Considerations related to vaping as a possible gateway into cigarette smoking : an analytical review [ version 1 ; peer review : 2 approved with reservations ]

Toxicant levels are much lower in e-cigarettes than cigarettes. Background: 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 fueled by cohort studies showing vaping predicts subsequent smoking initiation in young people. In this discussion paper, we consider various aspects of the gateway Methods: 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-in effects of vaping. Finally, we examine trends in e-cigarette and smoking prevalence in youths based on national surveys. First, we demonstrate that although studies report that vaping Results: 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. We have shown that the existence of any true gateway-in effect Conclusions: in youth is not yet clearly demonstrated and that the population health impact of introducing e-cigarettes is still likely to be beneficial.


Introduction
Recent publications made clear that, in youths, vaping (i.e., use of e-cigarettes) and cigarette smoking (subsequently referred to as "smoking") are strongly associated. In the U.S., for example, a survey of ninth and tenth grade students in Hawaii in 2014(Wills et al., 2017 revealed 195 ever-users of both products, 250 ever-vapers only, 37 ever-smokers only, and 820 never-users of either. From these data, the odds ratio (OR) relating ever-vaping to ever-smoking can be estimated as 17.3 (95% confidence interval (CI) 11.8-25.3). A strong association can also be seen for sixth to twelfth grade students in Texas in 2014(Cooper et al., 2016, with an OR of 28.8 (25.0-33.1), as well as nationally in 2012 (Dutra & Glantz, 2014), with an OR of 31.9 (27.6-36.8). Similar strong relationships are reported also in Canada (Aleyan et al., 2018), France (Dautzenberg et al., 2016), Great Britain (Eastwood et al., 2015), Poland (Goniewicz et al., 2014), and Korea (Lee et al., 2014). Theoretically, this association may arise if vaping encourages smoking, if smoking encourages vaping, and/or if other factors link to use of both products.
Concern that vaping may encourage subsequent smoking, the so-called "gateway-in" effect, was fuelled by a recent paper by Soneji et al. (2017). In this manuscript, the authors combined epidemiological evidence from nine U.S. cohort studies in young people that linked smoking initiation to previous vaping. Among baseline never-smokers, baseline ever-vaping strongly predicted smoking initiation within six to 18 months (OR 3.62, 95% CI 2.42-5.41) after adjusting for various predictors of initiation. Similarly, baseline past 30-day vaping predicted subsequent 30-day cigarette use (OR 4.28,).
Based on these results and those from one additional study (Hammond et al., 2017), the National Academies Press on the Public Health Consequences of E-Cigarettes (National Academies of Sciences Engineering and Medicine, 2018) recently concluded that "there is substantial evidence that e-cigarette use increases risk of ever using combustible tobacco cigarettes among youth and young adults, noting that the relevant studies had adjusted for "a wide range of covariates" and considering that it was "unlikely that confounding entirely accounts for the association because reductions in estimates of association from unadjusted to adjusted models were not consistently observed in the literature."

Methods
This review considers various methodological aspects of the gateway issue in youths.

Search parameters
First, based on PubMed searches carried out at intervals starting in May 2017 on "ecigarettes" or "e-cigarettes" or "e-cigs" or "electronic cigarette" or "ecigarette" or "e-cigarette", we identified papers and reviews that presented results from prospective studies of young people relating to the gateway-in effect. Further publications were also sought from reference lists of selected papers and reviews. For this review, we also considered studies that provided data on trends over time in cigarette smoking by youths in relation to e-cigarette use, and information on initiation of smoking and e-cigarette smoking by youths relevant to the gateway effect.
A list of factors other than e-cigarette use that were associated with the initiation of cigarette smoking was obtained from US Surgeon General (1994) and from a separate ongoing review on determinants of smoking initiation which aims to present metaanalyses of associations between initiation of cigarette smoking and the various other factors. This identified references using an Embase search in April 2017 with the following search string: 'smoking'/de OR 'smoking' AND ('initiation'/de OR 'initiation') AND ('prevalence'/de OR 'prevalence' OR 'incidence'/de OR 'incidence' OR 'proportion' OR 'age '/de OR 'age' OR 'dual use' OR 'combined use' OR 'psychosocial'/de OR 'psychosocial' OR 'beliefs'/de OR 'beliefs' OR 'attitudes'/de OR 'attitudes' OR 'perceptions'/de OR 'perceptions' OR 'opinions' OR 'acceptance'/de OR 'acceptance' OR 'predictors'/de OR 'predictors' OR 'friend'/de OR 'friend' OR 'family'/de OR 'family' OR 'parent'/de OR 'parent' OR 'propensity score'/de OR 'propensity score'

) AND [humans]/ lim AND [english]/lim AND ([embase]/lim OR [medline]/lim)
For the current paper, we only use the results from this search to list those factors shown in one or more studies to strongly associate with initiation of smoking, and to consider the extent to which the identified gateway studies take these factors into account.

Analysis of search results
In addition, separate sheets of an Excel Program (Lee, 2018) were developed to: (a) estimate the effects of omission of a confounding variable, or misclassification of it, on the association between vaping and subsequent initiation of smoking, and to (b) determine how the prevalence of smoking might be affected by way of any true gateway-in effects of vaping.
Full details are given in, respectively, Additional Files 2 and 3 (Lee, 2018). Both sheets are used to provide illustrative examples of how the effects depend on the parameter values assumed.
In the first sheet, the use may determine how the observed gateway-in effect depends on the following parameters: • The proportion of vapers in never smokers (P 1 ) • The proportion with a particular smoking predictor present in never smokers (P 2 ) • The concordance (odds ratio) between vaping and the smoking predictor (K) • The probability of initiating smoking during follow-up in those who neither smoke nor have the predictor (P A ) • The odds ratio for initiating smoking during follow-up associated with vaping (the true gateway-in effect -G E ) • The odds ratio for initiating smoking during follow-up associated with the smoking predictor (G P ) • The proportion with the predictor who are misclassified as not having it (M 1 ), and • The proportion without the predictor who are misclassified as having it (M 2 ) P 1 , P 2 and K define the baseline population in which N never smokers are subdivided in a 2 x 2 table according to presence or absence of e-cigarettes and of the smoking predictor, while P A , G E and G P determine the distribution of the population after a single follow-up period. M 1 and M 2 are misclassification rates of the predictor.
In the second sheet, the program considers individuals, divided into five equal strata, who initially are all never users of either cigarettes or e-cigarettes. The probability of initiating smoking or vaping increases progressively over the strata, the strata being intended to represent sets of individuals with an increasing susceptibility to tobacco. Over five time intervals, the individuals may transfer to four other groups: current vapers only, current smokers only, current dual users and former users. Users may modify the values of seven parameters: • The initiation rate of vaping in the first stratum (P 1 ) • The relative odds of initiation for successive strata (R 1 ) • The relative odds of initiation with smoking compared to vaping (R 2 ) • The relative odds of quitting compared to initiation (R 3 ) • The relative odds of re-initiation compared to initiation (R 4 ) • The relative odds of initiating smoking for vapers compared to tobacco never-users ("gateway-in" effect) • The relative odds of quitting smoking for dual users compared to smokers only ("gateway-out" effect) Lastly, the review also includes an analysis of trends in e-cigarette and smoking prevalence in youths based on national surveys which provided annual information over a period of at least 10 years. We identified four surveys from the U. Based on linear regression of log e (p/(1−p)) a year, where p is prevalence, observed prevalences in the years 2014-2016 (a period where the advent of e-cigarettes might have had a measurable effect on smoking prevalence if an important gateway-in effect existed) was compared with those expected based on the trend in the previous years for which results were available.

Results
Evidence relating vaping in youths to subsequent initiation of cigarette smoking From the literature search, we identified a key systematic review and meta-analysis by Soneji et al. (2017) on the association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adult. Table 1 provides details of the nine U.S. studies analyzed by Soneji et al. (2017).
A total of five studies, conducted with participants with a maximum age of 24-30 years old, collected information using internetbased surveys. The four other studies, involving participants aged less than 20 years old were based on self-completed questionnaires. The follow-up period was 6 months in one study (Hornik et al., 2016), 1 year in five studies (Leventhal et al., 2015;Primack et al., 2015;Spindle et al., 2017;Unger et al., 2016;Wills et al., 2017), and 13 to 18 months in the other three studies (Barrington-Trimis et al., 2016;Miech et al., 2017;Primack et al., 2016). Most studies concerned ever-and never-use, and two studies considered current and noncurrent use. Each study found that baseline vaping significantly predicted subsequent smoking, regardless of covariate adjustment. Adjusted ORs were lower than unadjusted ORs in six studies, particularly in two (Hornik et al., 2016;Leventhal et al., 2015). However, two studies (Primack et al., 2015;Primack et al., 2016) showed a moderately increased OR after adjustment.  et al., 2018;Watkins et al., 2018), two from the U.K. (Best et al., 2018;Conner et al., 2018), and one each from Canada (Hammond et al., 2017) and Mexico (Loukas et al., 2018). These results showed an association that reduced but remained significant after adjustment. Two further studies could not be included in Table 1, as they did not provide comparable results. One was a study on young adults in the Chicago area (Selya et al., 2018) that used path analysis and concluded that "E-cigarette use was not significantly associated with later conventional smoking…" The other was a study in the Netherlands (Treur et al., 2018) that reported a strong association after covariate adjustment but did not present unadjusted results for comparison.
There were an additional 15 publications (Amato et al., 2016;Ambrose, 2017;Barrington-Trimis et al., 2015;Bold et al., 2016;de Lacy et al., 2017;Doran et al., 2017;Etter & Bullen, 2014;Hanewinkel & Isensee, 2015;Huh & Leventhal, 2016;Kaufman et al., 2015;Leventhal et al., 2016;Loukas et al., 2015;Sutfin et al., 2015;Westling et al., 2017;Zhong et al., 2016) identified in our searches as of possible relevance based on the abstract. However, none of these provided useful data for various reasons. These included studies which: were conducted in adults; predicted e-cigarette use rather than cigarette smoking; did not present results in a form allowing the gateway effect to be estimated; were superseded by later publications; concerned intent to smoke cigarettes and not actual smoking; or even not considering e-cigarettes at all.
To avoid unfeasibly long questionnaires or huge studies, further work is needed to define an agreed minimum list of factors.
For the 15 studies included in Table 1, Additional File 1 (Lee, 2018) describes in detail how these risk factors were taken into account. While standard demographics were generally considered, and smoking susceptibility, substance use, risk-taking behavior, other tobacco use, parental education, family smoking, and peer smoking were considered in at least five studies, many other relevant factors were rarely considered. These include internalizing/externalizing disorders, outcome expectancies, school performance, anxiety, parental smoking, and peer attitudes to smoking. The studies vary in the number of factors accounted for: some (Conner et al., 2018;Leventhal et al., 2015;Watkins et al., 2018;Wills et al., 2017) considered more than 10 factors, others (Miech et al., 2017;Unger et al., 2016) only five. As shown in Additional File 1 (Lee, 2018), the questions used to assess these factors also varied considerably between studies.
Unfortunately, no study reported which factors contributed most to their adjustment. Thus, for example, Leventhal et al. (2015) adjusted for the most factors and found that the unadjusted OR of 7.78 (6.15-9.84) reduced dramatically after adjustment to 1.75 (1.10-2.78); however, the authors did not clarify which factors mainly contributed to this reduction.
Bias in the estimated gateway-in effect due to failure to adjust for a relevant covariate -simple confounding What effect might failure to adjust for a relevant predictor of smoking have on the estimated gateway-in effect observed between vaping and smoking? To answer to this question, we consider (using the first sheet of the Excel program we developed) a hypothetical baseline population of N never-smokers, of which P 1 vape, and P 2 have the smoking predictor. P 1 and P 2 are correlated, with a concordance ratio of K. We assume that, during follow up, those who neither have smoked nor have the predictor have a probability of initiating smoking of P A and that the odds of smoking initiation are independently increased by G E for vapers and by G P in those with the smoking predictor.
The illustrative results in Table 2, supported by mathematical detail in Additional File 2 (Lee, 2018), demonstrate the problem.
In the basic Situation 1, we set P 1 = 0.2, P 2 = 0.2, K = 5, P A = 0.05, G E = 1, and G P = 4. Instead of observing the true gateway effect (G E ) of 1, we observe a spurious gateway effect of 1.633 due to the uncontrolled confounding. In Situations 2 through 6, G E remains at 1, but other parameters are varied. There is a clear tendency for the confounding effect to increase with increasing K (Situation 2) and G P (Situation 3), but varying P A (Situation 4), P 1 (Situation 5), or P 2 (Situation 6) have less effect. Increasing G E , with the other parameters fixed (Situation 7), increases the observed gateway effect, but the proportional increase in the OR is reduced.
Bias in the estimated gateway-in effect due to adjustment for an inaccurately measured covariate -residual confounding While confounding effects are well understood by epidemiologists, which is why the authors of the gateway-in papers made some adjustments, "residual confounding," arising from inaccurately  Greenland (1980) noted that "misclassification of a confounder" leads to "partial loss of ability to control confounding," while Tzonou et al. (1986) later noted that "even misclassification rates as low as 10% can prevent adequate control of confounding." Other publications (Ahlbom & Steineck, 1992;Fewell et al., 2007;Greenland & Robins, 1985;Phillips & Smith, 1994;Savitz & Barón, 1989) show that if X is an inaccurately measured true cause of disease, and Y, precisely measured, is not a cause but is correlated with X, one may conclude incorrectly that Y, not X, is the cause. None of the gateway papers cited in Table 1, nor Soneji et al. (2017), refer to residual confounding as a potential source of bias, although all except two (Primack et al., 2015;Primack et al., 2016) mention possible bias from incomplete adjustment. Table 3 (see also Additional File 2 (Lee, 2018)) gives illustrative examples of the effect of residual confounding. Situations 1 to 4 concern misclassification occurring in each direction corresponding to situations 1, 2(a), 3(a), and 7(a) in Table 2, where no misclassification is assumed, whereas in Table 2, the smoking predictor is assumed to be measured accurately, and adjustment for it would correct the observed gateway-in effect back to its true value (G E ); this is not true when misclassification is present. Thus, in Situation 1, where G E is set at 1, adjustment for the misclassified predictor does not fully correct for the confounding. Adjustment is useless where the misclassification rate is 50% with the observed gateway-in effect staying at its unadjusted value of 1.633. With a 10% misclassification rate, the value reduces to 1.281 so that about half (281/633 = 44.4%) of the spurious increase in the OR remains. As misclassification rates reduce, the adjusted rate approaches its true value.
The pattern is similar in Situations 2 and 3, where G E remains at 1, but K and R P are varied. Again, all of the bias remains with 50% misclassification, and almost half remains with 10% misclassification. This is also observed in Situation 4, where G E = 2. Situations 5 and 6 are similar to Situation 1, except misclassification is assumed to be unidirectional. The bias is similar to that observed in Situation 1, where only false positives occur (Situation 5), but less where only false negatives are present (Situation 6). Poor specificity is more important than poor sensitivity as a source of bias.
How might smoking prevalence be affected by any true gateway effects of vaping?
It is important to understand how vaping might affect the prevalence of cigarette smoking. Not only might there be "gatewayin" effects, with vaping increasing the probability of subsequent smoking, but there might also be also "gateway-out" effects, with vaping by smokers increasing their probability of smoking cessation. In an attempt to gain some understanding, we used the second sheet of the Excel program we developed (see also Additional File 3 (Lee, 2018)). The program considers 2,000 individuals in each of five strata that represent groups with varying smoking susceptibility. All individuals start as never-users of either product, and over time, may switch to become current users of either product only, current dual users, or former users.
Where no gateway effects apply, the user may vary the values of P 1 , R 1 , R 2 , R 3 and R 4 as defined in the methods section. These parameters are then used to update tobacco use status over time. Where gateway effects apply, the user may define G 1 , the gateway-in effect, and G 2 , the gateway-out effect. Table 4 presents illustrative examples where no gateway-in effects apply. The five parameters are varied in turn, with the other parameters held constant. Increasing e-cigarette initiation rates by increasing P 1 (Block 1) reduces never-users, with a compensating increase in dual users and in concordance. In Block 1, it is assumed that there is no quitting or re-initiation Notes: A misclassification rate of x% in both directions (considered in situations 1 to 4) implies that x% of those who have the factor that predicts smoking (e.g. risk takers) are wrongly classified as not having it, and x% of those who do not have the factor are wrongly classified as having it. Situations 5 and 6 consider misclassification in one direction.
All models have P 1 = P 2 = 0.2 and P A = 0.5.
(R 3 = R 4 = 0), the relative odds of initiation for successive strata (R 1 ) are set at 5, and initiation rates are assumed the same for both tobacco products (R 2 = 1). Increasing R 1 (Block 2), which increases the between stratum variation in smoking susceptibility, markedly increases the concordance ratio. The other Blocks keep P 1 at 0.0002 and R 1 at 5. Increasing R 2 (Block 3) increases the frequency of smoking relative to vaping but also to dual use and the concordance ratio. Introducing quitting factor (Block 4) reduces smokers, but concordance is less affected. Furthermore, introduction of re-initiation factor (Block 5) has little effect; in fact, the chance of someone initiating, quitting, then re-initiating in this period is small. Table 5-Table 7 show how the relative odds of smoking are affected by gateway-in effects only, gateway-out effects only, or both, respectively. All these results set P 1 = 0.0002, R 1 = 5, and R 4 = 0. With no gateway effects, there are 11.28% cigarette smokers at the end of follow up: 7.43% smoking cigarettes only, and 3.85% dual users.
As the gateway-in effect (G 1 ) increases to 5 (Table 5), the percentage of cigarette smokers rises to 13.96%, giving an OR for smoking of 1.28 compared with G 1 = 1. The increase in the percentage of smokers is much less than the increase in G 1 ; in fact , there are typically far more never-smokers who initiate smoking than e-cigarette only users. Table 5 also shows that the gateway-in effect becomes less important as the relative initiation rate of cigarettes compared with that of e-cigarettes (R 2 ) increases.
As the gateway-out effect (G 2 ) increases from 1 to 5 (Table 6), the percentage of smokers declines. The effect is less than for increasing G 1 , and it is in the opposite direction. This effect is increased as R 3 , the relative frequency of quitting to initiation, is increased. Table 6 also shows that the gateway-out effect becomes more important as R 2 increases.
In Table 7, both gateway effects are varied, with the relative odds of smoking shown for combinations of G 1 and G 2 = 1, 2, or 5 and for varying values of R 2 and R 3 . The highest relative odds of smoking (1.497) are seen for the highest values of G 1 and R 2 and the lowest values of G 2 and R 3 , while the lowest odds of smoking (0.890) are seen in the reverse situation.
The effects sometimes approximately cancel out. Given R 3 < 1 (and it seems unlikely that quit rates would exceed initiation rates), gateway-in effects tend to exceed gateway-out effects where initiation rates are similar for vaping and smoking. However, where initiation rates for smoking are substantially higher (R 2 = 4), similar gateway-in and gateway-out effects also P1, initiation rate of vaping in stratum 1; R1, relative odds of initiation for successive strata; R2, relative odds of initiation for smoking compared to vaping; R3, relative odds of quitting compared to initiation; R4, relative odds of re-initiation compared to initiation. G 1 is the gateway-out effect. R 2 is the relative odds of initiation for smoking compared to vaping. R 3 is the relative odds of quitting compared to initiation.
The assumed values of the other parameters are P 1 = 0.0002, R 1 = 5, R 4 = 0, and G 1 = 1. See text for the definitions of these other parameters.  G 1 is the gateway-in effect. G 2 is the gateway-out effect. R 2 is the relative odds of initiation for smoking compared to vaping. R 3 is the relative odds of quitting compared to initiation.
The assumed values of the other parameters are P 1 = 0.0002, R 1 = 5, and R 4 = 0. See text for the definitions of these other parameters.
approximately cancel out. Here, with relatively more smokers to quit, gateway-out effects are more relevant.
While the above results are illustrative, one can derive five main general conclusions from them: 1. Any increase in smoking initiation due to vaping is proportionately much smaller than any increase in smoking prevalence.
4. Gateway-out effects increase as R 2 and R 3 increase, where there are more smokers who can quit.
5. With both gateway effects present, the overall effect on smoking prevalence may be in either direction.
Has introducing e-cigarettes affected smoking trends?
Smoking prevalence in U.S. youths had declined substantially even before vaping became popular. For example, the YRBS reported a decline in past 30-day smoking from 34.8% in 1995 to 15.7% in 2013. Has introducing e-cigarettes halted or even reversed this decline?
The NYTS is the only U.S. youth survey providing trend data on e-cigarette use over a reasonably long time period. In that survey, the prevalence of past 30-day vaping in each year from 2011-2015 was 1.0%, 2.0%, 2.9%, 9.2%, and 11.1%, respectively, for students in grades 6-12. In 2011-2013, vaping seems too rare to allow for assessment of any effect on smoking prevalence. To detect any effect, it seems more appropriate to compare the prevalence in 2014-2016 with that predicted from pre-existing trends. These analyses, though clearly limited, do not suggest that introducing e-cigarettes has adversely affected smoking prevalence trends. If there were any gateway effect, it would be clearly outweighed by other issues, such as vaping providing an alternative to cigarettes for tobacco users, or changes in attitudes to smoking resulting from anti-tobacco prevention measures.
Additional evidence on the likelihood of any marked gateway-in effect on smoking prevalence Additional evidence highlighted the improbability of any substantial gateway-in effect of vaping on smoking prevalence. Based on the U.S. PATH study, Pearson et al. (2018) recently reported that only a small percentage of nonsmokers would be interested in using a hypothetical modified risk tobacco product. While these analyses were based on adults, it was possible to confirm this finding from the publicly available data files for 18-24-year-olds. Thus, the proportion "very or somewhat likely" to use such a product was much lower in those who had never used tobacco (57/1745 = 3.3%) than in those who had ever done so (2375/7282 = 32.7%). This difference was similar in both sexes. Those aged 12-17 years old were also asked in the PATH study if they had seen a tobacco product that claims to be safer than other tobacco products in the past 12 months and their likelihood of using such a product in the next 30 days. Again, the proportion answering "very or somewhat likely" was much lower in tobacco never-users (174/4673 = 3.72%) than in ever-users (264/1565 = 16.87%).
Based on the Canadian COMPASS study, Aleyan et al. (2018) compared rates of smoking initiation over a two-year follow-up period among a sample of 9,501 students between grades 9 and 11 (cigarette never-smokers), classified at baseline into four groups by current e-cigarette use and susceptibility to smoke and assessed using a three-item validated measure. Though the data in Figure 1 of that paper showed that in both unsusceptible and susceptible never-smokers, rates of smoking initiation were higher in current than noncurrent e-cigarette users, only an estimated 33/2646 = 1.2% of those who had tried smoking during the follow-up period were unsusceptible current e-cigarette users. These results strongly indicate that even when there is some gateway-in effect, any resulting increase in smoking prevalence would be quite small.
How might any true gateway effect modify the population health impact of introducing e-cigarettes? Users of alternative tobacco products can certainly have similar nicotine exposure to that of cigarette smokers but potentially much lower risk of smoking-related diseases (SRD), as research has suggested, for example, from the experience and prevalence of Swedish snuff ("snus") use (Agewall et al., 2002;Bolinder et al., 1997a;Bolinder et al., 1997b;Lee, 2011;Lee, 2013). As vaping presents significantly reduced exposure to toxicants and harmful and potentially harmful constituents compared with smoking (National Academies of Sciences Engineering and Medicine, 2018), it would be expected that any harmful effects would be much lower. This hypothesis was subsequently endorsed by an expert group in 2014 (Nutt et al., 2014).
This hypothesis is also further supported by various theoretical beneficial and adverse effects of vaping (see Table 9). The first major benefit of vaping (B1) concerns individuals who, in the absence of e-cigarettes, would have initiated smoking but instead take up vaping. They should have a much lower risk of SRDs than if they had smoked, given, for example, that the expert panel (Nutt et al., 2014) estimated that relative to cigarettes, e-cigarettes likely pose a reduction in harm of up to 95%. This likely reduction in risk and harm also applies to benefit B2, where smokers who would otherwise have continued to smoke instead switch to vaping. While the benefits would take longer to emerge, they should be a considerable proportion relative to quitting smoking. The health benefit would even be greater for established smokers, where vaping helps them quit (B3).
Among the three adverse vaping effects listed in Table 9, smokers who are vaping in addition to their usual cigarette consumption (A3) is probably implausible, because most dual users are likely to control their nicotine intake and actually reduce their cigarette consumption, partially replacing cigarettes with e-cigarettes (Berry et al., 2018;McNeill et al., 2014;McRobbie et al., 2014). If so, the effect should be beneficial rather than adverse. The adverse effect for smokers switching to vaping instead of quitting smoking (A2) is not ideal but would still confer some benefits, given what is known regarding the reduced harm and toxicity of e-cigarettes.
Clearly, the greatest adverse effect arises for individuals who otherwise would not have smoked but are encouraged by vaping to do so (A1). However, considering the overall population health impact of this gateway-in effect, two points require emphasis. First, this is only likely to affect young people. Older people who decided not to start tobacco product use seem unlikely to start vaping, and even if they did so, it would probably be because they believe vaping to be much safer than the smoking they have already rejected (Majeed et al., 2017;Popova et al., 2018;Xu et al., 2016;Zhu et al., 2013). Second, the arguments made earlier suggest that the number of new smokers originating from any true gateway-in effect should be quite low. Any adverse effects resulting from gateway effects in young people are likely to be outweighed in the general population by beneficial effect B1, as evidenced by the substantial numbers who have vaped but not smoked. The reduction in risk of SRDs for such individuals is likely to be almost as great as any increase resulting from a gateway-in effect.
While various authors (Hill & Camacho, 2017;Levy et al., 2018;Nutt et al., 2014) estimate that the introduction of ecigarettes will have a beneficial population health impact, a recent publication Soneji et al. (2018) argues the opposite, presenting analyses estimating that "e-cigarette use in 2014 would lead to 1,510,000 years of life lost in the U.S. population." The study reported that the large adverse effects from an estimated 168,000 cigarette never-smokers between 12 and 29 years old initiating cigarette smoking in 2015 and going on to become daily smokers would heavily outweigh the very small beneficial effects from an additional 2,070 current cigarette smoking adults aged 25-69 quitting smoking in 2015 and remaining abstinent for seven or more years.
These analyses have various weaknesses. First, they assume that there is no reduction in risk of SRDs for cigarette smokers who become dual users but do not quit cigarettes. Dual users are likely to reduce cigarette consumption (Brose et al., 2015;Etter & Bullen, 2014;Farsalinos et al., 2016;Farsalinos et al., 2017;McRobbie et al., 2014) and hence their risk of SRD. Second, their analysis showing large adverse effects in youths relies heavily on the estimate of the gateway effect given by Soneji et al. (2017), which may largely result from confounding. Third, their analysis showing very small benefits from increased smoking quit rates in adults depended on an estimate derived from a prior review and meta-analysis by Kalkhoran & Glantz (2016) that estimated the OR of quitting among smokers with an interest in quitting. The estimate, and indeed the whole meta-analysis, has since drawn strong criticism from experts in the field for being inaccurate and misleading (West et al., 2016).

Discussion
Our analyses yield four major conclusions: 1. While the consistently increased adjusted gateway-in effect estimates appear compelling (Table 1), they may be severely biased by ignoring established predictors of smoking initiation and residual confounding -a true causal effect remains to be demonstrated.
2. If a true gateway effect were to exist, it will probably have little effect on smoking prevalence.
3. No available evidence exists that increasing e-cigarette use has slowed the decline in smoking prevalence; indeed, the decline appears to have accelerated.
4. Finally, introducing e-cigarettes should lead to a reduced risk of SRD.
Though some publications have used the evidence for gateway effects in youth to advocate for regulations to curb vaping in the general population (Miech et al., 2017;Primack et al., 2015;Soneji et al., 2017), others suggest that this over-interprets the evidence. Kozlowski & Warner (2017) point out the need "to better understand and assess confounding variables" and consider that the prospective studies merely "support that a minority of the relatively small number of e-cigarette triers-who haven't also been experimenting with other tobacco products already-will go on to some experimentation with cigarettes." Also, Levy et al. (2018) present calculations showing that "a strategy of replacing cigarette smoking with vaping would yield substantial life year gains, even under pessimistic assumptions [. . .]." Our argument that the gateway-in estimates are subject to relevant uncontrolled confounding is also supported by publications showing that vaping is associated with many factors associated with smoking ( Barrington-Trimis et al., 2015;Hanewinkel & Isensee, 2015;Temple et al., 2017). Unlike the implicit conclusion of the National Academies report (National Academies of Sciences Engineering and Medicine, 2018), which found that control of confounding in the gateway-in studies has been adequate, our conclusions would suggest otherwise. Our calculations indicating a lack of effect of e-cigarettes on smoking trends are further supported by Dutra & Glantz (2017), who concluded that "the introduction of e-cigarettes was not associated with a change in the linear decline in cigarette smoking among youth." While studies (McNeill et al., 2014) have argued against the likely population benefit of e-cigarettes due to their pervasive use among youths and young adults, as we have discussed, these analyses often fail to fully consider the limitations and weaknesses of the current evidence of the associated probabilities of e-cigarette use and cigarette smoking initiation in youths.
Nevertheless, some limitations of our work and of the available evidence must be noted. First, many risk factors for smoking initiation are correlated, and had even more been adjusted for, this might not have significantly changed the adjusted effect estimates. Second, while we suggest that residual confounding may be important, we have not considered data on how inaccurately specific risk factors are measured. Third, we did not consider evidence (Doran et al., 2017;Leventhal et al., 2016) that vaping may be associated with heavier smoking, although this factor may also be subject to confounding problems. Fourth, our evidence on trends is based on data where the prevalence of vaping is little more than 10%. Where vaping is infrequent, any true gateway-in effect will only slightly increase smoking prevalence, as cigarette never-smokers who have not vaped will far outweigh those who have. It is thus important to continue to monitor smoking and e-cigarette use trends for further conclusive insights.

Conclusions
The existence of any true gateway-in effect has not been clearly demonstrated. Even if it does exist, and subsequently, some individuals who otherwise would not have done so start to smoke cigarettes, any effect on smoking prevalence will be quite small, and any overall population health impact of introducing e-cigarettes still likely be beneficial.

Data availability
Additional File 1. Confounding factors considered by studies of vaping as a possible gateway to smoking. The file lists those factors with published evidence of a relationship with smoking and gives those factors not considered in any of the 15 studies considered in Table 1.
Additional File 2. Estimating the effects of omission of a confounding variable, or misclassification of it, on the association between vaping and subsequent initiation of smoking. The file presents details of the methodology used.
Additional File 3. How might the prevalence of smoking be affected by any true gateway effects of vaping? The file presents details of the methodology used.
Gateway calcs. The file is the Excel program used to produce the results presented in Additional Files 2 and 3. Author contributions PNL conceived the paper, developed the analyses reported in Additional Files 2 and 3, and drafted the manuscript. KJC discussed the paper with PNL, searched for relevant literature, checked tables, and commented on drafts. EFA was responsible for the work described in Additional File 1, collation of data used in Table 8, commented on drafts, and contributed to the writing of the manuscript. This ambitious paper by Lee and colleagues seems to have several aims, and I fear that the paper suffers from including them all, rather than separating them out into two or more focused manuscripts.

Grant information
The authors provide a review of the literature discussing what is currently known about the association between use of combusted and electronic tobacco products, they evaluate the likelihood of electronic products providing a gateway in to smoking based on seemingly informal, qualitative trend analyses, and they provide an important discussion about the potential for residual or otherwise uncontrolled confounding to explain or partially explain associations that have been noted in the literature. They also provide data from a series of simulations. The simulations demonstrate the interdependence among various tobacco use patterns in predicting population harm or benefit resulting from electronic tobacco products.
The most important points from the literature-based sections of the paper are a) the difference between predicted and observed smoking prevalence over time, which should reduce the fear that a large gateway to smoking effect is currently operating; b) the likelihood that misclassification and uncontrolled confounding play a role in the results reported by others, and probably lead to an overestimate of gateway-in effect.
The key findings from the simulations are a bit harder to uncover, due to a lack of detail in the methods section and some glossing over of assumptions. The authors found that a gateway-in effect is unlikely to be large enough to substantially affect population health. This is based on the assumptions that a) the current situation, in which smoking is far more prevalent than electronic cigarette use, will remain stable in at least the short run; b) the current higher likelihood of initiating tobacco use with cigarettes vs electronic products will remain stable in at least the short run; and c) the assumptions that the risks associated with electronic products are understood and are definitely lower than risks associated with smoking. If any of these input assumptions were varied, the simulation results would probably be different, and possibly much different. Some of the authors' strongest statements are due to differences between relative and absolute effects: Small percentages of a large underlying population (smokers) affect a large number of people, and large percentages of a small underlying population (electronic product users or initiators) affect a small number of people.
Apart from these conceptual concerns, it would strengthen the paper if the input into the simulations were better defined and justified.
The analyses presented in Table 8 seem to be the most informative of the simulations. Estimating the rate 2. 3.

7.
necessary to include all modelled effects, especially since not all are discussed. This makes this section at times difficult to follow. A better way to structure the Tables might be to link these explicitly to the 5 conclusions presented on pages 9 and 10. Terminology: I am not sure that the term "gateway-out" is helpful. At the very least, it would not represent the only way to construe this concept insofar as it could be argued that in a hypothetical scenario the opposite to the "gateway-in" might be the avoidance of uptake of smoking in the first place (another "gateway-out" not related to smoking cessation where adolescents who would have smoked try an e-cigarette and never start smoking). It might therefore be helpful to clarify the difference between those gateway effects away from smoking Change in smoking trends: I am not entirely convinced by this analysis for a number of reasons. First, very little information is provided about the actual analysis undertaken. I would expect to see fitting parameters for various functions to model trends in smoking prevalence in the period prior to 2014. Are these assumed to be linear, logarithmic, polynomial etc.? More detail is required here. A more appropriate way to analyse the data would be to conduct segmented regression or timeseries analysis. Second, based on the current analysis, the problem (somewhat acknowledged on top of page 11) is that it might still be the case that e-cigarettes functioned as a "gateway in" since we do not know what smoking prevalence would have been in the absence of e-cigarettes (and may have been even lower). I don't think this is likely but this issue should be acknowledged more explicitly, perhaps making it clear that such arguments would require "special pleading", i.e. one would need to postulate additional environmental changes which could explain a continued decrease in smoking prevalence even in the presence of a gateway effect. Second paragraph on page 11 does not seem to add anything (it's not clear how these cognitive measures in PATH relate to actual behaviour and so it's quite weak evidence). Suggest dropping this. Table 9: The counterpart to A3 is not presented, i.e. dual use which results in reduction of combustible cigarettes. If there is a commensurate reduction in cigarette consumption associated with an increase in e-cigarette use (if dual users reduce cigarette consumption sufficiently) there may be beneficial health effects (so perhaps this effect should be added). In fact, perhaps a more nuanced discussion of dual use is needed on page 12, given that it may have both positive and negative effects and encompasses such a wide range of behavioural patterns. Please note that the statement on page 11 "The adverse effect for smokers switching to vaping instead of quitting smoking (A2) is not ideal but would still confer some benefits, given what is known regarding the reduced harm and toxicity of e-cigarettes" is logically inconsistent. If a smoker intending to quit starts using e-cigarettes instead of quitting, there is no benefit (as e-cigarette use even if less harmful than smoking does not confer any benefit over stopping completely). The discussion on end of page 11 and top of page 12 feels rather qualitative. Either provide more quantitative analysis of modelled impact on health effects or move this to the discussion. You don't really provide any primary data to support your conclusion "Finally, introducing e-cigarettes should lead to a reduced risk of SRD." This is rather speculative at this stage without more rigorous modelling as was done e.g. in the NASEM report.

If applicable, is the statistical analysis and its interpretation appropriate? Partly
Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly No competing interests were disclosed. Competing Interests: Reviewer Expertise: Epidemiology, Health Psychology, Tobacco Control, Psychopharmacology, Neuroscience I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
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