ArticleDrug and alcohol dependence reports2026
Causal effect estimates of online e-cigarette marketing exposure on future e-cigarette harm perception and use.
Article in Drug and alcohol dependence reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
8 authors.
Funding
Abstract
Background: Globally, over 10 million youth use e-cigarettes; United States use, in particular, dramatically rose in the late 2010's, exceeding year-over-year increases from any other substance in over four decades. This rise has been partially attributed to youth online e-cigarette marketing exposure, but has not been appropriately studied. Methods: We examined youth data from the Population Assessment of Tobacco and Health (PATH) spanning the dramatic increase period (Waves 4, 4.5, and 5; 2016-2019). We estimated average risk differences (RD) for Wave 5 e-cigarette harmfulness perception and use comparing all youth versus no youth reporting past-month online e-cigarette marketing exposure in Waves 4 and 4.5. We used a doubly robust, nonparametric targeted minimum loss-based estimator (TMLE) to estimate RD, incorporating PATH survey weights. Initial analyses adjusted for demographics, mental health issues, and other forms of e-cigarette marketing. Subsequent analyses adjusted for frequency of social media use, other substance use, and tobacco (non-e-cigarette) use. Results: Initial analyses estimated that online marketing was associated with a 9% decrease in e-cigarette harmfulness perception (RD=-0.09, 95% C=-0.12, -0.05), or a Risk Ratio (RR) of 0.85 (95% CI=0.80, 0.91), as well as a 4% increase in current e-cigarette use (RD=0.04, 95% CI=0.02, 0.06; RR=1.36, 95% CI=1.15, 1.62). However, after adjusting for additional potential time-varying confounding variables, point estimates were close to null with 95% confidence intervals spanning the null. Discussion: Frequency of social media use, other substance use, and/or tobacco use may be important confounding variables related to marketing and e-cigarette use that require further investigation.
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