Causal and associational language in observational health research: A systematic evaluation.

This is the final version. Available from Oxford University Press via the DOI in this record. Data, data analysis code, and materials are available on the Open Science Framework project https://osf.io/jtdaz/.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 and screened for 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, three 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 34.2%, Moderate 33.2%, and Strong 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.Marie Skłodowska-Curie grantAustralian Research CouncilNational Institute of Mental HealthNational Institute of Mental HealthNational Institute of Biomedical Imaging and BioengineeringNational Center for Advancing Translational Sciences UCLA Clinical Translational Science InstituteBloomberg American Health InitiativeKaren Toffler Charity Trus