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

Authors :
Cathrine Axfors
Arthur Chatton
Elizabeth A. Stuart
Ariadne E. Rivera Aguirre
Julia M. Rohrer
Ian Schmid
Palwasha Khan
Daloha Rodríguez-Molina
Sebastián Peña
Sophie Pilleron
Camila Olarte Parra
Mark Kelson
Saman Khalatbari-Soltani
Jessie Seiler
Mi-Suk Kang Dufour
Eleanor J Murray
Peter W. G. Tennant
Anna Booman
Meg G. Salvia
Daniel J. Dunleavy
Taym M. Alsalti
Thomas Rhys Evans
Philipp Schoenegger
Rachel A. Hoopsick
Sarah Wieten
Sze Tung Lam
Gideon Meyerowitz-Katz
Stefanie Do
Rebekah Baglini
Sarah E. Twardowski
Sarah J Howcutt
Matthew P. Fox
Mari Takashima
Onyebuchi A. Arah
Julia Dabravolskaj
Clemence Leyrat
Emily Riederer
Shashank Suresh
Ashley L. O’Donoghue
Alberto Antonietti
Noah Haber
Eric Au
Nnaemeka U. Odo
Taylor McLinden
José Andrés Calvache
Alison E. Simmons
Talal S. Alshihayb
Nicholas Judd
Andreea Steriu
Source :
Haber, N, Wieten, S, Rohrer, J, Arah, O, Tennant, P, Stuart, E, Murray, E, Pilleron, S, Lam, S T, Riederer, E, Howcutt, S J, Simmons, A, Leyrat, C, Schoenegger, P, Booman, A, Dufour, M-S K, O'Donoghue, A & Baglini, R B 2022, ' Causal and associational language in observational health research: A systematic evaluation ', American Journal of Epidemiology . https://doi.org/10.1101/2021.08.25.21262631
Publication Year :
2022
Publisher :
eScholarship, University of California, 2022.

Abstract

Background Avoiding “causal” language with observational study designs is common publication practice, often justified as being a more cautious approach to interpretation. Objectives We aimed to i) estimate the degree to which causality was implied by both the language linking exposures to outcomes and by action recommendations in the high-profile health literature, ii) examine disconnects between language and recommendations, iii) identify which linking phrases were most common, and iv) generate estimates by which these phrases imply causality. Methods We identified 18 of the most prominent general medical/public health/epidemiology journals, and searched and screened for articles published from 2010 to 2019 that investigated exposure/outcome pairs until we reached 65 non-RCT articles per journal (n=1,170). Two independent reviewers and an arbitrating reviewer rated the degree to which they believed causality had been implied by the language in abstracts based on written guidance. Reviewers then rated causal implications of linking words in isolation. For comparison, additional review was performed for full texts and for a secondary sample of RCTs. Results Reviewers rated the causal implication of the sentence and phrase linking the exposure and outcome as None (i.e., makes no causal implication) in 13.8%, Weak in 34.2%, Moderate in 33.2%, and Strong in 18.7% of abstracts. Reviewers identified an action recommendation in 34.2% of abstracts. Of these action recommendations, reviewers rated the causal implications as None in 5.3%, Weak in 19.0%, Moderate in 42.8% and Strong in 33.0% of cases. The implied causality of action recommendations was often higher than the implied causality of linking sentences (44.5%) or commensurate (40.3%), with 15.3% being weaker. The most common linking word root identified in abstracts was “associate” (n=535/1,170; 45.7%) (e.g. “association,” “associated,” etc). There were only 16 (1.4%) abstracts using “cause” in the linking or modifying phrases. Reviewer ratings for causal implications of word roots were highly heterogeneous, including those commonly considered non-causal. Discussion We found substantial disconnects between causal implications used to link an exposure to an outcome and the action implications made. This undercuts common assumptions about what words are often considered non-causal and that policing them eliminates causal implications. We recommend that instead of policing words, editors, researchers, and communicators should increase efforts at making research questions, as well as the potential of studies to answer them, more transparent. Summary box

Details

ISSN :
00029262
Database :
OpenAIRE
Journal :
Haber, N, Wieten, S, Rohrer, J, Arah, O, Tennant, P, Stuart, E, Murray, E, Pilleron, S, Lam, S T, Riederer, E, Howcutt, S J, Simmons, A, Leyrat, C, Schoenegger, P, Booman, A, Dufour, M-S K, O'Donoghue, A & Baglini, R B 2022, ' Causal and associational language in observational health research: A systematic evaluation ', American Journal of Epidemiology . https://doi.org/10.1101/2021.08.25.21262631
Accession number :
edsair.doi.dedup.....6b2a830009842e8233432bca87c78c52