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The Consistency Assumption for Causal Inference in Social Epidemiology: When a Rose Is Not a Rose

  • Social Epidemiology (JM Oakes, Section Editor)
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Abstract

The assumption that exposures as measured in observational settings have clear and specific definitions underpins epidemiologic research and allows us to use observational data to predict outcomes in interventions. This leap between exposures as measured and exposures as intervened upon is typically supported by the consistency assumption. The consistency assumption has received extensive attention in risk factor epidemiology but relatively little emphasis in social epidemiology. However, violations of the consistency assumption may be especially important to consider when understanding how social and economic exposures influence health. Efforts to clarify the definitions of our exposures, thus bolstering the consistency assumption, will help guide interventions to improve population health and reduce health disparities. This article focuses on the consistency assumption as considered within social epidemiology. We explain how this assumption is articulated in the causal inference literature and give examples of how it might be violated for three common exposures in social epidemiology research: income, education, and neighborhood characteristics. We conclude that there is good reason to worry about consistency assumption violations in much of social epidemiology research. Theoretically motivated explorations of mechanisms along with empirical comparisons of research findings under alternative operationalizations of exposure can help identify consistency violations. We recommend that future social epidemiology studies be more explicit to name and discuss the consistency assumption when describing the exposure of interest, including reconciling disparate results in the literature.

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Authors and Affiliations

  1. Department of Medicine, Stanford University School of Medicine, 1070 Arastradero Road, Palo Alto, CA, 94304, USA

    David H. Rehkopf

  2. Department of Epidemiology and Biostatistics, University of California, San Francisco, School of Medicine, 550 16th Street, San Francisco, CA, 94158, USA

    M. Maria Glymour

  3. Division of Epidemiology and Community Health, University of Minnesota School of Public Health, West Bank Office Building, Suite, 300, 1300 S. Second Street, Minneapolis, MN, 55454, USA

    Theresa L. Osypuk

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  1. David H. Rehkopf
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Correspondence to David H. Rehkopf.

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David H. Rehkopf, M. Maria Glymour, and Theresa L. Osypuk declare that they have no conflict of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

Funding

David H. Rehkopf was supported by the National Institute on Aging of the National Institutes of Health under award number K01AG047280. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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This article is part of the Topical Collection on Social Epidemiology

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Rehkopf, D.H., Glymour, M.M. & Osypuk, T.L. The Consistency Assumption for Causal Inference in Social Epidemiology: When a Rose Is Not a Rose. Curr Epidemiol Rep 3, 63–71 (2016). https://doi.org/10.1007/s40471-016-0069-5

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