Full length articleLatent class analysis of current e-cigarette and other substance use in high school students
Introduction
E-cigarettes are gaining popularity among all age groups in the U.S. despite limited research on their safety. Of particular concern, rates of e-cigarette use are growing exponentially among youth. With regard to past-month use, the 2014 National Youth Tobacco Survey indicated that e-cigarette use tripled among high school (HS) students from 4.5% in 2013 to 13.4% in 2014, surpassing all other tobacco use, including traditional cigarettes (9.2%; Arrazola et al., 2015). Similarly, results from a recent, large survey study conducted in Connecticut found that 12.0% of HS students reported past-month e-cigarette use (Krishnan-Sarin et al., 2015).
Research suggests that e-cigarette use among adolescents is associated with cigarette smoking, and there is evidence that e-cigarette use may maintain nicotine addiction among current tobacco users or promote dual use of e-cigarettes and other tobacco products (Krishnan-Sarin et al., 2015, Camenga et al., 2014, Dutra and Glantz, 2014). There also is concern that e-cigarette use among never-smokers may lead to nicotine addiction and/or serve as gateway to the use of other tobacco products including cigarettes. For instance, in a large, recent survey study, 24.8% of e-cigarette users who had never smoked a cigarette reported initiating e-cigarette use with e-cigarettes that did not contain nicotine and subsequently switching to using e-cigarettes containing nicotine (Krishnan-Sarin et al., 2015). Furthermore, a recent study indicated that e-cigarette use among youth prospectively predicts the initiation of cigarette smoking one year later (Primack et al., 2015). Finally, there is emerging evidence that adolescent e-cigarette users are more likely than non-users to engage in polysubstance use (e.g., Camenga et al., 2014, Miech et al., 2015, Morean et al., 2015), perhaps reflecting a broader, underlying profile of substance-related risk. Results of a recent study that analyzed data from Monitoring the Future indicated that e-cigarette users are more likely than non-users to smoke cigarettes, binge drink, smoke marijuana, and take prescription medications that were not prescribed to them by a doctor (i.e., amphetamines, sedatives including barbiturates, tranquilizers, and/or narcotics; Miech et al., 2015). Adolescent e-cigarette users also have been shown to be more likely than adolescents who smoke cigarettes exclusively to smoke hookah and blunts (Camenga et al., 2014). Finally, a recent study indicated that adolescent e-cigarette users are using e-cigarettes to vaporize marijuana at concerning rates (Morean et al., 2015). As e-cigarettes continue to gain popularity, developing a better understanding of the link between adolescent e-cigarette use and the use of a wide range of other commonly used substances is critical to identifying the role e-cigarettes may play in substance use among youth.
Prior research indicates that latent class analysis (LCA), which empirically identifies subgroups of participants based on similar patterns of responses, is a valuable statistical tool for identifying youth substance use profiles (e.g., Bohnert et al., 2014, Lanza and Rhoades, 2013, Miech et al., 2015, Tomczyk et al., 2015). However, only one study of which we are aware has used LCA to identify adolescent substance use profiles that include e-cigarettes; Miech et al. (2015) used LCA to identify substance use profiles based on past-month e-cigarette use, cigarette use, marijuana use, binge drinking, and prescription medication misuse. Among 10th grade students and 12th grade students, classes were identified that largely comprised abstainers (probability of substance use ranged from 0.01 for cigarettes to 0.07 for e-cigarettes in 10th grade and from 0.02 for cigarettes to 0.08 for marijuana in 12th grade) and that comprised polysubstance users (probability of substance use ranged from 0.26 for prescription medications to 0.70 for marijuana in 10th grade and from 0.30 for prescription medications to 0.83 for marijuana in 12th grade). Among 12th grade students, a second class of polysubstance users was identified that comprised predominantly e-cigarette users (probability of substance use ranged from 0.01 for prescription medications to 0.93 for e-cigarettes). The study conducted by Miech et al. (2015) makes an important contribution to the literature. However, the study did not examine the use of other tobacco products that previously have been linked to cigarette and/or e-cigarette use like cigars, hookah, and blunts (e.g., Camenga et al., 2014). Thus, it remains important to evaluate how the use of a wide range of tobacco products contributes to adolescent substance use profiles across the full range of high school students.
In the current study, we examined HS students’ current use of e-cigarettes, cigarettes, cigars, smokeless tobacco, hookah, blunts, marijuana, and alcohol. We first examined past-month use rates of each product within the analytic sample (i.e., the sample of adolescents who had non-missing past-month substance use data for all substances). Latent class analysis was then used to determine profiles of past-month product use. Within the same model, multinomial logistic regression was used to evaluate the extent to which demographic characteristics (i.e., age, sex, race) were associated with class membership. Consistent with prior research, we hypothesized that one of the identified latent classes would represent students who engaged in little or no past month substance use (i.e., abstainers) and that one group would represent users of multiple substances. However, given the relative novelty of the current study, we did not outline any additional hypotheses regarding what substance use profiles may be identified via LCA or how the demographic variables would relate to the identified classes.
Section snippets
Participants
In November 2013, adolescents attending 4 HSs (N = 3614) in Southeastern CT completed an anonymous survey assessing attitudes toward and use of e-cigarettes and other tobacco products. Due to a request from the administration of one HS that questions explicitly assessing the quantity and frequency of alcohol and marijuana use be omitted from the survey, 2737 students received questions assessing tobacco, e-cigarette, marijuana, and alcohol use. Given the aims of the current study, the analytic
Prevalence of current product use
Product use rates ranged from 2.8% (smokeless tobacco) to 20.7% (alcohol), with 11.6% reporting e-cigarette use. Participant demographics and product use rates are presented in Table 1.
Latent class analysis
A 4 latent class solution was deemed optimal for several reasons. First, it had the lowest associated BIC value (which is one of the most reliable criterion) and was the largest class solution to have both significant LMR and bootstrapped LR tests (p-values < .001; see Table 2; Collins and Lanza, 2010). Second, the
Discussion
The current study adds to the research literature on adolescent substance use in the context of the increased popularity of e-cigarettes. Using LCA, we found that two classes evidenced elevated rates of e-cigarette use: the E-cigarette–Alcohol class and the All Products class. Also of note, a class emerged that comprised individuals who largely used marijuana and alcohol, but who engaged in low rates of e-cigarette and other tobacco product use.
Of interest, demographic variables were associated
Conclusions
In addition to the unknown health consequences of e-cigarette use, young e-cigarette users appear to be using of a wide range of substances. Additional data are needed to determine if the demographic profiles associated with elevated e-cigarette use observed in the current study are replicable. Future longitudinal research also is needed to determine whether e-cigarettes serve as a gateway product for the initiation of other forms of substance use or are taken up by adolescents who already are
Conflict of interest
None.
Funding
This research was supported in part by (1) NIH supplement through NIDA grant P50DA009241, (2) NIAAA grant 5T32AA015496, (3) NIDA1K12DA033012-01A1, and (4) CTSA grants UL1 TR000142 and KL2 TR000140. These sponsors had no role in the study design; collection, analysis or interpretation of the data; writing the manuscript; or the decision to submit the paper for publication.
Contributors
Dr. Morean contributed to conceptualization of the study and the development of the self-report survey, developed and tested the hypotheses reported in the manuscript, ran all statistical analyses, and wrote the primary manuscript draft.
Dr. Kong contributed to conceptualization of the study and the development of the self-report survey and critically reviewed drafts of the manuscript.
Dr. Camenga contributed to conceptualization of the study and the development of the self-report survey and
Acknowledgement
None.
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