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      “Good Epidemiology Practice” Guidelines for Pesticide Exposure Assessment

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          Abstract

          Both toxicology and epidemiology are used to inform hazard and risk assessment in regulatory settings, particularly for pesticides. While toxicology studies involve controlled, quantifiable exposures that are often administered according to standardized protocols, estimating exposure in observational epidemiology studies is challenging, and there is no established guidance for doing so. However, there are several frameworks for evaluating the quality of published epidemiology studies. We previously developed a preliminary list of methodology and reporting standards for epidemiology studies, called Good Epidemiology Practice (GEP) guidelines, based on a critical review of standardized toxicology protocols and available frameworks for evaluating epidemiology study quality. We determined that exposure characterization is one of the most critical areas for which standards are needed. Here, we propose GEP guidelines for pesticide exposure assessment based on the source of exposure data (i.e., biomonitoring and environmental samples, questionnaire/interview/expert record review, and dietary exposures based on measurements of residues in food and food consumption). It is expected that these GEP guidelines will facilitate the conduct of higher-quality epidemiology studies that can be used as a basis for more scientifically sound regulatory risk assessment and policy making.

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          Most cited references30

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          Does nondifferential misclassification of exposure always bias a true effect toward the null value?

          The authors present some examples to demonstrate that in certain nondifferential misclassification conditions with polychotomous exposure variables, estimates of odds ratios for categories at intermediate level of risk can be biased away from the null or can change direction. In addition, the authors present two examples to demonstrate that the slope of the dose-response trend for the true distributions can change direction, creating a false inverse trend, even if the misclassification is nondifferential.
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            Differential misclassification arising from nondifferential errors in exposure measurement.

            Misclassification into exposure categories formed from a continuous variable arises from measurement error in the continuous variable. Examples and mathematical results are presented to show that if the measurement error is nondifferential (independent of disease status), the resulting misclassification will often be differential, even in cohort studies. The degree and direction of differential misclassification vary with the exposure distribution, the category definitions, the measurement error distribution, and the exposure-disease relation. Failure to recognize the likelihood of differential misclassification may lead to incorrect conclusions about the effects of measurement error on estimates of relative risk when categories are formed from continuous variables, such as dietary intake. Simulations were used to examine some effects of nondifferential measurement error. Under the conditions used, nondifferential measurement error reduced relative risk estimates, but not to the degree predicted by the assumption of nondifferential misclassification. When relative risk estimates were corrected using methods appropriate for nondifferential misclassification, the "corrected" relative risks were almost always higher than the true relative risks, sometimes considerably higher. The greater the measurement error, the more inaccurate was the correction. The effects of exposure measurement errors need more critical evaluation.
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              Guidelines and recommendations for ensuring Good Epidemiological Practice (GEP): a guideline developed by the German Society for Epidemiology

              Objective To revise the German guidelines and recommendations for ensuring Good Epidemiological Practice (GEP) that were developed in 1999 by the German Society for Epidemiology (DGEpi), evaluated and revised in 2004, supplemented in 2008, and updated in 2014. Methods The executive board of the DGEpi tasked the third revision of the GEP. The revision was arrived as a result of a consensus-building process by a working group of the DGEpi in collaboration with other working groups of the DGEpi and with the German Association for Medical Informatics, Biometry and Epidemiology, the German Society of Social Medicine and Prevention (DGSMP), the German Region of the International Biometric Society (IBS-DR), the German Technology, Methods and Infrastructure for Networked Medical Research (TMF), and the German Network for Health Services Research (DNVF). The GEP also refers to related German Good Practice documents (e.g. Health Reporting, Cartographical Practice in the Healthcare System, Secondary Data Analysis). Results The working group modified the 11 guidelines (after revision: 1 ethics, 2 research question, 3 study protocol and manual of operations, 4 data protection, 5 sample banks, 6 quality assurance, 7 data storage and documentation, 8 analysis of epidemiological data, 9 contractual framework, 10 interpretation and scientific publication, 11 communication and public health) and modified and supplemented the related recommendations. All participating scientific professional associations adopted the revised GEP. Conclusions The revised GEP are addressed to everyone involved in the planning, preparation, execution, analysis, and evaluation of epidemiological research, as well as research institutes and funding bodies.
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                15 July 2020
                July 2020
                : 17
                : 14
                : 5114
                Affiliations
                [1 ]Gradient, One Beacon Street, 17th Floor, Boston, MA 02108, USA
                [2 ]Gradient, 600 Stewart Street, Suite 1900, Seattle, WA 98101, USA; rprueitt@ 123456gradientcorp.com
                [3 ]Stony Brook Cancer Center, Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA; paolo.boffetta@ 123456gmail.com
                [4 ]Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
                [5 ]Lancaster Environment Center, Lancaster University, Lancaster LA1 4YQ, UK; c.halsall@ 123456lancaster.ac.uk (C.H.); a.sweetman@ 123456lancaster.ac.uk (A.S.)
                Author notes
                [* ]Correspondence: jgoodman@ 123456gradientcorp.com ; Tel.: +1-617-395-5525
                Author information
                https://orcid.org/0000-0001-9230-8536
                Article
                ijerph-17-05114
                10.3390/ijerph17145114
                7400458
                32679916
                42620209-d064-491c-85ef-ab76da498fa6
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 May 2020
                : 09 July 2020
                Categories
                Article

                Public health
                epidemiology,methodology,exposure assessment,pesticides
                Public health
                epidemiology, methodology, exposure assessment, pesticides

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