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April 18, 2026Drug and Alcohol Dependence Reports0 citationsOpen Access

Causal Effect Estimates of Online E-cigarette Marketing Exposure on Future E-cigarette Harm Perception and Use

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PHPaul T. HarrellNWNicholas WilliamsKRKara E. Rudolph

Key Points

  • To investigate how online marketing of e-cigarettes influences youth harm perception and e-cigarette usage over time.
  • Analyzed youth data from the PATH study from Waves 4, 4.5, and 5 (2016-2019)
  • Estimated risk differences (RD) for harmfulness perception and current use based on past-month marketing exposure
  • Used a doubly robust targeted minimum loss-based estimator (TMLE) for analysis
  • Adjusted for various demographics, mental health conditions, and other forms of marketing
  • Further adjusted for frequency of social media and other substance use
  • Initial analyses linked online marketing exposure to a 9% decrease in harm perception (RD=-0.09)
  • Showed a 4% increase in e-cigarette use (RD=0.04) after initial adjustments
  • Subsequent adjustments negated these effects, indicating potential confounding from social media and substance use

Abstract

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. 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. Initial analyses estimated that online marketing 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. 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. • U.S. youth e-cigarette use rose dramatically in the late 2010’s. • Fixed effects analysis supports online marketing (OM) as a cause. • Causal effect analysis (CEA) adjusts for time-varying exposure-confounder feedback. • Initial CEA indicated OM increased current e-cigarette use. • The effect was lost after adjusting for online activity and other substance use.

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Cite This Study

Harrell et al. (2026) studied this question.

synapsesocial.com/papers/69e31f1a40886becb653e847https://doi.org/10.1016/j.dadr.2026.100436
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