Evidence map›Paper›PMID 35925053›Full record

ArticleAmerican journal of epidemiology2022

Causal and Associational Language in Observational Health Research: A Systematic Evaluation.

Noah A Haber, Sarah E Wieten, Julia M Rohrer, Onyebuchi A Arah, Peter W G Tennant, Elizabeth A Stuart, Eleanor J Murray, Sophie Pilleron, Sze Tung Lam, Emily Riederer and 39 more

Abstract read
In one paragraph

Article in American journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

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

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Observational
  11. Article
  12. Overview of Quantitative Research.Family medicine · 2026
    Review
  13. Article
  14. Causal clarity in statistical software.International journal of epidemiology · 2025
    Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Review
  20. Article
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

49 authors.

Noah A Haber
Sarah E Wieten
Julia M Rohrer
Onyebuchi A Arah
Peter W G Tennant
Elizabeth A Stuart
Eleanor J Murray
Sophie Pilleron
Sze Tung Lam
Emily Riederer
Sarah Jane Howcutt
Alison E Simmons
Clémence Leyrat
Philipp Schoenegger
Anna Booman
Mi-Suk Kang Dufour
Ashley L O'Donoghue
Rebekah Baglini
Stefanie Do
Mari De La Rosa Takashima
Thomas Rhys Evans
Daloha Rodriguez-Molina
Taym M Alsalti
Daniel J Dunleavy
Gideon Meyerowitz-Katz
Alberto Antonietti
Jose A Calvache
Mark J Kelson
Meg G Salvia
Camila Olarte Parra
Saman Khalatbari-Soltani
Taylor McLinden
Arthur Chatton
Jessie Seiler
Andreea Steriu
Talal S Alshihayb
Sarah E Twardowski
Julia Dabravolskaj
Eric Au
Rachel A Hoopsick
Shashank Suresh
Nicholas Judd
Sebastián Peña
Cathrine Axfors
Palwasha Khan
Ariadne E Rivera Aguirre
Nnaemeka U Odo
Ian Schmid
Matthew P Fox

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
PREMIERE: A PREdictive Model Index and Exchange REpositoryR01EB027650 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BUI, ALEX · 2019 to 2023
$3.0M
Integrating data for causal inference in behavioral healthT32MH122357 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Rashelle Jean Musci, Elizabeth A. Stuart · 2020 to 2026
$1.8M
Causal mediation methods for studying mechanisms in mental healthR01MH115487 · NIMH · JOHNS HOPKINS UNIVERSITY · PI STUART, ELIZABETH A. · 2018 to 2021
$1.3M
Medical Research Council MR/T032448/1NCATS NIH HHS UL1 TR001881NIBIB NIH HHS R01 EB027650NIMH NIH HHS R01 MH115487NIMH NIH HHS T32 MH122357
6 · The paper itself

Abstract

We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations. Reviewers rated the causal implication of exposure/outcome linking language as none (no causal implication) in 13.8%, weak in 34.2%, moderate in 33.2%, and strong in 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was "associate" (45.7%). Reviewers' ratings of linking word roots were highly heterogeneous; over half of reviewers rated "association" as having at least some causal implication. This research undercuts the assumption that avoiding "causal" words leads to clarity of interpretation in medical research.

Indexed as

Biomedical ResearchLanguageCausalityHumansassociationcausal inferencecausal languageobservational study

Identifiers

PMID35925053
PMCPMC11043784

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.