Considerations related to vaping as a possible gateway into cigarette smoking: an analytical review

Prevalence, demographic, socio-economic, and lifestyle factors associated with cigarette, e-cigarette, and dual use: evidence from the 2017-2021 Scottish Health Survey

Understanding the correlation between demographic, socio-economic, and lifestyle factors with e-cigarette use, cigarette smoking, and dual use is essential for targeted public health interventions. This study examines the prevalence of these behaviors in Scotland and identifies the associated factors. We conducted a repeated cross-sectional analysis of the Scottish Health Survey data from 2017 to 2021, leveraging data from 12,644 participants aged 16 and older: 2271 cigarette smokers, 687 e-cigarette users, 428 dual users, and 9258 never users. Weighted prevalences were calculated by age group, sex, and survey year, followed by weighted multinomial logistic regression to explore associated factors. The overall prevalences were 72.0% (95% CI 70.9-73.1) for never users, 18.9% (95% CI 17.9-19.9) for cigarette smokers, 5.5% (95% CI 5.0-6.1) for e-cigarette users, and 3.6% (95% CI 3.2-4.0) for dual users. From 2017 to 2021, cigarette smoking declined from 21.7% (95% CI 19.6-23.9) to 13.1% (95% CI 11.5-15.0), e-cigarette use from 6.5% (95% CI 5.4-7.8) to 4.8% (95% CI 3.6-6.4), and dual use from 3.7% (95% CI 2.9-4.6) to 2.7% (95% CI 1.9-3.7). Age was a critical factor, with the 25-34 age group more likely to use e-cigarettes (p = 0.007) and the 35-44 age group more likely to engage in dual use (p = 0.006) compared to the 16-24 age group. Males had higher odds of e-cigarette use than females (p = 0.031). White individuals had higher odds of using e-cigarettes (p = 0.023) and being dual users (p = 0.017) compared to non-whites. Previously married individuals had higher odds of dual use than singles (p = 0.031). Larger household sizes were linked to reduced odds of all three behaviors (p = 0.001). Rural residents were less likely to use e-cigarettes compared to urban residents (p = 0.025). Higher education correlated with lower odds of all three behaviors (p = 0.001). Manual occupation increased the likelihood of dual use (p = 0.042). Lower income and higher deprivation significantly increased the odds of all three behaviors (p < 0.001). Excessive alcohol consumption was associated with increased odds of the three behaviors (p < 0.001). Poor sleep quality correlated with increased odds of dual use (p = 0.002) and cigarette smoking (p < 0.001). Adherence to physical activity guidelines was associated with reduced odds of all three behaviors (cigarette smoking p < 0.001, e-cigarette use p = 0.031, dual use p = 0.016). In conclusion, this study showed a decline in the prevalence of cigarette smoking, e-cigarette use, and dual usage from 2017 to 2021 in Scotland. Significant associations with demographic, socio-economic, and lifestyle factors highlight the need for targeted public health interventions.

S100A4-dependent glycolysis promotes lymphatic vessel sprouting in tumor

Tumor-induced lymphangiogenesis promotes the formation of new lymphatic vessels, contributing to lymph nodes (LNs) metastasis of tumor cells in both mice and humans. Vessel sprouting appears to be a critical step in this process. However, how lymphatic vessels sprout during tumor lymphangiogenesis is not well-established. Here, we report that S100A4 expressed in lymphatic endothelial cells (LECs) promotes lymphatic vessel sprouting in a growing tumor by regulating glycolysis. In mice, the loss of S100A4 in a whole body (S100A4^-/-), or specifically in LECs (S100A4^ΔLYVE1) leads to impaired tumor lymphangiogenesis and disrupted metastasis of tumor cells to sentinel LNs. Using a 3D spheroid sprouting assay, we found that S100A4 in LECs was required for the lymphatic vessel sprouting. Further investigations revealed that S100A4 was essential for the position and motility of tip cells, where it activated AMPK-dependent glycolysis during lymphatic sprouting. In addition, the expression of S100A4 in LECs was upregulated under hypoxic conditions. These results suggest that S100A4 is a novel regulator of tumor-induced lymphangiogenesis. Targeting S100A4 in LECs may be a potential therapeutic strategy for lymphatic tumor metastasis.

Population-level counterfactual trend modelling to examine the relationship between smoking prevalence and e-cigarette use among US adults

BACKGROUND

Studies have suggested that some US adult smokers are switching away from smoking to e-cigarette use. Nationally representative data may reflect such changes in smoking by assessing trends in cigarette and e-cigarette prevalence. The objective of this study is to assess whether and how much smoking prevalence differs from expectations since the introduction of e-cigarettes.

METHODS

Annual estimates of smoking and e-cigarette use in US adults varying in age, race/ethnicity, and sex were derived from the National Health Interview Survey. Regression models were fitted to smoking prevalence trends before e-cigarettes became widely available (1999-2009) and trends were extrapolated to 2019 (counterfactual model). Smoking prevalence discrepancies, defined as the difference between projected and actual smoking prevalence from 2010 to 2019, were calculated, to evaluate whether actual smoking prevalence differed from those expected from counterfactual projections. The correlation between smoking discrepancies and e-cigarette use prevalence was investigated.

RESULTS

Actual overall smoking prevalence from 2010 to 2019 was significantly lower than counterfactual predictions. The discrepancy was significantly larger as e-cigarette use prevalence increased. In subgroup analyses, discrepancies in smoking prevalence were more pronounced for cohorts with greater e-cigarette use prevalence, namely adults ages 18-34, adult males, and non-Hispanic White adults.

CONCLUSION

Population-level data suggest that smoking prevalence has dropped faster than expected, in ways correlated with increased e-cigarette use. This population movement has potential public health implications.

Estimating the reduction in US mortality if cigarettes were largely replaced by e-cigarettes

BACKGROUND

Recent estimates indicated substantially replacing cigarettes by e-cigarettes would, during 2016-2100, reduce US deaths and life-years lost (millions) by 6.6 and 86.7 (Optimistic Scenario) and 1.6 and 20.8 (Pessimistic). To provide additional insight we use alternative modelling based on a shorter period (1991-2040), four main smoking-associated diseases, deaths aged 30-79 years, and a full product history. We consider variations in: assumed effective dose of e-cigarettes versus cigarettes (F); their relative quitting rate (Q); proportions smoking after 10 years (X); and initiation rate (I) of vaping, relative to smoking.

METHODS

We set F = 0.05, X = 5%, Q = 1.0 and I = 1.0 (Main Scenario) and F = 0.4, X = 10%, Q = 0.5 and I = 1.5 (Pessimistic Scenario). Sensitivity Analyses varied Main Scenario parameters singly; F from 0 to 0.4, X 0.01% to 15%, and Q and I 0.5 to 1.5. To allow comparison with prior work, individuals cannot be dual users, re-initiate, or switch except from cigarettes to e-cigarettes.

RESULTS

Main Scenario reductions were 2.52 and 26.23 million deaths and life-years lost; Pessimistic Scenario reductions were 0.76 and 8.31 million. These were less than previously, due to the more limited age-range and follow-up, and restriction to four diseases. Reductions in deaths (millions) varied most for X, from 3.22 (X = 0.01%) to 1.31 (X = 15%), and F, 2.74 (F = 0) to 1.35 (F = 0.4). Varying Q or I had little effect.

CONCLUSIONS

Substantial reductions in deaths and life-years lost were observed even under pessimistic assumptions. Estimates varied most for X and F. These findings supplement literature indicating e-cigarettes can importantly impact health challenges from smoking.

Association between electronic nicotine delivery systems and electronic non-nicotine delivery systems with initiation of tobacco use in individuals aged < 20 years. A systematic review and meta-analysis

BACKGROUND

This systematic review described the association between electronic nicotine delivery systems and electronic non-nicotine delivery systems (ENDS/ENNDS) use among non-smoking children and adolescents aged <20 years with subsequent tobacco use.

METHODS

We searched five electronic databases and the grey literature up to end of September 2020. Prospective longitudinal studies that described the association between ENDS/ENNDS use, and subsequent tobacco use in those aged < 20 years who were non-smokers at baseline were included. The Joanna Briggs Institute Critical Appraisal Checklist was used to assess risk of bias. Data were extracted by two reviewers and pooled using a random-effects meta-analysis. We generated unadjusted and adjusted risk ratios (ARRs) describing associations between ENDS/ENNDS and tobacco use.

FINDINGS

A total of 36 publications met the eligibility criteria, of which 25 were included in the systematic review (23 in the meta-analysis) after exclusion of overlapping studies. Sixteen studies had high to moderate risk of bias. Ever users of ENDS/ENNDS had over three times the risk of ever cigarette use (ARR 3·01 (95% CI: 2·37, 3·82; p<0·001, I2: 82·3%), and current cigarette use had over two times the risk (ARR 2·56 (95% CI: 1·61, 4·07; p<0·001, I2: 77·3%) at follow up. Among current ENDS/ENNDS users, there was a significant association with ever (ARR 2·63 (95% CI: 1·94, 3·57; p<0·001, I2: 21·2%)), but not current cigarette use (ARR 1·88 (95% CI: 0·34, 10·30; p = 0·47, I2: 0%)) at follow up. For other tobacco use, ARR ranged between 1·55 (95% CI 1·07, 2·23) and 8·32 (95% CI: 1·20, 57·04) for waterpipe and pipes, respectively. Additionally, two studies examined the use of ENNDS (non-nicotine devices) and found a pooled adjusted RR of 2·56 (95% CI: 0·47, 13·94, p = 0.035).

CONCLUSION

There is an urgent need for policies that regulate the availability, accessibility, and marketing of ENDS/ENNDS to children and adolescents. Governments should also consider adopting policies to prevent ENDS/ENNDS uptake and use in children and adolescents, up to and including a ban for this group.

Skeletal System Biology and Smoke Damage: From Basic Science to Medical Clinic

Cigarette smoking has a negative impact on the skeletal system, as it reduces bone mass and increases fracture risk through its direct or indirect effects on bone remodeling. Recent evidence demonstrates that smoking causes an imbalance in bone turnover, making bone vulnerable to osteoporosis and fragility fractures. Moreover, cigarette smoking is known to have deleterious effects on fracture healing, as a positive correlation between the daily number of cigarettes smoked and years of exposure has been shown, even though the underlying mechanisms are not fully understood. It is also well known that smoking causes several medical/surgical complications responsible for longer hospital stays and a consequent increase in the consumption of resources. Smoking cessation is, therefore, highly advisable to prevent the onset of bone metabolic disease. However, even with cessation, some of the consequences appear to continue for decades afterwards. Based on this evidence, the aim of our review was to evaluate the impact of smoking on the skeletal system, especially on bone fractures, and to identify the pathophysiological mechanisms responsible for the impairment of fracture healing. Since smoking is a major public health concern, understanding the association between cigarette smoking and the occurrence of bone disease is necessary in order to identify potential new targets for intervention.

Gateway or common liability? A systematic review and meta-analysis of studies of adolescent e-cigarette use and future smoking initiation

BACKGROUND AND AIMS

Studies have consistently found a longitudinal association between e-cigarette use (vaping) and cigarette smoking. Many have interpreted such association as causal. This systematic review and meta-analysis evaluated the plausibility of a causal interpretation by (1) estimating the effect of adolescent vaping on smoking initiation, adjusted for study quality characteristics, (2) evaluating the sufficiency of adjustment for confounding based on the social development model (SDM) and the social ecological model (SEM) and E-value analyses and (3) investigating sample attrition and publication bias.

METHODS

Systematic review and meta-analysis of longitudinal studies that examined the association between e-cigarette use at baseline and smoking at follow-up. Participants were non-smokers aged < 18 at baseline.

RESULTS

Meta-analysis of 11 studies showed a significant longitudinal association between vaping and smoking [adjusted odds ratio (aOR) = 2.93, 95% confidence interval (CI) = 2.22, 3.87]. Studies with sample sizes < 1000 had a significantly higher odds ratio (OR = 6.68, 95% CI = 3.63, 12.31) than studies with sample sizes > 1000 (OR = 2.49, 95% CI = 1.97, 3.15). Overall, the attrition rate was very high (median = 30%). All but one study reported results from complete sample analysis, despite those dropping out having higher risk profiles. Only two studies comprehensively adjusted for confounding. The median E-value was 2.90, indicating that the estimates were not robust against unmeasured confounding.

CONCLUSIONS

There is a longitudinal association between adolescent vaping and smoking initiation; however, the evidence is limited by publication bias, high sample attrition and inadequate adjustment for potential confounders.

Estimating the Population Health Impact of Recently Introduced Modified Risk Tobacco Products: A Comparison of Different Approaches

INTRODUCTION

Various approaches have been used to estimate the population health impact of introducing a Modified Risk Tobacco Product (MRTP).

AIMS AND METHODS

We aimed to compare and contrast aspects of models considering effects on mortality that were known to experts attending a meeting on models in 2018.

RESULTS

Thirteen models are described, some focussing on e-cigarettes, others more general. Most models are cohort-based, comparing results with or without MRTP introduction. They typically start with a population with known smoking habits and then use transition probabilities either to update smoking habits in the "null scenario" or joint smoking and MRTP habits in an "alternative scenario". The models vary in the tobacco groups and transition probabilities considered. Based on aspects of the tobacco history developed, the models compare mortality risks, and sometimes life-years lost and health costs, between scenarios. Estimating effects on population health depends on frequency of use of the MRTP and smoking, and the extent to which the products expose users to harmful constituents. Strengths and weaknesses of the approaches are summarized.

CONCLUSIONS

Despite methodological differences, most modellers have assumed the increase in risk of mortality from MRTP use, relative to that from cigarette smoking, to be very low and have concluded that MRTP introduction is likely to have a beneficial impact. Further model development, supplemented by preliminary results from well-designed epidemiological studies, should enable more precise prediction of the anticipated effects of MRTP introduction.

IMPLICATIONS

There is a need to estimate the population health impact of introducing modified risk nicotine-containing products for smokers unwilling or unable to quit. This paper reviews a variety of modeling methodologies proposed to do this, and discusses the implications of the different approaches. It should assist modelers in refining and improving their models, and help toward providing authorities with more reliable estimates.

Investigating the effect of e-cigarette use on quitting smoking in adults aged 25 years or more using the PATH study

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.

Investigating the effect of e-cigarette use on quitting smoking in adults aged 25 years or more using the PATH study

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.

Further investigation of gateway effects using the PATH study

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.

Further investigation of gateway effects using the PATH study

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.

Association of Electronic Cigarette Regulations With Electronic Cigarette Use Among Adults in the United States

IMPORTANCE

Millions of Americans use electronic cigarettes (e-cigarettes). A growing number of state and local governments have started to draft and implement laws regarding the sale, marketing, and use of e-cigarettes. The association of US state regulations regarding e-cigarettes with e-cigarette use remains unknown.

OBJECTIVE

To examine the association of US state regulations regarding e-cigarettes with current e-cigarette use among adults in the United States.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study included adults aged 18 years or older from the 2016 and 2017 Behavioral Risk Factor Surveillance System, which is a nationwide, telephone-administered survey that collects state-representative data on health-related risk behaviors, chronic health conditions, and use of preventive services. Data analysis was performed from February 1, 2019, to April 31, 2019.

EXPOSURES

United States state laws regulating e-cigarette use, including prohibiting e-cigarette use in indoor areas of private workplaces, restaurants, and bars; requiring retailers to purchase a license to sell e-cigarettes; prohibiting self-service displays of e-cigarettes; prohibiting sales of tobacco products, including e-cigarettes, to persons younger than 21 years; and e-cigarette taxes.

MAIN OUTCOMES AND MEASURES

Current use of e-cigarettes.

RESULTS

Among 894 997 participants aged 18 years or older (503 688 women [51.3%], 679 443 non-Hispanic white [62.6%], 71 730 non-Hispanic black [16.3%], 69 823 Hispanic [11.4%], and 74 001 non-Hispanic other races [9.8%]), 28 907 (weighted prevalence, 4.4%) were currently using e-cigarettes. The age-standardized weighted prevalence of current e-cigarette use varied across US states and territories, from 1.0% in Puerto Rico to 6.2% in Guam. After adjustment for demographic, socioeconomic, and lifestyle factors, including conventional cigarette use, the odds ratios of current e-cigarette use were 0.90 (95% CI, 0.83-0.98) for state laws prohibiting e-cigarette use in indoor areas of private workplaces, restaurants, and bars; 0.90 (95% CI, 0.85-0.95) for state laws requiring retailers to purchase a license to sell e-cigarettes; 1.04 (95% CI, 0.99-1.09) for state laws prohibiting self-service displays of e-cigarettes; 0.86 (95% CI, 0.74-0.99) for state laws prohibiting sales of tobacco products, including e-cigarettes, to persons younger than 21 years; and 0.89 (95% CI, 0.83-0.96) for state laws applying taxes to e-cigarettes.

CONCLUSIONS AND RELEVANCE

These findings suggest that several state regulations regarding e-cigarettes may be associated with reduced e-cigarette use among US adults.

Investigating gateway effects using the PATH study

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.

Investigating gateway effects using the PATH study

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.