Electronic Cigarette Use and Smoking Initiation in Taiwan: Evidence from the First Prospective Study in Asia

Chien, Yu Ning; Gao, Wayne; Sanna, Mattia; Chen, Ping Ling; Chen, Yi Hua; Glantz, Stanton A.; Chiou, Hung Yi

doi:10.3390/ijerph16071145

Cited by 26 publications

(37 citation statements)

References 42 publications

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“…The authors only cite a portion of these studies. In detail, young people have been recruited in the US and Canada 2-15 , UK [16][17][18] , Finland 19 , Mexico 20 , the Netherlands 21 , Romania 22 , Taiwan 23 , and Germany 24 . More than 100,000 young people have been recruited for these studies with observational periods of up to 24 months.…”

Section: Data Availabilitymentioning

confidence: 99%

Investigating gateway effects using the PATH study

Fry

2019

F1000Res

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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.

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Section: Data Availabilitymentioning

confidence: 99%

Investigating gateway effects using the PATH study

Fry

2019

F1000Res

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“…Since the time that we published our earlier analysis [8] and our paper on general considerations relating to vaping as a possible gateway into cigarette smoking [7] a number of other authors have presented evidence from prospective studies [9][10][11][12][13][14][15]. The studies vary in the extent to which potential confounding variables have been adjusted for, with large OR estimates tending to be reported in studies with more limited control.…”

Section: Discussionmentioning

confidence: 99%

“…Apart from a US study [14] ,which reported an OR of 6.8 (1.7-28.3), following adjustment for ten covariates independently associated with initiation of smoking, most of the other studies which appear to have better control for confounding gave lower estimates. These included a study in Taiwan [10] which reported an OR of 2.14 (1.66-2.75), a study in Germany [12] which reported an OR of 2.18 (1.65-2.87) and a study in Finland [11] which reported that adjustment reduced the OR from 11.52 (4.91-26.56) to 2.92 (1.09-7.85). Notably, a study in Great Britain [16] reported an OR of 11.89 (3.56-39.72) estimated using the usual logistic method, but a reduced value of 1.34 (1.05-1.72) using causal mediation analysis.…”

Section: Discussionmentioning

confidence: 99%

Further investigations of gateway effects using the PATH study

Lee¹,

Fry²

2020

Preprint

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Background: Considerable interest exists in whether e-cigarette use (“vaping”) by youths increases the risk of initiating cigarette smoking. Based on Waves 1 and 2 of the Population Assessment of Tobacco and Health study we reported that adjustment for propensity for vaping using Wave 1 variables explained about 80% of the unadjusted relationship. This analysis may be over-adjusted had vaping at Wave 1 affected some variables recorded then. Here we present analyses using Waves 1 to 3 to avoid this possibility. Methods: Our main analysis M1 concerned those who had never smoked by Wave 2 and never vaped by Wave 1. Wave 2 vaping was linked to smoking initiation by Wave 3, adjusting for Wave 1 predictors. Sensitivity analyses excluded other tobacco product users at Wave 1, included other tobacco product use as an additional predictor, or were based on propensity for ever smoking or ever any tobacco use, rather than ever vaping. Other analyses adjusted for propensity as derived originally, or ignored Wave 1 data. Other analyses used grouped age (only available originally) or exact age (available now) as a confounder variable, attempted residual confounding adjustment by modifying values of predictor variables using data later recorded, or considered interactions with age. Results: In M1, propensity adjustment removed about 50% of the excess odds ratio (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 16 variables making up the propensity score. Many factors studied hardly affected the results, including using grouped or exact age, consideration of other tobacco products, including interactions, or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment only explained about 50% of the excess OR whereas analyses subject to over-adjustment explained about 80%. Conclusions: Although much of the unadjusted gateway effect results from uncontrolled confounding, our current analysis provides stronger evidence of a causal effect of vaping than did our earlier analysis. However, some doubts remain about the completeness of confounder adjustment.

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“…There is considerable uncertainty regarding the chemical constituents of the e-liquids delivered by the e-cigarettes in the studies included in the review. Where evidence on nicotine content was available, it indicated that a substantial majority of e-cigarettes in those studies delivered nicotine [22][23][24][25]. Many publications noted considerable uncertainty regarding nicotine content, including apparent mislabelling, and the need for greater clarity and reliability on this point.…”

Section: Study Selectionmentioning

confidence: 99%

E-cigarette use and combustible tobacco cigarette smoking uptake among non-smokers, including relapse in former smokers: umbrella review, systematic review and meta-analysis

Baenziger

Ford

Yazidjoglou³

et al. 2020

Preprint

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Introduction: Combustible tobacco smoking is a leading cause of death and disability worldwide. E-cigarettes are promoted for smoking cessation, but evidence on how their use relates to smoking uptake is limited. Methods: We searched PubMed, Scopus, Web of Science, PsychINFO (Ovid), Medline (Ovid) and Wiley Cochrane Library in April 2020. Studies of non-smokers - never, not current, and former smokers - with a baseline measure of e-cigarette use and an outcome measure of combustible smoking uptake were included. Results: Of 6,225 studies identified, 3 systematic reviews (incorporating 13 primary research studies) and 12 additional studies were included in umbrella and top-up systematic reviews, respectively. All 25 studies found increased risk of smoking uptake with e-cigarette exposure, although magnitude varied substantially. Using a random-effects model, comparing e-cigarette users versus non-e-cigarette users, among never-smokers at baseline the odds ratio (OR) for smoking initiation was 3.25 (95%CI 2.61-4.05, I2 85.7%) and among non-smokers at baseline the OR for current smoking was 2.87 (95%CI 1.97-4.19, I2 90.1%). Among former smokers, smoking relapse was higher in e-cigarette users versus non-users (OR=2.40, 95% CI 1.50-3.83, I2 12.3%). Conclusions: Across multiple settings, non-smokers who use e-cigarettes are consistently more likely than non-e-cigarettes users to initiate combustible cigarette smoking and become current smokers; risk magnitude varied, with an average of around three times the odds. Former smokers using e-cigarettes have over twice the odds of relapse as non-e-cigarettes users. This study is the first to our knowledge to review and pool data on the latter topic.

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Electronic Cigarette Use and Smoking Initiation in Taiwan: Evidence from the First Prospective Study in Asia

Cited by 26 publications

References 42 publications

Investigating gateway effects using the PATH study

Investigating gateway effects using the PATH study

Further investigations of gateway effects using the PATH study

E-cigarette use and combustible tobacco cigarette smoking uptake among non-smokers, including relapse in former smokers: umbrella review, systematic review and meta-analysis

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