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      Occupational exposure to pesticides and neurobehavioral outcomes. Impact of different original and recalled exposure measures on the associations

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          Abstract

          Background

          Several measures of occupational exposure to pesticides have been used to study associations between exposure to pesticides and neurobehavioral outcomes. This study assessed the impact of different exposure measures for glyphosate and mancozeb on the association with neurobehavioral outcomes based on original and recalled self-reported data with 246 smallholder farmers in Uganda.

          Methods

          The association between the 6 exposure measures and 6 selected neurobehavioral test scores was investigated using linear multivariable regression models. Exposure measures included original exposure measures for the previous year in 2017: (i) application status (yes/no), (ii) number of application days, (iii) average exposure-intensity scores (EIS) of an application and (iv) number of EIS-weighted application days. Two additional measures were collected in 2019: (v) recalled application status and (vi) recalled EIS for the respective periods in 2017.

          Results

          Recalled applicator status and EIS were between 1.2 and 1.4 times more frequent and higher for both pesticides than the original application status and EIS. Adverse associations between the different original measures of exposure to glyphosate and 4 neurobehavioral tests were observed. Glyphosate exposure based on recalled information and all mancozeb exposure measures were not associated with the neurobehavioral outcomes.

          Conclusions

          The relation between the different original self-reported glyphosate exposure measures and neurobehavioral test scores appeared to be robust. When based on recalled exposure measures, associations observed with the original exposure measures were no longer present. Therefore, future epidemiological studies on self-reported exposure should critically evaluate the potential bias towards the null in observed exposure–response associations.

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          Most cited references16

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          Efficient measurement error correction with spatially misaligned data.

          Association studies in environmental statistics often involve exposure and outcome data that are misaligned in space. A common strategy is to employ a spatial model such as universal kriging to predict exposures at locations with outcome data and then estimate a regression parameter of interest using the predicted exposures. This results in measurement error because the predicted exposures do not correspond exactly to the true values. We characterize the measurement error by decomposing it into Berkson-like and classical-like components. One correction approach is the parametric bootstrap, which is effective but computationally intensive since it requires solving a nonlinear optimization problem for the exposure model parameters in each bootstrap sample. We propose a less computationally intensive alternative termed the "parameter bootstrap" that only requires solving one nonlinear optimization problem, and we also compare bootstrap methods to other recently proposed methods. We illustrate our methodology in simulations and with publicly available data from the Environmental Protection Agency.
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            Measurement error in two-stage analyses, with application to air pollution epidemiology.

            Public health researchers often estimate health effects of exposures (e.g., pollution, diet, lifestyle) that cannot be directly measured for study subjects. A common strategy in environmental epidemiology is to use a first-stage (exposure) model to estimate the exposure based on covariates and/or spatio-temporal proximity and to use predictions from the exposure model as the covariate of interest in the second-stage (health) model. This induces a complex form of measurement error. We propose an analytical framework and methodology that is robust to misspecification of the first-stage model and provides valid inference for the second-stage model parameter of interest. We decompose the measurement error into components analogous to classical and Berkson error and characterize properties of the estimator in the second-stage model if the first-stage model predictions are plugged in without correction. Specifically, we derive conditions for compatibility between the first- and second-stage models that guarantee consistency (and have direct and important real-world design implications), and we derive an asymptotic estimate of finite-sample bias when the compatibility conditions are satisfied. We propose a methodology that (1) corrects for finite-sample bias and (2) correctly estimates standard errors. We demonstrate the utility of our methodology in simulations and an example from air pollution epidemiology.
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              Does more accurate exposure prediction necessarily improve health effect estimates?

              A unique challenge in air pollution cohort studies and similar applications in environmental epidemiology is that exposure is not measured directly at subjects' locations. Instead, pollution data from monitoring stations at some distance from the study subjects are used to predict exposures, and these predicted exposures are used to estimate the health effect parameter of interest. It is usually assumed that minimizing the error in predicting the true exposure will improve health effect estimation. We show in a simulation study that this is not always the case. We interpret our results in light of recently developed statistical theory for measurement error, and we discuss implications for the design and analysis of epidemiologic research.
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                Author and article information

                Contributors
                Journal
                Ann Work Expo Health
                Ann Work Expo Health
                annhyg
                Annals of Work Exposures and Health
                Oxford University Press (UK )
                2398-7308
                2398-7316
                July 2024
                04 June 2024
                04 June 2024
                : 68
                : 6
                : 657-664
                Affiliations
                Institute for Risk Assessment Sciences (IRAS), Utrecht University , Yalelaan 1, 3584 CL Utrecht, The Netherlands
                Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH) , Kreuzstrasse 2, 4123 Allschwil, Switzerland
                University of Basel , Petersplatz 1, 4001 Basel, Switzerland
                Centre for Human Exposure Science, Institute of Occupational Medicine (IOM) , Research Avenue North Riccarton, Edinburgh, United Kingdom
                Uganda National Association of Community and Occupational Health (UNACOH) , Building, plot 37, YMCA, 41 Buganda Rd, Kampala, Uganda
                School of Public Health, Makarere University , New Mulago Hill Road, Mulago, Kampala, Uganda
                Institute for Risk Assessment Sciences (IRAS), Utrecht University , Yalelaan 1, 3584 CL Utrecht, The Netherlands
                Centre for Occupational and Environmental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester , Crawford House, Booth St E, Manchester M13 9QS, United Kingdom
                Centre for Human Exposure Science, Institute of Occupational Medicine (IOM) , Research Avenue North Riccarton, Edinburgh, United Kingdom
                Centre for Occupational and Environmental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester , Crawford House, Booth St E, Manchester M13 9QS, United Kingdom
                Centre for Occupational and Environmental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester , Crawford House, Booth St E, Manchester M13 9QS, United Kingdom
                Health and Safety Executive (HSE) , Harpur Hill, Buxton SK17 9JN, United Kingdom
                Centre for Human Exposure Science, Institute of Occupational Medicine (IOM) , Research Avenue North Riccarton, Edinburgh, United Kingdom
                Institute for Risk Assessment Sciences (IRAS), Utrecht University , Yalelaan 1, 3584 CL Utrecht, The Netherlands
                Author notes
                Corresponding author: Email: samuel.fuhrimann@ 123456swisstph.ch
                Author information
                https://orcid.org/0000-0002-1861-1737
                https://orcid.org/0000-0001-8712-736X
                https://orcid.org/0000-0001-7708-3017
                https://orcid.org/0000-0002-1205-1898
                https://orcid.org/0000-0001-8923-2999
                Article
                wxae025
                10.1093/annweh/wxae025
                11229323
                38832717
                a0681a26-2d3e-4f74-bc55-cc182ae1e60c
                © The Author(s) 2024. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.

                History
                : 07 September 2023
                : 15 January 2024
                : 25 April 2024
                : 09 February 2024
                Page count
                Pages: 8
                Categories
                Short Communications
                AcademicSubjects/MED00640

                exposure assessment,exposure misclassification,farmers,glyphosate,mancozeb,neurobehavioral outcomes,pesticides,recall,uganda

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