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. Author manuscript; available in PMC: 2023 Jul 27.
Published in final edited form as: Am J Health Behav. 2021 Jul 26;45(4):711–722. doi: 10.5993/AJHB.45.4.10

Adolescent E-cigarette Users at Highest Risk of Cigarette Smoking Intention

Olusegun Owotomo 1, Julie Maslowsky 2
PMCID: PMC10372813  NIHMSID: NIHMS1908433  PMID: 34340738

Abstract

Objectives:

Adolescent e-cigarette users are at risk of developing smoking intention, an established predictor of conventional cigarette smoking. In this study, we identify subgroups of adolescent e-cigarette users who are most likely to intend to smoke conventional cigarettes.

Methods:

Cross-sectional data on 1357 8th and 10th grade e-cigarette users who had never smoked conventional cigarettes were obtained from 2014–2017 Monitoring the Future Surveys. We conducted latent class analysis to identify subgroups of adolescent e-cigarette users; through latent class regression analysis, we examined the association between subgroup membership and smoking intention.

Results:

We identified 3 subgroups of adolescent e-cigarette users: socially-protected (56.6%), peer-driven (29.8%), and market-vulnerable (13.6%). The peer-driven class reported the highest number of peers who smoke and the lowest proportion of friends who strongly disapproved of daily cigarette smoking. They were significantly more likely than the socially-protected and market-vulnerable classes to have smoking intention (AOR=2.46; 95% CI 1.84–3.28, and AOR=2.29; 95% CI 1.48–3.53, respectively).

Conclusion:

Our findings provide insights on the constellation of risk and protective factors that contribute to smoking intention among adolescent e-cigarette users. It highlights peer influence as an important area of emphasis for adolescent smoking prevention programs.

Keywords: e-cigarettes, e-cigarette use, smoking intention, adolescents, cigarette smoking, smoking initiation

Preventing cigarette smoking initiation among adolescents is a crucial step in efforts to reduce tobacco-related morbidity and mortality.1Although adolescent smoking rates have declined significantly over the past 3 decades, from 28.3% in 1996 to 3.7% in 2019,2 emergence of new tobacco products such as e-cigarettes threaten current efforts at tobacco control and prevention among adolescents.3 E-cigarettes have become more popular than conventional cigarettes and can deliver high concentrations of nicotine to the developing adolescent brain, potentially increasing the risk of nicotine addiction.4–6 Between 2017 and 2018, prevalence of e-cigarette use increased by 78% (11.7% to 20.8%) among high school students and by 49% (3.3% to 4.9%) among middle school students.7 As of 2019, 27.5% of high school students and 10.5% of middle school students in the United Sates (US) reported current e-cigarette use.8 E-cigarette use is a major risk for transitioning to cigarette smoking among adolescents.9

Although adolescent e-cigarette users are at heightened risk of progressing to conventional cigarette smoking,10–16 not all adolescent e-cigarette users go on to use conventional cigarettes. The factors underlying this progression for some but not others remain unclear. Based on the Theory of Planned Behavior (TPB), intention is the most proximal antecedent to behavior.17,18Accordingly, smoking intention predicts smoking initiation.19,20 Adolescent e-cigarette use can lead to smoking intention,13,21–23 which can subsequently increase the likelihood of initiating conventional cigarette smoking.19,20 Characterizing adolescent e-cigarette users based on their likelihood to exhibit smoking intention is an important first step in understanding the factors underlying the progression from e-cigarette use to conventional cigarette smoking.

According to the TPB, predictors of behavioral intention include background characteristics and exposures, attitudes and subjective norms toward the behavior, and perceived behavioral control.17,18,24 Background characteristics (such as risk-taking propensity and sociodemographic factors) and prior exposures (including pro- and anti-tobacco media exposures) inform attitudes and norms toward conventional cigarette smoking.25–28 These smoking-related attitudes and norms predict smoking intention, which in turn, predicts smoking initiation. For example, smoking intention among adolescents is often determined by their perceptions of the harm and addictiveness of conventional cigarette smoking.29 Smoking intention is also predicted by subjective norms – adolescents’ perceptions of the social pressure in support of or against conventional cigarette smoking.18,24 Subjective norms include both injunctive norms (perceptions of whether friends and/or family approve or disapprove of conventional cigarette smoking) and descriptive norms (perceptions of how prevalent conventional cigarette smoking is among friends and/or family).18,30 Two subjective norms in particular, peer smoking and friends’ approval/disapproval of smoking, are strong predictors of adolescent smoking behavior.2,31A final construct that predicts behavioral intention is perceived behavioral control, a perception of how easy or difficult it is to perform a behavior. Perceived behavioral control is formed by control beliefs (beliefs about factors that can facilitate or constrain the performance of a behavior).17,18,24 Control beliefs about access to cigarettes and parental monitoring are established predictors of smoking intention among adolescents.32–35 Classifying adolescent e-cigarette users based on their background characteristics, smoking-related attitudes and norms, and exposures to factors that can facilitate or hinder conventional cigarette smoking will provide additional insights into their likelihood of exhibiting smoking intention, an important precursor to smoking initiation.

Most studies investigating smoking intention among adolescent e-cigarette users have approached data analysis using a variable-centered approach that provides information on how individual variables predict an outcome of interest.36,37 For example, logistic regression models demonstrate associations between e-cigarette use and smoking intention.21–24 In contrast, a person-centered approach classifies individuals into distinct groups based on shared characteristics or their response patterns to selected variables36 and is more advantageous for research that aims to examine cluster of traits within people and determine group differences in specific outcomes of interest.37 Person-centered analysis is common in studies of patterns of adolescent substance use,38–42 but none specifically investigate smoking intention among adolescent e-cigarette users.

The Current Study

The current study applies a person-centered approach to identify subgroups of adolescent e-cigarette users who had never smoked conventional cigarettes and determine the subgroups most likely to intend to smoke conventional cigarettes. We hypothesize that there will be distinct subgroups of adolescent e-cigarette users based on variations in underlying smoking-related characteristics, and the subgroup with highest endorsements of pro-smoking characteristics will be the most likely to intend to smoke conventional cigarettes.

METHODS

Study Participants

We obtained cross-sectional data on 8th and 10th grade students from the Monitoring the Future Surveys of 2014–2017.43 Students included in the current study were current (past 30 days) e-cigarette users who had never smoked conventional cigarettes. Current e-cigarette use was measured via one item: “During the last 30 days, on how many days (if any) have you used electronic cigarettes (e-cigarettes)?” Response was on a 6-point scale ranging from “none” to “20–30 days”. Students reporting no e-cigarette use or those who had ever smoked conventional cigarettes were excluded, resulting in a sample of 1357 youth. Table 1 provides descriptive statistics on the sample’s characteristics.

Table 1.

Demographic and Smoking-related Characteristics for Total Sample and EachLatent Class of Adolescent E-cigarette Users

Total sample Socially-Protected Class Peer-Driven Class Market-Vulnerable Class
Composition (N) (%) 1357 (100) 768 (56.6)  405 (29.8) 184 (13.6)
Smoking Intention (% Yes) 41.3 35.5a 55.3b 34.4a
Race/ethnicity (%)
Black 11 8.7a 11.8a 18.3b
Hispanic 27.7 22.7a 31.1b 40.9c
White 61.4 68.6a 57.1b 40.8c
Sex (%)
Male 53.6 50.6a 54.9a 63.9b
Female 46.4 49.4a 45.1a 36.1b
Grade (%)
8th grade 41.4 37.8a 41.0a 57.1b
10th grade 58.6 62.2a 59.0a 42.9b
Urbanicity (%)
Rural 14.1 12.1a 16.5b 17.7b
Urban 85.9 87.9a 83.5b 82.3b
Indicator Variables
Ownership of tobacco branded merchandise (% Yes) 11.8 9.0a 12.4a 22.2b
Friends’ feelings about smoking cigarettes occasionally
Not disapprove (% Yes) 10.7 0.0a 21.3b 32.2b
Disapprove (% Yes) 16.4 0.5a 47.5b 15.1c
Strongly disapprove (% Yes) 72.8 99.5a 31.3b 52.7c
Friends’ feelings about smoking cigarettes everyday
Not disapprove (% Yes) 18.2 1.5a 42.8b 33.8c
Disapprove (% Yes) 41.2 36.8a 55.0b 29.3c
Strongly disapprove (% Yes) 40.6 61.7a 2.2b 36.8c
Friends’ feelings about smoking ≥1 pack cigarettes per day
Not disapprove (% Yes) 12.0 0a 25.0b 33.9c
Disapprove (% Yes) 24.4 0.1a 71.9b 18.2c
Strongly disapprove (% Yes) 63.5 99.9a 3.1b 47.9c
M (SD) M (SD) M (SD) M (SD)
Peer smoking 1.73 (0.87) 1.60 (0.87)a 1.95 (0.87)b 1.79 (0.87)b
Perception of harm of conventional cigarette smoking 3.36 (0.97) 3.75 (0.39)a 3.62 (0.39)b 1.09 (0.39)c
Perception of addictiveness of conventional cigarette smoking 4.21 (1.04) 4.35 (1.03)a 4.08 (1.03)b 3.86 (1.03)b
Perceived influence of antismoking ads 3.00 (1.55) 3.26 (1.52)a 2.72 (1.52)b 2.55 (1.52)b
Access to conventional cigarettes 4.09 (1.26) 4.27 (1.24)a 3.95 (1.24)b 3.55 (1.24)c
Parental monitoring 4.20 (0.89) 4.31 (0.88)a 4.09 (0.88)b 3.97 (0.88)b
Risk propensity 3.41 (1.14) 3.47 (1.14)a 3.40 (1.14)a,b 3.24 (1.14)b
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Note.

Higher scores on the variables indicate higher peer smoking, higher perceived harm and addictiveness of conventional cigarette smoking, higher perceived influence of antismoking ads, higher perceived access to conventional cigarettes, higher parental monitoring, and higher risk-taking propensity. ANOVA and Tukey’s posthoc test was used to test statistically significant differences in means and proportions of the indicator variables between latent classes. Matching superscripts

Measures

Intention to smoke conventional cigarettes.

Smoking intention was measured using a single item: “If you have never smoked, do you think you will try smoking cigarettes sometime this year?” Responses were on a 4-point scale: 1 = “I definitely will”, 2 = “I probably will”, 3 = “I probably will not,” and 4 = “I definitely will not”. Consistent with previous studies,22,25,28,31 responses were dichotomized, with the response “I definitely will not” indicating firm intention not to smoke conventional cigarettes and other responses suggesting smoking intention.

Background media exposures.

Perceived influence of antismoking advertisements was measured via one item: “To what extent do you think such ads (anti-smoking commercials or “spots” that are intended to discourage cigarette smoking) on TV, radio, billboards or in magazines and newspapers have made you less favorable toward smoking cigarettes?” Responses were on a 5-point scale ranging from “not at all” (1) to “to a very great extent” (5).

Ownership of tobacco promotional items was a dichotomous variable indicating ownership of tobacco promotional items, such as clothing, hats, bags, or other items.

Risk-taking propensity was measured by deriving the average of 2 items: “I get a real kick out of doing things that are a little dangerous” and “I like to test myself every now and then by doing something a little risky.” Responses for each of the 2 items were on a 5-point scale ranging from “disagree” (1) to “agree” (5). Higher values on the averaged items indicated higher risk-taking propensity.

Attitudes toward conventional cigarette smoking.

Attitudes were assessed using 2 variables: perceived addictiveness of conventional cigarette smoking and perceived harm of conventional cigarette smoking.

Perceived addictiveness of conventional cigarettes smoking was a composite variable derived from 2 items asking the extent to which youth agreed with the following statements: “I could smoke a pack a day for a year or more and still be able to quit if I wanted to” and “At my age, smoking is not too dangerous because you can always quit later.”29,44,45 Responses for each measure were on a 5-point scale from “disagree” (1) to “agree” (5). These 2 items were reverse coded and averaged to form a composite variable. Higher scores reflected higher perceived addictiveness.

Perceived harm of cigarette smoking was measured via one item: “How much do you think people risk harming themselves (physically or in other ways), if they smoke one or more packs of cigarettes per day?” Responses were on a 4-point scale ranging from “no risk” (1) to “great risk” (4).

Subjective norms toward conventional cigarette smoking.

Subjective norms toward conventional cigarette smoking were measured as 2 separate constructs (injunctive and descriptive norms) per the TPB:18

Friends’ disapproval of smoking (injunctive norm) was measured using 3 separate items asking how youth think their close friends feel or would feel about doing each of the following things: smoking one or more packs of cigarettes per day, smoking cigarettes occasionally, and smoking cigarettes every day. Responses to each question were (1) “not disapprove”, (2) “disapprove”, and (3) “strongly disapprove”.

Peer smoking (descriptive norm) was measured via one item: “How many of your friends would you estimate smoke cigarettes?” Responses were on a 5-point scale ranging from (1) “none” to (5) “all”.

Control beliefs/factors over conventional cigarette smoking.

Facilitating and constraining factors were operationalized as access to conventional cigarettes and parental monitoring, respectively.

Access to conventional cigarettes was measured via one item: “How difficult do you think it would be for you to get cigarettes, if you wanted some?” Responses were on a 5-point scale from (1) “probably impossible” to (5) “very easy.”

Parental monitoring was a composite variable derived by averaging responses from 3 items:46 (1) “My parents know where I am after school,” (2) “When I go out at night, my parents know whom I am with,” and (3) “When I go out at night, my parents know where I am.” Responses ranged from “never” (1) to “always” (5). Higher values indicate higher parental monitoring.

Covariates

Tobacco-related background characteristics that we included as covariates for both the latent class analysis and regression analyses include grade, race/ethnicity, sex, urbanicity, country region, and parent education level.

Data Analysis

A latent class regression analysis was conducted in Mplus version 8 47 using the classical 3-step analytic approach including a latent class analysis, model fit determination and class enumeration, and regression of the identified latent classes on an observed dependent variable.48

A latent class analysis was first conducted to identify underlying homogenous subgroups (classes) of adolescent e-cigarette users using their responses to the 11smoking-related indicator variables discussed above. The optimal number of classes was determined by running models iteratively, starting with one-class model followed by a point increment in number of classes (from one to 6) while examining fit indices for each class compared to the previous one.39,40,47,49

The best fitting model was determined based on quality of fit, parsimony, and interpretability.36,49,50 The 2 major fit indices used for model fit determination were sample-size adjusted Bayesian Information Criterion (BIC), including a BIC scree plot,49 and Bootstrap Likelihood Ratio Test (BLRT).51 The model with the fewest number of interpretable classes, smallest BIC value, and a statistically significant (p <.05) BLRT value was considered the best fit and reported. Next, analysis of co-variance (ANCOVA) and Tukey’s HSD (honestly significant difference) posthoc tests were conducted to determine statistically significant differences in specific indicator variables across the identified latent classes.

Multivariable logistic regression was conducted to determine the adolescent e-cigarette user subgroup that was most likely to have smoking intention. The regression model examined the association between the latent classes of adolescent e-cigarette users and smoking intention while controlling for covariates highlighted above. Two regression models were conducted with the reference category (latent class) changed for each model, in order to compare each latent class to all others. For the entire analysis, study sample weights were applied to achieve national representativeness of sample,43 and missing data was handled using full information maximum likelihood (FIML).52

RESULTS

Descriptive Statistics

In the total sample of 8th and 10th grade adolescents, 18.6% had already tried conventional cigarette smoking (at least once in their lifetime), 4.4% were current cigarette smokers (smoked cigarettes at least once in past 30 days – 3% of 8th graders and 5.8% of 10th graders), and 10.3% had used e-cigarettes (7.7% of 8th graders and 12.9% of 10th graders). Of those who had used e-cigarettes, 25.6% had also smoked conventional cigarettes.

The current study included only adolescent e-cigarette users who had never smoked conventional cigarettes (N=1357) to investigate their intention to smoke. Adolescents who had already smoked cigarettes were excluded and not part of the study sample. In the current national sample of adolescent e-cigarette users who had never smoked conventional cigarettes, 41.3% had intention to smoke conventional cigarettes in the current year. Table 1 presents descriptive statistics.

Latent Class Analysis

A 3-class solution was determined to be the best fit for the data based on the model fit indices, BIC scree plot, parsimony, and substantive meaning of the latent classes. Table 2 provides the BIC and BLRT values and p-values obtained for each model specification. Although the 4-class model had the lowest BIC, the BIC scree plot showed that the substantial decline and flattening out of the BIC occurred in the 3-class model (Figure 1). Also, upon interpretation of the latent classes, there was no clear distinction between the latent classes for the 4-class model. Figure 2 shows the plots depicting these classes, and Table 1 shows the demographic and smoking-related characteristics of each latent class.

Table 2.

Fit Statistics to Determine LCA Solution (N=1357)

1 class 2 class 3 class 4 class 5 class 6 class
Log likelihood −17565.30 −16672.17 −16031.86 −15801.78 −15611.20 −15443.71
Parameters 21 36 51 66 81 96
BIC 35282.07 33604.00 32431.59 32079.63 31806.66 31579.86
Sample-size adjusted BIC 35215.37 33489.64 32269.59 31869.97 31549.36 31274.91
(aBIC)
Entropy 0.925 0.957 0.965 0.962 0.964
LMR p-value 0.000 0.0000 0.01 0.729 0.768
Distribution of profiles 36.2%, 63.8% 13.6%, 29.8%,
56.6%		 13.4%, 7.0%,
55.6%,
24.0%		 12.5%
5.9%
20.2%
51.2%
10.1%		 5.7%
20.2%
8.7%
41.0%
51.2%
10.1%
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Note.

Variances constrained to be equal for all variables.

Figure 1.

Figure 1

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BIC Scree Plot

Figure 2.

Figure 2

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Plots Depicting the 3 Latent Classes of Adolescent E-cigarette Users

Note.

*Values represent proportions (eg, 22.2% = 0.22).

**Values represent means of responses to items.

Class Interpretation

The 3 identified classes were named as socially-protected, peer-driven, and market-vulnerable. The socially-protected class (N=768; 56.6%) reported the fewest number of peers who smoke, the most friends who strongly disapproved of conventional cigarette smoking, and the highest scores on parental monitoring (Table 1). They were the most likely to rate conventional cigarette smoking as harmful and addictive and reported the highest score on perceived influence of antismoking ads. The peer-driven class (N=405; 29.8%) reported the lowest proportion of friends who strongly disapproved of daily cigarette smoking, higher number of peers who smoke, and a lower score on parental monitoring than the socially-protected class. The market-vulnerable class (N=184; 13.6%) ranked highest in ownership of tobacco promotional items and were least likely to rate conventional cigarette smoking as harmful. Table 1 shows more details on the demographic and smoking-related characteristics of the latent classes of adolescent e-cigarette users.

Multivariable Logistic Regression

Two regression models were conducted to ensure each of the 3 latent classes was compared with the others. In Model 1, the peer-driven class was more likely than the socially-protected class to have smoking intention (AOR=2.46; 95% CI, 1.84 – 3.28; Table 3). There was no statistically significant difference between the socially-protected class and the market-vulnerable class regarding smoking intention. In Model 2, the peer-driven class was more likely to have smoking intention (AOR= 2.29; 95% CI, 1.48 – 3.53, see Table 3) compared with the market-vulnerable class.

Table 3.

Adjusted Multivariable Logistic Regression Showing Association between Latent Classes and Smoking Intention (N=1357)

Model 1 (Reference: Socially-Protected) Model 2 (Reference: Market-Vulnerable)
B (SE) AOR 95% CI B (SE) AOR 95% CI
Latent Classes
(Reference: Socially-Protected)
Market-Vulnerable 0.07 (0.21) 1.07 0.71 – 1.62 -- -- --
Peer Driven 0.90 (0.15) 2.46*** 1.84 – 3.28 -- -- --
(Reference: Market-Vulnerable)
Peer-Driven -- -- -- 0.90 (0.15) 2.29*** 1.48 – 3.53
Socially-Protected -- -- -- −0.07 (0.21) 0.93 0.62 – 1.41
Sex (Reference: Female)
Male −0.53 (0.14) 0.59*** 0.45 – 0.77 −0.53 (0.14) 0.59*** 0.45 – 0.77
Grade (Reference: 10th)
8th 0.16 (0.14) 1.18 0.89 – 1.55 0.16 (0.14) 1.18 0.89 – 1.55
Race/ethnicity (Reference: White)
Black −0.93 (0.23) 0.40*** 0.45 – 0.77 −0.93 (0.23) 0.40*** 0.45 – 0.77
Hispanic 0.03 (0.19) 1.03 0.71 – 1.49 0.03 (0.19) 1.03 0.71 – 1.49
Urbanicity (Reference: Urban)
Rural 0.30 (0.18) 1.35 0.95 – 1.93 0.30 (0.18) 1.35 0.95 – 1.93
Country Region (Reference: West)
Northeast 0.08 (0.24) 1.08 0.68 – 1.72 0.08 (0.24) 1.08 0.68 – 1.72
Northcentral 0.25 (0.22) 1.29 0.84 – 1.98 0.25 (0.22) 1.29 0.84 – 1.98
South 0.18 (0.19) 1.13 0.78 – 0.64 0.18 (0.19) 1.13 0.78 – 0.64
Parent Education 0.09 (0.06) 1.09 0.94 – 1.07 0.09 (0.06) 1.09 0.94 – 1.07
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Note.

***

p<.001

AOR: Adjusted Odds Ratio; CI: Confidence Interval. SE: Standard Error.

DISCUSSION

Using a theory-driven, person-centered approach, we identified 3distinct subgroups of adolescent e-cigarette users: peer-driven, socially-protected, and market-vulnerable. These classes had statistically significant differences in their background exposures to tobacco and anti-tobacco messages, risk-taking propensity, attitudes and norms toward conventional cigarette smoking, access to cigarettes, and parental monitoring, which were associated with differential risk of exhibiting smoking intentions.

About one-third (29.8%) of the sample of adolescent e-cigarette users who had never smoked conventional cigarettes were in the peer-driven class and were more than twice as likely as the other 2 classes to have smoking intention. The peer-driven class was primarily distinguished from the other 2 classes by their lowest percentage of friends who disapproved of conventional cigarette smoking. They also were distinct from the socially-protected class by their higher percentage of friends who smoke, lower perceptions of harm and addictiveness of smoking, and lower scores on parental monitoring.

The majority of adolescent e-cigarette users in the sample (56.6%) were in the socially-protected class, which was characterized by the highest score on parental monitoring, the lowest score on peer smoking, and the highest percentage of friends who disapprove of smoking. Although e-cigarette users in this group reported the highest access to cigarettes, they also were the most likely to report being influenced by anti-smoking ads and had the highest perceptions of the harm and addictiveness of conventional cigarette smoking.

Finally, 13.6% of the sample were in the market-vulnerable class and were highly exposed to tobacco marketing. For example, 22% of adolescent e-cigarette users in this subgroup owned tobacco promotional items, a known risk factor for conventional cigarette smoking among adolescents.53African Americans, Hispanics, males, and younger adolescents were more likely to be in this class, an unsurprising result given the tobacco industry strategy of targeting preteens and racial/ethnic minorities with tobacco promotional items.54,55

A combination of risk and protective factors differentiated the groups on their likelihood of exhibiting smoking intention. The socially-protected and market-vulnerable classes were less likely to have smoking intention than the peer-driven class, possibly because of their more balanced combination of risk and protective factors. For example, although the socially-protected class had the highest access to cigarettes, members of this class also reported the highest scores on protective factors including antismoking attitudes and norms and parental monitoring. Similarly, although the market-vulnerable class ranked highest in ownership of tobacco promotional items and had the least perceptions of harm of conventional cigarette smoking, they also reported the least access to cigarettes and had relatively higher proportion of friends who strongly discouraged daily cigarette smoking. The peer-driven class was the most likely to exhibit smoking intention and had the lowest scores on protective factors such as friends’ disapproval of smoking. Notably, the peer-driven class did not differ from the other 2 classes on risk-taking propensity. Whereas risk-taking propensity may be a major driver of e-cigarette use among adolescents as reported by prior studies;56–58 our findings suggest that it is not a major factor differentiating adolescent e-cigarette users on their likelihood to exhibit smoking intention.

Limitations

Our study had several limitations. First, the cross-sectional nature of the study means that causality cannot be inferred. Future longitudinal studies should examine the smoking trajectories of each of adolescent e-cigarette user subgroups identified in the current study. The cross-sectional data limitation is somewhat offset by the advantages of using a large national sample, that encompasses US 8th and 10th grade never-smoker e-cigarette user subpopulation that identify as white non-Hispanic, black non-Hispanic, and Hispanic. However, a more detailed breakdown of race and ethnicity was not possible in the publicly accessible data files used for this study. Future studies examining smoking intention in well-characterized, diverse adolescent samples are warranted. An additional limitation is that the measure of ownership of tobacco promotional items does not specify which tobacco product is branded on the item owned by survey participants. Future national surveys should use more specific measures of owning promotional items for e-cigarettes, which may leave adolescents vulnerable to using tobacco products.59,60 Finally, nicotine addiction, a potential risk factor for smoking initiation among adolescent e-cigarette users,61 was not accounted for in the study.

Conclusions

Our results provide a logical first step in defining the constellation of risk and protective factors that contribute to smoking intention among adolescent e-cigarette users who had never smoked conventional cigarettes. We identified a subset of adolescent e-cigarette users at most risk of exhibiting smoking intention and found peer smoking and friends’ approval of smoking to be major distinctive characteristics of this subset. The 2 other subsets of adolescent e-cigarette users were less likely to exhibit smoking intention, possibly due to the presence of protective factors such as parental monitoring, high perceptions of the harm and addictiveness of conventional cigarette smoking, and low access to cigarettes.

In the current adolescent e-cigarette use epidemic, our study findings highlight factors that may increase risk of progression from e-cigarette use to conventional cigarette smoking. Our findings suggest that tobacco control interventions such as educational campaigns addressing negative attitudes and norms toward conventional cigarette smoking, which were instrumental in reducing the prevalence of conventional cigarette smoking among adolescents, also may be effective in curbing smoking initiation among adolescent e-cigarette users. Because many adolescents may use e-cigarettes for sensation seeking,56–58 educational campaigns should emphasize the risk of nicotine addiction associated with e-cigarette use.61 Parental monitoring also should be further encouraged to reduce adolescent risk behaviors.32,62 Existing policies designed to protect racial/ethnic minorities from targeted tobacco marketing should be revisited. Finally, age-restriction policies that increase minimum cigarette purchase age to 21 should be enforced locally and nationally to reduce access.

Acknowledgements

Our research was supported by grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHHD) (K01HD091416 and P2CHD042849) and from the William T. Grant Foundation Scholars Program. NICHHD and the William T. Grant Foundation had no role in the study design, collection, data analysis, interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.

Footnotes

Conflict of Interest Disclosure Statement

The authors have no conflicts of interest or financial relationships relevant to this article to disclose.

Human Subjects Approval Statement

We used publicly available data only; thus, we did not require institutional review board approval.

Contributor Information

Olusegun Owotomo, Children’s National Medical Center, Washington, DC, United States..

Julie Maslowsky, Joe R. & Teresa Lozano Long Endowed Associate Professor of Health Behavior & Health Education, Department of Kinesiology & Health Education, University of Texas at Austin, Austin, TX, United States..

References

  • 1.US Department of Health and Human Services. The health consequences of smoking—50 years of progress: a report of the surgeon general. Atlanta, GA: U.S. Department of Health and Human Services, CDC; 2014. http://www.surgeongeneral.gov/library/reports/50-years-of-progress/full-report.pdf [Google Scholar]
  • 2.Johnston LD, Miech RA, O’Malley PM, et al. Monitoring the future national survey results on drug use 1975–2019: Overview, Key findings on Adolescent Drug Use. Ann Arbor, MI: Institute for Social Research, University of Michigan; 2020. http://www.monitoringthefuture.org/pubs/monographs/mtf-overview2019.pdf [Google Scholar]
  • 3.US Department of Health and Human Services. E-cigarette use among youth and young adults: a report of the surgeon general. Atlanta, GA: U.S. Department of Health and Human Services, CDC, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2016.https://www.cdc.gov/tobacco/data_statistics/sgr/e-cigarettes/index.htm. [Google Scholar]
  • 4.Kandel DB, Chen K. Extent of smoking and nicotine dependence in the United States: 1991–1993. Nicotine Tob Res. 2000;2(3):263–274. 10.1080/14622200050147538 [DOI] [PubMed] [Google Scholar]
  • 5.Spear LP. The adolescent brain and age-related behavioral manifestations. Neurosci Biobehav Rev. 2000;24(4):417–463. 10.1016/s0149-7634(00)00014-2 [DOI] [PubMed] [Google Scholar]
  • 6.U.S. Department of Health and Human Services. (2018). surgeon general’s advisory on e-cigarette use among youth. Washington, DC: US Department of Health and Human Services, Office of the Surgeon General 2018. https://e-cigarettes.surgeongeneral.gov/documents/surgeon-generals-advisory-on-e-cigarette-use-among-youth-2018.pdf [Google Scholar]
  • 7.Gentzke AS, Creamer M, Cullen KA, et al. Vital signs: tobacco product use among middle and high school students - United States, 2011–2018. MMWR Morb Mortal Wkly Rep. 2019;68(6):157–164. Published 2019 Feb 15. 10.15585/mmwr.mm6806e1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Cullen KA, Gentzke AS, Sawdey MD, et al. E-cigarette use among youth in the United States, 2019. JAMA. 2019;322(21):2095–2103. doi: 10.1001/jama.2019.18387 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.National Academies of Sciences, Engineering, and Medicine. Public health consequences of e-cigarettes. Washington, DC: The National Academies Press; 2018. 10.17226/24952 [DOI] [PubMed] [Google Scholar]
  • 10.Barrington-Trimis JL, Urman R, Berhane K, et al. E-cigarettes and future cigarette use. Pediatrics. 2016;138(1):e20160379. 10.1542/peds.2016-0379 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Leventhal AM, Strong DR, Kirkpatrick MG, et al. Association of electronic cigarette use with initiation of combustible tobacco product smoking in early adolescence. JAMA. 2015;314(7):700–707. 10.1001/jama.2015.8950 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Miech R, Patrick ME, O’Malley PM, et al. E-cigarette use as a predictor of cigarette smoking: results from a 1-year follow-up of a national sample of 12th grade students. Tob Control. 2017;26(e2):e106–e111. 10.1136/tobaccocontrol-2016-053291 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Primack BA, Soneji S, Stoolmiller M, et al. Progression to traditional cigarette smoking after electronic cigarette use among US adolescents and young adults. JAMA Pediatr. 2015;169(11):1018–1023. 10.1001/jamapediatrics.2015.1742 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Soneji S, Barrington-Trimis JL, Wills TA, et al. Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: a systematic review and meta-analysis [published correction appears in JAMA Pediatr. 2018 Jan 1;172(1):92–93] [published correction appears in JAMA Pediatr. 2018 Jan 1;172(1):98] [published correction appears in JAMA Pediatr. 2020 May 1;174(5):509]. JAMA Pediatr. 2017;171(8):788–797. 10.1001/jamapediatrics.2017.1488 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Unger JB, Soto DW, Leventhal A. E-cigarette use and subsequent cigarette and marijuana use among Hispanic young adults. Drug Alcohol Depend. 2016;163:261–264. 10.1016/j.drugalcdep.2016.04.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Wills TA, Knight R, Sargent JD, et al. Longitudinal study of e-cigarette use and onset of cigarette smoking among high school students in Hawaii. Tob Control. 2017;26(1):34–39. 10.1136/tobaccocontrol-2015-052705 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Ajzen I, Albarracin D. Predicting and changing behavior: a reasoned action approach. In Ajzen I, Albarracin D, Hornik R, (Eds). Prediction and change of health behavior: applying the reasoned action approach. Mahwah, NJ: Lawrence Erlbaum Associates; 2007:3–18. [Google Scholar]
  • 18.Ajzen I Martin Fishbein’s legacy: the reasoned action approach. Ann Am Acad Pol Soc Sci. 2012;640(1):11–27. 10.1177/0002716211423363 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.DeVries HD, Backbier E, Kok G, et al. The impact of social influences in the context of attitude, self-efficacy, intention, and previous behavior as predictors of smoking onset. J Appl Soc Psychol. 1995;25(3):237–257. 10.1111/j.1559-1816.1995.tb01593.x [DOI] [Google Scholar]
  • 20.Pierce JP, Choi WS, Gilpin EA, et al. Validation of susceptibility as a predictor of which adolescents take up smoking in the United States. Health Psychol. 1996;15(5):355–361. 10.1037/0278-6133.15.5.355 [DOI] [PubMed] [Google Scholar]
  • 21.Bunnell RE, Agaku IT, Arrazola RA, et al. Intentions to smoke cigarettes among never-smoking US middle and high school electronic cigarette users: National Youth Tobacco Survey, 2011–2013. Nicotine Tob Res. 2015;17(2):228–235. 10.1093/ntr/ntu166 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Wills TA, Sargent JD, Knight R, et al. E-cigarette use and willingness to smoke: a sample of adolescent nonsmokers. Tob Control. 2016;25(e1):e52–e59. 10.1136/tobaccocontrol-2015-052349 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Owotomo O, Maslowsky J. Adolescent smoking susceptibility in the current tobacco context: 2014–2016. Am J Health Behav. 2018;42(3):102–113. 10.5993/AJHB.42.3.10 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Ajzen I The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211. 10.1016/0749-5978(91)90020-T [DOI] [Google Scholar]
  • 25.Dube SR, Arrazola RA, Lee J, et al. Pro-tobacco influences and susceptibility to smoking cigarettes among middle and high school students--United States, 2011. J Adolesc Health. 2013;52(5 Suppl):S45–S51. 10.1016/j.jadohealth.2012.07.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Farrelly MC, Davis KC, Haviland ML, et al. Evidence of a dose-response relationship between “truth” antismoking ads and youth smoking prevalence. Am J Public Health. 2005;95(3):425–431. 10.2105/AJPH.2004.049692 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Sargent JD, Tanski S, Stoolmiller M, et al. Using sensation seeking to target adolescents for substance use interventions. Addiction. 2010;105(3):506–514. 10.1111/j.1360-0443.2009.02782.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Soneji S, Pierce JP, Choi K, et al. Engagement with online tobacco marketing and associations with tobacco product use among U.S. Youth. J Adolesc Health. 2017;61(1):61–69. 10.1016/j.jadohealth.2017.01.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Halpern-Felsher BL, Biehl M, Kropp RY, et al. Perceived risks and benefits of smoking: differences among adolescents with different smoking experiences and intentions. Prev Med. 2004;39(3):559–567. 10.1016/j.ypmed.2004.02.017 [DOI] [PubMed] [Google Scholar]
  • 30.Fishbein M, Ajzen I. Predicting and changing behavior: the reasoned action approach. New York, NY: Psychology Press; 2010. [Google Scholar]
  • 31.Gritz ER, Prokhorov AV, Hudmon KS, et al. Predictors of susceptibility to smoking and ever smoking: a longitudinal study in a triethnic sample of adolescents. Nicotine Tob Res. 2003;5(4):493–506. 10.1080/1462220031000118568 [DOI] [PubMed] [Google Scholar]
  • 32.Bohnert KM, Ríos-Bedoya CF, Breslau N. Parental monitoring at age 11 and smoking initiation up to age 17 among Blacks and Whites: a prospective investigation. Nicotine Tob Res. 2009;11(12):1474–1478. 10.1093/ntr/ntp160 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Forrester K, Biglan A, Severson HH, et al. Predictors of smoking onset over two years. Nicotine Tob Res. 2007;9(12):1259–1267. 10.1080/14622200701705357 [DOI] [PubMed] [Google Scholar]
  • 34.Spivak AL, Monnat SM. Prohibiting juvenile access to tobacco: violation rates, cigarette sales, and youth smoking. Int J Drug Policy. 2015;26(9):851–859. 10.1016/j.drugpo.2015.03.006 [DOI] [PubMed] [Google Scholar]
  • 35.White MM, Gilpin EA, Emery SL, et al. Facilitating adolescent smoking: who provides the cigarettes? Am J Health Promot. 2005;19(5):355–360. 10.4278/0890-1171-19.5.355 [DOI] [PubMed] [Google Scholar]
  • 36.Berlin KS, Parra GR, Williams NA. An introduction to latent variable mixture modeling (part 2): longitudinal latent class growth analysis and growth mixture models. J Pediatr Psychol. 2014;39(2):188–203. 10.1093/jpepsy/jst085 [DOI] [PubMed] [Google Scholar]
  • 37.Laursen BP, Hoff E. Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Quarterly. 2006;52(3):377–389. 10.1353/mpq.2006.0029 [DOI] [Google Scholar]
  • 38.Dutra LM, Glantz SA, Lisha NE, et al. Beyond experimentation: five trajectories of cigarette smoking in a longitudinal sample of youth. PLoS One. 2017;12(2):e0171808. Published 2017 Feb 9. 10.1371/journal.pone.0171808 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Lisha NE, Thrul J, Ling PM. Latent class analysis to examine patterns of smoking and other tobacco products in young adult bar patrons. J Adolesc Health. 2019;64(1):93–98. 10.1016/j.jadohealth.2018.06.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Morean ME, Kong G, Camenga DR, et al. Latent class analysis of current e-cigarette and other substance use in high school students. Drug Alcohol Depend. 2016;161:292–297. 10.1016/j.drugalcdep.2016.02.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Nasim A, Blank MD, Cobb CO, et al. Patterns of alternative tobacco use among adolescent cigarette smokers. Drug Alcohol Depend. 2012;124(1–2):26–33. 10.1016/j.drugalcdep.2011.11.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Yu M, Sacco P, Choi HJ, et al. Identifying patterns of tobacco use among US middle and high school students: A latent class analysis. Addict Behav. 2018;79:1–7. 10.1016/j.addbeh.2017.11.034 [DOI] [PubMed] [Google Scholar]
  • 43.Johnston LD, Bachman JG, Schulenberg JE, et al. Monitoring the future: a continuing study of American youth (8th and 10th Grade Surveys). Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2017. 10.3886/ICPSR36799.v1 [DOI] [Google Scholar]
  • 44.Arnett JJ. Optimistic bias in adolescent and adult smokers and nonsmokers. Addict Behav. 2000;25(4):625–632. 10.1016/s0306-4603(99)00072-6 [DOI] [PubMed] [Google Scholar]
  • 45.Owotomo Owotomo O, Maslowsky J, Loukas A. Perceptions of the harm and addictiveness of conventional cigarette smoking among adolescent e-cigarette users. J Adolesc Health. 2018;62(1):87–93. 10.1016/j.jadohealth.2017.08.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Dever BV, Schulenberg JE, Dworkin JB, et al. Predicting risk-taking with and without substance use: the effects of parental monitoring, school bonding, and sports participation. Prev Sci. 2012;13(6):605–615. 10.1007/s11121-012-0288-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Muthén LK, Muthén BO. Mplus user’s guide. 7th ed. Los Angeles, CA: Muthén&Muthén; 1998–2017. [Google Scholar]
  • 48.Feingold A, Tiberio SS, Capaldi DM. New approaches for examining associations with latent categorical variables: applications to substance abuse and aggression. Psychol Addict Behav. 2014;28(1):257–267. 10.1037/a0031487 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.PorcuM GiambonaF. Introduction to latent class analysis with applications. J Early Adolesc. 2017;37(1):129–158. 10.1177/0272431616648452 [DOI] [Google Scholar]
  • 50.Chung H, Flaherty BP, Schafer JL. Latent class logistic regression: application to marijuana use and attitudes among high school seniors. J R Stat Soc Ser A Stat Soc. 2006;169(4):723–743. 10.1111/j.1467-985X.2006.00419.x [DOI] [Google Scholar]
  • 51.Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Modeling. 2007;14(4):535–569. 10.1080/10705510701575396 [DOI] [Google Scholar]
  • 52.Pigott TD. A review of methods for missing data: educational research and evaluation. Educ Res Eval. 2001;7(4):353–383. 10.1076/edre.7.4.353.8937 [DOI] [Google Scholar]
  • 53.Gilpin EA, Pierce JP, Rosbrook B. Are adolescents receptive to current sales promotion practices of the tobacco industry? Prev Med. 1997;26(1):14–21. 10.1006/pmed.1996.9980 [DOI] [PubMed] [Google Scholar]
  • 54.Feighery EC, Schleicher NC, Boley Cruz T, et al. An examination of trends in amount and type of cigarette advertising and sales promotions in California stores, 2002–2005. Tob Control. 2008;17(2):93–98. 10.1136/tc.2007.022046 [DOI] [PubMed] [Google Scholar]
  • 55.Henriksen L, Schleicher NC, Dauphinee AL, et al. Targeted advertising, promotion, and price for menthol cigarettes in California high school neighborhoods. Nicotine Tob Res. 2012;14(1):116–121. 10.1093/ntr/ntr122 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Etter JF. Gateway effects and electronic cigarettes. Addiction. 2018;113(10):1776–1783. 10.1111/add.13924 [DOI] [PubMed] [Google Scholar]
  • 57.Kong G, Morean ME, Cavallo DA, et al. Reasons for electronic cigarette experimentation and discontinuation among adolescents and young adults. Nicotine Tob Res. 2015;17(7):847–854. 10.1093/ntr/ntu257 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Patrick ME, Miech RA, Carlier C, et al. Self-reported reasons for vaping among 8th, 10th, and 12th graders in the US: Nationally-representative results. Drug Alcohol Depend. 2016;165:275–278. 10.1016/j.drugalcdep.2016.05.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Pokhrel P, Fagan P, Kehl L, et al. Receptivity to e-cigarette marketing, harm perceptions, and e-cigarette use. Am J Health Behav. 2015;39(1):121–131. 10.5993/AJHB.39.1.13 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Gilpin EA, Distefan JM, Pierce JP. Population receptivity to tobacco advertising/promotions and exposure to anti-tobacco media: effect of Master Settlement Agreement in California: 1992–2002. Health Promot Pract. 2004;5(3 Suppl):91S–98S. 10.1177/1524839904264601 [DOI] [PubMed] [Google Scholar]
  • 61.Jackler RK, Ramamurthi D. Nicotine arms race: JUUL and the high-nicotine product market. Tob Control. 2019;28(6):623–628. 10.1136/tobaccocontrol-2018-054796 [DOI] [PubMed] [Google Scholar]
  • 62.Lippold MA, Greenberg MT, Graham JW, et al. Unpacking the effect of parental monitoring on early adolescent problem behavior: mediation by parental knowledge and moderation by parent-youth warmth. J Fam Issues. 2014;35(13):1800–1823. 10.1177/0192513X13484120 [DOI] [PMC free article] [PubMed] [Google Scholar]

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