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Avoiding overadjustment bias in social epidemiology through appropriate covariate selection: a primer

  • Anita van Zwieten
    Affiliations
    The University of Sydney School of Public Health, Faculty of Medicine and Health, Sydney, New South Wales, Australia

    Centre for Kidney Research, The Children's Hospital at Westmead, Westmead, New South Wales, Australia
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  • Peter W.G. Tennant
    Affiliations
    Leeds Institute for Data Analytics, University of Leeds, Leeds, UK

    Faculty of Medicine and Health, University of Leeds, Leeds, UK

    Alan Turing Institute, London, UK
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  • Michelle Kelly-Irving
    Affiliations
    Equity Research Team, CERPOP, Université de Toulouse, Inserm, Université Paul Sabatier, Toulouse, France

    Institut fédératif d’études et recherche interdisciplinaire santé société (Iferiss), Université Toulouse III Paul Sabatier, Toulouse, France
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  • Fiona M. Blyth
    Affiliations
    The University of Sydney School of Public Health, Faculty of Medicine and Health, Sydney, New South Wales, Australia

    ARC Centre of Excellence in Population Aging Research (CEPAR), University of Sydney, Sydney, Australia
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  • Armando Teixeira-Pinto
    Affiliations
    The University of Sydney School of Public Health, Faculty of Medicine and Health, Sydney, New South Wales, Australia

    Centre for Kidney Research, The Children's Hospital at Westmead, Westmead, New South Wales, Australia
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  • Saman Khalatbari-Soltani
    Correspondence
    Corresponding authors. The University of Sydney School of Public Health, Faculty of Medicine and Health, New South Wales, Australia. Tel.: +61 2 8627 9813; fax: +61 2 9036 0000.
    Affiliations
    The University of Sydney School of Public Health, Faculty of Medicine and Health, Sydney, New South Wales, Australia

    ARC Centre of Excellence in Population Aging Research (CEPAR), University of Sydney, Sydney, Australia
    Search for articles by this author

      Abstract

      Obtaining accurate estimates of the causal effects of socioeconomic position (SEP) on health is important for public health interventions. To do this, researchers must identify and adjust for all potential confounding variables, while avoiding inappropriate adjustment for mediator variables on a causal pathway between the exposure and outcome. Unfortunately, ‘overadjustment bias’ remains a common and under-recognized problem in social epidemiology. This paper offers an introduction on selecting appropriate variables for adjustment when examining effects of SEP on health, with a focus on overadjustment bias. We discuss the challenges of estimating different causal effects including overadjustment bias, provide guidance on overcoming them, and consider specific issues including the timing of variables across the life-course, mutual adjustment for socioeconomic indicators, and conducting systematic reviews. We recommend three key steps to select the most appropriate variables for adjustment. First, researchers should be clear about their research question and causal effect of interest. Second, using expert knowledge and theory, researchers should draw causal diagrams representing their assumptions about the interrelationships between their variables of interest. Third, based on their causal diagram(s) and causal effect(s) of interest, researchers should select the most appropriate set of variables, which maximizes adjustment for confounding while minimizing adjustment for mediators.

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