Temporal Changes in the Correlates of U.S. Adolescent Electronic Cigarette Use and Utilization in Tobacco Cessation,2011 to 2013

Abstract

Objective. To examine temporal changes in the correlates of experimental and current e-cigarette use and associations with tobacco quit attempts. Method. Repeated cross-sectional analyses of data from the 2011 (n = 17,741), 2012 (n = 23,194), and 2013 (n = 16,858) National Youth Tobacco Surveys—a nationally representative sample of U.S. middle and high school students. Respondents were asked about lifetime and past-month e-cigarette use, conventional cigarette use, and number of quit attempts made in the prior year among current smokers. Results. Multinomial logistic regression models indicate higher odds of experimental or current e-cigarette use versus abstinence in the 2012 and 2013 survey years (vs. 2011). Respondents in the 2013 survey were more likely to use e-cigarettes in 2012, and they were significantly more likely to be current versus experimental users. Temporal increases in past-month e-cigarette use were uneven across demographic subgroups. Among current smokers of conventional cigarettes, fully adjusted models show that across all survey years the probability of past-month e-cigarette use was not significantly different for those who attempted to quit using tobacco in the past year from those who did not. Conclusions. Adolescent e-cigarette use has become more common between 2011 and 2013 and shows signs of becoming increasingly characterized by current rather than experimental use. Temporal patterns suggest that recent population increases were initially driven by select subgroups (e.g., Whites), with other subgroups contributing to the increase more recently (e.g., Black). Tobacco cessation has remained unrelated to current e-cigarette use over time, suggesting that e-cigarette use may be contributing to increased nicotine use.

Keywords

adolescence electronic cigarettes nicotine tobacco United States

Electronic cigarettes (e-cigarettes) have become increasingly popular in recent years—among adults and adolescents. Between 2010 and 2013, the percentage of U.S. adults that had tried an e-cigarette doubled (King, Patel, Nguyen, & Dube, 2015) while conventional cigarette smoking declined (Centers for Disease Control and Prevention, 2014). Over roughly the same period, adolescent use tripled (Centers for Disease Control and Prevention, 2015), sparking debate among health scientists over the harm-reduction potential of e-cigarettes and risks linked with adolescent nicotine initiation (Brandon et al., 2015; Chapman, 2014; Riker, Lee, Darville, & Hahn, 2012).

Due to their lower toxicity and potential as cessation aids (Bullen et al., 2010; Caponnetto et al., 2013; Polosa et al., 2011), e-cigarettes may reduce tobacco-related morbidity and mortality. However, lax regulatory measures (Williams, Derrick, & Ribisl, 2015) and the directed advertising of teen-friendly e-cigarette products (Duke et al., 2014; Grana & Ling, 2014) could jeopardize recent reductions in teen nicotine use. Indeed, evidence suggests that e-cigarettes are overtaking conventional cigarettes in popularity, with 16% of 10th and 17.1% of 12th graders in the 2014 Monitoring the Future Study reporting past-month e-cigarette use versus 7.2% and 13.6% reporting past-month conventional cigarette use (L. D. Johnston, O’Malley, Miech, Bachman, & Schulenberg, 2015).

Health scholars warn that widespread e-cigarette use could offset recent reductions in adolescent tobacco use, and that regulation is needed across several target areas (Riker et al., 2012). First, ambiguous and weak regulatory measures make e-cigarettes easily accessible to minors. As of late 2014, one fifth of U.S. states permitted e-cigarette sales to minors (Marynak et al., 2014), and Internet sales remain largely unregulated, even in states requiring age verification (Williams et al., 2015).

Second, an expanding line of e-cigarette products is both appealing and purposefully targeted to adolescents. Flavored “e-juice” comes in palatable, candy-like options, including pina colada, bubble gum, and cookies and cream. Use of flavorings in combustible cigarettes was prohibited by the U.S. Congress in 2009 to discourage smoking initiation among minors (U.S. House of Representatives, 2009), but e-cigarettes remain exempt from this policy.

Third, policies regulating the advertising of nicotine to minors have not been modified to include e-cigarettes. Scholars find that teen-directed e-cigarette advertisements have increased in recent years (Duke et al., 2014) and frequently contain messages about the health claims of e-cigarettes as well as their association with modernity (73%), heightened social status (44%), and celebrity-like behavior (22%; Grana & Ling, 2014). Such messages parallel late-20th century tobacco advertisements portraying smoking as glamorous and benign (Bayer, 2008) and are associated with higher odds of teenage e-cigarette use (Agaku & Ayo-Yusuf, 2014).

Concerns regarding weak regulation of sales, products with teen appeal, and teen-directed marketing have emerged against the backdrop of increasing adolescent e-cigarette use. This is alarming partly because it runs counter to the general decline of adolescent tobacco use between 1997 and 2013 (D. A. Johnston, O’Malley, Bachman, & Schulenberg, 2014). Furthermore, results from prospective data suggest that e-cigarette use increases the risk of conventional cigarette initiation at follow-up (Leventhal et al., 2015; Sutfin et al., 2015; Wills et al., 2016).

Though recent studies have identified key correlates of teenage e-cigarette use—including male gender, non-Hispanic White race, older age, and conventional tobacco use (Dutra & Glantz, 2014; Lippert, 2014)—it remains unclear how these correlates have shifted over time, and whether e-cigarettes have become more important for teenage tobacco cessation. It is possible that as the cessation applications of e-cigarettes have become incorporated into the public discourse on harm reduction among adult smokers, younger smokers have increasingly reached for e-cigarettes as less harmful options. Indeed, recent work finds that e-cigarette using adolescents attribute fewer harms to e-cigarettes than nonusers (Wills, Knight, Williams, Pagano, & Sargent, 2015). Similar patterns have been noted among younger users of low-nitrosamine smokeless tobacco (snus) in Europe (Lund & Lund, 2014), though reports from the United States suggest American health care providers may be ill-equipped to discuss e-cigarettes with adolescent patients (Pepper, McRee, & Gilkey, 2014). Moreover, recent qualitative evidence suggests that young people hold complex views on the role of e-cigarettes in tobacco cessation and simultaneously attribute risks and benefits to e-cigarettes (Camenga et al., 2015). Thus, an alternative hypothesis is that increases in U.S. adolescent e-cigarette use was driven by experimentation or “polynicotine use” among teenage smokers, and not by tobacco cessation.

This study uses repeated cross-sectional data from the 2011, 2012, and 2013 waves of the National Youth Tobacco Survey (NYTS) to address three main questions: (1) How have e-cigarette experimentation and current (active) usage shifted between 2011 and 2013? (2) How have the correlates of adolescent e-cigarette use changed over time? (3) Has the association between tobacco quit attempts and e-cigarette use among current teenage smokers changed between 2011 and 2013?

Method

Data

The NYTS is a nationally representative sample of U.S. public and private school students in Grades 6 to 12, covering all 50 states and the District of Columbia. The NYTS used a three-stage cluster-based sampling design where primary sampling units (counties, several small counties, or portions of larger counties) were selected without replacement followed by schools within primary sampling units and students within schools. Selected participants self-administered pencil-and-paper questionnaires containing items on tobacco use (including conventional cigarettes, smokeless tobacco, and e-cigarettes), tobacco-related beliefs, attitudes, exposure to second-hand tobacco, and basic demographic variables.

The 2011 survey—the first to include e-cigarette measures—covered 178 schools and 18,866 students (72.7% response rate); the 2012 sample covered 228 schools and 24,658 students (73.6% response rate); the 2013 sample covered 187 schools and 18,406 students (67.8% response rate). The pooled sample size across all rounds of NYTS was 61,930. After making exclusions for missing data on e-cigarette use, conventional smoking, sex, age, grade, and race (n = 4,137, 6.7%), the analytic sample was 57,793. Supplemental analyses based on multiply-imputed data using chained equations (Acock, 2005) revealed similar results to those presented here.

Outcome

Two dependent variables are examined. The first is constructed by combining responses to questions on both lifetime and past-month e-cigarette use, assessed as dichotomous variables with those students who indicated use of “electronic cigarettes or e-cigarettes, such as Ruyan or NJOY to the question “Which of the following tobacco products have you ever tried (used in the past 30 days), even just 1 time?” and was coded “1” for affirmative responses, “0” otherwise. Combining measures of lifetime and past-month use yields the following: (0) never tried an e-cigarette; (1) have tried an e-cigarette, but have not used within the past month; and (2) have used an e-cigarette in the past month. For parsimony, these categories are referred to as “abstainers,” “experimental users,” and “current users,” respectively. A second dependent variable—past-month e-cigarette use (i.e., current use)—is used in select models based on the subsample of current conventional cigarette smokers.

Independent Variables

Conventional cigarette smoking was captured in a three-category variable with categories for “never,” “experimenter,” and “current.” Never smokers had never smoked or tried cigarettes; current smokers had smoked more than 100 cigarettes (5 packs) in their lifetimes and smoked at least once in the past month; and experimenters had met one of two criteria: Either they had smoked at least once in their lives but less than 100 cigarettes in their lifetimes, or they had smoked 100 or more cigarettes but not within the past month. Although this typology follows conventions in the literature, exploratory analyses of the pooled NYTS data revealed a small proportion of experimental smokers who had smoked 100 or more cigarettes in their lifetimes but not in the past month (n = 252 respondents). Supplementary analyses omitting these individuals did not change the results.

Among current smokers, an additional variable is constructed measuring past-year quit attempts. Based on self-reports, current smokers are assigned a value of “1” if they reported having attempted to quit smoking one or more times in the past year, and “0” otherwise.

Control variables include survey year, age, sex, age appropriate for grade, race/ethnicity, and conventional cigarette usage. Survey year was defined as the calendar year of survey and specified with indicators for 2011, 2012, and 2013. Age and sex were based on self-reports. Age was treated as a continuous variable and standardized around its mean. Age appropriate for grade was calculated as a proxy for poor academic performance which may have resulted in students repeating a grade. A dichotomous indicator (= 1) was created to indicate high age for grade (vs. 0 otherwise) according to the following criteria: age >12 years in Grade 6; >13 years in Grade 7; >14 years in Grade 8; >15 years in Grade 9; >16 years in Grade 10; >17 years in Grade 11; and >18 years in Grade 12. Race/ethnicity is self-reported and grouped into the following: Non-Hispanic White (reference), Non-Hispanic Black, Mexican American,¹ other Hispanic, and other race.

Analysis

Descriptive statistics are presented in Table 1. Table 2 summarizes results from multinomial logistic regression models analyzing the association between the trichotomous e-cigarette use variable and independent variables, including survey year, age, sex, race/ethnicity, age appropriate for grade, and conventional smoking. In models not shown, the reference category for survey year is rotated once (omitting 2012 as the reference year) to achieve full pairwise comparisons. Figure 1 presents year-specific predicted probabilities of past-month e-cigarette use for age, sex, and racial/ethnic subgroups; and Figure 2 shows predicted probabilities of past-month e-cigarette use for conventional cigarette smokers, experimenters, and abstainers. Estimates in the figures are derived from separate logistic regression models adjusting for sex, age, race/ethnicity, age for grade, smoking status, and survey year (including main effects and interaction terms between survey year and independent variables). Wald tests were conducted to determine the significance of (1) differences between year-specific probabilities of past-month e-cigarette use between various subgroups (e.g., females) and their reference categories (e.g., males), and (2) year-to-year changes in the probabilities of e-cigarette use within certain subgroups (e.g., from 2011-2012 among Whites). Figure 3 displays year-specific predicted probabilities of past-month e-cigarette use among current cigarette smokers with and without past-year tobacco quit attempts. Estimates in Figure 3 are based on fully adjusted models including interaction terms between tobacco quit attempts and survey year. Analyses are weighted by the inverse of the sampling fraction and standard errors are corrected for within-school homogeneity by clustering estimates by school identifiers.

Table 1.

Sample Description.

	2011 (n = 17,741)		2012 (n = 23,194)		2013 (n = 16,858)		Total ( N = 57,793)
Student characteristics	n	%	n	%	n	%	n	%
Electronic cigarette use
Abstainer	17,193	96.7	21,664	93.3	15,528	91.8	54,385	94.0
Experimenter	370	2.2	1,058	4.7	834	5.2	2,262	4.0
Current user	178	1.1	472	2.0	496	3.0	1,146	2.0
Age, mean (SE)	14.65 (0.15)		14.63 (0.13)		14.64 (0.14)		14.64 (0.08)
Sex
Female	8,967	49.3	11,665	49.5	8,560	49.5	29,192	49.4
Male	8,774	50.7	11,529	50.5	8,298	50.5	28,601	50.6
Race
White	7,609	60.6	12,844	59.3	8,623	57.7	29,076	59.2
Black	3,333	14.6	3,186	14.4	3,209	16.0	9,728	15.0
Mexican American	3,800	12.9	3,712	13.5	1,767	10.5	9,260	12.3
Hispanic, other	2,066	7.2	1,811	7.8	2,100	10.6	5,976	8.5
Other race	935	4.7	1,659	4.9	1,159	5.2	3,753	4.9
Age-for-grade
Appropriate	16,446	92.9	21,959	94.6	15,763	93.9	54,168	93.8
High	1,295	7.1	1,235	5.4	1,095	6.1	3,625	6.2
Conventional cigarette smoking
Abstainer	12,242	70.4	17,172	74.0	12,488	74.8	41,902	73.1
Experimenter	4,688	24.5	5,116	22.0	3,730	21.3	13,534	22.6
Current smoker	811	5.1	906	4.0	640	3.9	2,357	4.4
Past-year quit attempts (smokers only)
Did not attempt to quit smoking	280	39.4	367	45.5	230	40.3	877	41.5
Made one or more quit attempts	431	60.6	419	54.6	333	59.7	1,183	58.5

Note. Based on unweighted counts and weighted percentages and means.

Table 2.

Multinomial Logistic Regression Models Examining E-Cigarette Abstinence, Experimentation, and Current Use (N = 57,793).

	Panel A. Experimental e-cigarette user versus abstainer		Panel B. Current e-cigarette user versus abstainer		Panel C. Current e-cigarette user versus experimental user
Variables	OR	95% CI	OR	95% CI	OR	95% CI
Year
2011	1.00		1.00		1.00
2012	3.10***	(2.51, 3.84)	3.01***	(2.25, 4.01)	0.97	(0.73, 1.29)
2013	4.01***^a	(3.21, 5.01)	5.38***^a	(3.98, 7.27)	1.32*^a	(1.01, 1.78)
Age (standardized)	1.31***	(1.21, 1.41)	1.05	(0.94, 1.16)	0.80***	(0.73, 0.88)
Female	0.85**	(0.75, 0.95)	0.62***	(0.52, 0.74)	0.73**	(0.60, 0.89)
Race
White	1.00		1.00		1.00
Black	0.32***	(0.25, 0.42)	0.59***	(0.44, 0.78)	1.82***	(1.33, 2.48)
Mexican American	0.51***	(0.41, 0.62)	0.77*	(0.61, 0.98)	1.51**	(1.18, 1.94)
Hispanic, other	0.62***	(0.51, 0.76)	1.03	(0.80, 1.32)	1.66***	(1.30, 2.13)
Other race	0.53***	(0.40, 0.71)	0.72	(0.49, 1.07)	1.36	(0.87, 2.13)
High age-for-grade	0.70**	(0.57, 0.87)	0.78	(0.57, 1.06)	1.11	(0.79, 1.55)
Conventional cigarette smoking
Never	1.00		1.00		1.00
Experimenter	21.09***	(17.47, 25.47)	16.31***	(12.22, 21.76)	0.77	(0.57, 1.06)
Current	94.32***	(74.73, 119.04)	155.67***	(112.20, 215.97)	1.65**	(1.16, 2.34)

Note. Italicized estimates should be interpreted cautiously due to their imprecision and wide confidence intervals. e-cigarette = electronic cigarette; OR = odds ratio; 95% CI = 95% confidence interval.

The coefficient for 2013 is positive and significantly larger compared to 2012.

*p < .05. **p < .01. ***p < .001.

Results

Results in Table 1 indicate shifting patterns of e-cigarette use from 2011 to 2013: Experimental use rose from 2.2% in 2011 to 5.2% in 2013, and current use increased from 1.1% to 3% over the same period. Conversely, use of combustible cigarettes declined over the same period, with 24.5% and 5.1% of the sample meeting criteria for experimental or current cigarette use in 2011, respectively, compared to 21.3% and 3.9% in 2013. Over half of current smokers in each NYTS cross section reported one or more past-year quit attempts.

Panel A of Table 2 shows that the odds ratio (OR) for the 2012 survey year is 3.1, indicating that those interviewed in 2012 versus 2011 had 210% higher odds of being experimental e-cigarette users than abstainers (210% = [3.1 − 1.0] × 100). Similarly, those interviewed in 2013 versus 2011 had 301% higher odds of being experimental users than abstainers (OR = 4.01, p < .001, 95% confidence interval [CI] = 3.21, 5.01). Supplementary models treating the 2012 survey year as the reference category indicate that the odds of being an experimental e-cigarette user versus abstainer are higher for those interviewed in 2013 versus 2012 (OR = 1.29, p < .01, 95% CI = 1.09, 1.54). Additional contrasts in Panel A show that the odds of experimental use versus abstinence are higher with increasing age, for males versus females, Whites versus racial/ethnic minorities, those of appropriate versus high age for grade, and experimental or current users of conventional cigarettes versus abstainers. Estimates associated with conventional smoking must be interpreted cautiously due to wide confidence intervals.

Results in Panel B parallel those from Panel A: Those interviewed in both 2012 and 2013 have higher odds of being current e-cigarette users versus abstainers when compared to those interviewed in 2011. Supplementary results also indicate that the 2013 interviewees have higher odds of being current e-cigarette users versus abstainers than those interviewed in 2012 (OR = 1.79, p < .001, 95% CI = 1.38, 2.32). The odds of being a current user versus abstainer are lower for females versus males, Blacks and Mexican Americans versus Whites, and higher for experimental and current users of conventional cigarettes versus abstainers.²

Panel C presents the odds of being a current versus experimental e-cigarette user. Results indicate that the odds of being a current versus experimental user were 32% higher for those interviewed in 2013 versus 2011 (OR = 1.32, p < .05, 95% CI = 1.01, 1.78). A supplemental model treating 2012 as the omitted survey year reveals that those interviewed in 2013 also have higher odds of being current versus experimental users (OR = 1.38, p < .01, 95% CI = 1.13, 1.70). The odds of being a current versus experimental e-cigarette user are lower with higher age and among females versus males, and higher among Blacks, Mexican Americans, and “other” Hispanics relative to Whites, and among current smokers versus abstainers.

Figure 1 illustrates changes over time in past-month e-cigarette use across demographic subgroups. Age trends indicate that throughout the study period, adolescents aged 12 and under had low probabilities of current e-cigarette use. Conversely, with each successive year, the probability of current e-cigarette use increased for all but one of the other age groups (13-14 year olds) and by 2013 was significantly higher in each relative to the youngest age group. In 2011, those aged 17+ were significantly less likely to use e-cigarettes compared to 13 to 14 year olds, though the prevalence of e-cigarette use between the two groups was statistically indistinguishable in 2012 and 2013.

Sex-specific trends show significantly lower rates of past-month e-cigarette use among females versus males in 2011, 2012, and 2013. Year-to-year increases in the probability of e-cigarette use were also significant for both males and females across 2011 to 2013. Variations by race/ethnicity indicate modest differences in year-specific probabilities of current e-cigarette use. Across racial/ethnic subgroups, significant increases in the probability of current e-cigarette use were noted from 2011 to 2013, as well as from 2011 to 2012 and 2012 to 2013 for Whites and Mexican Americans, 2011 to 2012 for “other” Hispanics, and 2012 to 2013 for Blacks.

Figure 2 illustrates temporal patterns in current e-cigarette use by conventional cigarette smoking. The results show that across all survey years, those who experimented with cigarettes or were current smokers were significantly more likely to be current e-cigarette users than those who abstained from smoking. The probability of current e-cigarette use increased significantly from 2011 to 2013 for all groups, and from 2012 to 2013 for cigarette abstainers, 2011 to 2012 and 2012 to 2013 for cigarette experimenters, and 2011 to 2012 for current smokers.

Figure 3 illustrates results from a logistic regression model, adjusted for controls and based on the subsample of current smokers, predicting past-month e-cigarette use as a function of past-year quit attempts. Results indicate that among active smokers in 2011, the probability of past-month e-cigarette use was nearly identical between those who had attempted to quit smoking at least once in the previous year (11.6%) and those who had not (10.2%). In 2012, the probability of current e-cigarette use rose to 23.9% among those who tried to quit smoking and 30.5% among those who did not; and in 2013, these figures were 32.1% and 35.3%, respectively.

Discussion

While e-cigarettes may reduce the disease burden associated with adult smoking (Bullen et al., 2010; Caponnetto et al., 2013; Polosa et al., 2011), concerns persist over the potential for e-cigarettes to facilitate adolescent nicotine use (Leventhal et al., 2015; Wills et al., 2016). These concerns are buttressed by reports identifying a number of lapses in regulation and industry practices that make e-cigarettes available and appealing to adolescents (Duke et al., 2014; Williams et al., 2015).

Research is needed to understand how e-cigarette experimentation and current use figure into the recent population shifts in adolescent e-cigarette use. It also remains unclear whether the population-level increase in teen e-cigarette use has occurred evenly across demographic subgroups. Furthermore, because e-cigarettes have become frequent talking points in the discourse on adult tobacco cessation, adolescent smokers wishing to quit tobacco could have become more likely to use e-cigarettes over time—for health-positive reasons. Given these knowledge gaps, the current study was framed around three questions: (1) How have e-cigarette experimentation and current usage shifted between 2011 and 2013? (2) How have the correlates of adolescent e-cigarette use changed over time? (3) Have quit attempts become more important predictors of adolescent e-cigarette use among current smokers?

Study results show that e-cigarette experimentation and current use increased between 2011 and 2013. Additionally, those interviewed in 2013 were more likely to be e-cigarette experimenters or current users versus abstainers compared to those interviewed in 2012. While these findings are generally consistent with prior reports (Centers for Disease Control and Prevention, 2014, 2015), two additional findings have not been documented previously. First, the odds of current e-cigarette use versus experimentation were higher in 2013 versus 2012 and 2011. One interpretation of this is that regular use of e-cigarettes is becoming more common relative to experimental use. Second, among adolescents who had tried e-cigarettes, Blacks and Hispanics were more likely to be current versus experimental users compared to Whites. This suggests that e-cigarette using Black and Hispanic teens are a more selective group given to active rather than experimental use. These patterns could also reflect racial differences in the purpose of use, with Blacks and Hispanics using e-cigarettes to quit—or augment—their smoking habits. More research is needed to understand these racial/ethnic differences.

Study findings also demonstrate important subgroup differences in the temporal shifts in e-cigarette use. For instance, the probability of current e-cigarette use rose significantly from 2011 to 2012 and from 2012 to 2013 for Whites and Mexican Americans, but among Blacks, this was true from 2012 to 2013 only. This suggests that population-level increases in the rate of teen e-cigarette use may have been driven by increases in use among certain subgroups (e.g., Whites and Hispanics), with contributions from other groups coming in later years.

Results show associations between conventional and electronic cigarette use, with significant increases in the probability of current e-cigarette use occurring for both experimental and current smokers between 2011 and 2012, and between 2012 and 2013 for the former group only. Among current cigarette smokers, the probability of current e-cigarette use was not significantly different between those who had or had not attempted to quit using tobacco in the past year. This was true across all survey years and suggests that adolescent e-cigarette users are concurrently using conventional cigarettes rather than substituting one for the other. Furthermore, supplementary analyses (see Note 2) revealed that current cigarette smokers were more likely to use e-cigarettes than a small subgroup of former smokers. These results provide additional evidence that e-cigarettes play trivial roles in teenage tobacco cessation, though caution should be taken when evaluating this very small group of quitters. Like adults (Brandon et al., 2015), adolescent smokers may strategically use e-cigarettes in circumstances where smoking is prohibited, such as public areas or their parents’ homes (Kong et al., 2015).

The study has several limitations. First, the cross-sectional design of the NYTS prevents conclusions being drawn about the sequence of e-cigarette and conventional cigarette initiation. Second, the NYTS includes a limited range of sociodemographic measures and almost no information on socioeconomic, geographic, or family background measures. Omitting such measures could bias the results. Finally, the NYTS questions concerning e-cigarettes do not include references to vaping pens, electronic hookahs, or hookah pens. This may underestimate the prevalence of use (Richtel, 2014).

Despite these limitations, this study provides useful insights into recent trends in adolescent e-cigarette use. Notwithstanding their lower toxicity relative to combustible cigarettes, e-cigarettes contain nicotine which has known implications for neurological development during adolescence (Dwyer, McQuown, & Leslie, 2009; Goriounova & Mansvelder, 2012; Liao, Chen, Lee, Lu, & Chen, 2012). While the harm reduction aspects of e-cigarettes have been emphasized to adult smokers through media messages (Pepper, Emery, Ribisl, Southwell, & Brewer, 2014) and physician interactions (Kandra, Ranney, Lee, & Goldstein, 2014), the health risks of e-cigarettes remain unclear and may be more pronounced during the sensitive life course stage of adolescence. These and other concerns lend merit to the U.S. Food and Drug Administration’s proposed rule to regulate e-cigarettes in 2014. While the scope of potential Food and Drug Administration regulations is unclear, some health scholars have argued that bringing electronic cigarettes under specific regulatory provisions of the 2009 Family Smoking Prevention and Tobacco Control Act could discourage use among younger people (Pesko, Kenkel, Wang, & Hughes, 2016). These include eliminating the use of flavoring in e-cigarettes, restricting advertisements and Internet sales, adopting uniform age restrictions, and prohibiting the use of e-cigarettes in places where conventional cigarettes are similarly prohibited. Additional steps could include taxing e-cigarettes in similar ways as conventional tobacco products, a measure that several states have already taken (Gourdet, Chriqui, & Chaloupka, 2014).

Footnotes

Author’s Note

The data of the NYTS study are available for download at .

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Notes

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