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Background: The evidence on harms and benefits of e-cigarettes partly concerns whether their use encourages smokers to quit. We addressed this using data from the nationally representative PATH study, with detailed accounting for potential confounding variables. Methods: We considered adults aged 25+. Our original analyses, reported in version 1 of this paper, used data for Waves 1 to 3, separate analyses considering Waves 1 to 2, 2 to 3 and 1 to 3. These related baseline ever e-cigarette use (or e-product use at Wave 2) to quitting at follow-up, adjusting for confounders derived from 55 candidates. Sensitivity analyses omitted ever other product users, linked quitting to current e-cigarette use, and used values of some predictors modified using follow-up data. Additional analyses used data for Waves 1 to 4, separately considering sustained, delayed and temporary quitting during Waves 1 to 3, 2 to 4 and 1 to 4. Sensitivity analyses considered 30-day quitting, restricted attention to smokers attempting to quit, and considered ever smokeless tobacco or snus use. Results: In the original analyses, unadjusted odds ratios (ORs) of quitting smoking for ever e-cigarette use were 1.29 (95% CI 1.01-1.66), 1.52 (1.26-1.83) and 1.47 (1.19-1.82) for the Wave 1 to 2, 2 to 3, and 1 to 3 analyses. These reduced after adjustment, to 1.23 (0.94-1.61), 1.51 (1.24-1.85) and 1.39 (1.11-1.74). Quitting rates remained elevated in users in all sensitivity analyses. The additional analyses found associations of e-cigarette use with sustained, delayed and temporary quitting, associations little affected by considering 30-day quitting, and only slightly reduced restricting attention to quit attempters. Ever use of smokeless tobacco or snus also predicted increased quitting. Conclusions: As does most evidence from clinical trials, other analyses of PATH, and other epidemiological studies, our results suggest using e-cigarettes helps adult smokers to quit.

Background: Modelling disease risk from e-cigarette use requires knowing how much e-cigarette uptake affects a smoker’s cigarette consumption. From Waves 1-3 of the US PATH study and other evidence, we sought answers to three questions: (Q1) Does consumption differ between dual users and exclusive smokers? (Q2) Does initiating e-cigarettes affect cigarette consumption? (Q3) Is baseline consumption related to later initiation of e-cigarettes? Methods: Consumption data from PATH were corrected for clear recording errors, with e-cigarette use classified as established or every day, and adjustment made for demographics, use of alcohol, drugs and other tobacco products, and age of starting smoking. Searches identified other studies published since 2008, with Q1 answered from cross-sectional studies, and Q2 and Q3 also from prospective studies. Results: (Q1) In PATH, consumption in current every day e-cigarette users was 2-3 cigarettes per day lower in dual users, with no difference seen in established users. From 31 published studies, consumption was no lower in dual users for current or ever e-cigarette users. (Q2) In PATH study consistent decreases in consumption occurred in those becoming every day dual users with increases seen in those reverting to exclusive smoking. Thirteen published studies consistently showed reduced consumption in those becoming dual users. (Q3) Three studies consistently showed greater consumption in smokers subsequently initiating e-cigarettes, but no significant difference was seen for PATH. Conclusions: Assessment is complicated by the few every day e-cigarette users in PATH, and between-study variability in quantifying e-cigarette use and presentation of results. As taking up e-cigarettes is associated with reducing consumption, finding no clear cross-sectional difference between dual users and exclusive smokers suggests smokers taking up e-cigarettes had higher consumption initially, limited evidence supporting this. Given the much lower disease risk of e-cigarettes vs. cigarettes, smokers becoming dual users should somewhat reduce their disease risk.

Background: The evidence on harms and benefits of e-cigarettes partly concerns whether their use encourages smokers to quit. We addressed this using data from the nationally representative PATH study, with detailed accounting for potential confounding variables. Methods: We considered adults aged 25+. Our original analyses, reported in version 1 of this paper, used data for Waves 1 to 3, separate analyses considering Waves 1 to 2, 2 to 3 and 1 to 3. These related baseline ever e-cigarette use (or e-product use at Wave 2) to quitting at follow-up, adjusting for confounders derived from 55 candidates. Sensitivity analyses omitted ever other product users, linked quitting to current e-cigarette use, and used values of some predictors modified using follow-up data. Additional analyses used data for Waves 1 to 4, separately considering sustained, delayed and temporary quitting during Waves 1 to 3, 2 to 4 and 1 to 4. Sensitivity analyses considered 30-day quitting, restricted attention to smokers attempting to quit, and considered ever smokeless tobacco or snus use. Results: In the original analyses, unadjusted odds ratios (ORs) of quitting smoking forever e-cigarette use were 1.29 (95% CI 1.01-1.66), 1.52 (1.26-1.83) and 1.47 (1.19-1.82) for the Wave 1 to 2, 2 to 3, and 1 to 3 analyses. These reduced after adjustment, to 1.23 (0.94-1.61), 1.51 (1.24-1.85) and 1.39 (1.11-1.74). Quitting rates remained elevated in users in all sensitivity analyses. The additional analyses found associations of e-cigarette use with sustained, delayed and temporary quitting, associations little affected by considering 30-day quitting, and only slightly reduced restricting attention to quit attempters. Ever use of smokeless tobacco or snus also predicted increased quitting. Conclusions: As does most evidence from clinical trials, other analyses of PATH, and other epidemiological studies, our results suggest using e-cigarettes helps adult smokers to quit.

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: Part of the evidence on harms and benefits of e-cigarettes concerns whether using e-cigarettes encourages smokers to quit. With limited results from controlled trials, and weaknesses in much epidemiological data, we addressed this using nationally representative prospective study data, with detailed accounting for factors associated with quitting. Methods: Analyses used data for adults aged 25+ years from Waves 1 to 3 of the US PATH study. Separate analyses concerned follow-up from Waves 1 to 2, 2 to 3 and 1 to 3. The main analyses related baseline ever e-cigarette use (or e-product use at Wave 2) to having quit at follow-up, adjusting for predictors of quitting derived from 55 candidates. Sensitivity analyses omitted adults who had never used other products, linked quitting to current rather than ever e-cigarette use, used modified values of some predictors using later recorded data, or (in Wave 1 to 3 analysis only) also adjusted for quitting by Wave 2. Results: In the main analyses, unadjusted odds ratios (ORs) of quitting for ever e-cigarette use were 1.29 (95% CI 1.01-1.66), 1.52 (1.26-1.83) and 1.47 (1.19-1.82) for the Wave 1 to 2, 2 to 3, and 1 to 3 analyses. These estimates reduced after adjustment, to 1.23 (0.94-1.61), 1.51 (1.24-1.85) and 1.39 (1.11-1.74). The final models, including between six and nine predictors, always included household income, everyday/someday smoking, wanting to smoke after waking and having tried quitting, with other variables included in specific analyses. Quitting rates remained elevated in e-cigarette users in all sensitivity analyses. ORs were increased where other product users were omitted. Adjusted ORs of quitting for current e-cigarette use were 1.41 (1.06-1.89), 1.30 (1.01-1.67) and 1.56 (1.21-2.00). Conclusions: The results suggest e-cigarettes may assist adult smokers to quit, particularly in individuals not using other nicotine products, and who are current e-cigarette users.

Background: Modelling disease risk from e-cigarette use requires knowing how much e-cigarette uptake affects a smoker’s cigarette consumption. From Waves 1-3 of the US PATH study and other evidence, we sought answers to three questions: (Q1) Does consumption differ between dual users and exclusive smokers? (Q2) Does initiating e-cigarettes affect cigarette consumption? (Q3) Is baseline consumption related to later initiation of e-cigarettes? Methods: Consumption data from PATH were corrected for clear recording errors, with e-cigarette use classified as established or every day, and adjustment made for demographics, use of alcohol, drugs and other tobacco products, and age of starting smoking. Searches identified other studies published since 2008, with Q1 answered from cross-sectional studies, and Q2 and Q3 also from prospective studies. Results: (Q1) In PATH, consumption in current every day e-cigarette users was 2-3 cigarettes per day lower in dual users, with no difference seen in established users. From 31 published studies, consumption was no lower in dual users for current or ever e-cigarette users. (Q2) In PATH study consistent decreases in consumption occurred in those becoming every day dual users with increases seen in those reverting to exclusive smoking. Thirteen published studies consistently showed reduced consumption in those becoming dual users. (Q3) Three studies consistently showed greater consumption in smokers subsequently initiating e-cigarettes, but no significant difference was seen for PATH. Conc lusions: Assessment is complicated by the few every day e-cigarette users in PATH, and between-study variability in quantifying e-cigarette use and presentation of results. As taking up e-cigarettes is associated with reducing consumption, finding no clear cross-sectional difference between dual users and exclusive smokers suggests smokers taking up e-cigarettes had higher consumption initially, limited evidence supporting this. Given the much lower disease risk of e-cigarettes vs. cigarettes, smokers becoming dual users should somewhat reduce their disease risk.

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.