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 meta-analyses 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.

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

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)

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)

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.S., the Youth Risk Behavior Survey (YRBS: available from https://www.cdc.gov/healthyyouth/data/yrbs/index.htm), the National Youth Tobacco Survey (NYTS: available from https://www.cdc.gov/tobacco/data_statistics/surveys/nyts/index.htm), the Monitoring the Future study (MTF: available from https://www.icpsr.umich.edu/icpsrweb/NAHDAP/series/35), and the National Survey on Drug Use and Health (NSDUH: available from https://www.samhsa.gov/data/data-we-collect/nsduh-national-survey-drug-use-and-health). We also identified two surveys from the UK, the Smokefree Youth Survey in Great Britain (SYSGB: available from http://ash.org.uk/download/use-of-electronic-cigarettes-among-children-in-great-britain/) and the Office for National Statistics (ONS: available from https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/drugusealcoholandsmoking/datasets/ecigaretteuseingreatbritain). All six databases were accessed on 16 Oct 2017.

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.

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 internet-based 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.

Table 1 also includes results from six later studies cited in a recent review ( Glasser et al., 2018): two from the U.S. ( Loukas 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.

Initiation of smoking is long-established to be associated with various sociodemographic, environmental, and behavioral factors. Those identified by an authoritative source over 20 years ago ( US Surgeon General, 1994) include low education level ( Gritz et al., 1998; Sargent et al., 1997), internalizing/externalizing disorders ( de Leon et al., 2002; Ernst et al., 2010; Rohde et al., 2003), outcome expectancies ( Barrington-Trimis et al., 2015; Simons-Morton et al., 1999), susceptibility to smoking ( Huang et al., 2005; Jackson, 1998), conduct problems ( Dalton et al., 2003; Scal et al., 2003), substance use ( Reed et al., 2007; Scal et al., 2003), risk-taking behavior ( Coogan et al., 1998; Dalton et al., 2003), poor school performance ( O'Connor et al., 2003; Sargent et al., 1997), anxiety ( Coogan et al., 1998; Scal et al., 2003), household smoking ( Picotte et al., 2006; Sargent et al., 1997), peer smoking ( Barrington-Trimis et al., 2015; Picotte et al., 2006), peer attitudes to smoking ( Barrington-Trimis et al., 2015; Daly et al., 1993), low self-esteem ( Dalton et al., 2003; Weiss et al., 2006), and other tobacco product use ( Barrington-Trimis et al., 2016; Jordan et al., 2014). Currently available evidence reveals that many of these predictors consistently show a strong association with initiation, with reported ORs often exceeding 10. While these predictors are clearly not all independent, their number illustrates the difficulty in ensuring that gateway studies consider an adequate list. 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.

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.

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 determining confounding variables, is rarely considered. Many statisticians have highlighted this problem of residual confounding. Almost 40 years ago, 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.

It is important to understand how vaping might affect the prevalence of cigarette smoking. Not only might there be “gateway-in” 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 (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 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.

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.

Table 8 presents results from this comparison based on the U.S. NYTS, YRBS, MTF, and NSDUH surveys. All estimates are for past 30-day smoking. In all the surveys, smoking prevalence in 2014–2016 was less than predicted from the underlying trend, contrary to expectations of a definitive gateway-in effect being present. The tendency for the decline in prevalence to accelerate over 2014–2016 is evident regardless of sex or age.

The Smokefree Youth Survey in Great Britain also reported increasing vaping, with percentages of 4%, 6%, 11%, and 10% each year from 2013–2016, respectively, for 11–18-year-olds. As shown in Table 8, ONS data (for an older age group) also show no tendency for a rise in cigarette smoking given increasing vaping. Here, very small annual declines (about 0.3% in males and 0.7% in females) over 2006–2015 were followed by a much larger decline of 7% between 2015 and 2016.

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 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.

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 e-cigarettes 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).