Q2. Why were the longitudinal negative binomial models used?
Longitudinal negative binomial models with time-varying and time-invariant covariates were used because comparisons indicated a better fit relative to alternative choices (e.g., Poisson).
Q3. What is the main cause of premature death in the US?
20% of adults and high school students use cigarettes, and tobacco remains the primary cause of premature death (DHHS, 2014).
Q4. How many participants reported smoking during the six months pre-baseline?
During the six months pre-baseline, 19% of participants reported no e-cigarette use, 32% 1–3 uses, 27% 1–2 uses/month, 10% weekly use, 6% 2–4 uses/week, and 6% daily/almost daily use.
Q5. What is the effect of e-cigarettes on smoking?
Findings suggest e-cigarette use by young adult non-daily smokers leads to greater cigarette consumption, and thus greater risk for tobacco dependence.
Q6. What were the main variables used to assess the effects of age restrictions?
Bivariate tests were used to assess relationships between demographic, predictor and outcome variables, and to assess the impact of age restrictions enacted during data collection.
Q7. What were the demographic characteristics measured by self-report at baseline?
Demographic characteristics were measured by self-report at baseline, and included age, sex, race, ethnicity, and student status.
Q8. How many cigarettes were used in the previous 6 months?
At baseline, those who reported ≥4 e-cigarette uses in the previous 6 months were smoking 1.15 cigarettes per day, compared with 0.96 for less frequent e-cigarette users.
Q9. What variables were used to determine the likelihood of e-cigarette use?
Predictors included binary (sex, student status, significant other who smoked), categorical (race/ethnicity), count (smokers in participants' households), and continuous (intent to quit cigarettes in thenext year, 1–7 scale) variables.
Q10. How many participants were missing data at the three, 6 and 12 month timepoints?
In terms of cigarette and ecigarette use over time, the proportions of data missing at 3 month, 6 month, 9 month, and 12 month timepoints were relatively low: 3%, 11%, 14%, and 9%, respectively.
Q11. What is the effect of e-cigarette use on smoking?
The fact that lagged analyses produced larger effects than the analyses accounting for e-cigarette use over 12 months suggests recent e-cigarette use may have greater impact on cigarette smoking than consistency of e-cigarette use over a longer period.
Q12. What were the responses to the e-cigarette frequency?
Response options included: 0 times; 1–3 times; 1–2 times per month; weekly; 2–4 times per week; and daily/almost daily (prebaseline e-cigarette frequency).
Q13. Who provided financial support for this study?
This work was supported by the National Institutes of Health (grant R01 DA037217 to N.D.), who provided financial support but had no other role in this project.
Q14. What is the reason why the study was not sufficiently sensitive to detect it?
it may be that heavier e-cigarette use increases risk for progressive smoking, but the studywasnot sufficiently sensitive to detect it.
Q15. How many participants denied smoking in the past 12 months?
While cessation was not directly assessed, 44 participants (11.2%) denied smoking in the past 14 days at 12 months, and 23 of these (5.9%) had given the same response for the 9 days of assessment at 9 months.
Q16. Why was e-cigarette stability included in the analysis?
their analyses included e-cigarette stability as a predictor measuring aggregate e-cigarette use over time, rather than current or recent, but not cumulative, use.