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      Inferential challenges when assessing racial/ethnic health disparities in environmental research

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          Abstract

          Numerous epidemiologic studies have documented environmental health disparities according to race/ethnicity (R/E) to inform targeted interventions aimed at reducing these disparities. Yet, the use of R/E under the potential outcomes framework implies numerous underlying assumptions for epidemiologic studies that are often not carefully considered in environmental health research. In this commentary, we describe the current state of thinking about the interpretation of R/E variables in etiologic studies. We then discuss how such variables are commonly used in environmental epidemiology. We observed three main uses for R/E: i) as a confounder, ii) as an effect measure modifier and iii) as the main exposure of interest either through descriptive analysis or under a causal framework. We identified some common methodological concerns in each case and provided some practical solutions. The use of R/E in observational studies requires particular cautions in terms of formal interpretation and this commentary aims at providing a practical resource for future studies assessing racial/ethnic health disparities in environmental research.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12940-020-00689-5.

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          Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.

          Ideally, questions about comparative effectiveness or safety would be answered using an appropriately designed and conducted randomized experiment. When we cannot conduct a randomized experiment, we analyze observational data. Causal inference from large observational databases (big data) can be viewed as an attempt to emulate a randomized experiment-the target experiment or target trial-that would answer the question of interest. When the goal is to guide decisions among several strategies, causal analyses of observational data need to be evaluated with respect to how well they emulate a particular target trial. We outline a framework for comparative effectiveness research using big data that makes the target trial explicit. This framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls.
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            Estimating causal effects of treatments in randomized and nonrandomized studies.

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              Mediation Analysis: A Practitioner's Guide

              This article provides an overview of recent developments in mediation analysis, that is, analyses used to assess the relative magnitude of different pathways and mechanisms by which an exposure may affect an outcome. Traditional approaches to mediation in the biomedical and social sciences are described. Attention is given to the confounding assumptions required for a causal interpretation of direct and indirect effect estimates. Methods from the causal inference literature to conduct mediation in the presence of exposure-mediator interactions, binary outcomes, binary mediators, and case-control study designs are presented. Sensitivity analysis techniques for unmeasured confounding and measurement error are introduced. Discussion is given to extensions to time-to-event outcomes and multiple mediators. Further flexible modeling strategies arising from the precise counterfactual definitions of direct and indirect effects are also described. The focus throughout is on methodology that is easily implementable in practice across a broad range of potential applications.
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                Author and article information

                Contributors
                tbenmarhnia@ucsd.edu
                anjumh@uw.edu
                jay.kaufman@mcgill.ca
                Journal
                Environ Health
                Environ Health
                Environmental Health
                BioMed Central (London )
                1476-069X
                12 January 2021
                12 January 2021
                2021
                : 20
                : 7
                Affiliations
                [1 ]GRID grid.217200.6, ISNI 0000 0004 0627 2787, Department of Family Medicine and Public Health & Scripps Institution of Oceanography University of California, San Diego, ; 9500 Gilman Drive, La Jolla, CA 92093 USA
                [2 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Epidemiology, , University of Washington, ; Seattle, WA USA
                [3 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Department of Epidemiology, Biostatistics, and Occupational Health, , McGill University, ; Montreal, QC Canada
                Author information
                http://orcid.org/0000-0002-4018-3089
                Article
                689
                10.1186/s12940-020-00689-5
                7802337
                33430882
                9d6981b1-549c-4abc-8ec9-3fa831337b82
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 3 February 2020
                : 29 December 2020
                Categories
                Commentary
                Custom metadata
                © The Author(s) 2021

                Public health
                air pollution and health,race/ethnicity,causal inference,social epidemiology
                Public health
                air pollution and health, race/ethnicity, causal inference, social epidemiology

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