Dual Use of Nicotine and Cannabis Through Vaping Among Adolescents.

INTRODUCTION: This study seeks to identify adolescent nicotine and cannabis vaping patterns and the characteristics of those adolescents who comprised each pattern. METHODS: This prospective longitudinal survey study measured the relationship between nicotine and cannabis vaping among 1,835 adolescents from 4 public high schools outside of Philadelphia, Pennsylvania. Adolescents completed in-classroom surveys, including questions of lifetime and past 30-day nicotine and cannabis vaping, at Wave 1 (fall 2016, ninth grade) and 6-month intervals for the following 36 months (fall 2019, 12th grade). Data were analyzed in 2021. RESULTS: A sequential processes growth mixture model revealed 4 latent conjoint classes of nicotine and cannabis vaping: early, declining dual use (Class 1: n=259); rapidly increasing dual use (Class 2: n=128); later, slower dual use (Class 3: n=313); and no use (Class 4: n=1,136). Increased odds of belonging to Class 1 and Class 2 versus belonging to Class 4 were significantly associated with cigarette smoking (OR=3.71, OR=2.21), alcohol use (OR=2.55, OR=4.39), peer vaping (OR=1.24, OR=1.20), sensation seeking (OR=1.03, OR=1.11), positive E-cigarette expectations (OR=1.21, OR=1.17), and cigar smoking (OR=2.39 Class 2 only). Increased odds of belonging to Class 3 versus Class 4 were significantly associated with alcohol use (OR=1.66), perceived benefits of E-cigarette use (OR=1.03), positive E-cigarette expectations (OR=1.08), depressive symptoms (OR=1.02), and sensation seeking (OR=1.03). CONCLUSIONS: From middle to late adolescence, vaping of nicotine and cannabis develop in close parallel. Regulatory policy and prevention interventions should consider the interplay between these 2 substances during this period of adolescence.

The Indirect Effect of Cigarette Smoking on e-Cigarette Progression via Substitution Beliefs.

PURPOSE: This study sought to evaluate whether associations between adolescent combustible cigarette smoking and e-cigarette progression were explained by beliefs that e-cigarettes substitute for cigarettes in nonsmoking situations. METHODS: Adolescents (N = 1,799) from public high schools outside of Philadelphia, PA, completed in-classroom surveys at wave 1 (fall 2016, beginning of ninth grade) and at 6-month intervals for the following 36 months (fall 2019, beginning of 12th grade). RESULTS: A parallel process latent growth curve model revealed that the pathway from baseline smoking to e-cigarette use trend through baseline e-cigarette substitution beliefs was significant (B = .02, z = 2.16, p = .03), indicating that the positive effect of greater baseline cigarette smoking on the rate of e-cigarette progression was channeled through e-cigarette substitution beliefs. The indirect effect from baseline smoking to e-cigarette use trend via e-cigarette substitution beliefs trend was also significant, albeit negative (B = -.05, z = -2.61, p = .009). The negative indirect effect suggested that the strong positive effect of baseline cigarette smoking on baseline substitution beliefs was followed by a slowing of the overall rate of change from baseline. Given that the total effect from baseline smoking to e-cigarette trend was not significant (p = .91), the results suggested complete mediation. CONCLUSIONS: Among adolescents with greater use of combustible cigarettes, beliefs regarding the substitutability of e-cigarettes in nonsmoking situations appear to foster progression in e-cigarette use. Addressing these beliefs in the school setting and through media campaign messaging early in adolescence may help to prevent the use of e-cigarettes and combustible cigarettes.

Conjoint Developmental Trajectories of Adolescent E-cigarette and Combustible Cigarette Use.

OBJECTIVES: In this study, we sought to identify which adolescents progress to regular electronic cigarette (e-cigarette) use (without cigarette smoking), which adolescents become dual users of both types of cigarettes, and how dual use develops across time. METHODS: Adolescents (N = 1808) from public high schools outside Philadelphia, Pennsylvania, completed in-classroom surveys at wave 1 (fall 2016, beginning of ninth grade) and at 6-month intervals for the following 36 months (fall 2019, beginning of 12th grade). RESULTS: A sequential processes growth mixture model identified 4 conjoint latent classes: later, rapid e-cigarette uptake (class 1: n = 230); no use of e-cigarettes or combustible cigarettes (class 2: n = 1141); earlier, steady e-cigarette uptake (class 3: n = 265); and dual use of e-cigarettes and combustible cigarettes (class 4: n = 204). Using a rich set of potential risk factors, multinomial logistic regression assessed the likelihood of belonging to each conjoint class compared with the comparison class (dual use). Adolescents in the dual use class were characterized by a greater number and severity of e-cigarette and combustible cigarette risk factors. Adolescents in the 2 e-cigarettes-only classes were characterized by either e-cigarette-specific risk factors (earlier onset) or no risk factors (later onset). The no use class had an absence of risk factors for e-cigarette and cigarette use. CONCLUSIONS: This study provides new prospective evidence for distinct patterns and profiles of adolescents who progress to current e-cigarette use, including adolescents who were initially cigarette smokers. The findings have implications for prevention intervention timing, tobacco product focus, content, and the adolescent subgroups to target.

Adolescent depression symptoms and e-cigarette progression.

BACKGROUND: Depression symptoms are associated with the initiation of cigarette smoking and progression to regular use. Whether similar relationships exist between depression symptoms and adolescent e-cigarette progression has not been firmly established. METHODS: This prospective longitudinal survey study measured the relationship between depression symptoms and e-cigarette use among 1822 adolescents from four public high schools outside of Philadelphia, PA. Adolescents completed in-classroom surveys at wave 1 (fall 2016, 9th grade) and 6-month intervals for the following 36 months (fall 2019, 12th grade). E-cigarette use, depression symptoms, and potential covariates were measured at each wave. A latent growth curve model was used to assess the longitudinal relationship between e-cigarette use and depression symptoms. RESULTS: Baseline depressive symptoms had a significant effect on e-cigarette use trend (b = 0.01, z = 4.29, p < 0.0001) while holding other variables constant. A standard deviation increase in depressive symptoms at baseline was associated with a 0.25 standard deviation increase in the rate of e-cigarette progression across the following 36 months. By contrast, the path from baseline e-cigarette use to depressive symptoms trend was not significant (p = 0.74). CONCLUSIONS: The present study provides new evidence for the effects of depression symptoms on adolescent e-cigarette progression. Greater depressive symptoms at age 14 years old were associated with a faster rate of e-cigarette escalation. However, e-cigarette use was not related to the development of depression symptoms over time. It will be important to examine whether adolescents with elevated depression symptoms respond similarly to e-cigarette prevention campaigns as adolescents in general.

Adolescent E-Cigarette Onset and Escalation: Associations With Internalizing and Externalizing Symptoms.

PURPOSE: We sought to evaluate if internalizing symptoms (i.e., anxiety and depression) and/or externalizing symptoms (i.e., impulsivity, sensation seeking, and substance use) were risk factors for the onset of 30-day e-cigarette use and escalation in the number of days used across the following 30 months among adolescents. METHODS: Adolescents (N = 1,808) from public high schools outside Philadelphia, PA, completed in-classroom surveys at wave 1 (fall 2016, beginning of ninth grade) and at 6-month intervals for the following 30 months (spring 2019, end of 11th grade). RESULTS: A two-part latent growth curve model of e-cigarette use revealed significant positive associations between externalizing factors, past 30-day e-cigarette use, and the number of days of e-cigarette use only at wave 1 (p values <.05). Cigarette smoking was associated with a slowing in the rate of onset of 30-day e-cigarette use across the 30-month follow-up (ß = -.24, z = -2.41, p = .02). Depression was associated with an increased rate of escalation in the number of days of e-cigarette use across the following 30 months (ß = .01, z = 2.52, p = .01), whereas anxiety was associated with a decreased rate (ß = -.72, z = -2.36, p = .02). CONCLUSIONS: The findings highlight two groups of adolescents at risk for e-cigarette use: adolescents whose e-cigarette use reflects a higher-risk profile with early e-cigarette onset, and adolescents who have a lower-risk profile, at least initially, with later e-cigarette use onset. The timing and content of prevention efforts should be tailored to each group.