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Background: Interest exists in whether youth e-cigarette use (“vaping”) increases risk of initiating cigarette smoking. Using Waves 1 and 2 of the US PATH study we previously reported adjustment for vaping propensity using Wave 1 variables explained about 80% of the unadjusted relationship. Here data from Waves 1 to 3 are used to avoid over-adjustment if Wave 1 vaping affected variables recorded then. Methods: Main analyses M1 and M2 concerned Wave 2 never smokers who never vaped by Wave 1, linking Wave 2 vaping to Wave 3 smoking initiation, adjusting for predictors of vaping based on Wave 1 data using differing propensity indices. M3 was similar but derived the index from Wave 2 data. Sensitivity analyses excluded Wave 1 other tobacco product users, included other product use as another predictor, or considered propensity for smoking or any tobacco use, not vaping. Alternative analyses used exact age (not previously available) as a confounder not grouped age, attempted residual confounding adjustment by modifying predictor values using data recorded later, or considered interactions with age. Results: In M1, adjustment removed about half the excess OR (i.e. OR–1), the unadjusted OR, 5.60 (95% CI 4.52-6.93), becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16), depending whether adjustment was for propensity as a continuous variable, as quintiles, or the variables making up the propensity score. Many factors had little effect: using grouped or exact age; considering other products; including interactions; or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment explained about half the excess OR, whereas analyses subject to over-adjustment explained about 80%. Conclusions: Although much of the unadjusted gateway effect results from confounding, we provide stronger evidence than previously of some causal effect of vaping, though doubts still remain about the completeness of adjustment.

Background: Interest exists in whether youth e-cigarette use (“vaping”) increases risk of initiating cigarette smoking. Using Waves 1 and 2 of the US PATH study we reported that adjustment for vaping propensity using Wave 1 variables explained about 80% of the unadjusted relationship. Here we use data from Waves 1 to 3 to avoid over-adjustment if Wave 1 vaping affected variables recorded then. Methods: Our main analysis M1 concerned Wave 2 never smokers who never vaped by Wave 1, linking Wave 2 vaping to Wave 3 smoking initiation, adjusting for Wave 1 predictors. We conducted sensitivity analyses that: excluded Wave 1 other tobacco product users; included other product use as an extra predictor; or considered propensity for smoking or any tobacco use, rather than vaping. We also conducted analyses that: adjusted for propensity as derived originally; ignored Wave 1 data; used exact age (not previously available) as a confounder rather than grouped age; attempted residual confounding adjustment by modifying predictor values using data recorded later; or considered interactions with age. Results: In M1, adjustment removed about half the excess OR (i.e. OR–1), the unadjusted OR, 5.60 (95% CI 4.52-6.93), becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16), depending whether adjustment was for propensity as a continuous variable, as quintiles, or for the variables making up the propensity score. Many factors had little effect: using grouped or exact age; considering other products; including interactions; or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment explained about half the excess OR, whereas analyses subject to over-adjustment explained about 80%. Conclusions: Although much of the unadjusted gateway effect results from confounding, we provide stronger evidence than previously of some causal effect of vaping, though some doubts still remain about the completeness of adjustment.

Background: A recent meta-analysis of nine cohort studies in youths reported that baseline ever e-cigarette use strongly predicted cigarette smoking initiation in the next 6-18 months, with an adjusted odds ratio (OR) of 3.62 (95% confidence interval 2.42-5.41). A recent e-cigarette review agreed there was substantial evidence for this “gateway effect”. As the number of confounders considered in the studies was limited we investigated whether the effect might have resulted from inadequate adjustment, using Waves 1 and 2 of the US PATH study. Methods: Our main analyses considered Wave 1 never cigarette smokers who, at Wave 2, had data on smoking initiation.We constructed a propensity score for ever e-cigarette use from Wave 1 variables, using this to predict ever cigarette smoking. Sensitivity analyses accounted for other tobacco product use, linked current e-cigarette use to subsequent current smoking, or used propensity scores for ever smoking or ever tobacco product use as predictors. We also considered predictors using data from both waves, attempting to reduce residual confounding from misclassified responses. Results: Adjustment for propensity dramatically reduced the unadjusted OR of 5.70 (4.33-7.50) to 2.48 (1.85-3.31), 2.47 (1.79-3.42) or 1.85 (1.35-2.53), whether adjustment was made as quintiles, as a continuous variable or for the individual variables. Additional adjustment for other tobacco products reduced this last OR to 1.59 (1.14-2.20). Sensitivity analyses confirmed adjustment removed most of the gateway effect. Control for residual confounding also reduced the association. Conclusions: We found that confounding is a major factor, explaining most of the observed gateway effect. However, our analyses are limited by small numbers of new smokers considered and the possibility of over-adjustment if taking up e-cigarettes affects some predictor variables. Further analyses are intended using Wave 3 data to try to minimize these problems, and clarify the extent of any true gateway effect.

Background: A recent meta-analysis of nine cohort studies in youths reported that baseline ever e-cigarette use strongly predicted cigarette smoking initiation in the next 6-18 months, with an adjusted odds ratio of 3.62 (95% confidence interval 2.42-5.41). A recent review of e-cigarettes agreed there was substantial evidence for this “gateway effect”. However, the number of confounders considered in the studies was limited, so we investigated whether the effect might have resulted from inadequate adjustment, using Waves 1 and 2 of the Population Assessment of Tobacco and Health study. Methods: Our main analyses considered Wave 1 never cigarette smokers who, at Wave 2, had information available on smoking initiation. We constructed a propensity score for ever e-cigarette use from Wave 1 variables, using this to predict ever cigarette smoking. Sensitivity analyses accounted for use of other tobacco products, linked current e-cigarette use to subsequent current smoking, or used propensity scores for ever smoking or ever tobacco product use as predictors. We also considered predictors using data from both waves to attempt to control for residual confounding from misclassified responses. Results: Adjustment for propensity dramatically reduced the unadjusted odds ratio (OR) of 5.70 (4.33-7.50) to 2.48 (1.85-3.31), 2.47 (1.79-3.42) or 1.85 (1.35-2.53), whether adjustment was made as quintiles, as a continuous variable or for the individual variables. Additional adjustment for other tobacco products reduced this last OR to 1.59 (1.14-2.20). Sensitivity analyses confirmed adjustment removed most of the gateway effect. Control for residual confounding also reduced the association. Conclusions: We found that confounding is a major factor, explaining most of the observed gateway effect. However, our analyses are limited by small numbers of new smokers considered and the possibility of over-adjustment if taking up e-cigarettes affects some predictor variables. Further analyses are intended using Wave 3 data which should avoid these problems.

Background: Interest exists in whether youth e-cigarette use ("vaping") increases risk of initiating cigarette smoking. Using Waves 1 and 2 of the US PATH study we reported that adjustment for vaping propensity using Wave 1 variables explained about 80% of the unadjusted relationship. Here we use data from Waves 1 to 3 to avoid over-adjustment if Wave 1 vaping affected variables recorded then. Methods: Our main analysis M1 concerned Wave 2 never smokers who never vaped by Wave 1, linking Wave 2 vaping to Wave 3 smoking initiation, adjusting for Wave 1 predictors. We conducted sensitivity analyses that: excluded Wave 1 other tobacco product users; included other product use as an extra predictor; or considered propensity for smoking or any tobacco use, rather than vaping. We also conducted analyses that: adjusted for propensity as derived originally; ignored Wave 1 data; used exact age (not previously available) as a confounder rather than grouped age; attempted residual confounding adjustment by modifying predictor values using data recorded later; or considered interactions with age. Results: In M1, adjustment removed about half the excess OR (i.e. OR-1), the unadjusted OR, 5.60 (95% CI 4.52-6.93), becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16), depending whether adjustment was for propensity as a continuous variable, as quintiles, or for the variables making up the propensity score. Many factors had little effect: using grouped or exact age; considering other products; including interactions; or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment explained about half the excess OR, whereas analyses subject to over-adjustment explained about 80%. Conclusions: Although much of the unadjusted gateway effect results from confounding, we provide stronger evidence than previously of some causal effect of vaping, though some doubts still remain about the completeness of adjustment., Competing Interests: Competing interests: Both authors are long term consultants to many tobacco companies and organizations, including the funder of this study., (Copyright: © 2020 Lee PN and Fry JS.)

Background: Toxicant levels are much lower in e-cigarettes than cigarettes. Therefore, introducing e-cigarettes into the market seems likely to reduce smoking-related diseases (SRD). However, vaping might provide a gateway into cigarette smoking for those who otherwise would never have smoked, a concern fuelled by cohort studies showing vaping predicts subsequent smoking initiation in young people. Methods: In this discussion paper, we consider various aspects of the gateway issue in youths. We provide a descriptive critical review of results from prospective studies relating to the gateway effect and the extent to which the studies considered other potential confounding variables associated with smoking initiation. We then estimate the effects of omitting a confounding variable, or misclassifying it, on the association between vaping and subsequent smoking initiation, and determine how the prevalence of smoking might be affected by any true gateway effects of vaping. Finally, we examine trends in e-cigarette and smoking prevalence in youths based on national surveys. Results:  First, we demonstrate that although studies report that vaping significantly predicts smoking initiation following adjustment for various other predictors, the sets of predictors considered are quite incomplete. Furthermore, no study considered residual confounding arising from inaccurate measurement of predictors. More precise adjustment may substantially reduce the association. Second, we show any true gateway effect would likely affect smoking prevalence only modestly. Third, we show smoking prevalence in U.S. and U.K. youths in 2014-2016 declined somewhat faster than predicted by the preceding trend; a substantial gateway effect suggests the opposite. Finally, we argue that even if some gateway effect exists, introducing e-cigarettes still likely reduces SRDs. Conclusions: Given that the existence of any true gateway effect in youth is not yet clearly demonstrated the population health impact of introducing e-cigarettes is still likely to be beneficial., Competing Interests: Competing interests: PNL and KJC consult for various tobacco companies. EFA is a current employee of Philip Morris International.