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      A Review of Pesticide Exposure and Cancer Incidence in the Agricultural Health Study Cohort

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          We reviewed epidemiologic evidence related to occupational pesticide exposures and cancer incidence in the Agricultural Health Study (AHS) cohort.

          Data sources

          Studies were identified from the AHS publication list available at as well as through a Medline/PubMed database search in March 2009. We also examined citation lists. Findings related to lifetime-days and/or intensity-weighted lifetime-days of pesticide use are the primary focus of this review, because these measures allow for the evaluation of potential exposure–response relationships.

          Data synthesis

          We reviewed 28 studies; most of the 32 pesticides examined were not strongly associated with cancer incidence in pesticide applicators. Increased rate ratios (or odds ratios) and positive exposure–response patterns were reported for 12 pesticides currently registered in Canada and/or the United States (alachlor, aldicarb, carbaryl, chlorpyrifos, diazinon, dicamba, S-ethyl- N, N-dipropylthiocarbamate, imazethapyr, metolachlor, pendimethalin, permethrin, trifluralin). However, estimates of association for specific cancers were often imprecise because of small numbers of exposed cases, and clear monotonic exposure–response patterns were not always apparent. Exposure misclassification is also a concern in the AHS and may limit the analysis of exposure–response patterns. Epidemiologic evidence outside the AHS remains limited with respect to most of the observed associations, but animal toxicity data support the biological plausibility of relationships observed for alachlor, carbaryl, metolachlor, pendimethalin, permethrin, and trifluralin.


          Continued follow-up is needed to clarify associations reported to date. In particular, further evaluation of registered pesticides is warranted.

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          Most cited references 85

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          The Agricultural Health Study.

          The Agricultural Health Study, a large prospective cohort study has been initiated in North Carolina and Iowa. The objectives of this study are to: 1) identify and quantify cancer risks among men, women, whites, and minorities associated with direct exposure to pesticides and other agricultural agents; 2) evaluate noncancer health risks including neurotoxicity reproductive effects, immunologic effects, nonmalignant respiratory disease, kidney disease, and growth and development among children; 3) evaluate disease risks among spouses and children of farmers that may arise from direct contact with pesticides and agricultural chemicals used in the home lawns and gardens, and from indirect contact, such as spray drift, laundering work clothes, or contaminated food or water; 4) assess current and past occupational and nonoccupational agricultural exposures using periodic interviews and environmental and biologic monitoring; 5) study the relationship between agricultural exposures, biomarkers of exposure, biologic effect, and genetic susceptibility factors relevant to carcinogenesis; and 6) identify and quantify cancer and other disease risks associated with lifestyle factors such as diet, cooking practices, physical activity, smoking and alcohol consumption, and hair dye use. In the first year of a 3-year enrollment period, 26,235 people have been enrolled in the study, including 19,776 registered pesticide applicators and 6,459 spouses of registered farmer applicators. It is estimated that when the total cohort is assembled in 1997 it will include approximately 75,000 adult study subjects. Farmers, the largest group of registered pesticide applicators comprise 77% of the target population enrolled in the study. This experience compares favorably with enrollment rates of previous prospective studies. Images Figure 1. Figure 2. Figure 3. Figure 4.
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            Bias in occupational epidemiology studies.

            The design of occupational epidemiology studies should be based on the need to minimise random and systematic error. The latter is the focus of this paper, and includes selection bias, information bias and confounding. Selection bias can be minimised by obtaining a high response rate (and by appropriate selection of the control group in a case-control study). In general, it is important to ensure that information bias is minimised and is also non-differential (for example, that the misclassification of exposure is not related to disease status) by collecting data in a standardised manner. A major concern in occupational epidemiology studies usually relates to confounding, because exposure has not been randomly allocated, and the groups under study may therefore have different baseline disease risks. For each of these types of bias, the goal should be to avoid the bias by appropriate study design and/or appropriate control in the analysis. However, it is also important to attempt to assess the likely direction and strength of biases that cannot be avoided or controlled.
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              Proper interpretation of non-differential misclassification effects: expectations vs observations.

              Many investigators write as if non-differential exposure misclassification inevitably leads to a reduction in the strength of an estimated exposure-disease association. Unfortunately, non-differentiality alone is insufficient to guarantee bias towards the null. Furthermore, because bias refers to the average estimate across study repetitions rather than the result of a single study, bias towards the null is insufficient to guarantee that an observed estimate will be an underestimate. Thus, as noted before, exposure misclassification can spuriously increase the observed strength of an association even when the misclassification process is non-differential and the bias it produced is towards the null. We present additional results on this topic, including a simulation study of how often an observed relative risk is an overestimate of the true relative risk when the bias is towards the null. The frequency of overestimation depends on many factors: the value of the true relative risk, exposure prevalence, baseline (unexposed) risk, misclassification rates, and other factors that influence bias and random error. Non-differentiality of exposure misclassification does not justify claims that the observed estimate must be an underestimate; further conditions must hold to get bias towards the null, and even when they do hold the observed estimate may by chance be an overestimate.

                Author and article information

                Environ Health Perspect
                Environmental Health Perspectives
                National Institute of Environmental Health Sciences
                August 2010
                5 May 2010
                : 118
                : 8
                : 1117-1125
                Health Canada, Ottawa, Ontario, Canada
                Author notes
                Address correspondence to S. Weichenthal, Health Canada, 269 Laurier Avenue West, Ottawa, Ontario Canada, K1A 0K9. Telephone: (613) 957-2617. Fax: (613) 948-8482. E-mail: scott.weichenthal@

                The authors declare they have no actual or potential competing financial interests.

                This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.

                Public health

                agricultural health study, pesticides, review, cancer


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