal protective equipment used. In addition, a detailed take-home questionnaire was administered to collect further intensity- related exposure information such as maintenance or repair of mixing and application equipment, work practices and personal hygiene. More than 40% of the enrolled applicators responded to this detailed take-home questionnaire. Two algorithms were developed to identify applicators’ exposure scenarios using information from the enrollment and take-home questionnaires separately in the calculation of subject-specific intensity of exposure score to individual pesticides. The ‘general algorithm’ used four basic variables (i.e. mixing status, application method, equipment repair status and personal protective equipment use) from the enrollment questionnaire and measurement data from the published pesticide exposure literature to calculate estimated intensity of exposure to individual pesticides for each applicator. The ‘detailed’ algorithm was based on variables in the general algorithm plus additional exposure information from the take-home questionnaire, including types of mixing system used (i.e. enclosed or open), having a tractor with enclosed cab and/or charcoal filter, frequency of washing equipment after application, frequency of replacing old gloves, personal hygiene and changing clothes after a spill. Weighting factors applied in both algorithms were estimated using measurement data from the published pesticide exposure literature and professional judgment. For each study subject, chemical-specific lifetime cumulative pesticide exposure levels were derived by combining intensity of pesticide exposure as calculated by the two algorithms independently and duration/frequency of pesticide use from the questionnaire. Distributions of duration, intensity and cumulative exposure levels of 2,4-D and chlorpyrifos are presented by state, gender, age group and applicator type (i.e. farmer or commercial applicator) for the entire enrollment cohort and for the sub-cohort of applicators who responded to the take-home questionnaire. The distribution patterns of all basic exposure indices (i.e. intensity, duration and cumulative exposure to 2,4-D and chlorpyrifos) by state, gender, age and applicator type were almost identical in two study populations, indicating that the take-home questionnaire sub-cohort of applicators is representative of the entire cohort in terms of exposure