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

Abstract

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 (β = -.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 (β = .01, z = 2.52, p = .01), whereas anxiety was associated with a decreased rate (β = -.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.

Keywords: Adolescents; Anxiety; Depression; E-cigarettes; Substance use.

Figure 1.. Two-part latent growth curve model of the effects of internalizing and externalizing symptoms on adolescent e-cigarette onset and escalation.

Note: Binary level factor represents past 30-day e-cigarette use at baseline. Binary trend factor represents onset of past 30-day e-cigarette use across follow-up. Continuous level factor represents number of days of e-cigarette use in the past 30-days at baseline. Continuous trend factor represents escalation in the number of days of e-cigarette use in the past 30-days across follow-up.