The Consistency Assumption for Causal Inference in Social Epidemiology: When a Rose is Not a Rose.
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 exposure 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.
Stanford University School of Medicine, Department of Medicine, 1070 Arastradero Road, Palo Alto, CA 94304, , drehkopf@stanford.edu.
David H
DH
Rehkopf
University of California, San Francisco, School of Medicine, Department of Epidemiology & Biostatistics, 550 16 Street, San Francisco, CA 94158, , mglymour@epi.ucsf.edu.
M Maria
MM
Glymour
University of Minnesota School of Public Health, Division of Epidemiology and Community Health, West Bank Office Building, Suite 300, 1300 S. Second Street, Minneapolis, MN 55454, , tosypuk@umn.edu.
Theresa L
TL
Osypuk
bias; causal inference; epidemiology; potential outcomes; social class