Further investigations of gateway effects using the PATH study

Further investigations of gateway effects using the PATH study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Further investigations of gateway effects using the PATH study Peter N Lee, John S Fry This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-18592/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Nov, 2020 Read the published version in F1000Research → Version 1 posted You are reading this latest preprint version Abstract Background: Considerable interest exists in whether e-cigarette use (“vaping”) by youths increases the risk of initiating cigarette smoking. Based on Waves 1 and 2 of the Population Assessment of Tobacco and Health study we reported that adjustment for propensity for vaping using Wave 1 variables explained about 80% of the unadjusted relationship. This analysis may be over-adjusted had vaping at Wave 1 affected some variables recorded then. Here we present analyses using Waves 1 to 3 to avoid this possibility. Methods: Our main analysis M1 concerned those who had never smoked by Wave 2 and never vaped by Wave 1. Wave 2 vaping was linked to smoking initiation by Wave 3, adjusting for Wave 1 predictors. Sensitivity analyses excluded other tobacco product users at Wave 1, included other tobacco product use as an additional predictor, or were based on propensity for ever smoking or ever any tobacco use, rather than ever vaping. Other analyses adjusted for propensity as derived originally, or ignored Wave 1 data. Other analyses used grouped age (only available originally) or exact age (available now) as a confounder variable, attempted residual confounding adjustment by modifying values of predictor variables using data later recorded, or considered interactions with age. Results: In M1, propensity adjustment removed about 50% of the excess odds ratio (i.e. OR–1), the unadjusted OR, 5.60 (95% CI 4.52-6.93) becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16) depending whether adjustment was for propensity as a continuous variable, as quintiles, or for the 16 variables making up the propensity score. Many factors studied hardly affected the results, including using grouped or exact age, consideration of other tobacco products, including interactions, or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment only explained about 50% of the excess OR whereas analyses subject to over-adjustment explained about 80%. Conclusions: Although much of the unadjusted gateway effect results from uncontrolled confounding, our current analysis provides stronger evidence of a causal effect of vaping than did our earlier analysis. However, some doubts remain about the completeness of confounder adjustment. Health Policy Cigarettes confounding over-adjustment e-cigarettes gateway effects modelling propensity score Background In youths, use of e-cigarettes (“vaping”) has increased considerably in recent years in many countries (e.g. [ 1 – 3 ]). It is generally recognized that vaping significantly reduces exposure to harmful constituents compared to smoking [ 4 ], so one might expect risks from vaping to be much lower [ 5 ]. However, there are concerns about the rise in vaping. The concern of interest here is the possibility that vaping may encourage some individuals to start smoking who would otherwise not have done so, often referred to as the “gateway” effect. The concern that vaping may act as a gateway into smoking was originally brought sharply into focus by a 2017 meta-analysis [ 6 ] based on nine US cohort studies in young people linking previous vaping to subsequent initiation of smoking. This paper reported that among baseline never-smokers, ever vaping at baseline strongly predicted initiation in the next 6 to 18 months, with an odds ratio (OR) of 3.62 (95% confidence interval (CI) 2.42–5.41) after adjustment for various predictors of initiation. Similarly baseline past 30-day vaping also predicted subsequent 30-day cigarette use (OR 4.25, 95% CI 2.52–7.37). We have previously published two papers relating to the gateway effect. Our first paper [ 7 ] considered various general issues. It made a number of relevant points: Although studies reported that vaping significantly predicts smoking initiation following adjustment for various other predictors, the sets of predictors considered were generally quite incomplete. No study considered residual confounding arising from inaccurate measurement of predictors. More precise adjustment may have substantially reduced the association. Any true gateway effect would likely have affected smoking prevalence only modestly. Smoking prevalence in US and UK youths in 2014–2016 declined somewhat faster than predicted by the preceding trend, whereas a substantial gateway effect would suggest the opposite. Even were some gateway effect to exist, introducing e-cigarettes would still be likely to reduce deaths from smoking-related diseases. Our second paper [ 8 ] described results of our own analyses, based on data from Waves 1 and 2 of the Population Assessment of Tobacco and Health (PATH) study, a nationally representative longitudinal cohort study in the United States of tobacco use and how it affects the health of people. Wave 1 was conducted from 12 September 2013 to 15 December 2014, with Wave 2 the first annual follow-up. For each Wave, data are available separately for Youths (aged 12–17 years) and Adults (aged 18 + years), the Youth data including some information from the parents. Publicly available data files include extensive information on use of various types of tobacco products and on a range of variables linked to initiation of tobacco. Note that where youths become 18 between successive waves of the survey, their data will be available in the Adult data rather than the Youth data. Also additional youths who were under 12 at the time of Wave 1 are added into the Youth data when they reach the age of 12 at a subsequent wave. The main analyses we described considered Wave 1 never cigarette smoking youths who, at Wave 2, had information available on smoking initiation. Having constructed a propensity score for ever e-cigarette use from Wave 1 variables, we found that adjustment markedly reduced the unadjusted OR of 5.70 (95% CI 4.33–7.50) to 2.48 (1.85–3.31), 2.47 (1.79–3.42) or 1.85 (1.35–2.53), depending on whether adjustment was made for propensity as quintiles, for propensity as a continuous variable, or for the individual variables making up the propensity score. Various sensitivity analyses confirmed that adjustment removed most of the gateway effect. Although we found that confounding was a major factor, explaining most of the observed gateway effect, we were particularly concerned about the possibility of over-adjustment, if taking up e-cigarettes had affected the values of some of the Wave 1 predictor variables considered. At the time, we noted that the possibility of over-adjustment could be avoided using data from Waves 1, 2 and 3 of the PATH study, by relating initiation of cigarette smoking at Wave 3 to vaping at Wave 2, restricting attention to those who, at Wave 1, had never vaped, and using propensity indicators recorded at Wave 1 linked to uptake of e-cigarettes by Wave 2. Here we describe the results of extensive analyses conducted based on Waves 1, 2 and 3 which not only include the main analyses envisaged at the time of our earlier paper [ 8 ], but also a variety of sensitivity and alternative analyses. Methods Some aspects of the analyses described here are the same as those described earlier [ 8 ] and are not presented again here. The selection of demographic and other predictor variables is the same as before, except that in some analyses we use exact age (12, 13, 14, 15, 16 and 17) which could now be estimated from the age group (12–14, 15–17) at the three Waves and the Wave when youths became adults (18+) for the first time. Use of the person-level weights provided in the PATH study database is as before, as is the process by which a sequence of logistic regression analyses is used to develop the shorter list of demographic variables to be used in forming the propensity scores. Our main analysis M1 is based on those with data at Waves 1, 2 and 3 who had never smoked cigarettes by Wave 2 and had never used e-cigarettes by Wave 1. This analysis predicts Wave 3 ever smoking from Wave 2 ever e-product use, with adjustment based on Wave 1 predictors used to derive a propensity index for taking up e-products between Waves 1 and 2, and exact age being used in preference to grouped age. Note that, whereas in Wave 1 questions in PATH related only to e-cigarette use, in Waves 2 and 3 questions related to ever e-product use, which also included use of e-cigars, e-pipes and e-hookahs. Associated with main analysis M1 are four sensitivity analyses (S1 to S4) which are otherwise similar, except that: S1. Those who had ever used other tobacco products at Wave 1 are excluded; S2. Ever use of other tobacco products at Wave 1 is included as an additional predictor variable; S3. T he analysis is based on a propensity score for ever cigarette smoking rather than for ever vaping; or S4. The analysis is based on a propensity score for ever use of any tobacco product rather than for ever vaping. Note that in our original paper [ 8 ] we also presented results of a further sensitivity analysis, based on linking current vaping to current smoking. This was not repeated here as numbers of new current smokers in current vapers were very low. Main analysis M2 is similar to M1, except that analysis adjusts for the propensity index as originally derived [ 8 ], based on 12 variables recorded at Wave 1. Alternative versions of M2 substitute exact age rather than grouped age in deriving the propensity index, and/or included Wave 1 vapers in the analysis. Main analysis M3 adjusts for a propensity index derived by linking Wave 2 predictors to Wave 2 e-product use. This is a replicate of the analysis conducted originally [ 8 ], but using a different period of taking up cigarettes. Data for Wave 1 were ignored, except that where the data for a characteristic was “ever in last 12 months”, Wave 1 data were used to define “ever”. An alternative version of M3 replaces grouped age by exact age in deriving the propensity index. Apart from analyses linking Wave 2 e-product use to additional cigarette smoking at Wave 3 in those who had never smoked at Wave 2, two additional analyses (A1 and A2) were also conducted. Additional analysis A1 relates e-cigarette use at Wave 1 to cigarette smoking at Wave 2 as in our earlier publication [ 8 ], but based on individuals who provided data at all three Waves. One version of this uses the same 12 variables as before to develop the propensity index, the other replaces grouped age by exact age. The OR from this analysis can be combined with that reported for main analysis M2 to give a combined estimate of the gateway effect for Wave 1 to 2 initiation and Wave 2 to 3 initiation based on the same set of variables determined at Wave 1. Additional analysis A2 ignores Wave 2 data and relates e-cigarette use at Wave 1 to cigarette smoking at Wave 3 using the same 12 variables as before, but replacing grouped age by exact age. Consideration of residual confounding was also taken into account for three of the analyses described above (M1, M3, A1), all involving exact age. In each case, the list of predictor variables was unaltered from that used originally, but the values of the predictor variables and of the propensity index were revised based on data available at all three Waves. For age, individual year of age at Wave 1 was used, while gender and Hispanic origin did not change between Waves. For the other variables used to form the propensity index, we used all the available data, generally choosing the response most associated with increased e-cigarette use where response varied between Waves (see Additional File Table 1 for further details). Table 1 Predicting Wave 2 ever-cigarette use from 16 Wave 1 predictor variables (Main analysis M1) Variable a Levels N OR (95% CI) Exact age 12 1518 1.00 (base) 13 1474 1.71 (1.23–2.38) 14 1451 1.97 (1.43–2.71) 15 1376 2.25 (1.65–3.08) 16 1188 2.55 (1.86–3.51) 17 1051 3.75 (2.72–5.15) Ever been curious about 0.86 (0.76–0.97) b smoking a cigarette Think you will smoke a 0.59 (0.48–0.71) c cigarette in the next year Anyone who lives with you Cigarettes, cigars, 2140 1.00 (base) now use tobacco cigarillos, filtered cigars Smokeless or other 319 1.73 (1.26–2.37) tobacco only No-one living in the home 5599 0.78 (0.65–0.94) uses tobacco Ever used alcohol at all Yes 2483 1.00 (base) No 5575 0.53 (0.45–0.62) Agree/disagree: like new and Strongly agree 285 1.00 (base) exciting experiences, even if I Agree 1252 0.71 (0.52–0.97) have to break the rules Neither agree nor disagree 2107 0.64 (0.47–0.87) Disagree 2404 0.38 (0.28–0.53) Strongly disagree 2010 0.46 (0.32–0.65) Youth’s grade performance in Mostly A’s 2342 1.00 (base) school in past 12 months A’s or B’s 2849 1.30 (1.07–1.58) Mostly B’s 702 1.60 (1.22–2.10) B’s or C’s 1346 1.47 (1.17–1.85) Mostly C’s 325 2.16 (1.52–3.09) C’s or D’s 334 2.74 (1.95–3.86) Mostly D’s 45 2.09 (0.90–4.87) D’s or F’s 71 2.54 (1.34–4.81) Mostly F’s 10 1.85 (0.26–12.91) School is ungraded 34 1.80 (0.54–6.06) How often you visit your Several times a day 2464 1.00 (base) Facebook, Google Plus, About once a day 2284 0.67 (0.56–0.80) MySpace, Twitter or other 3–5 days a week 1006 0.73 (0.58–0.92) 1–2 days a week 732 0.51 (0.37–0.69) Never 1572 0.40 (0.31–0.53) Agree/disagree: I think I Strongly agree 18 1.00 (base) would enjoy using tobacco Agree 95 0.42 (0.14–1.31) Disagree 1517 0.57 (0.20–1.58) Strongly disagree 6428 0.35 (0.12–1.01) Hispanic origin Hispanic 2332 1.00 (base) Not Hispanic 5726 0.67 (0.57–0.79) Became very distressed when Past month 1940 1.00 (base) something reminded of past 2–12 months 1137 0.86 (0.70–1.07) Over a year 906 0.71 (0.55–0.92) Never 4075 0.74 (0.62–0.89) Cigarettes or tobacco might be Yes 1057 1.00 (base) available to youth at parent No 7001 0.65 (0.52–0.80) or guardian’s home Money received in total during None 2771 1.00 (base) an average week Less than $ 1 331 1.34 (0.91–1.96) $ 1 to $ 5 1234 1.26 (0.99–1.61) $ 6 to $ 10 1019 1.40 (1.10–1.79) $ 11 to $ 20 1289 1.42 (1.14–1.77) $ 21 to $ 50 751 1.36 (1.06–1.75) $ 51 to $ 100 337 1.53 (1.11–2.10) $ 101 to $ 150 160 2.02 (1.33–3.06) $ 151 or more 166 1.96 (1.29–2.99) Last time 2 + times: had a hard Past month 2700 1.00 (base) time paying attention at 2–12 months 1402 0.75 (0.62–0.92) school, work or home Over a year 819 0.84 (0.64–1.09) Never 3137 0.72 (0.59–0.87) Number of times seen Movie 4 Never 6839 1.00 (base) Once 858 0.91 (0.73–1.11) Twice 190 1.24 (0.83–1.86) 3 or more times 171 1.91 (1.29–2.82) Think you will try a cigarette 1.99 (1.17–3.37) d soon Note: The model is based on 8058 youths with data on all 16 predictors who neither smoked nor used e-cigarettes at Wave 1. a The variables are shown in order of their inclusion into the model b The OR is per unit of the graded variable which represents decreasing curiosity c The OR is per unit of the graded variable which represents decreasing likelihood d The OR is per unit of the graded variable which represents decreasing likelihood, with those originally entered as missing because they thought that they would not smoke a cigarette in the next year scored as “definitely not” (Level 4) For analyses M1, M3 and A1, alternative versions were also run in which the number of variables adjusted for was increased by also including interactions of age with each of the other three predictors most strongly linked to the relevant gateway effect. Results M1 Relating initiation of cigarette smoking between Waves 2 and 3 to ever e-product use at Wave 2, with adjustment for Wave 1 predictors linked to uptake of e-cigarettes between Waves 1 and 2 Initial analyses linked exact age, four other demographic variables (gender, Hispanic origin, race and census region) and 60 other selected predictor variables to ever e-product use at Wave 2 in those who had not smoked or used e-cigarettes at Wave 1. A propensity index based on 16 variables was derived using the three step process described earlier [ 8 ]. Additional File Table 2 shows the steps at which different variables were eliminated from consideration, while Table 1 gives the fitted equation for the propensity index. Table 1 about here As shown in Table 2 adjustment for propensity removed about half the excess OR (i.e. OR − 1), the unadjusted OR of 5.60 (95% CI 4.52–6.93) reducing to either 3.37 (2.65–4.28) or 3.11 (2.47–3.92), depending on whether adjustment was as a continuous variable or as quintiles. A similar reduction in the OR, to 3.27 (2.57–4.16), was achieved by adjusting for the 16 variables individually. It can also be seen that, for the first seven variables adjusted for, the adjusted OR decreased steadily, to 3.25. Further adjustment had little or no effect, with introducing additional variables sometimes slightly increasing the estimated OR and sometimes slightly decreasing it. Table 2 Relating Wave 3 ever smoking to Wave 2 ever e-product use (Main analysis M1) Adjustment variables OR (95% CI) None 5.60 (4.52–6.93) Propensity score as quintiles 3.11 (2.47–3.92) Propensity score as a continuous variable 3.37 (2.65–4.28) Exact age 4.87 (3.91–6.06) +Ever been curious about smoking a cigarette 4.27 (3.41–5.34) +Think you will smoke a cigarette in the next year 3.84 (3.06–4.82) +Anyone who lives with you now use tobacco 3.73 (2.97–4.69) +Ever used alcohol at all 3.48 (2.76–4.38) +Agree/disagree: Like new and exciting experiences even if I have to break the rules 3.39 (2.68–4.28) +Youth’s grade performance in school in past 12 months 3.25 (2.57–4.12) +How often you visit your Facebook, Google Plus, MySpace, Twitter or other 3.17 (2.50–4.01) +I think I would enjoy using tobacco 3.17 (2.50–4.02) +Hispanic origin 3.22 (2.54–4.09) +Last time a significant problem with: becoming very distressed when something reminded of past 3.19 (2.51–4.05) +Cigarettes or tobacco might be available to youth at parent or guardian’s home 3.17 (2.50–4.02) +Money received in total during an average week 3.25 (2.56–4.13) +Last time 2 + times: Had a hard time paying attention at school, work or home 3.22 (2.53–4.09) +Number of times seen Movie 4 3.28 (2.57–4.17) +Think you will try a cigarette soon 3.27 (2.57–4.16) Notes: The table shows the effects of adjustment based on the Wave 1 predictors used to derive a propensity index for taking up e-products between Wave 1 and 2. The analyses are based on those with data at Waves 1, 2 and 3 who had never smoked cigarettes by Wave 2 and had never used e-cigarettes by Wave 1. Between Waves 2 and 3261/7367 (3.54%) of never users of e-products at Wave 2 took up smoking, while 148/893 (16.57%) of ever users did so. For individuals who were 16–17 at Wave 1, adult data were used to determine e-product use and cigarette smoking at later Waves. The table includes the results of a stepwise regression based on successively including the most significant adjustment variables, given that ever e-product use at Wave 2 was included in the model. Four sensitivity analyses of M1 were carried out, fuller details being given in Tables 3 to 6 of the Additional File. Compared to M1, S1 excluded those who had ever used products other than cigarettes or e-cigarettes at Wave 1, both in the construction of the propensity index and in estimating the gateway effect. Whereas M1 involved 8260 youths, of which 409 initiated smoking between Waves 2 and 3, S1 involved 7945, of which 359 took up smoking. The propensity index developed for S1 involved all the 16 variables shown in Table 2 , except for “Number of times seen Movie 4” and “Think you will try a cigarette soon”. Here, the pattern of results is similar to that for Table 2 , with the unadjusted OR of 5.66 (95% CI 4.49–7.13) reducing to either 3.45 (2.67–4.46), 3.24 (2.53–4.15), or 3.23 (2.49–4.18), depending on whether adjustment was made for propensity as a continuous variable, propensity as quintiles, or all the 14 variables individually. Compared to M1, the only difference for S2 was that ever smoked other tobacco products at Wave 1 was added to the 16 variables used in M1 to make up the propensity score, and was forced into the regression models. Starting with the same unadjusted OR as M1, the adjusted ORs were very similar; 3.37 (2.64–4.29), 3.07 (2.44–3.87) and 3.20 (2.50–4.08), after adjustment for propensity (continuous), propensity (quintiles), or all the individual variables. Whereas M1 (and S1 and S2) adjusted for variables found to be predictive of initiating e-product use at Wave 2, S3 adjusted for variables predictive of cigarette smoking. Here, the final model included 27 variables. The unadjusted OR of 5.65 (95% CI 4.55–7.01) slightly differed from that in M1 as the individuals considered had to have non-missing data on 27 variables rather than 16. However, the overall effect of adjustment was again similar, with the OR reducing to 3.28 (2.56–4.22) after adjustment for all 27 variables. As for M1, adjustment for the first four variables had the most effect. Adjustment for the first seven variables reduced the OR to 3.26 (2.57–4.13), similar to the OR after adjustment for all 27. Propensity adjustment was not carried out in S3. Compared to M1, S4 adjusted for variables predictive of take-up of any tobacco product between Waves 1 and 2. Here, the propensity index was based on 18 variables, with the unadjusted OR of 5.74 (4.55–7.23) reducing to 3.31 (95% CI 2.56–4.28), 3.19 (2.48–4.09), or 3.21 (2.47–4.18), after adjustment for propensity (continuous), propensity (quintiles), or all the individual variables. Adjustment for all 18 variables had a similar effect to adjustment for the most important 10 variables, where the OR was 3.20 (2.47–4.14). M2 Relating initiation of cigarette smoking between Waves 2 and 3 to ever e-product use at Wave 2, with adjustment for the same Wave 1 predictors as previously reported [ 8 ] Here, instead of deriving the Wave 1 predictors linked to uptake of e-cigarettes between Waves 1 and 2, analysis M2 uses the same set of Wave 1 predictors used in our earlier work [ 8 ], the results being shown in Table 3 . Here, the unadjusted OR of 5.74 (95% CI 4.62–7.13) reduced to 3.54 (2.81–4.45) after adjustment for propensity as quintiles and to 3.45 (2.72–4.37) after adjusting for the individual variables. While adjustment here removed about half the excess OR, the reduction was less, to 4.53 (3.62–5.68) after adjustment for propensity as a continuous variable. The reductions were similar if exact age rather than age group was included in the list of variables. Here the unadjusted OR was reduced to 3.51 (2.79–4.41) after adjustment for propensity as quintiles, 4.59 (3.66–5.74) after adjustment for propensity as a continuous variable, and 3.39 (2.67–4.30) after adjustment for the individual variables. Table 3 Relating Wave 3 ever smoking to Wave 2 ever e-product use (Main analysis M2) Adjustment variables OR (95% CI) None 5.74 (4.62–7.13) Propensity score as quintiles 3.54 (2.81–4.45) Propensity score as continuous variable 4.53 (3.62–5.68) Age range 5.20 (4.17–6.49) + Ever used alcohol at all 4.45 (3.54–5.58) + Ever been curious about smoking a cigarette 4.10 (3.26–5.16) + Think you will smoke a cigarette in the next year 3.70 (2.94–4.68) + Agree/disagree: Prefer friends who are exciting and unpredictable 3.65 (2.89–4.61) + Reaction if parent/guardian found you using tobacco 3.64 (2.88–4.60) + Gender 3.63 (2.87–4.58) + Agree/disagree: I think I would enjoy using tobacco 3.63 (2.87–4.59) + Agree/disagree: Some products are safer than others 3.63 (2.87–4.59) + Ever used prescription drug not prescribed to you: Ritalin or Adderall 3.67 (2.90–4.64) + Has a Facebook, Google Plus, MySpace, Twitter or other social networking 3.53 (2.79–4.47) + Anyone who lives with you now use tobacco 3.45 (2.72–4.37) Notes: The table shows the effects of adjustment based on the same Wave 1 predictors as used in our original paper [ 8 ]. The analyses are based on those with data at Waves 1, 2 and 3 who had never smoked cigarettes by Wave 2 and had never used e-cigarettes by Wave 1. Between Waves 2 and 3, 249/7133 (3.49%) of never users of e-products at Wave 2 took up smoking, while 146/880 (16.59%) of ever users did so. For individuals who were 16–17 at Wave 1, adult data were used to determine e-product use and cigarette smoking at later Waves. The table includes the results of a stepwise regression based on successively including the most significant adjustment variables, given that ever e-product use at Wave 2 was included in the model. Similar analyses were also run which did not exclude those who had used e-cigarettes by Wave 1. This increased the number of ever e-product users who took up smoking from 146 to 201, and slightly increased the unadjusted OR to 5.95 (4.89–7.23). However, the pattern of decline following adjustment was quite similar. For example, the OR adjusted for the individual variables reduced to 3.31 (2.65–4.12) using grouped age and to 3.26 (2.62–4.06) using exact age. M3 Relating initiation of cigarette smoking between Waves 2 and 3 to ever e-product use at Wave 3, with adjustment for Wave 2 predictors As noted in the Methods section, M3 is essentially a replicate of our earlier work [ 8 ], but using a different period of introduction of cigarettes. The propensity score developed was based on 18 variables, using age group or exact age as alternatives. The results, shown in Table 4 , indicate that, as earlier [ 8 ], a large proportion of the unadjusted association can be explained by adjustment. The largest proportion was explained by adjusting for the 18 variables making up the propensity score, with the unadjusted OR of 6.70 (95% CI 5.40–8.32) reducing to 2.25 (1.74–2.91) or 2.75 (1.75–2.93) depending on whether the list of variables included age range or exact age. However, most of this reduction could be explained by adjustment for propensity. Table 4 Relating Wave 3 ever smoking to Wave 2 ever e-product use (Main analysis M3) Adjustment variables Using age group OR (95% CI) Using exact age OR (95% CI) None 6.70 (5.40–8.32) 6.70 (5.40–8.32) Propensity score as quintiles 2.77 (2.19–3.50) 2.74 (2.17–3.48) Propensity score as a continuous variable 2.57 (1.98–3.33) 2.60 (2.00-3.36) Age range 5.78 (4.62–7.22) - Exact age - 5.45 (4.36–6.83) +Last time a significant problem with: feeling very trapped, lonely, sad, blue, depressed 5.22 (4.17–6.54) 4.95 (3.94–6.21) +Reaction if parent/guardian found you using tobacco 4.89 (3.89–6.14) 4.66 (3.70–5.87) +Money received in total during an average week 4.65 (3.69–5.86) 4.52 (3.59–5.71) +Number of times seen Movie 3 4.31 (3.41–5.44) 4.20 (3.32–5.31) +Number of times seen Movie 4 4.12 (3.25–5.21) 4.02 (3.18–5.10) +Ever been curious about smoking a cigarette 3.45 (2.71–4.38) 3.36 (2.64–4.28) +Think you will smoke a cigarette in the next year 2.89 (2.26–3.70) 2.86 (2.24–3.66) +Ever used alcohol at all 2.63 (2.05–3.37) 2.63 (2.05–3.38) +In past 12 months, youth’s grade performance at school 2.51 (1.95–3.22) 2.51 (1.95–3.23) +Agree/disagree: using tobacco would help me calm down when I am angry 2.43 (1.89–3.12) 2.43 (1.89–3.13) +How often you visit your social media accounts 2.43 (1.88–3.12) 2.45 (1.90–3.15) +Would smoke if one of your friends offered you one 2.37 (1.84–3.06) 2.39 (1.86–3.09) +Anyone who lives with you now use tobacco 2.34 (1.81–3.02) 2.36 (1.83–3.04) +Think you will try a cigarette soon 2.33 (1.81–3.01) 2.35 (1.82–3.03) +Agree disagree: some tobacco products are safer than others 2.30 (1.78–2.97) 2.32 (1.79–2.99) +Youth has a curfew or set time to be home on school nights 2.29 (1.77–2.95) 2.30 (1.78–2.98) +Ever used prescription drug not prescribed to you: Ritalin or Adderall 2.25 (1.74–2.91) 2.27 (1.75–2.93) Notes: The table shows the effects of adjustment based on Wave 2 predictors linked to use of e-products in Wave 2. The analyses are based on those with data at Waves 2 and 3 ignoring data from Wave 1. Between Waves 2 and 3, 228/8233 (2.77%) of never users of e-products at Wave 2 took up smoking, while 145/949 (15.28%) of ever users did so. For individuals who were 17 at Wave 2, adult data were used to determine cigarette smoking at Wave 3. The table includes the results of a stepwise regression based on successively including the most significant adjustment variables, given that ever e-product use at Wave 2 was included in the model. The first set of ORs is based on a model including age group, while the second is based on a model including exact age. Combining the Wave 2 to 3 results shown in Table 4 with the Wave 1 to 2 results reported earlier [ 8 ] by fixed-effect meta-analysis, gives an unadjusted OR of 6.30 (5.31–7.46) which is reduced to 2.65 (2.24–3.18), 2.53 (2.07–3.10) or 2.08 (1.70–2.54) depending on whether adjustment is for propensity (quintiles), propensity (continuous) or all the variables making up the propensity score. This represents reductions in the excess OR of, respectively, 68.9%, 71.1% or 79.8%. A1 Relating initiation of cigarette smoking between Waves 1 and 2 to ever e-cigarette use at Wave 1, based on individuals who provided data at all three Waves Table 5 summarizes the main results of these analyses and compares them with those reported earlier [ 8 ]. While the original analyses were based on 9423 youths, 421 of whom initiated smoking, the new analyses were based on 8700 youths, 389 of whom initiated smoking. As can be seen, the results in the original analysis, based on grouped age, were similar to those from the new analyses, whether grouped or exact age was used. Table 5 Relating Wave 2 ever smoking to Wave 1 ever e-cigarette use - original [ 8 ] and A1 ORs Adjustment variables Data on two Waves Data on all three Waves Originally reported Grouped age Exact age OR (95% CI) OR (95% CI) OR (95% CI) None 5.70 (4.33–7.50) 5.99 (4.52–7.95) 5.99 (4.52–7.95) Propensity score as quintiles 2.48 (1.85–3.31) 2.65 (1.96–3.58) 2.59 (1.92–3.50) Propensity score as continuous variable 2.47 (1.79–3.42) 2.67 (1.92–3.72) 2.64 (1.89–3.68) Grouped age 4.81 (3.64–6.35) 5.04 (3.78–6.72) - Exact age - - 4.81 (3.60–6.42) + 11 further variables 1.85 (1.35–2.53) 1.97 (1.42–2.73) 1.98 (1.43–2.75) Notes: Each set of ORs is based on those who had never smoked cigarettes by Wave 1. The first analysis is as summarized in Table 1 . The last two analyses only exclude those without data at Wave 3. The results from analysis A1 for grouped age may theoretically be combined with those from analysis M2 shown in Table 3 , as they both use the Wave 1 predictors from our original paper [ 8 ], with exact age replacing grouped age, and are both based on individuals with data at all three Waves. However, as illustrated by the results adjusted for all 12 variables, where the ORs are 3.45 (95% CI 2.72–4.37) from Tables 3 and 1.97 (1.42–2.73) from Table 5 , these estimates are heterogeneous (p < 0.001), providing a random-effects combined estimate of 2.64 (1.52–4.57). A2 Relating Wave 3 ever smoking to Wave 1 e-cigarette use, ignoring Wave 2 data This analysis is similar to that reported originally [ 8 ] but relates to a longer follow-up period, and uses exact rather than grouped age. The results of this analysis, shown in Table 6 , are quite similar to those shown in Table 5 . Again, an unadjusted OR is markedly reduced by adjusting for propensity, whether as quintiles or as a continuous variable, and is further reduced by adjusting for all the 12 individual variables considered. Table 6 Relating Wave 3 ever smoking to Wave 1 ever e-cigarette use using exact age Adjustment variables OR (95% CI) None 5.65 (4.50–7.10) Propensity score as quintiles 2.48 (1.95–3.16) Propensity score as continuous variable 2.61 (2.00-3.40) Exact age 4.69 (3.71–5.93) + 11 further variables 1.97 (1.51–2.56) Notes: The table shows the effects of adjustment based on the same Wave 1 predictors as used in our original paper [ 8 ] but replacing age range by exact age. The set of ORs is based on those with data at Waves 1, 2 and 3 who had never smoked cigarettes by Wave 1. Between Waves 1 and 3, 716/8334 (8.59%) of never users of e-cigarettes at Wave 1 took up smoking, while 123/366 (33.61%) of ever users did so. The table includes the results of a stepwise regression based on successively including the most significant adjustment variables, given that ever e-product use at Wave 1 was included in the model. Attempting to account for residual confounding Table 7 summarizes the main results shown in Table 2 for main analysis M1, which make no allowance for residual confounding, and compares them with the results of an analysis using the same list of predictor variables, but with values modified in an attempt to adjust for residual confounding. As can be seen, markedly more of the unadjusted association was explained when allowance for residual confounding was made, with the adjusted ORs in the range 2.36 to 2.46 when allowance was made, compared with 3.11 to 3.37 when it was not. Note that the unadjusted ORs in the two sets of results vary slightly, as missing values in some individuals in the original analyses were replaced by estimates taken from other Waves. Table 7 Effect of allowance for residual confounding in main analysis M1 Adjustment variables M1 – no allowance M1 – allowance OR (95% CI) OR (95% CI) None 5.60 (4.52–6.93) 5.65 (4.58–6.98) Propensity score as quintiles 3.11 (2.47–3.92) 2.40 (1.91–3.02) Propensity score as a continuous variable 3.37 (2.65–4.28) 2.46 (1.93–3.14) All 16 variables individually 3.27 (2.57–4.16) 2.36 (1.85–3.02) Notes: The “no allowance” results correspond to those in Table 6 . The analyses are based on those with data at Waves 1, 2 and 3 who had never smoked cigarettes by Wave 2 and had never used e-cigarettes by Wave 1. Between Waves 2 and 3 261/7367 (3.54%) of never users of e-products at Wave 2 took up smoking, while 148/893 (16.57%) of ever users did so in the population considered in the “no allowance” analyses The corresponding figures in the “allowance” analyses were 267/7682 (3.48%) and 150/915 (16.39%). For individuals who were 16–17 at Wave 1, adult data were used to determine e-product use and cigarette smoking at later Waves. The table includes the results of a stepwise regression based on successively including the most significant adjustment variables, given that ever e-product use at Wave 2 was included in the model. While allowance for residual confounding has quite a marked effect for analysis M1, the analysis which avoided the possibility of over-adjustment, it did not for analyses M3 and A2, which did not avoid this possibility. Detailed results are shown in Tables 7 and 8 in the Additional File. Table 8 Summary of results from analyses Baseline Follow-up Unadjusted % Excess OR explained a Analysis Wave Wave Predictor Age Comment OR P as Q b P as C c 6 variables All variables A Original 1 2 Ever e-cigs Grouped As published ( [ 8 ]) 5.70 68.5 68.7 78.1 81.9 B M1 2 3 Ever e-cigs Exact Predictors revised based on those who were not Wave 1 e-users 5.60 54.1 48.5 48.0 50.7 C M1/S1 2 3 Ever e-cigs Exact As M1 but excludes Wave 1 other product users 5.66 51.9 47.4 47.9 52.1 D M1/S2 2 3 Ever e-cigs Exact As M1 but Wave 1 other product use included as predictor 5.60 55.0 48.3 50.2 52.2 E M1/S3 2 3 Ever cigs Exact As M1 but adjusting for predictors of ever cigarette smoking 5.65 - - 48.4 51.0 F M1/S4 2 3 Ever any product Exact As M1 but adjusting for predictors of ever any tobacco use 5.74 53.8 51.3 45.1 53.4 G M2 2 3 Ever e-cigs Grouped Original 12 predictors 5.74 46.4 25.5 44.3 48.3 H M2 (variant) Did not exclude Wave 1 e-users 5.95 50.3 28.7 49.3 53.3 I M2 (variant) 2 3 Ever e-cigs Exact Original 12 predictors 5.74 47.0 24.3 46.0 49.6 J M2 (variant) Did not exclude Wave 1 e-users 5.95 50.7 28.3 50.5 54.3 K M3 2 3 Ever e-cigs Grouped Predictors revised essentially ignoring Wave 1 data 6.70 68.9 72.5 45.3 78.1 L M3 (variant) Exact As above but using exact age 6.70 69.5 71.9 47.0 77.8 M A1 1 2 Ever e-cigs Grouped As original but based on those with data on all three Waves 5.99 66.9 66.5 76.8 80.6 N A1 (variant) Exact As above but using exact age 5.99 68.1 67.1 77.0 80.4 O A2 1 3 Ever e-cigs Exact Original predictors but ignoring Wave 2 5.65 68.2 65.4 74.4 79.1 P M1 (variant) 2 3 Ever-e-cigs Exact As M1 but allows for residual confounding 5.65 69.9 68.6 60.0 70.8 Q M3 (variant) 2 3 Ever e-cigs Exact As M3 but allows for residual confounding 6.67 75.3 74.3 51.5 80.2 R A1 (variant) 1 2 Ever e-cigs Exact As A1 but allows for residual confounding 6.10 69.0 68.0 65.1 76.7 a % excess explained = 100*(OR u – OR A ) / (OR u –1) where OR u is the unadjusted OR, and OR A is the adjusted OR b P as Q = Propensity as quintiles c P as C = Propensity as a continuous variable Investigating whether introducing some interactions explains more of the gateway effect Versions of analyses M1, M3 and A1 were also seen, in which the number of variables adjusted for was extended by also including interactions of age with each of the other three predictors most strongly linked to the gateway effect. For analysis M1, allowance for these interactions had virtually no effect, the original estimate of 3.27 (95% CI 2.57–4.16) shown in Table 2 without including interactions changing to 3.26 (2.55–4.15) when interactions were included in the model. For analysis M3, the estimate changed only from 2.27 (1.75–2.93) to 2.35 (1.81–3.05), while for analysis A1, it changed from 1.98 (1.43–2.75) to 2.06 (1.48–2.88). Summary of results Table 8 summarizes the results from 18 of the analyses described above, expressing the extent to which adjustment explained the unadjusted OR using the statistic 100 x (OR U – OR A ) / (OR U – 1) where OR U is the unadjusted OR, and OR A is the adjusted OR. The most obvious impression from the table is that the results largely fall into two groups. Table 8 about here Results from the original analysis and for analyses M3, A1 and A2 (rows A, K to O, and Q to R of Table 8 ) all show that as much as about 80% of the unadjusted excess OR can be explained by adjustment for the full set of variables in the model, with somewhat less, typically about 70%, explained using propensity as quintiles or as a continuous variable. In contrast, results from virtually all of analyses M1 and M2 (rows B to K) show that only about 50% of the unadjusted excess OR can be explained by adjustment for the full set of variables, with propensity as quintiles giving generally similar results. The difference between these two groups is that the first set of results are subject to the problem of over-adjustment, with the values of the predictors used possibly having been affected by having used e-cigarettes. This is mainly so where the baseline wave was Wave 1, but was also true for analysis M3 where Wave 1 data were essentially ignored. In contrast the second set of results avoided over-adjustment by considering follow-up from Wave 2 to 3, with predictors based on Wave 1 data in youths who had never used e-cigarettes. However in this second set of results the variables used were not as up-to-date as in the first analyses. The variant analysis of M1, allowing for residual confounding (row P) gives an intermediate result with about 70% of the excess risk being explained, whether by the full set of variables or by propensity. This analysis, however, does not avoid the problem of over-adjustment as it incorporates some information from waves where individuals were already using e-cigarettes. It is clear from Table 8 that many of the variables studied had little effect on the pattern of results. These included use of grouped or exact age, taking into account use of other products, and using predictors of cigarette smoking or any tobacco use rather than predictors of e-cigarette use. Two other conclusions may be drawn from Table 8 . One is that adjustment for propensity as quintiles or as a continuous variable generally gives very similar results, with the exception of analysis M2 and its variants, where propensity as a continuous variable explained substantially less of the unadjusted excess OR. Inspection of the detailed modelling results showed that, whereas in other analyses, the logarithm of the OR increased fairly linearly with quintiles of propensity, in the case of analysis M2 and its variants it did not. Thus, in M1 for example, the log ORs by quintile were 0, 0.73, 1.11, 1.66 and 2.52 while in M2 they were 0, 0.21, 0.96, 1.51 and 2.19 with very little rise between quintiles 1 and 2. The other is that adjustment for the first six variables in the model generally explained a very substantial part of the unadjusted excess OR explained by the full set. Though this was not true for analysis M2, it was still true that adjustment for the last eight or nine variables explained far less of the excess OR than did the first eight or nine. Discussion In our publication based on Waves 1 and 2 [ 8 ] our analyses showed that an unadjusted estimate of the gateway effect 5.70 (85% CI 4.33–7.50) could be considerably reduced by adjustment, to 1.59 (1.14–2.20) in the most striking case. Because of the marked reduction in the OR following adjustment, and the possibility of incomplete control for confounding we regarded it as “unclear whether prior vaping actually increases uptake of cigarette smoking”. However, we did note the possibility of over-adjustment, with vaping at Wave 1 possibly having affected the recorded values of some of the variables used for adjustment. At that time we noted that this possibility of over-adjustment could be addressed in analyses relating initiation of cigarette smoking at Wave 3 to vaping at Wave 2, restricting attention to those youths who, at Wave 1, had never vaped, and using adjustment variables recorded at Wave 1. This we have done in the analyses reported here, and our major finding is that adjustment reduced the excess risk far less, by only about 50% rather than about 80%, in our main analysis M1. While these results more strongly support the existence of a true gateway effect of taking up vaping, there must still remain doubt about its magnitude. One reason is that predictors recorded a year before the baseline may not fully account for the characteristics of the youth at the start of follow-up. A second reason is that, although the PATH study records data on a whole range of possibly relevant characteristics, there may be some relevant predictors not considered. A third reason is that the answers to some of the questions may have been inaccurately measured. We have attempted to address this problem of residual confounding by amending values of predictors recorded at Wave 1 to take into account data recorded at later Waves. However, this problem re-introduces the problem of over-adjustment as Wave 2 and 3 values may have been affected by vaping. Theoretically, one could use data from Waves 1 to 4, using data for Waves 1 and 2 from youths who have never vaped to produce more accurate estimates of the predictors to use for a study of gateway effects between Waves 3 and 4. But this would add to the problem of using predictors recorded some time before follow-up. Since the time that we published our earlier analysis [ 8 ] and our paper on general considerations relating to vaping as a possible gateway into cigarette smoking [ 7 ] a number of other authors have presented evidence from prospective studies [ 9 – 15 ]. The studies vary in the extent to which potential confounding variables have been adjusted for, with large OR estimates tending to be reported in studies with more limited control. Thus, a study in the Netherlands [ 15 ], which adjusted only for sex, age education and a single indicator of propensity to smoke, reported an OR of 11.90 (95% CI 3.36–42.11) for the relationship between ever use of e-cigarettes with nicotine and initiation of cigarette smoking during follow-up. Also, a study in the US [ 9 ], which adjusted only for demographic variables and use of other tobacco products, reported ORs of 7.08 (2.34–21.42) and 3.87 (1.86–2.06) depending on the follow-up period studied, while another US study [ 13 ], with limited control for confounding variables, reported an OR of 3.57 (1.96–6.45). Apart from a US study [ 14 ] ,which reported an OR of 6.8 (1.7–28.3), following adjustment for ten covariates independently associated with initiation of smoking, most of the other studies which appear to have better control for confounding gave lower estimates. These included a study in Taiwan [ 10 ] which reported an OR of 2.14 (1.66–2.75), a study in Germany [ 12 ] which reported an OR of 2.18 (1.65–2.87) and a study in Finland [ 11 ] which reported that adjustment reduced the OR from 11.52 (4.91–26.56) to 2.92 (1.09–7.85). Notably, a study in Great Britain [ 16 ] reported an OR of 11.89 (3.56–39.72) estimated using the usual logistic method, but a reduced value of 1.34 (1.05–1.72) using causal mediation analysis. Generally our results are consistent with the literature in confirming that a substantial proportion, but not all, of the observed association between e-cigarette use and subsequent initiation of cigarette smoking can be explained by adjustment for factors linked to susceptibility to tobacco. However, large cohort studies with high quality, accurate, data on a wide range of predictive factors recorded at regular intervals will be needed to gain better insight into the magnitude of any true causal effect of vaping. The PATH study with its multiple waves and comprehensive questionnaire should prove more and more useful in the future. There are a number of theoretical beneficial and adverse effects of e-cigarettes [ 7 ]. Beneficial effects relate to individuals who would otherwise have smoked vaping instead, smokers who would otherwise have continued to smoke switching instead to vaping, vaping helping established smokers to quit, and vaping helping established smokers to reduce their cigarette consumption. Apart from vaping encouraging initiation of smoking, theoretical adverse effects occur if smokers intending to quit switch to vaping instead, or smokers add vaping to their normal cigarette consumption. It is clearly important, therefore, to take all these considerations into account when attempting to estimate the health impact of e-cigarettes. Conclusions By using data from three Waves of the PATH study, the analyses of the gateway effect reported here improve on those reported earlier [ 8 ] based on the first two Waves by allowing potential confounding variables to be determined at a time before vaping started. Whereas the earlier analyses suggested that the adjustment for confounding explained about 80% of the unadjusted relationship between vaping and subsequent initiation of smoking, our current analyses suggest that adjustment explains only about 50%. This provides stronger evidence of a true effect of vaping, although doubt still remains about its true magnitude for reasons discussed. Abbreviations CI confidence interval; OR odds ratio; PATH Population Assessment of Tobacco and Health Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The data from the PATH study are publicly available at https://pathstudyinfo.nih.gov Competing interests Both authors are long term consultants to the tobacco industry Funding Financial support was provided by Philip Morris Products SA, through Project Agreement No. 29 with P.N. Lee Statistics and Computing Ltd. While some technical comments were provided by the funder on drafts of the statistical plan and this publication, the final versions remain the responsibility of the authors. Authors’ contributions PNL drafted the statistical plan, checked the analyses conducted, and drafted this publication. JSF commented on the plan, carried out the analyses, and commented on the publication. Both authors agreed the final publication. Acknowledgements We thank Esther Afolalu for assistance in acquiring the data from the PATH study, and Zheng Sponsiello-Wang and Christelle Chrea for providing technical comments at various stages. We also thank Jan Hamling for assistance in running the analyses, and Yvonne Cooper and Diana Morris for typing the various drafts of the paper. References Barrington-Trimis JL, Urman R, Leventhal AM, Gauderman WJ, Cruz TB, Gilreath TD, et al. E-cigarettes, cigarettes, and the prevalence of adolescent tobacco use. Pediatrics 2016;138(2).(Epub 20160711): doi:10.1542/peds.2015-3983. Best C, van der Sluijs W, Haseen F, Eadie D, Stead M, MacKintosh AM, et al. Does exposure to cigarette brands increase the likelihood of adolescent e-cigarette use? A cross-sectional study. BMJ Open 2016;6(2):e008734.(Epub 20160223): doi:10.1136/bmjopen-2015-008734. Miech R, Johnston L, O'Malley PM, Bachman JG, Patrick ME. Trends in adolescent vaping, 2017-2019 (Letter). N Engl J Med 2019;381(15):1490-1.(Epub 20190918): doi:10.1056/NEJMc1910739. National Academies of Sciences Engineering and Medicine. Public health consequences of e-cigarettes. Washington DC: The National Academies Press; 2018. Available: https://doi.org/10.17226/24952 . Nutt DJ, Phillips LD, Balfour D, Curran HV, Dockrell M, Foulds J, et al. Estimating the harms of nicotine-containing products using the MCDA approach. Eur Addict Res 2014;20(5):218-25. doi:10.1159/000360220. Soneji S, Barrington-Trimis JL, Wills TA, Leventhal AM, Unger JB, Gibson LA, et al. Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: A systematic review and meta-analysis. JAMA Pediatr 2017;171(8):788-97.(Epub 20170626): doi:10.1001/jamapediatrics.2017.1488. Lee PN, Coombs KJ, Afolalu EF. Considerations related to vaping as a possible gateway into cigarette smoking: an analytical review [version 3; peer review: 2 approved]. F1000Research 2018;7(1915):( https://doi.org/10.12688/f1000research.6928.3 ). doi:doi.org/10.12688/f1000research.16928.3. Lee PN, Fry J. Investigating gateway effects using the PATH study [version 1; peer review: 1 approved]. F1000Research 2019;8(264):( https://doi.org/10.12688/f1000research.8354.1 ). doi:10.12688/f1000research.18354.1. Bold KW, Kong G, Camenga DR, Simon P, Cavallo DA, Morean ME, et al. Trajectories of e-cigarette and conventional cigarette use among youth. Pediatrics 2018;141(1).(Epub 20171204): doi:10.1542/peds.2017-1832. Chien YN, Gao W, Sanna M, Chen PL, Chen YH, Glantz S, et al. Electronic cigarette use and smoking initiation in Taiwan: Evidence from the First Prospective Study in Asia. Int J Environ Res Public Health 2019;16(7).(Epub 20190330): doi:10.3390/ijerph16071145. Kinnunen JM, Ollila H, Minkkinen J, Lindfors PL, Timberlake DS, Rimpela AH. Nicotine matters in predicting subsequent smoking after e-cigarette experimentation: A longitudinal study among Finnish adolescents. Drug Alcohol Depend 2019;201:182-7.(Epub 20190619): doi:10.1016/j.drugalcdep.2019.04.019. Morgenstern M, Nies A, Goecke M, Hanewinkel R. E-Cigarettes and the use of conventional cigarettes. Dtsch Arztebl Int 2018;115(14):243-8. Pénzes M, Foley KL, Nadasan V, Paulik E, Ábrám Z, Urbán R. Bidirectional associations of e-cigarette, conventional cigarette and waterpipe experimentation among adolescents: A cross-lagged model. Addict Behav 2018;80:59-64.(Epub 20180111): doi:10.1016/j.addbeh.2018.01.010. Primack BA, Shensa A, Sidani JE, Hoffman BL, Soneji S, Sargent JD, et al. Initiation of traditional cigarette smoking after electronic cigarette use among tobacco-naive US young adults. Am J Med 2018;131(4):443.e1-.e9.(Epub 20171211): doi:10.1016/j.amjmed.2017.11.005. Treur JL, Rozema AD, Mathijssen JJP, van Oers H, Vink JM. E-cigarette and waterpipe use in two adolescent cohorts: cross-sectional and longitudinal associations with conventional cigarette smoking. Eur J Epidemiol 2018;33(3):323-34.(Epub 20171219): doi:10.1007/s10654-017-0345-9. East K, Hitchman SC, Bakolis I, Williams S, Cheeseman H, Arnott D, et al. The association between smoking and electronic cigarette use in a cohort of young people. J Adolesc Health 2018;62(5):539-47.(Epub 20180221): doi:10.1016/j.jadohealth.2017.11.301. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-18592","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":440744,"identity":"97f386af-2446-44e3-a464-24a08be5122d","order_by":1,"name":"Peter N Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBAC+wYgwQgk+IHUAaggM14tBgeYIVokgZhELQYHiNZyvP/g48oGGznj270HDnyoYJDnZ+AxNsCnxb7nMLPh2YY0Y7M75xIOzjjDYDizgcc4Aa8tEslsko0NhxO33cgxOMzbxpBgcIDH+ABeLfKP2X82Nvyv3zwDqOXvP2K0SDCzMTY2HEgwkABqAYYDWAt+h/EkGwMdlmw4484Zg4M9xyQMZzazFeP1vgH7wYcfG//YyfPP7jF88KPGRp6fvXmzBD4tCCABI/HHCqaWUTAKRsEoGAWYAAC9hUoF5WNWQQAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Peter","middleName":"N","lastName":"Lee","suffix":""},{"id":440745,"identity":"d9e9c2ec-cef6-42e4-9a01-cff225e49b6a","order_by":2,"name":"John S Fry","email":"","orcid":"","institution":"RoeLee Statistics Ltd","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"S","lastName":"Fry","suffix":""}],"badges":[],"createdAt":"2020-03-20 11:48:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-18592/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-18592/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.12688/f1000research.24289.2","type":"published","date":"2020-11-09T18:59:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":13495405,"identity":"c8a30069-5138-44e0-8b8e-dde5844ec7be","added_by":"auto","created_at":"2021-09-16 22:45:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":415178,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-18592/v1/9031f34b-f8f5-4ee2-a1b5-bc698c1dee37.pdf"},{"id":715715,"identity":"911da826-7f26-4f92-b21e-a75f78605b0e","added_by":"auto","created_at":"2020-03-26 15:29:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":56787,"visible":true,"origin":"","legend":"","description":"","filename":"GatewaypaperforBMCPublicHealthAdditionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-18592/v1/Gateway paper for BMC Public Health_Additional file - 2.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eFurther investigations of gateway effects using the PATH study\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eIn youths, use of e-cigarettes (\u0026ldquo;vaping\u0026rdquo;) has increased considerably in recent years in many countries (e.g. [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]). It is generally recognized that vaping significantly reduces exposure to harmful constituents compared to smoking [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], so one might expect risks from vaping to be much lower [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, there are concerns about the rise in vaping. The concern of interest here is the possibility that vaping may encourage some individuals to start smoking who would otherwise not have done so, often referred to as the \u0026ldquo;gateway\u0026rdquo; effect. The concern that vaping may act as a gateway into smoking was originally brought sharply into focus by a 2017 meta-analysis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] based on nine US cohort studies in young people linking previous vaping to subsequent initiation of smoking. This paper reported that among baseline never-smokers, ever vaping at baseline strongly predicted initiation in the next 6 to 18 months, with an odds ratio (OR) of 3.62 (95% confidence interval (CI) 2.42\u0026ndash;5.41) after adjustment for various predictors of initiation. Similarly baseline past 30-day vaping also predicted subsequent 30-day cigarette use (OR 4.25, 95% CI 2.52\u0026ndash;7.37).\u003c/p\u003e \u003cp\u003eWe have previously published two papers relating to the gateway effect. Our first paper [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] considered various general issues. It made a number of relevant points:\u003c/p\u003e \u003cp\u003e \u003cdiv mark=\"Bullet\" class=\"UnorderedList\"\u003e \u003cdiv class=\"ItemContent\"\u003e \u003cdiv id=\"Par16\" class=\"OListPara\" name=\"Para\"\u003eAlthough studies reported that vaping significantly predicts smoking initiation following adjustment for various other predictors, the sets of predictors considered were generally quite incomplete.\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"ItemContent\"\u003e \u003cdiv id=\"Par17\" class=\"OListPara\" name=\"Para\"\u003eNo study considered residual confounding arising from inaccurate measurement of predictors.\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"ItemContent\"\u003e \u003cdiv id=\"Par18\" class=\"OListPara\" name=\"Para\"\u003eMore precise adjustment may have substantially reduced the association.\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"ItemContent\"\u003e \u003cdiv id=\"Par19\" class=\"OListPara\" name=\"Para\"\u003eAny true gateway effect would likely have affected smoking prevalence only modestly.\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"ItemContent\"\u003e \u003cdiv id=\"Par20\" class=\"OListPara\" name=\"Para\"\u003eSmoking prevalence in US and UK youths in 2014\u0026ndash;2016 declined somewhat faster than predicted by the preceding trend, whereas a substantial gateway effect would suggest the opposite.\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"ItemContent\"\u003e \u003cdiv id=\"Par21\" class=\"OListPara\" name=\"Para\"\u003eEven were some gateway effect to exist, introducing e-cigarettes would still be likely to reduce deaths from smoking-related diseases.\u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eOur second paper [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] described results of our own analyses, based on data from Waves 1 and 2 of the Population Assessment of Tobacco and Health (PATH) study, a nationally representative longitudinal cohort study in the United States of tobacco use and how it affects the health of people. Wave 1 was conducted from 12 September 2013 to 15 December 2014, with Wave 2 the first annual follow-up. For each Wave, data are available separately for Youths (aged 12\u0026ndash;17\u0026nbsp;years) and Adults (aged 18\u0026thinsp;+\u0026thinsp;years), the Youth data including some information from the parents. Publicly available data files include extensive information on use of various types of tobacco products and on a range of variables linked to initiation of tobacco. Note that where youths become 18 between successive waves of the survey, their data will be available in the Adult data rather than the Youth data. Also additional youths who were under 12\u0026nbsp;at the time of Wave 1 are added into the Youth data when they reach the age of 12\u0026nbsp;at a subsequent wave.\u003c/p\u003e \u003cp\u003eThe main analyses we described considered Wave 1 never cigarette smoking youths who, at Wave 2, had information available on smoking initiation. Having constructed a propensity score for ever e-cigarette use from Wave 1 variables, we found that adjustment markedly reduced the unadjusted OR of 5.70 (95% CI 4.33\u0026ndash;7.50) to 2.48 (1.85\u0026ndash;3.31), 2.47 (1.79\u0026ndash;3.42) or 1.85 (1.35\u0026ndash;2.53), depending on whether adjustment was made for propensity as quintiles, for propensity as a continuous variable, or for the individual variables making up the propensity score. Various sensitivity analyses confirmed that adjustment removed most of the gateway effect.\u003c/p\u003e \u003cp\u003eAlthough we found that confounding was a major factor, explaining most of the observed gateway effect, we were particularly concerned about the possibility of over-adjustment, if taking up e-cigarettes had affected the values of some of the Wave 1 predictor variables considered. At the time, we noted that the possibility of over-adjustment could be avoided using data from Waves 1, 2 and 3 of the PATH study, by relating initiation of cigarette smoking at Wave 3 to vaping at Wave 2, restricting attention to those who, at Wave 1, had never vaped, and using propensity indicators recorded at Wave 1 linked to uptake of e-cigarettes by Wave 2.\u003c/p\u003e \u003cp\u003eHere we describe the results of extensive analyses conducted based on Waves 1, 2 and 3 which not only include the main analyses envisaged at the time of our earlier paper [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but also a variety of sensitivity and alternative analyses.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eSome aspects of the analyses described here are the same as those described earlier [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and are not presented again here. The selection of demographic and other predictor variables is the same as before, except that in some analyses we use exact age (12, 13, 14, 15, 16 and 17) which could now be estimated from the age group (12\u0026ndash;14, 15\u0026ndash;17) at the three Waves and the Wave when youths became adults (18+) for the first time. Use of the person-level weights provided in the PATH study database is as before, as is the process by which a sequence of logistic regression analyses is used to develop the shorter list of demographic variables to be used in forming the propensity scores.\u003c/p\u003e \u003cp\u003eOur main analysis M1 is based on those with data at Waves 1, 2 and 3 who had never smoked cigarettes by Wave 2 and had never used e-cigarettes by Wave 1. This analysis predicts Wave 3 ever smoking from Wave 2 ever e-product use, with adjustment based on Wave 1 predictors used to derive a propensity index for taking up e-products between Waves 1 and 2, and exact age being used in preference to grouped age. Note that, whereas in Wave 1 questions in PATH related only to e-cigarette use, in Waves 2 and 3 questions related to ever e-product use, which also included use of e-cigars, e-pipes and e-hookahs.\u003c/p\u003e \u003cp\u003eAssociated with main analysis M1 are four sensitivity analyses (S1 to S4) which are otherwise similar, except that:\u003c/p\u003e \u003cp\u003eS1. Those who had ever used other tobacco products at Wave 1 are excluded;\u003c/p\u003e \u003cp\u003eS2. Ever use of other tobacco products at Wave 1 is included as an additional predictor variable;\u003c/p\u003e \u003cp\u003eS3. \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eT\u003c/span\u003ehe analysis is based on a propensity score for ever cigarette smoking rather than for ever vaping; or\u003c/p\u003e \u003cp\u003eS4. The analysis is based on a propensity score for ever use of any tobacco product rather than for ever vaping.\u003c/p\u003e \u003cp\u003eNote that in our original paper [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] we also presented results of a further sensitivity analysis, based on linking current vaping to current smoking. This was not repeated here as numbers of new current smokers in current vapers were very low.\u003c/p\u003e \u003cp\u003eMain analysis M2 is similar to M1, except that analysis adjusts for the propensity index as originally derived [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], based on 12 variables recorded at Wave 1. Alternative versions of M2 substitute exact age rather than grouped age in deriving the propensity index, and/or included Wave 1 vapers in the analysis.\u003c/p\u003e \u003cp\u003eMain analysis M3 adjusts for a propensity index derived by linking Wave 2 predictors to Wave 2 e-product use. This is a replicate of the analysis conducted originally [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but using a different period of taking up cigarettes. Data for Wave 1 were ignored, except that where the data for a characteristic was \u0026ldquo;ever in last 12 months\u0026rdquo;, Wave 1 data were used to define \u0026ldquo;ever\u0026rdquo;. An alternative version of M3 replaces grouped age by exact age in deriving the propensity index.\u003c/p\u003e \u003cp\u003eApart from analyses linking Wave 2 e-product use to additional cigarette smoking at Wave 3 in those who had never smoked at Wave 2, two additional analyses (A1 and A2) were also conducted.\u003c/p\u003e \u003cp\u003eAdditional analysis A1 relates e-cigarette use at Wave 1 to cigarette smoking at Wave 2 as in our earlier publication [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but based on individuals who provided data at all three Waves. One version of this uses the same 12 variables as before to develop the propensity index, the other replaces grouped age by exact age. The OR from this analysis can be combined with that reported for main analysis M2 to give a combined estimate of the gateway effect for Wave 1 to 2 initiation and Wave 2 to 3 initiation based on the same set of variables determined at Wave 1.\u003c/p\u003e \u003cp\u003eAdditional analysis A2 ignores Wave 2 data and relates e-cigarette use at Wave 1 to cigarette smoking at Wave 3 using the same 12 variables as before, but replacing grouped age by exact age.\u003c/p\u003e \u003cp\u003eConsideration of residual confounding was also taken into account for three of the analyses described above (M1, M3, A1), all involving exact age. In each case, the list of predictor variables was unaltered from that used originally, but the values of the predictor variables and of the propensity index were revised based on data available at all three Waves. For age, individual year of age at Wave 1 was used, while gender and Hispanic origin did not change between Waves. For the other variables used to form the propensity index, we used all the available data, generally choosing the response most associated with increased e-cigarette use where response varied between Waves (see Additional File Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for further details).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003ePredicting Wave 2 ever-cigarette use from 16 Wave 1 predictor variables (Main analysis M1)\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVariable\u003csup\u003ea\u003c/sup\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eLevels\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eN\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact age\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1518\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e13\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1474\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.71 (1.23\u0026ndash;2.38)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e14\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1451\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.97 (1.43\u0026ndash;2.71)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1376\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.25 (1.65\u0026ndash;3.08)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e16\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1188\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.55 (1.86\u0026ndash;3.51)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e17\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1051\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.75 (2.72\u0026ndash;5.15)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver been curious about\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.86 (0.76\u0026ndash;0.97)\u003csup\u003eb\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003esmoking a cigarette\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eThink you will smoke a\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.59 (0.48\u0026ndash;0.71)\u003csup\u003ec\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ecigarette in the next year\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAnyone who lives with you\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eCigarettes, cigars,\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2140\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003enow use tobacco\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ecigarillos, filtered cigars\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSmokeless or other\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e319\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.73 (1.26\u0026ndash;2.37)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003etobacco only\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo-one living in the home\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5599\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.78 (0.65\u0026ndash;0.94)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003euses tobacco\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver used alcohol at all\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2483\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5575\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.53 (0.45\u0026ndash;0.62)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAgree/disagree: like new and\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eStrongly agree\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e285\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eexciting experiences, even if I\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAgree\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1252\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.71 (0.52\u0026ndash;0.97)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ehave to break the rules\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNeither agree nor disagree\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2107\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.64 (0.47\u0026ndash;0.87)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDisagree\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2404\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.38 (0.28\u0026ndash;0.53)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eStrongly disagree\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2010\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.46 (0.32\u0026ndash;0.65)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eYouth\u0026rsquo;s grade performance in\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMostly A\u0026rsquo;s\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2342\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eschool in past 12 months\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eA\u0026rsquo;s or B\u0026rsquo;s\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2849\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.30 (1.07\u0026ndash;1.58)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMostly B\u0026rsquo;s\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e702\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.60 (1.22\u0026ndash;2.10)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eB\u0026rsquo;s or C\u0026rsquo;s\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1346\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.47 (1.17\u0026ndash;1.85)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMostly C\u0026rsquo;s\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e325\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.16 (1.52\u0026ndash;3.09)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eC\u0026rsquo;s or D\u0026rsquo;s\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e334\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.74 (1.95\u0026ndash;3.86)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMostly D\u0026rsquo;s\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e45\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.09 (0.90\u0026ndash;4.87)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eD\u0026rsquo;s or F\u0026rsquo;s\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e71\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.54 (1.34\u0026ndash;4.81)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMostly F\u0026rsquo;s\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.85 (0.26\u0026ndash;12.91)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSchool is ungraded\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e34\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.80 (0.54\u0026ndash;6.06)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHow often you visit your\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSeveral times a day\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2464\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFacebook, Google Plus,\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAbout once a day\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2284\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.67 (0.56\u0026ndash;0.80)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMySpace, Twitter or other\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u0026ndash;5 days a week\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1006\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.73 (0.58\u0026ndash;0.92)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u0026ndash;2 days a week\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e732\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.51 (0.37\u0026ndash;0.69)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNever\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1572\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.40 (0.31\u0026ndash;0.53)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAgree/disagree: I think I\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eStrongly agree\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e18\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ewould enjoy using tobacco\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAgree\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e95\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.42 (0.14\u0026ndash;1.31)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDisagree\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1517\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.57 (0.20\u0026ndash;1.58)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eStrongly disagree\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e6428\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.35 (0.12\u0026ndash;1.01)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHispanic origin\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHispanic\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2332\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot Hispanic\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5726\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.67 (0.57\u0026ndash;0.79)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBecame very distressed when\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePast month\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1940\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003esomething reminded of past\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u0026ndash;12 months\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1137\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.86 (0.70\u0026ndash;1.07)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eOver a year\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e906\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.71 (0.55\u0026ndash;0.92)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNever\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4075\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.74 (0.62\u0026ndash;0.89)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCigarettes or tobacco might be\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eYes\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1057\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eavailable to youth at parent\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNo\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e7001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.65 (0.52\u0026ndash;0.80)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eor guardian\u0026rsquo;s home\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMoney received in total during\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNone\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2771\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ean average week\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eLess than \u003cspan\u003e$\u003c/span\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e331\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.34 (0.91\u0026ndash;1.96)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan\u003e$\u003c/span\u003e1 to \u003cspan\u003e$\u003c/span\u003e5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1234\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.26 (0.99\u0026ndash;1.61)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan\u003e$\u003c/span\u003e6 to \u003cspan\u003e$\u003c/span\u003e10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1019\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.40 (1.10\u0026ndash;1.79)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan\u003e$\u003c/span\u003e11 to \u003cspan\u003e$\u003c/span\u003e20\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1289\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.42 (1.14\u0026ndash;1.77)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan\u003e$\u003c/span\u003e21 to \u003cspan\u003e$\u003c/span\u003e50\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e751\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.36 (1.06\u0026ndash;1.75)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan\u003e$\u003c/span\u003e51 to \u003cspan\u003e$\u003c/span\u003e100\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e337\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.53 (1.11\u0026ndash;2.10)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan\u003e$\u003c/span\u003e101 to \u003cspan\u003e$\u003c/span\u003e150\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e160\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.02 (1.33\u0026ndash;3.06)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan\u003e$\u003c/span\u003e151 or more\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e166\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.96 (1.29\u0026ndash;2.99)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLast time 2\u0026thinsp;+\u0026thinsp;times: had a hard\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePast month\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2700\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003etime paying attention at\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u0026ndash;12 months\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1402\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.75 (0.62\u0026ndash;0.92)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eschool, work or home\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eOver a year\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e819\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.84 (0.64\u0026ndash;1.09)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNever\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3137\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.72 (0.59\u0026ndash;0.87)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNumber of times seen Movie 4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNever\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e6839\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.00 (base)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eOnce\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e858\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.91 (0.73\u0026ndash;1.11)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTwice\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e190\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.24 (0.83\u0026ndash;1.86)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3 or more times\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e171\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.91 (1.29\u0026ndash;2.82)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eThink you will try a cigarette\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.99 (1.17\u0026ndash;3.37)\u003csup\u003ed\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003esoon\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: The model is based on 8058 youths with data on all 16 predictors who neither smoked nor used e-cigarettes at Wave 1.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003e The variables are shown in order of their inclusion into the model\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003eb\u003c/sup\u003e The OR is per unit of the graded variable which represents \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003edecreasing\u003c/span\u003e curiosity\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ec\u003c/sup\u003e The OR is per unit of the graded variable which represents \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003edecreasing\u003c/span\u003e likelihood\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ed\u003c/sup\u003e The OR is per unit of the graded variable which represents \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003edecreasing\u003c/span\u003e likelihood, with those originally entered as missing because they thought that they would not smoke a cigarette in the next year scored as \u0026ldquo;definitely not\u0026rdquo; (Level 4)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor analyses M1, M3 and A1, alternative versions were also run in which the number of variables adjusted for was increased by also including interactions of age with each of the other three predictors most strongly linked to the relevant gateway effect.\u003c/p\u003e "},{"header":"Results","content":" \u003cp\u003e \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eM1 Relating initiation of cigarette smoking between Waves 2 and 3 to ever e-product use at Wave 2, with adjustment for Wave 1 predictors linked to uptake of e-cigarettes between Waves 1 and 2\u003c/span\u003e \u003c/p\u003e \u003cp\u003eInitial analyses linked exact age, four other demographic variables (gender, Hispanic origin, race and census region) and 60 other selected predictor variables to ever e-product use at Wave 2 in those who had not smoked or used e-cigarettes at Wave 1. A propensity index based on 16 variables was derived using the three step process described earlier [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additional File Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the steps at which different variables were eliminated from consideration, while Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e gives the fitted equation for the propensity index.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eabout here\u003c/span\u003e\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e adjustment for propensity removed about half the excess OR (i.e. OR\u0026thinsp;\u0026minus;\u0026thinsp;1), the unadjusted OR of 5.60 (95% CI 4.52\u0026ndash;6.93) reducing to either 3.37 (2.65\u0026ndash;4.28) or 3.11 (2.47\u0026ndash;3.92), depending on whether adjustment was as a continuous variable or as quintiles. A similar reduction in the OR, to 3.27 (2.57\u0026ndash;4.16), was achieved by adjusting for the 16 variables individually. It can also be seen that, for the first seven variables adjusted for, the adjusted OR decreased steadily, to 3.25. Further adjustment had little or no effect, with introducing additional variables sometimes slightly increasing the estimated OR and sometimes slightly decreasing it.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eRelating Wave 3 ever smoking to Wave 2 ever e-product use (Main analysis M1)\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdjustment variables\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNone\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.60 (4.52\u0026ndash;6.93)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as quintiles\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.11 (2.47\u0026ndash;3.92)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as a continuous variable\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.37 (2.65\u0026ndash;4.28)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact age\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.87 (3.91\u0026ndash;6.06)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Ever been curious about smoking a cigarette\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.27 (3.41\u0026ndash;5.34)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Think you will smoke a cigarette in the next year\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.84 (3.06\u0026ndash;4.82)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Anyone who lives with you now use tobacco\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.73 (2.97\u0026ndash;4.69)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Ever used alcohol at all\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.48 (2.76\u0026ndash;4.38)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Agree/disagree: Like new and exciting experiences even if I have to break the rules\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.39 (2.68\u0026ndash;4.28)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Youth\u0026rsquo;s grade performance in school in past 12 months\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.25 (2.57\u0026ndash;4.12)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+How often you visit your Facebook, Google Plus, MySpace, Twitter or other\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.17 (2.50\u0026ndash;4.01)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+I think I would enjoy using tobacco\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.17 (2.50\u0026ndash;4.02)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Hispanic origin\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.22 (2.54\u0026ndash;4.09)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Last time a significant problem with: becoming very distressed when something reminded of past\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.19 (2.51\u0026ndash;4.05)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Cigarettes or tobacco might be available to youth at parent or guardian\u0026rsquo;s home\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.17 (2.50\u0026ndash;4.02)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Money received in total during an average week\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.25 (2.56\u0026ndash;4.13)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Last time 2\u0026thinsp;+\u0026thinsp;times: Had a hard time paying attention at school, work or home\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.22 (2.53\u0026ndash;4.09)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Number of times seen Movie 4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.28 (2.57\u0026ndash;4.17)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Think you will try a cigarette soon\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.27 (2.57\u0026ndash;4.16)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNotes: The table shows the effects of adjustment based on the Wave 1 predictors used to derive a propensity index for taking up e-products between Wave 1 and 2. The analyses are based on those with data at Waves 1, 2 and 3 who had never smoked cigarettes by Wave 2 and had never used e-cigarettes by Wave 1. Between Waves 2 and 3261/7367 (3.54%) of never users of e-products at Wave 2 took up smoking, while 148/893 (16.57%) of ever users did so. For individuals who were 16\u0026ndash;17\u0026nbsp;at Wave 1, adult data were used to determine e-product use and cigarette smoking at later Waves. The table includes the results of a stepwise regression based on successively including the most significant adjustment variables, given that ever e-product use at Wave 2 was included in the model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFour sensitivity analyses of M1 were carried out, fuller details being given in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e to \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e of the Additional File.\u003c/p\u003e \u003cp\u003eCompared to M1, S1 excluded those who had ever used products other than cigarettes or e-cigarettes at Wave 1, both in the construction of the propensity index and in estimating the gateway effect. Whereas M1 involved 8260 youths, of which 409 initiated smoking between Waves 2 and 3, S1 involved 7945, of which 359 took up smoking. The propensity index developed for S1 involved all the 16 variables shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, except for \u0026ldquo;Number of times seen Movie 4\u0026rdquo; and \u0026ldquo;Think you will try a cigarette soon\u0026rdquo;. Here, the pattern of results is similar to that for Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, with the unadjusted OR of 5.66 (95% CI 4.49\u0026ndash;7.13) reducing to either 3.45 (2.67\u0026ndash;4.46), 3.24 (2.53\u0026ndash;4.15), or 3.23 (2.49\u0026ndash;4.18), depending on whether adjustment was made for propensity as a continuous variable, propensity as quintiles, or all the 14 variables individually.\u003c/p\u003e \u003cp\u003eCompared to M1, the only difference for S2 was that ever smoked other tobacco products at Wave 1 was added to the 16 variables used in M1 to make up the propensity score, and was forced into the regression models. Starting with the same unadjusted OR as M1, the adjusted ORs were very similar; 3.37 (2.64\u0026ndash;4.29), 3.07 (2.44\u0026ndash;3.87) and 3.20 (2.50\u0026ndash;4.08), after adjustment for propensity (continuous), propensity (quintiles), or all the individual variables.\u003c/p\u003e \u003cp\u003eWhereas M1 (and S1 and S2) adjusted for variables found to be predictive of initiating e-product use at Wave 2, S3 adjusted for variables predictive of cigarette smoking. Here, the final model included 27 variables. The unadjusted OR of 5.65 (95% CI 4.55\u0026ndash;7.01) slightly differed from that in M1 as the individuals considered had to have non-missing data on 27 variables rather than 16. However, the overall effect of adjustment was again similar, with the OR reducing to 3.28 (2.56\u0026ndash;4.22) after adjustment for all 27 variables. As for M1, adjustment for the first four variables had the most effect. Adjustment for the first seven variables reduced the OR to 3.26 (2.57\u0026ndash;4.13), similar to the OR after adjustment for all 27. Propensity adjustment was not carried out in S3.\u003c/p\u003e \u003cp\u003eCompared to M1, S4 adjusted for variables predictive of take-up of any tobacco product between Waves 1 and 2. Here, the propensity index was based on 18 variables, with the unadjusted OR of 5.74 (4.55\u0026ndash;7.23) reducing to 3.31 (95% CI 2.56\u0026ndash;4.28), 3.19 (2.48\u0026ndash;4.09), or 3.21 (2.47\u0026ndash;4.18), after adjustment for propensity (continuous), propensity (quintiles), or all the individual variables. Adjustment for all 18 variables had a similar effect to adjustment for the most important 10 variables, where the OR was 3.20 (2.47\u0026ndash;4.14).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eM2 Relating initiation of cigarette smoking between Waves 2 and 3 to ever e-product use at Wave 2, with adjustment for the same Wave 1 predictors as previously reported\u003c/span\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eHere, instead of deriving the Wave 1 predictors linked to uptake of e-cigarettes between Waves 1 and 2, analysis M2 uses the same set of Wave 1 predictors used in our earlier work [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], the results being shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Here, the unadjusted OR of 5.74 (95% CI 4.62\u0026ndash;7.13) reduced to 3.54 (2.81\u0026ndash;4.45) after adjustment for propensity as quintiles and to 3.45 (2.72\u0026ndash;4.37) after adjusting for the individual variables. While adjustment here removed about half the excess OR, the reduction was less, to 4.53 (3.62\u0026ndash;5.68) after adjustment for propensity as a continuous variable. The reductions were similar if exact age rather than age group was included in the list of variables. Here the unadjusted OR was reduced to 3.51 (2.79\u0026ndash;4.41) after adjustment for propensity as quintiles, 4.59 (3.66\u0026ndash;5.74) after adjustment for propensity as a continuous variable, and 3.39 (2.67\u0026ndash;4.30) after adjustment for the individual variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eRelating Wave 3 ever smoking to Wave 2 ever e-product use (Main analysis M2)\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdjustment variables\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNone\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.74 (4.62\u0026ndash;7.13)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as quintiles\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.54 (2.81\u0026ndash;4.45)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as continuous variable\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.53 (3.62\u0026ndash;5.68)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAge range\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.20 (4.17\u0026ndash;6.49)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver used alcohol at all\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.45 (3.54\u0026ndash;5.58)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver been curious about smoking a cigarette\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.10 (3.26\u0026ndash;5.16)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eThink you will smoke a cigarette in the next year\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.70 (2.94\u0026ndash;4.68)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAgree/disagree: Prefer friends who are exciting and unpredictable\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.65 (2.89\u0026ndash;4.61)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eReaction if parent/guardian found you using tobacco\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.64 (2.88\u0026ndash;4.60)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eGender\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.63 (2.87\u0026ndash;4.58)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAgree/disagree: I think I would enjoy using tobacco\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.63 (2.87\u0026ndash;4.59)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAgree/disagree: Some products are safer than others\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.63 (2.87\u0026ndash;4.59)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver used prescription drug not prescribed to you: Ritalin or Adderall\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.67 (2.90\u0026ndash;4.64)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHas a Facebook, Google Plus, MySpace, Twitter or other social networking\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.53 (2.79\u0026ndash;4.47)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAnyone who lives with you now use tobacco\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.45 (2.72\u0026ndash;4.37)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: The table shows the effects of adjustment based on the same Wave 1 predictors as used in our original paper [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The analyses are based on those with data at Waves 1, 2 and 3 who had never smoked cigarettes by Wave 2 and had never used e-cigarettes by Wave 1. Between Waves 2 and 3, 249/7133 (3.49%) of never users of e-products at Wave 2 took up smoking, while 146/880 (16.59%) of ever users did so. For individuals who were 16\u0026ndash;17\u0026nbsp;at Wave 1, adult data were used to determine e-product use and cigarette smoking at later Waves. The table includes the results of a stepwise regression based on successively including the most significant adjustment variables, given that ever e-product use at Wave 2 was included in the model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSimilar analyses were also run which did not exclude those who had used e-cigarettes by Wave 1. This increased the number of ever e-product users who took up smoking from 146 to 201, and slightly increased the unadjusted OR to 5.95 (4.89\u0026ndash;7.23). However, the pattern of decline following adjustment was quite similar. For example, the OR adjusted for the individual variables reduced to 3.31 (2.65\u0026ndash;4.12) using grouped age and to 3.26 (2.62\u0026ndash;4.06) using exact age.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eM3 Relating initiation of cigarette smoking between Waves 2 and 3 to ever e-product use at Wave 3, with adjustment for Wave 2 predictors\u003c/span\u003e \u003c/p\u003e \u003cp\u003eAs noted in the \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section, M3 is essentially a replicate of our earlier work [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but using a different period of introduction of cigarettes. The propensity score developed was based on 18 variables, using age group or exact age as alternatives. The results, shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, indicate that, as earlier [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], a large proportion of the unadjusted association can be explained by adjustment. The largest proportion was explained by adjusting for the 18 variables making up the propensity score, with the unadjusted OR of 6.70 (95% CI 5.40\u0026ndash;8.32) reducing to 2.25 (1.74\u0026ndash;2.91) or 2.75 (1.75\u0026ndash;2.93) depending on whether the list of variables included age range or exact age. However, most of this reduction could be explained by adjustment for propensity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eRelating Wave 3 ever smoking to Wave 2 ever e-product use (Main analysis M3)\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdjustment variables\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eUsing age group\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eUsing exact age\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNone\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.70 (5.40\u0026ndash;8.32)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.70 (5.40\u0026ndash;8.32)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as quintiles\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.77 (2.19\u0026ndash;3.50)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.74 (2.17\u0026ndash;3.48)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as a continuous variable\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.57 (1.98\u0026ndash;3.33)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.60 (2.00-3.36)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAge range\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.78 (4.62\u0026ndash;7.22)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact age\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.45 (4.36\u0026ndash;6.83)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Last time a significant problem with: feeling very trapped, lonely, sad, blue, depressed\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.22 (4.17\u0026ndash;6.54)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.95 (3.94\u0026ndash;6.21)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Reaction if parent/guardian found you using tobacco\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.89 (3.89\u0026ndash;6.14)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.66 (3.70\u0026ndash;5.87)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Money received in total during an average week\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.65 (3.69\u0026ndash;5.86)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.52 (3.59\u0026ndash;5.71)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Number of times seen Movie 3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.31 (3.41\u0026ndash;5.44)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.20 (3.32\u0026ndash;5.31)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Number of times seen Movie 4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.12 (3.25\u0026ndash;5.21)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.02 (3.18\u0026ndash;5.10)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Ever been curious about smoking a cigarette\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.45 (2.71\u0026ndash;4.38)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.36 (2.64\u0026ndash;4.28)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Think you will smoke a cigarette in the next year\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.89 (2.26\u0026ndash;3.70)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.86 (2.24\u0026ndash;3.66)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Ever used alcohol at all\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.63 (2.05\u0026ndash;3.37)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.63 (2.05\u0026ndash;3.38)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+In past 12 months, youth\u0026rsquo;s grade performance at school\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.51 (1.95\u0026ndash;3.22)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.51 (1.95\u0026ndash;3.23)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Agree/disagree: using tobacco would help me calm down when I am angry\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.43 (1.89\u0026ndash;3.12)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.43 (1.89\u0026ndash;3.13)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+How often you visit your social media accounts\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.43 (1.88\u0026ndash;3.12)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.45 (1.90\u0026ndash;3.15)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Would smoke if one of your friends offered you one\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.37 (1.84\u0026ndash;3.06)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.39 (1.86\u0026ndash;3.09)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Anyone who lives with you now use tobacco\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.34 (1.81\u0026ndash;3.02)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.36 (1.83\u0026ndash;3.04)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Think you will try a cigarette soon\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.33 (1.81\u0026ndash;3.01)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.35 (1.82\u0026ndash;3.03)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Agree disagree: some tobacco products are safer than others\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.30 (1.78\u0026ndash;2.97)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.32 (1.79\u0026ndash;2.99)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Youth has a curfew or set time to be home on school nights\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.29 (1.77\u0026ndash;2.95)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.30 (1.78\u0026ndash;2.98)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+Ever used prescription drug not prescribed to you: Ritalin or Adderall\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.25 (1.74\u0026ndash;2.91)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.27 (1.75\u0026ndash;2.93)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: The table shows the effects of adjustment based on Wave 2 predictors linked to use of e-products in Wave 2. The analyses are based on those with data at Waves 2 and 3 ignoring data from Wave 1. Between Waves 2 and 3, 228/8233 (2.77%) of never users of e-products at Wave 2 took up smoking, while 145/949 (15.28%) of ever users did so. For individuals who were 17\u0026nbsp;at Wave 2, adult data were used to determine cigarette smoking at Wave 3. The table includes the results of a stepwise regression based on successively including the most significant adjustment variables, given that ever e-product use at Wave 2 was included in the model. The first set of ORs is based on a model including age group, while the second is based on a model including exact age.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCombining the Wave 2 to 3 results shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e with the Wave 1 to 2 results reported earlier [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] by fixed-effect meta-analysis, gives an unadjusted OR of 6.30 (5.31\u0026ndash;7.46) which is reduced to 2.65 (2.24\u0026ndash;3.18), 2.53 (2.07\u0026ndash;3.10) or 2.08 (1.70\u0026ndash;2.54) depending on whether adjustment is for propensity (quintiles), propensity (continuous) or all the variables making up the propensity score. This represents reductions in the excess OR of, respectively, 68.9%, 71.1% or 79.8%.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eA1 Relating initiation of cigarette smoking between Waves 1 and 2 to ever e-cigarette use at Wave 1, based on individuals who provided data at all three Waves\u003c/span\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes the main results of these analyses and compares them with those reported earlier [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While the original analyses were based on 9423 youths, 421 of whom initiated smoking, the new analyses were based on 8700 youths, 389 of whom initiated smoking. As can be seen, the results in the original analysis, based on grouped age, were similar to those from the new analyses, whether grouped or exact age was used.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eRelating Wave 2 ever smoking to Wave 1 ever e-cigarette use - original [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and A1 ORs\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdjustment variables\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eData on two Waves\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eData on all three Waves\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eOriginally reported\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eGrouped age\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact age\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNone\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.70 (4.33\u0026ndash;7.50)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.99 (4.52\u0026ndash;7.95)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.99 (4.52\u0026ndash;7.95)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as quintiles\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.48 (1.85\u0026ndash;3.31)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.65 (1.96\u0026ndash;3.58)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.59 (1.92\u0026ndash;3.50)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as continuous variable\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.47 (1.79\u0026ndash;3.42)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.67 (1.92\u0026ndash;3.72)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.64 (1.89\u0026ndash;3.68)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eGrouped age\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.81 (3.64\u0026ndash;6.35)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.04 (3.78\u0026ndash;6.72)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact age\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.81 (3.60\u0026ndash;6.42)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u0026thinsp;11 further variables\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.85 (1.35\u0026ndash;2.53)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.97 (1.42\u0026ndash;2.73)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.98 (1.43\u0026ndash;2.75)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Each set of ORs is based on those who had never smoked cigarettes by Wave 1. The first analysis is as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The last two analyses only exclude those without data at Wave 3.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results from analysis A1 for grouped age may theoretically be combined with those from analysis M2 shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, as they both use the Wave 1 predictors from our original paper [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], with exact age replacing grouped age, and are both based on individuals with data at all three Waves. However, as illustrated by the results adjusted for all 12 variables, where the ORs are 3.45 (95% CI 2.72\u0026ndash;4.37) from Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and 1.97 (1.42\u0026ndash;2.73) from Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, these estimates are heterogeneous (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), providing a random-effects combined estimate of 2.64 (1.52\u0026ndash;4.57).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eA2 Relating Wave 3 ever smoking to Wave 1 e-cigarette use, ignoring Wave 2 data\u003c/h2\u003e \u003cp\u003eThis analysis is similar to that reported originally [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] but relates to a longer follow-up period, and uses exact rather than grouped age. The results of this analysis, shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, are quite similar to those shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Again, an unadjusted OR is markedly reduced by adjusting for propensity, whether as quintiles or as a continuous variable, and is further reduced by adjusting for all the 12 individual variables considered.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eRelating Wave 3 ever smoking to Wave 1 ever e-cigarette use using exact age\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdjustment variables\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNone\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.65 (4.50\u0026ndash;7.10)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as quintiles\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.48 (1.95\u0026ndash;3.16)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as continuous variable\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.61 (2.00-3.40)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact age\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.69 (3.71\u0026ndash;5.93)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e11 further variables\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.97 (1.51\u0026ndash;2.56)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: The table shows the effects of adjustment based on the same Wave 1 predictors as used in our original paper [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] but replacing age range by exact age. The set of ORs is based on those with data at Waves 1, 2 and 3 who had never smoked cigarettes by Wave 1. Between Waves 1 and 3, 716/8334 (8.59%) of never users of e-cigarettes at Wave 1 took up smoking, while 123/366 (33.61%) of ever users did so. The table includes the results of a stepwise regression based on successively including the most significant adjustment variables, given that ever e-product use at Wave 1 was included in the model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eAttempting to account for residual confounding\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e summarizes the main results shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for main analysis M1, which make no allowance for residual confounding, and compares them with the results of an analysis using the same list of predictor variables, but with values modified in an attempt to adjust for residual confounding. As can be seen, markedly more of the unadjusted association was explained when allowance for residual confounding was made, with the adjusted ORs in the range 2.36 to 2.46 when allowance was made, compared with 3.11 to 3.37 when it was not. Note that the unadjusted ORs in the two sets of results vary slightly, as missing values in some individuals in the original analyses were replaced by estimates taken from other Waves.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eEffect of allowance for residual confounding in main analysis M1\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdjustment variables\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM1 \u0026ndash; no allowance\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eM1 \u0026ndash; allowance\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR (95% CI)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNone\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.60 (4.52\u0026ndash;6.93)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.65 (4.58\u0026ndash;6.98)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as quintiles\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.11 (2.47\u0026ndash;3.92)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.40 (1.91\u0026ndash;3.02)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePropensity score as a continuous variable\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.37 (2.65\u0026ndash;4.28)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.46 (1.93\u0026ndash;3.14)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAll 16 variables individually\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.27 (2.57\u0026ndash;4.16)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.36 (1.85\u0026ndash;3.02)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: The \u0026ldquo;no allowance\u0026rdquo; results correspond to those in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analyses are based on those with data at Waves 1, 2 and 3 who had never smoked cigarettes by Wave 2 and had never used e-cigarettes by Wave 1. Between Waves 2 and 3 261/7367 (3.54%) of never users of e-products at Wave 2 took up smoking, while 148/893 (16.57%) of ever users did so in the population considered in the \u0026ldquo;no allowance\u0026rdquo; analyses The corresponding figures in the \u0026ldquo;allowance\u0026rdquo; analyses were 267/7682 (3.48%) and 150/915 (16.39%). For individuals who were 16\u0026ndash;17\u0026nbsp;at Wave 1, adult data were used to determine e-product use and cigarette smoking at later Waves. The table includes the results of a stepwise regression based on successively including the most significant adjustment variables, given that ever e-product use at Wave 2 was included in the model.\u003c/p\u003e \u003cp\u003eWhile allowance for residual confounding has quite a marked effect for analysis M1, the analysis which avoided the possibility of over-adjustment, it did not for analyses M3 and A2, which did not avoid this possibility. Detailed results are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e in the Additional File.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eSummary of results from analyses\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eBaseline\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eFollow-up\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eUnadjusted\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e% Excess OR explained\u003csup\u003ea\u003c/sup\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAnalysis\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eWave\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eWave\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003ePredictor\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eAge\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eComment\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eOR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003eP as Q\u003csup\u003eb\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003eP as C\u003csup\u003ec\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e6 variables\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003eAll variables\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eOriginal\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eGrouped\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eAs published (\u0026nbsp;[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e])\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.70\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e68.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e68.7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e78.1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e81.9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eB\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003ePredictors revised based on those who were not Wave 1 e-users\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.60\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e54.1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e48.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e48.0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e50.7\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM1/S1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eAs M1 but excludes Wave 1 other product users\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.66\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e51.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e47.4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e47.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e52.1\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eD\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM1/S2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eAs M1 but Wave 1 other product use included as predictor\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.60\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e55.0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e48.3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e50.2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e52.2\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eE\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM1/S3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eAs M1 but adjusting for predictors of ever cigarette smoking\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.65\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e48.4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e51.0\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eF\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM1/S4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver any product\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eAs M1 but adjusting for predictors of ever any tobacco use\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.74\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e53.8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e51.3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e45.1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e53.4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eG\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eGrouped\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eOriginal 12 predictors\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.74\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e46.4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e25.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e44.3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e48.3\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eH\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM2 (variant)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eDid not exclude Wave 1 e-users\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.95\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e50.3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e28.7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e49.3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e53.3\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM2 (variant)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eOriginal 12 predictors\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.74\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e47.0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e24.3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e46.0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e49.6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eJ\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM2 (variant)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eDid not exclude Wave 1 e-users\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.95\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e50.7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e28.3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e50.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e54.3\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eK\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eGrouped\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003ePredictors revised essentially ignoring Wave 1 data\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.70\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e68.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e72.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e45.3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e78.1\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eL\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM3 (variant)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eAs above but using exact age\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.70\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e69.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e71.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e47.0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e77.8\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eM\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eA1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eGrouped\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eAs original but based on those with data on all three Waves\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.99\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e66.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e66.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e76.8\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e80.6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eN\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eA1 (variant)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eAs above but using exact age\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.99\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e68.1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e67.1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e77.0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e80.4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eO\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eA2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eOriginal predictors but ignoring Wave 2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.65\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e68.2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e65.4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e74.4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e79.1\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eP\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM1 (variant)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver-e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eAs M1 but allows for residual confounding\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.65\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e69.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e68.6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e60.0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e70.8\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eQ\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM3 (variant)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eAs M3 but allows for residual confounding\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.67\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e75.3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e74.3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e51.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e80.2\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eA1 (variant)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eEver e-cigs\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eExact\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eAs A1 but allows for residual confounding\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e69.0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e68.0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003e65.1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003e76.7\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003ea\u003c/sup\u003e % excess explained\u0026thinsp;=\u0026thinsp;100*(OR\u003csub\u003eu\u003c/sub\u003e \u0026ndash; OR\u003csub\u003eA\u003c/sub\u003e) / (OR\u003csub\u003eu\u003c/sub\u003e\u0026ndash;1) where OR\u003csub\u003eu\u003c/sub\u003e is the unadjusted OR, and OR\u003csub\u003eA\u003c/sub\u003e is the adjusted OR\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003eb\u003c/sup\u003e P as Q\u0026thinsp;=\u0026thinsp;Propensity as quintiles\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003csup\u003ec\u003c/sup\u003e P as C\u0026thinsp;=\u0026thinsp;Propensity as a continuous variable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eInvestigating whether introducing some interactions explains more of the gateway effect\u003c/h2\u003e \u003cp\u003eVersions of analyses M1, M3 and A1 were also seen, in which the number of variables adjusted for was extended by also including interactions of age with each of the other three predictors most strongly linked to the gateway effect. For analysis M1, allowance for these interactions had virtually no effect, the original estimate of 3.27 (95% CI 2.57\u0026ndash;4.16) shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e without including interactions changing to 3.26 (2.55\u0026ndash;4.15) when interactions were included in the model. For analysis M3, the estimate changed only from 2.27 (1.75\u0026ndash;2.93) to 2.35 (1.81\u0026ndash;3.05), while for analysis A1, it changed from 1.98 (1.43\u0026ndash;2.75) to 2.06 (1.48\u0026ndash;2.88).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eSummary of results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e summarizes the results from 18 of the analyses described above, expressing the extent to which adjustment explained the unadjusted OR using the statistic 100 x (OR\u003csub\u003eU\u003c/sub\u003e \u0026ndash; OR\u003csub\u003eA\u003c/sub\u003e) / (OR\u003csub\u003eU\u003c/sub\u003e \u0026ndash; 1) where OR\u003csub\u003eU\u003c/sub\u003e is the unadjusted OR, and OR\u003csub\u003eA\u003c/sub\u003e is the adjusted OR. The most obvious impression from the table is that the results largely fall into two groups.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e \u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eabout here\u003c/span\u003e\u003c/p\u003e \u003cp\u003eResults from the original analysis and for analyses M3, A1 and A2 (rows A, K to O, and Q to R of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) all show that as much as about 80% of the unadjusted excess OR can be explained by adjustment for the full set of variables in the model, with somewhat less, typically about 70%, explained using propensity as quintiles or as a continuous variable.\u003c/p\u003e \u003cp\u003eIn contrast, results from virtually all of analyses M1 and M2 (rows B to K) show that only about 50% of the unadjusted excess OR can be explained by adjustment for the full set of variables, with propensity as quintiles giving generally similar results.\u003c/p\u003e \u003cp\u003eThe difference between these two groups is that the first set of results are subject to the problem of over-adjustment, with the values of the predictors used possibly having been affected by having used e-cigarettes. This is mainly so where the baseline wave was Wave 1, but was also true for analysis M3 where Wave 1 data were essentially ignored. In contrast the second set of results avoided over-adjustment by considering follow-up from Wave 2 to 3, with predictors based on Wave 1 data in youths who had never used e-cigarettes. However in this second set of results the variables used were not as up-to-date as in the first analyses.\u003c/p\u003e \u003cp\u003eThe variant analysis of M1, allowing for residual confounding (row P) gives an intermediate result with about 70% of the excess risk being explained, whether by the full set of variables or by propensity. This analysis, however, does not avoid the problem of over-adjustment as it incorporates some information from waves where individuals were already using e-cigarettes.\u003c/p\u003e \u003cp\u003eIt is clear from Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e that many of the variables studied had little effect on the pattern of results. These included use of grouped or exact age, taking into account use of other products, and using predictors of cigarette smoking or any tobacco use rather than predictors of e-cigarette use.\u003c/p\u003e \u003cp\u003eTwo other conclusions may be drawn from Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. One is that adjustment for propensity as quintiles or as a continuous variable generally gives very similar results, with the exception of analysis M2 and its variants, where propensity as a continuous variable explained substantially less of the unadjusted excess OR. Inspection of the detailed modelling results showed that, whereas in other analyses, the logarithm of the OR increased fairly linearly with quintiles of propensity, in the case of analysis M2 and its variants it did not. Thus, in M1 for example, the log ORs by quintile were 0, 0.73, 1.11, 1.66 and 2.52 while in M2 they were 0, 0.21, 0.96, 1.51 and 2.19 with very little rise between quintiles 1 and 2.\u003c/p\u003e \u003cp\u003eThe other is that adjustment for the first six variables in the model generally explained a very substantial part of the unadjusted excess OR explained by the full set. Though this was not true for analysis M2, it was still true that adjustment for the last eight or nine variables explained far less of the excess OR than did the first eight or nine.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eIn our publication based on Waves 1 and 2 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] our analyses showed that an unadjusted estimate of the gateway effect 5.70 (85% CI 4.33\u0026ndash;7.50) could be considerably reduced by adjustment, to 1.59 (1.14\u0026ndash;2.20) in the most striking case. Because of the marked reduction in the OR following adjustment, and the possibility of incomplete control for confounding we regarded it as \u0026ldquo;unclear whether prior vaping actually increases uptake of cigarette smoking\u0026rdquo;. However, we did note the possibility of over-adjustment, with vaping at Wave 1 possibly having affected the recorded values of some of the variables used for adjustment.\u003c/p\u003e \u003cp\u003eAt that time we noted that this possibility of over-adjustment could be addressed in analyses relating initiation of cigarette smoking at Wave 3 to vaping at Wave 2, restricting attention to those youths who, at Wave 1, had never vaped, and using adjustment variables recorded at Wave 1. This we have done in the analyses reported here, and our major finding is that adjustment reduced the excess risk far less, by only about 50% rather than about 80%, in our main analysis M1.\u003c/p\u003e \u003cp\u003eWhile these results more strongly support the existence of a true gateway effect of taking up vaping, there must still remain doubt about its magnitude. One reason is that predictors recorded a year before the baseline may not fully account for the characteristics of the youth at the start of follow-up. A second reason is that, although the PATH study records data on a whole range of possibly relevant characteristics, there may be some relevant predictors not considered. A third reason is that the answers to some of the questions may have been inaccurately measured. We have attempted to address this problem of residual confounding by amending values of predictors recorded at Wave 1 to take into account data recorded at later Waves. However, this problem re-introduces the problem of over-adjustment as Wave 2 and 3 values may have been affected by vaping. Theoretically, one could use data from Waves 1 to 4, using data for Waves 1 and 2 from youths who have never vaped to produce more accurate estimates of the predictors to use for a study of gateway effects between Waves 3 and 4. But this would add to the problem of using predictors recorded some time before follow-up.\u003c/p\u003e \u003cp\u003eSince the time that we published our earlier analysis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and our paper on general considerations relating to vaping as a possible gateway into cigarette smoking [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] a number of other authors have presented evidence from prospective studies [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The studies vary in the extent to which potential confounding variables have been adjusted for, with large OR estimates tending to be reported in studies with more limited control. Thus, a study in the Netherlands [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which adjusted only for sex, age education and a single indicator of propensity to smoke, reported an OR of 11.90 (95% CI 3.36\u0026ndash;42.11) for the relationship between ever use of e-cigarettes with nicotine and initiation of cigarette smoking during follow-up. Also, a study in the US [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which adjusted only for demographic variables and use of other tobacco products, reported ORs of 7.08 (2.34\u0026ndash;21.42) and 3.87 (1.86\u0026ndash;2.06) depending on the follow-up period studied, while another US study [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], with limited control for confounding variables, reported an OR of 3.57 (1.96\u0026ndash;6.45). Apart from a US study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] ,which reported an OR of 6.8 (1.7\u0026ndash;28.3), following adjustment for ten covariates independently associated with initiation of smoking, most of the other studies which appear to have better control for confounding gave lower estimates. These included a study in Taiwan [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] which reported an OR of 2.14 (1.66\u0026ndash;2.75), a study in Germany [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] which reported an OR of 2.18 (1.65\u0026ndash;2.87) and a study in Finland [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] which reported that adjustment reduced the OR from 11.52 (4.91\u0026ndash;26.56) to 2.92 (1.09\u0026ndash;7.85). Notably, a study in Great Britain [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] reported an OR of 11.89 (3.56\u0026ndash;39.72) estimated using the usual logistic method, but a reduced value of 1.34 (1.05\u0026ndash;1.72) using causal mediation analysis.\u003c/p\u003e \u003cp\u003eGenerally our results are consistent with the literature in confirming that a substantial proportion, but not all, of the observed association between e-cigarette use and subsequent initiation of cigarette smoking can be explained by adjustment for factors linked to susceptibility to tobacco. However, large cohort studies with high quality, accurate, data on a wide range of predictive factors recorded at regular intervals will be needed to gain better insight into the magnitude of any true causal effect of vaping. The PATH study with its multiple waves and comprehensive questionnaire should prove more and more useful in the future.\u003c/p\u003e \u003cp\u003eThere are a number of theoretical beneficial and adverse effects of e-cigarettes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Beneficial effects relate to individuals who would otherwise have smoked vaping instead, smokers who would otherwise have continued to smoke switching instead to vaping, vaping helping established smokers to quit, and vaping helping established smokers to reduce their cigarette consumption. Apart from vaping encouraging initiation of smoking, theoretical adverse effects occur if smokers intending to quit switch to vaping instead, or smokers add vaping to their normal cigarette consumption. It is clearly important, therefore, to take all these considerations into account when attempting to estimate the health impact of e-cigarettes.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eBy using data from three Waves of the PATH study, the analyses of the gateway effect reported here improve on those reported earlier [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] based on the first two Waves by allowing potential confounding variables to be determined at a time before vaping started. Whereas the earlier analyses suggested that the adjustment for confounding explained about 80% of the unadjusted relationship between vaping and subsequent initiation of smoking, our current analyses suggest that adjustment explains only about 50%. This provides stronger evidence of a true effect of vaping, although doubt still remains about its true magnitude for reasons discussed.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval;\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio;\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePATH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePopulation Assessment of Tobacco and Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe data from the PATH study are publicly available at \u003ca href=\"https://pathstudyinfo.nih.gov\"\u003ehttps://pathstudyinfo.nih.gov\u003c/a\u003e\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eBoth authors are long term consultants to the tobacco industry\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eFinancial support was provided by Philip Morris Products SA, through Project Agreement No. 29 with P.N. Lee Statistics and Computing Ltd.\u0026nbsp; While some technical comments were provided by the funder on drafts of the statistical plan and this publication, the final versions remain the responsibility of the authors.\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026rsquo; contributions\u003c/h3\u003e\n\u003cp\u003ePNL drafted the statistical plan, checked the analyses conducted, and drafted this publication.\u0026nbsp; JSF commented on the plan, carried out the analyses, and commented on the publication.\u0026nbsp; Both authors agreed the final publication.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eWe thank Esther Afolalu for assistance in acquiring the data from the PATH study, and Zheng Sponsiello-Wang and Christelle Chrea for providing technical comments at various stages.\u0026nbsp; We also thank Jan Hamling for assistance in running the analyses, and Yvonne Cooper and Diana Morris for typing the various drafts of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003col\u003e\n\u003cli\u003eBarrington-Trimis JL, Urman R, Leventhal AM, Gauderman WJ, Cruz TB, Gilreath TD, et al. E-cigarettes, cigarettes, and the prevalence of adolescent tobacco use. Pediatrics 2016;138(2).(Epub 20160711): doi:10.1542/peds.2015-3983.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003eBest C, van der Sluijs W, Haseen F, Eadie D, Stead M, MacKintosh AM, et al. Does exposure to cigarette brands increase the likelihood of adolescent e-cigarette use? A cross-sectional study. BMJ Open 2016;6(2):e008734.(Epub 20160223): doi:10.1136/bmjopen-2015-008734.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003eMiech R, Johnston L, O'Malley PM, Bachman JG, Patrick ME. Trends in adolescent vaping, 2017-2019 (Letter). N Engl J Med 2019;381(15):1490-1.(Epub 20190918): doi:10.1056/NEJMc1910739.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003eNational Academies of Sciences Engineering and Medicine. Public health consequences of e-cigarettes. Washington DC: The National Academies Press; 2018. Available: \u003ca href=\"https://doi.org/10.17226/24952\"\u003ehttps://doi.org/10.17226/24952\u003c/a\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003eNutt DJ, Phillips LD, Balfour D, Curran HV, Dockrell M, Foulds J, et al. Estimating the harms of nicotine-containing products using the MCDA approach. Eur Addict Res 2014;20(5):218-25. doi:10.1159/000360220.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003eSoneji S, Barrington-Trimis JL, Wills TA, Leventhal AM, Unger JB, Gibson LA, et al. Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: A systematic review and meta-analysis. JAMA Pediatr 2017;171(8):788-97.(Epub 20170626): doi:10.1001/jamapediatrics.2017.1488.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003eLee PN, Coombs KJ, Afolalu EF. Considerations related to vaping as a possible gateway into cigarette smoking: an analytical review [version 3; peer review: 2 approved]. F1000Research 2018;7(1915):(\u003ca href=\"https://doi.org/10.12688/f1000research.6928.3\"\u003ehttps://doi.org/10.12688/f1000research.6928.3\u003c/a\u003e). doi:doi.org/10.12688/f1000research.16928.3.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"8\"\u003e\n\u003cli\u003eLee PN, Fry J. Investigating gateway effects using the PATH study [version 1; peer review: 1 approved]. F1000Research 2019;8(264):(\u003ca href=\"https://doi.org/10.12688/f1000research.8354.1\"\u003ehttps://doi.org/10.12688/f1000research.8354.1\u003c/a\u003e). doi:10.12688/f1000research.18354.1.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"9\"\u003e\n\u003cli\u003eBold KW, Kong G, Camenga DR, Simon P, Cavallo DA, Morean ME, et al. Trajectories of e-cigarette and conventional cigarette use among youth. Pediatrics 2018;141(1).(Epub 20171204): doi:10.1542/peds.2017-1832.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"10\"\u003e\n\u003cli\u003eChien YN, Gao W, Sanna M, Chen PL, Chen YH, Glantz S, et al. Electronic cigarette use and smoking initiation in Taiwan: Evidence from the First Prospective Study in Asia. Int J Environ Res Public Health 2019;16(7).(Epub 20190330): doi:10.3390/ijerph16071145.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"11\"\u003e\n\u003cli\u003eKinnunen JM, Ollila H, Minkkinen J, Lindfors PL, Timberlake DS, Rimpela AH. Nicotine matters in predicting subsequent smoking after e-cigarette experimentation: A longitudinal study among Finnish adolescents. Drug Alcohol Depend 2019;201:182-7.(Epub 20190619): doi:10.1016/j.drugalcdep.2019.04.019.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"12\"\u003e\n\u003cli\u003eMorgenstern M, Nies A, Goecke M, Hanewinkel R. E-Cigarettes and the use of conventional cigarettes. Dtsch Arztebl Int 2018;115(14):243-8.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"13\"\u003e\n\u003cli\u003eP\u0026eacute;nzes M, Foley KL, Nadasan V, Paulik E, \u0026Aacute;br\u0026aacute;m Z, Urb\u0026aacute;n R. Bidirectional associations of e-cigarette, conventional cigarette and waterpipe experimentation among adolescents: A cross-lagged model. Addict Behav 2018;80:59-64.(Epub 20180111): doi:10.1016/j.addbeh.2018.01.010.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"14\"\u003e\n\u003cli\u003ePrimack BA, Shensa A, Sidani JE, Hoffman BL, Soneji S, Sargent JD, et al. Initiation of traditional cigarette smoking after electronic cigarette use among tobacco-naive US young adults. Am J Med 2018;131(4):443.e1-.e9.(Epub 20171211): doi:10.1016/j.amjmed.2017.11.005.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"15\"\u003e\n\u003cli\u003eTreur JL, Rozema AD, Mathijssen JJP, van Oers H, Vink JM. E-cigarette and waterpipe use in two adolescent cohorts: cross-sectional and longitudinal associations with conventional cigarette smoking. Eur J Epidemiol 2018;33(3):323-34.(Epub 20171219): doi:10.1007/s10654-017-0345-9.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003col start=\"16\"\u003e\n\u003cli\u003eEast K, Hitchman SC, Bakolis I, Williams S, Cheeseman H, Arnott D, et al. The association between smoking and electronic cigarette use in a cohort of young people. J Adolesc Health 2018;62(5):539-47.(Epub 20180221): doi:10.1016/j.jadohealth.2017.11.301.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr /\u003e \u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cigarettes, confounding, over-adjustment, e-cigarettes, gateway effects, modelling, propensity score","lastPublishedDoi":"10.21203/rs.3.rs-18592/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-18592/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground:\u0026nbsp;Considerable interest exists in whether e-cigarette use (“vaping”) by youths increases the risk of initiating cigarette smoking.\u0026nbsp;Based on Waves 1 and 2 of the Population Assessment of Tobacco and Health study we reported that adjustment for propensity for vaping using Wave 1 variables explained about 80% of the unadjusted relationship.\u0026nbsp;This analysis may be over-adjusted had vaping at Wave 1 affected some variables recorded then.\u0026nbsp;Here we present analyses using Waves 1 to 3 to avoid this possibility. \u003c/p\u003e\u003cp\u003eMethods:\u0026nbsp;Our main analysis M1 concerned those who had never smoked by Wave 2 and never vaped by Wave 1.\u0026nbsp;Wave 2 vaping was linked to smoking initiation by Wave 3, adjusting for Wave 1 predictors.\u0026nbsp;Sensitivity analyses excluded other tobacco product users at Wave 1, included other tobacco product use as an additional predictor, or were based on propensity for ever smoking or ever any tobacco use, rather than ever vaping.\u0026nbsp;Other analyses adjusted for propensity as derived originally, or ignored Wave 1 data.\u0026nbsp;Other analyses used grouped age (only available originally) or exact age (available now) as a confounder variable, attempted residual confounding adjustment by modifying values of predictor variables using data later recorded, or considered interactions with age. \u003c/p\u003e\u003cp\u003eResults:\u0026nbsp;In M1, propensity adjustment removed about 50% of the excess odds ratio (i.e. OR–1), the unadjusted OR, 5.60 (95% CI 4.52-6.93) becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16) depending whether adjustment was for propensity as a continuous variable, as quintiles, or for the 16 variables making up the propensity score.\u0026nbsp;Many factors studied hardly affected the results, including using grouped or exact age, consideration of other tobacco products, including interactions, or using predictors of smoking or tobacco use rather than vaping.\u0026nbsp;The clearest conclusion was that analyses avoiding over-adjustment only explained about 50% of the excess OR whereas analyses subject to over-adjustment explained about 80%. \u003c/p\u003e\u003cp\u003eConclusions:\u0026nbsp;Although much of the unadjusted gateway effect results from uncontrolled confounding, our current analysis provides stronger evidence of a causal effect of vaping than did our earlier analysis.\u0026nbsp;However, some doubts remain about the completeness of confounder adjustment.\u003c/p\u003e","manuscriptTitle":"Further investigations of gateway effects using the PATH study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-03-26 15:29:06","doi":"10.21203/rs.3.rs-18592/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"278bcdb1-04ae-4516-a979-84581b0d2925","owner":[],"postedDate":"March 26th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":74911,"name":"Health Policy"}],"tags":[],"updatedAt":"2021-07-22T18:59:20+00:00","versionOfRecord":{"articleIdentity":"rs-18592","link":"https://doi.org/10.12688/f1000research.24289.2","journal":{"identity":"f1000research","isVorOnly":true,"title":"F1000Research"},"publishedOn":"2020-11-09 18:59:20","publishedOnDateReadable":"November 9th, 2020"},"versionCreatedAt":"2020-03-26 15:29:06","video":"","vorDoi":"10.12688/f1000research.24289.2","vorDoiUrl":"https://doi.org/10.12688/f1000research.24289.2","workflowStages":[]},"version":"v1","identity":"rs-18592","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-18592","identity":"rs-18592","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}