Evidence map›Paper›PMID 27326386›Full record

ArticleCurrent epidemiology reports2016

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

David H Rehkopf, M Maria Glymour, Theresa L Osypuk

Abstract read
In one paragraph

Article in Current epidemiology reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

78 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Gentrification, Neighborhood Change, and Population Health: a Systematic Review.Journal of urban health : bulletin of the New York Academy of Medicine · 2020
    Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Article
  6. A Novel Decision-Modeling Framework for Health Policy Analyses When Outcomes Are Influenced by Social and Disease Processes.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Real-world evidence for primary care: A primer on observational research.African journal of primary health care & family medicine · 2025
    Article
  14. Review
  15. Toward New Directions in Human Biology: A Roadmap for Anthropological Causal Inference With Observational Data.American journal of human biology : the official journal of the Human Biology Council · 2025
    Article
  16. Depicting patient-reported outcome measures within directed acyclic graphs: practice and implications for causal reasoning.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
    Article
  17. Causal clarity in statistical software.International journal of epidemiology · 2025
    Article
  18. Article
  19. Article
  20. Review

18 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

David H RehkopfStanford University School of Medicine, Department of Medicine, 1070 Arastradero Road, Palo Alto, CA 94304, , drehkopf@stanford.edu.
M Maria GlymourUniversity of California, San Francisco, School of Medicine, Department of Epidemiology & Biostatistics, 550 16 Street, San Francisco, CA 94158, , mglymour@epi.ucsf.edu.
Theresa L OsypukUniversity 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.

Funding

Minnesota Population Center Science and Technical CoreP2CHD041023 · NICHD · UNIVERSITY OF MINNESOTA · PI THERESA LOUISE OSYPUK · 2016 to 2026
$4.3M
Life course contexts and work - quantifying impacts on aging and chronic diseaseK01AG047280 · NIA · STANFORD UNIVERSITY · PI REHKOPF, DAVID H · 2014 to 2018
$632k
NIA NIH HHS K01 AG047280NICHD NIH HHS P2C HD041023
6 · The paper itself

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 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.

Indexed as

biascausal inferenceepidemiologypotential outcomessocial class

Identifiers

PMID27326386
PMCPMC4912021

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.