Journal article
Causal and Associational Language in Observational Health Research: A Systematic Evaluation
NA Haber, SE Wieten, JM Rohrer, OA Arah, PWG Tennant, EA Stuart, EJ Murray, S Pilleron, ST Lam, E Riederer, SJ Howcutt, AE Simmons, C Leyrat, P Schoenegger, A Booman, MS Kang Dufour, AL O’Donoghue, R Baglini, S Do, M De La Rosa Takashima Show all
American Journal of Epidemiology | OXFORD UNIV PRESS INC | Published : 2022
DOI: 10.1093/aje/kwac137
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 cau..
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Awarded by Australian Research Council
Funding Acknowledgements
No funding was granted specifically for the support of this study. The Meta-Research Innovation Center at Stanford University is supported by Arnold Ventures LLC (Houston, Texas), formerly the Laura and John Arnold Foundation. S.P. was funded by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant (agreement no. 842817). S.K.-S. is supported by the Australian Research Council Centre of Excellence in Population Aging Research (project number CE170100005). I.S. is supported by the National Institute of Mental Health (grant T32MH122357). E.A.S.'s time was supported by the National Institute of Mental Health (grant R01MH115487) and the Bloomberg American Health Initiative. A.L.O. is funded by a philanthropic gift from Google.org outside of the submitted work. O.A.A. is supported by the National Institute of Biomedical Imaging and Bioengineering (grant R01EB027650), National Center for Advancing Translational Sciences UCLA Clinical Translational Science Institute (grant UL1TR001881), and a philanthropic gift from the Karen Toffler Charity Trust. Data, data analysis code, and materials are available on the Open Science Framework project https://osf.io/jtdaz/.This work was supported by many people who made contributions to this work. Turki Althunian contributed to the screening process. Jess Rohmann contributed to the piloting process. This work was additionally supported by comments and contributions from Alyssa Bilinksi, Pascal Goldsetzer, Caroline Blaine, Otto Kalliokoski, Eero Raittio, Tanya Colyer, Tim Watkins, Alexander Breskin, Arindam Basu, Jessica L. Rohmann, Luke A McGuinness, Todd Johnson, Mario Mali.cki, Sebastian Skejo, Scott Graham, Michael Chaiton-Murray, John Edlund, Katelyn Smalley, Danielle Newby, Anita Williams, Cord Phelps, Colleen Derkatch, Alexander Wolthon, Pallavi Rohella, Damien Croteau-Chonka, Steven Goodman, and John Ioannidis. Presented at the Annual Meeting of the Society for Epidemiologic Research, June 14-17, 2022, Chicago, Illinois. A preprint of this article has been published online. (Haber, NA, Wieten SE, Rohrer JM, et al. Causal and Associational Language in Observational Health Research: A Systematic Evaluation. medRxiv. 2021. https://doi.org/10.1101/2021.08.25.21262631).All errors are the sole responsibility of the authors, and no funders had any role in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. The researchers were independent from funders, and all authors, external and internal, had full access to all of the data (including statistical reports and tables) in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. Conflict of interest: none declared.