Abstract
Objectives:
E-cigarette use has become increasingly prevalent among U.S. youth and young adults in recent years. Exposure to e-cigarette marketing may stimulate e-cigarette use. This study estimated the longitudinal association between e-cigarette marketing exposure and e-cigarette experimentation among U.S. youth and young adult never tobacco users.
Methods:
The analysis included nationally representative samples of youth (ages 12–17; N=8,121) and young adult (ages 18–24; N=1,683) never tobacco users from Wave 2 (2014–2015) and Wave 3 (2015–2016) of the Population Assessment of Tobacco and Health Study. The study measured past-month exposure to e-cigarette marketing through various places (e.g., websites and events) at Wave 2 and e-cigarette experimentation at Wave 3. Statistical analysis included multivariable regressions to examine the associations between Wave 2 e-cigarette marketing exposure and Wave 3 e-cigarette experimentation.
Results:
At Wave 2, 70.7% of youth and 73.9% of young adult never tobacco users reported past-month exposure to e-cigarette marketing; at Wave 3, 4.9% and 4.5% of youth and young adults experimented with e-cigarettes, respectively. Youth and young adults exposed to e-cigarette marketing at Wave 2 were more likely (AOR=1.53, CI=1.07–2.17; and AOR=2.73, CI=1.16–6.42, respectively) to have experimented with e-cigarettes at Wave 3 than those not exposed. Marketing exposure through each place at Wave 2 was associated with e-cigarette experimentation at Wave 3.
Conclusions:
E-cigarette marketing exposure predicted subsequent e-cigarette experimentation among youth and young adult never tobacco users. Increased restrictions on marketing through various channels may help minimize their exposure to e-cigarette marketing messages.
Table of contents summary:
Past-month exposure to e-cigarette marketing predicted future e-cigarette experimentation in this longitudinal cohort study of youth and young adult never tobacco users.
INTRODUCTION
E-cigarette use has become more prevalent among youth and young adults in the U.S. in recent years. In 2018, about 5% and 21% of U.S. middle and high school students, respectively, reported using e-cigarettes in the past 30 days, rising from 1% and 2% in 2011;1 and, in 2017, about 5% of young adults (ages 18–24) used e-cigarettes “some days” or “every day,” rising from 2% in 2012.2 E-cigarette products often contain nicotine, and nicotine exposure during adolescence and early adulthood can harm the developing brain.3 Although e-cigarette use alone is considered to produce fewer toxicants than smoking cigarettes,4 e-cigarette use can still cause respiratory health issues4 and may lead to nicotine addiction.5 Additionally, e-cigarette use among tobacco-naïve young people, even just experimentation, is associated with subsequent uptake of combustible cigarettes,6–8 which remains the leading cause of preventable death in the U.S.9 Therefore, minimizing the likelihood that tobacco-naïve young people experiment with e-cigarettes is a critical component of the effort to prevent adverse tobacco-related health outcomes nationwide.
One possible strategy to reduce e-cigarette use among this population is to reduce their exposure to e-cigarette marketing. E-cigarette marketing expenditures have continued to rise rapidly in the U.S.,10,11 with a corresponding increase observed in e-cigarette sales.12 In 2016, the Food and Drug Administration (FDA) finalized a rule extending its regulatory authority to e-cigarettes.7 New regulations by the agency require all e-cigarette packages and advertisements to include a prominent warning message about the presence and addictiveness of nicotine, although no restrictions have been applied to the placement or volume of e-cigarette marketing.7 Previous studies showed that significant proportions of youth and young adults are exposed to e-cigarette marketing through the Internet, newspapers/magazines, TV/movies, radio, and retail stores.15–18 One study found that as many as 80% of U.S. youth (about 21 million) were exposed to e-cigarette advertisements in 2016.15 Exposure to e-cigarette marketing may promote e-cigarette experimentation by forging positive perceptions about the behavior in the minds of youth and young adults, a pattern which has been observed for combustible cigarettes.19 Informed by theories of social influence and persuasion, a growing body of work has examined linkages between e-cigarette marketing exposure and e-cigarette use progression.16,20–24
These studies, however, have several limitations. First, many of these studies have either used regional or convenience samples16,22,24 which may have limited generalizability, or a cross-sectional design20,22 which cannot rule out reverse causation. Second, the existing longitudinal studies16,21,23 did not assess whether the associations between e-cigarette marketing exposure and e-cigarette use progression differ by e-cigarette use susceptibility at baseline. Theory and research on the stages of tobacco use progression indicate that tobacco use susceptibility serves as a precursor for subsequent tobacco use.25 Consequently, it is critical to assess whether those who are susceptible to e-cigarette use differ in the risk for future use after marketing exposure compared to those who are not susceptible. Third, most of the studies that examined e-cigarette marketing exposure have focused on youth16,20,23,24 but not young adults. Young adults have become increasingly vulnerable to the tobacco industry’s marketing tactics26 and likely to initiate tobacco products (including e-cigarettes).27
To overcome these limitations, this current study analyzed secondary data from the Population Assessment of Tobacco and Health (PATH) Study28 to assess longitudinal associations between e-cigarette marketing exposure and subsequent e-cigarette experimentation among youth (ages 12–17) and young adult (ages 18–24) never tobacco users, stratified by e-cigarette use susceptibility at the baseline. We hypothesized that the exposure to e-cigarette marketing at baseline may increase the likelihood of e-cigarette use experimentation among youth and young adults at one-year follow-up.
METHODS
Study Samples
This study used data from Wave 2 (2014–2015) and Wave 3 (2015–2016) youth and adult survey public-use files of the PATH Study, which includes nationally representative, longitudinal cohorts of civilian, non-institutionalized youth and adults in the U.S.28 The PATH Study’s weighted response rates at Wave 1 were 74.0% and 78.4% for adults and youth, respectively.29 The weighted retention rates for Waves 2 and 3 among Wave 1 respondents were 83.2% and 78.4% for adults, and 87.3% and 83.3% for youth, respectively.29 More details about the PATH Study, including both youth and adult surveys, can be found elsewhere.28,29 For this prospective analysis, we restricted the sample to youth (ages 12–17; n=8,121) and young adult (ages 18–24; n=1,683) respondents who completed both Waves 2 and 3 surveys and had never used any type of tobacco products (cigarettes, e-cigarettes, cigars, hookah, smokeless tobacco, tobacco pipes, bidis, and kreteks) at Wave 2. Ever tobacco users were excluded from the analysis in order to remove the potential confounding effect of prior tobacco use experience as an alternate pathway for e-cigarette use.
Response Variable: E-cigarette Experimentation Between Waves 2 and 3
Waves 2 and 3 of the survey asked youth and young adult respondents: “Have you ever used an electronic nicotine product, even once or two times (electronic nicotine products include e-cigarettes, e-cigars, e-hookahs, personal vaporizers, vape pens and hookah pens)?” We considered those who responded “Yes” at Wave 3 as having used e-cigarettes between Waves 2 and 3.
Predictor Variables: E-cigarette Marketing Exposure at Wave 2
At Wave 2, respondents were asked: “In the past 30 days, have you noticed e-cigarettes being advertised in any of the following places?” (“Yes” and “No” options were displayed for each of these places): “On posters or billboards,” “In newspapers or magazines,” “On websites or social media sites,” “On radio,” “On television,” and “At events like fairs, festivals, or sporting events.” We considered respondents who chose at least one place of exposure as exposed to e-cigarette marketing at baseline; conversely, we considered respondents who did not choose any of the places as unexposed. We also treated the number of places for e-cigarette marketing exposure as a continuous variable (range: 0–6).
Covariates
We used the following sociodemographic characteristics measured at Wave 2 as covariates: age, gender identity, race/ethnicity, annual household income, and highest educational attainment of the young adults or, in the case of the youth, their parents (see Table 1 for variable categories). Psychosocial covariates used for the analysis were past-month, self-reported internalizing problems (e.g., depression, anxiety, and distress) and externalizing problems (e.g., having a hard time paying attention, having a hard time listening to directions)30 Internalizing and externalizing problems, measured by the Global Appraisal of Individual Needs Short Screener (GAIN-SS),31 were found to predict a heightened likelihood of substance use (including tobacco products).30,32 GAIN-SS demonstrated moderate to high reliability using youth and young adult samples.33
Table 1.
Weighted socio-demographics, psychosocial characteristics, and e-cigarette experimentation by e-cigarette use susceptibility, PATH Study (Waves 2 and 3 interviews) 2014–2016
| Youth and Young Adult Sample Characteristics | ||||||
|---|---|---|---|---|---|---|
| Youth (ages 12–17) | Young Adult (ages 18–24) | |||||
| Overall | Susceptible to E-cigarette Use | Not Susceptible to E-cigarette Use | Overall | Susceptible to E-cigarette Use | Not Susceptible to E-cigarette Use | |
| N=8,121 | N=1,218 | N=6,530 | N=1,683 | N=454 | N=1,228 | |
| Weighted % (95% CI) | Weighted % (95% CI) | Weighted % (95% CI) | Weighted % (95% CI) | Weighted % (95% CI) | Weighted % (95% CI) | |
| Wave 2 Interview | ||||||
| Age | ||||||
| 12–14 years | 65.7 (65.0, 66.3) | 54.0 (51.1, 57.0) | 65.7 (64.9, 66.6) | ---1 | ||
| 15–17 years | 34.3 (33.7, 35.0) | 46.0 (43.1, 48.9) | 34.3 (33.4, 35.2) | |||
| Gender Identity | ||||||
| Male | 50.4 (49.8, 51.1) | 51.0 (47.8, 54.3) | 50.6 (49.6, 51.5) | 43.9 (41.5, 46.4) | 45.6 (39.5, 51.8) | 43.4 (40.2, 46.5) |
| Female | 49.6 (48.9, 50.2) | 49.0 (45.8, 52.2) | 49.4 (48.5, 50.4) | 56.1 (53.6, 58.5) | 54.4 (48.2, 60.5) | 56.6 (53.5, 59.8) |
| Race/Ethnicity | ||||||
| Non-Hispanic White | 53.6 (52.5, 54.8) | 56.8 (52.2, 61.3) | 51.4 (45.1, 57.6) | 52.8 (48.7, 56.9) | 48.4 (42.6, 54.2) | 54.3 (49.2, 59.4) |
| Non-Hispanic Black | 14.2 (13.4, 15.0) | 13.1 (11.1, 15.5) | 13.6 (10.4, 17.6) | 14.4 (12.4, 16.7) | 14.4 (11.3, 18.4) | 14.5 (12.1, 17.2) |
| Hispanic | 22.9 (22.0, 23.9) | 18.3 (15.3, 21.8) | 21.1 (17.1, 25.8) | 19.4 (16.8, 22.2) | 23.0 (19.2, 27.2) | 18.1 (15.2, 21.5) |
| Non-Hispanic Other | 9.3 (8.5, 10.1) | 11.8 (8.4, 16.2) | 13.9 (9.3, 20.3) | 13.4 (10.3, 17.2) | 14.2 (10.0, 19.9) | 13.1 (9.8, 17.3) |
| Annual Household Income | ||||||
| <$50,000 | 39.3 (37.6, 41.2) | 44.9 (41.5, 48.4) | 37.5 (35.6, 39.5) | 57.8 (55.0, 60.6) | 52.4 (46.3, 58.4) | 59.6 (56.2, 63.0) |
| ≥50,000 | 48.6 (46.9, 50.3) | 44.7 (41.1, 48.5) | 50.3 (48.4, 52.1) | 30.0 (27.5, 32.6) | 35.5 (29.7, 41.8) | 28.2 (25.2, 31.3) |
| Undetermined | 12.1 (11.1, 13.0) | 10.4 (8.1, 13.2) | 12.2 (11.2, 13.3) | 12.2 (10.3, 14.5) | 12.1 (8.9, 16.2) | 12.2 (9.9, 14.9) |
| Highest Educational Attainment | ||||||
| ≤High School | 29.7 (28.0, 31.5) | 35.2 (31.9, 38.6) | 28.1 (26.3, 29.9) | 42.3 (39.5, 45.2) | 41.5 (36.3, 46.9) | 42.5 (39.1, 46.0) |
| >High School | 62.7 (60.9, 64.4) | 57.9 (54.6, 61.2) | 64.2 (62.4, 66.0) | 57.7 (54.8, 60.5) | 58.5 (53.1, 63.7) | 57.5 (54.0, 60.9) |
| Undetermined | 7.6 (6.9, 8.3) | 6.9 (5.0, 9.3) | 7.7 (7.1, 8.4) | --- | ||
| Past-month Internalizing Problems | ||||||
| Yes | 50.8 (49.4, 52.2) | 63.1 (59.7, 66.5) | 49.4 (48.0, 50.9) | 38.4 (35,5, 41.3) | 46.9 (41.0, 52.8) | 35.6 (32.3, 39.0) |
| No | 49.2 (47.9, 50.6) | 36.9 (33.5, 40.4) | 50.6 (49.1, 52.0) | 61.6 (58.7, 64.5) | 53.1 (47.2, 59.0) | 64.4 (61.0, 67.7) |
| Past-month Externalizing Problems | ||||||
| Yes | 58.8 (57.5, 60.2) | 71.6 (68.2, 74.9) | 57.6 (56.2, 59.1) | 46.4 (43.4, 49.5) | 59.5 (54.0, 64.7) | 42.0 (38.6, 45.5) |
| No | 41.2 (39.8, 42.6) | 28.4 (25.1, 31.8) | 42.4 (40.9, 43.8) | 53.6 (50.5, 56.6) | 40.5 (35.3, 46.0) | 58.0 (54.5, 61.4) |
| E-cigarette Use Susceptibility | ||||||
| Yes | 14.5 (13.6, 15.4) | --- | 24.9 (22.5, 27.5) | --- | ||
| No | 85.5 (84.6, 86.4) | 75.1 (72.5, 77.5) | ||||
| Wave 3 Interview | ||||||
| E-cigarette Experimentation | ||||||
| Yes | 4.9 (4.3, 5.5) | 15.2 (13.0, 17.9) | 3.4 (2.9, 3.9) | 4.5 (3.5, 5.6) | 9.0 (6.5, 12.4) | 3.0 (2.1, 4.1) |
| No | 95.1 (94.5, 95.7) | 84.8 (82.1, 87.1) | 96.6 (96.1, 97.1) | 95.5 (94.4, 96.5) | 91.0 (87.6, 93.5) | 97.0 (95.9, 97.9) |
Notes: Percentages, confidence intervals are all weighted estimates.
Age categorization was not available for young adults (ages 18–24) from the PATH public use data files.
Stratification Variable: E-cigarette Use Susceptibility at Wave 2
We measured e-cigarette use susceptibility, defined as the absence of a firm commitment not to use e-cigarettes,25 using the following questions, each of which had four response options (definitely not, probably not, probably yes, and definitely yes): (1) at any time in the next year do you think you will use these products?; (2) do you think in the future you will experiment with these products?; and (3) if one of your best friends were to offer you these products would you use them? Consistent with previous work,25 we classified respondents as not susceptible to future e-cigarette use if they answered “definitely not” to all three questions; otherwise, we classified them as susceptible.
Statistical Analysis
We conducted the following statistical analyses using Stata 14.0 (StataCorp, College Station, TX) in both youth and young adult samples. First, we examined the respondent characteristics. Second, we gauged the prevalence of exposure to e-cigarette marketing via specific places of exposure. Third, we used multivariable regression models to examine the characteristics associated with e-cigarette marketing exposure at Wave 2. Lastly, we conducted separate multivariable logistic regressions to assess the associations between Wave 2 e-cigarette marketing exposure and Wave 3 e-cigarette experimentation. The predictor variables for these models included exposure to any e-cigarette marketing (yes or no), the number of places of marketing exposure (range: 0 to 6), and whether exposure occurred in each of the six places (yes or no). We stratified the samples by Wave 2 e-cigarette use susceptibility. Furthermore, we conducted sensitivity analysis to examine whether the relationships between exposure to e-cigarette marketing and experimentation change after controlling for having at least one close friend using e-cigarettes for youth and living with at least one person using e-cigarettes for young adults. Since the results were highly consistent, we kept simpler models for parsimony.
We used the Wave 3 weights when calculating proportions with 95% confidence intervals, adopting the balanced repeated replications (BRR) method with Fay’s adjustment of 0.3.29 Wave 3 weights also accounted for loss to follow-up from Wave 2 to Wave 3.29 We used imputed socioeconomic covariates and included an “undetermined” category for variables with missing values greater than 5%. For the regression procedures, we excluded observations with missing values by listwise deletion.34 This research only involved the use of de-identified data, which is not considered human subjects research and requires no IRB review or approval per National Institutes of Health policy and 45 CFR 46.
RESULTS
Demographic Characteristics
The sample of youth was balanced on gender (female: 50.4%; male: 49.6%) but had a higher proportion of younger youth (12–14 years: 65.7%; 15–17 years: 34.3%) (Table 1). The sample of young adults had slightly more females (female: 56.1%; male: 43.9%). About 14.5% and 24.9% of youth and young adults who had never used tobacco before were susceptible to e-cigarette use, respectively.
E-cigarette Marketing Exposure
Overall, 70.7% and 73.9% of youth and young adult never tobacco users (about 11 million and 7 million in the U.S., respectively) reported e-cigarette marketing exposure in the past month (Table 2). Those who were susceptible to e-cigarette use were more likely to report e-cigarette marketing exposure than those who were not susceptible for both samples (80.7% vs. 70.6% for youth; and 84.4% vs. 70.4% for young adults).
Table 2.
Prevalence of Exposure to E-cigarette Marketing in the Past Month at Wave 2, PATH Study (Wave 2 interview) 2014–2015
| Youth (ages 12–17) | Young Adults (ages 18–24) | |||||
|---|---|---|---|---|---|---|
| Overall | Susceptible to E-cigarette Use | Not Susceptible to E-cigarette Use | Overall | Susceptible to E-cigarette Use | Not Susceptible to E-cigarette Use | |
| E-cigarette Marketing Exposure to Any Places (Weighted %, 95% CI) | ||||||
| 70.7 (69.3, 72.1) | 80.7 (78.2, 82.9) | 70.6 (69.1, 72.0) | 73.9 (71.1, 76.6) | 84.4 (80.4, 87.8) | 70.4 (67.1, 73.5) | |
| Number of Places for E-cigarette Marketing Exposure (Weighted Mean, 95% CI) | ||||||
| 1.91 (1.86, 1.96) | 2.42 (2.32, 2.52) | 1.88 (1.82, 1.94) | 2.11 (2.01, 2.22) | 2.49 (2.29, 2.69) | 1.99 (1.87, 2.10) | |
| Places of Marketing Exposure (Weighted %, 95% CI) | ||||||
| Television | 60.6 (58.9, 62.2) | 73.8 (70.7, 76.7) | 60.0 (58.2, 61.8) | 61.3 (57.6, 64.9) | 74.8 (68.2, 80.4) | 57.3 (53.1, 61.5) |
| Posters or billboards | 60.3 (58.5, 62.0) | 73.4 (70.1, 76.4) | 60.1 (58.2, 62.0) | 65.1 (61.6, 68.5) | 77.7 (71.9, 82.5) | 61.3 (57.4, 65.0) |
| Websites or social media | 56.5 (54.7, 58.4) | 73.6 (70.4, 76.5) | 55.5 (53.5, 57.5) | 64.0 (60.4, 67.4) | 78.4 (72.9, 83.1) | 59.3 (55.1, 63.3) |
| Newspapers or magazines | 55.9 (54.4, 57.5) | 70.5 (66.9, 73.9) | 55.6 (53.9, 57.3) | 61.2 (57.4, 64.8) | 76.0 (70.0, 81.2) | 56.4 (52.2, 60.5) |
| Radio | 32.8 (30.6, 35.0) | 48.0 (43.4, 52.6) | 32.5 (30.2, 35.0) | 39.8 (35.7, 44.1) | 55.1 (45.9, 64.0) | 35.9 (31.7, 40.4) |
| Event like fairs and festivals | 30.3 (28.4, 32.4) | 46.4 (41.7, 51.1) | 29.8 (27.5, 32.2) | 39.0 (34.9, 43.2) | 60.5 (52.0, 68.4) | 32.4 (28.0, 37.2) |
Table 3 shows that in the multivariable regression model for youth, being non-Hispanic black (adjusted odds ratios or AOR=1.20, 95% CI=1.01–1.44), having past-month internalizing (AOR=1.50, 95% CI=1.30–1.73) and externalizing (AOR=1.81, 95% CI=1.31–1.82) problems, and being susceptible to e-cigarette use (AOR=1.54, 95% CI=1.31–1.82) was associated with e-cigarette marketing exposure. The results from the stratified analysis were similar to the results from the overall model for youth. As for young adults in general, being non-Hispanic black (AOR=0.59, 95% CI=0.42–0.85) and non-Hispanic other (AOR=0.47, 95% CI=0.25–0.89) were less likely to be associated with e-cigarette marketing exposure as compared to being non-Hispanic white. Similar risk factors were found for non-susceptible young adults. No significant covariates were found for susceptible young adults.
Table 3.
Logistic regressions for e-cigarette marketing exposure by e-cigarette use susceptibility at Wave 2, PATH Study (Wave 2 and 3 interviews) 2014–2016
| E-cigarette Marketing Exposure at Wave 2 | ||||||
|---|---|---|---|---|---|---|
| Youth (ages 12–17) | Young Adult (ages 18–24) | |||||
| Overall | Susceptible to E-cigarette Use | Not Susceptible to E-cigarette Use | Overall | Susceptible to E-cigarette Use | Not Susceptible to E-cigarette Use | |
| AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | |
| Age | ||||||
| 12–14 years | Reference | ----1 | ||||
| 15–17 years | 1.08 (0.95, 1.23) | 0.94 (0.64, 1.40) | 1.10 (0.96, 1.26) | |||
| Gender Identity | ||||||
| Male | Reference | Reference | ||||
| Female | 1.03 (0.90, 1.17) | 0.96 (0.68, 1.35) | 1.04 (0.91, 1.19) | 1.19 (0.89, 1.58) | 1.27 (0.59, 2.73) | 1.16 (0.84, 1.61) |
| Race/Ethnicity | ||||||
| Non-Hispanic White | Reference | Reference | ||||
| Non-Hispanic Black | 1.20 (1.01, 1.44) | 0.89 (0.57, 1.36) | 1.27 (1.04, 1.55) | 0.59 (0.42, 0.85) | 0.53 (0.21, 1.34) | 0.60 (0.40, 0.90) |
| Hispanic | 1.13 (0.96, 1.32) | 1.55 (1.01, 2.41) | 1.08 (0.91, 1.27) | 0.70 (0.47, 1.05) | 0.49 (0.24, 1.02) | 0.77 (0.51, 1.16) |
| Non-Hispanic Other | 0.80 (0.61, 1.03) | 0.60 (0.31, 1.10) | 0.82 (0.63, 1.06) | 0.47 (0.25, 0.89) | 0.55 (0.16, 1.94) | 0.45 (0.22, 0.92) |
| Annual Household Income | ||||||
| <$50,000 | Reference | Reference | ||||
| ≥50,000 | 1.05 (0.92, 1.20) | 1.05 (0.66, 1.65) | 1.06 (0.92, 1.24) | 1.61 (1.04, 2.50) | 1.79 (0.68, 4.70) | 1.56 (0.98, 2.48) |
| Undetermined | 0.91 (0.68, 1.22) | 2.66 (0.44, 16.07) | 0.85 (0.61, 1.18) | 0.80 (0.53, 1.20) | 1.22 (0.48, 3.09) | 0.74 (0.46, 1.18) |
| Highest Educational Attainment | ||||||
| ≤High School | Reference | Reference | ||||
| >High School | 1.06 (0.91, 1.24) | 1.34 (0.87, 2.07) | 1.02 (0.86, 1.21) | 1.32 (0.99, 1.77) | 1.42 (0.73, 2.78) | 1.33 (0.98, 1.79) |
| Undetermined | 1.00 (0.70, 1.44) | 0.28 (0.04, 1.94) | 1.10 (0.76, 1.60) | --- | ||
| Internalizing Problems | ||||||
| Yes | 1.50 (1.30, 1.73) | 1.49 (0.93, 2.39) | 1.51 (1.28, 1.77) | 1.33 (0.94, 1.88) | 1.31 (0.66, 2.60) | 1.35 (0.93, 1.95) |
| No | Reference | Reference | ||||
| Externalizing Problems | ||||||
| Yes | 1.81 (1.55, 2.11) | 1.56 (1.03, 2.36) | 1.84 (1.56, 2.17) | 1.80 (1.39, 2.32) | 1.31 (0.66, 2.60) | 1.83 (1.31, 2.57) |
| No | Reference | Reference | ||||
| E-cigarette Use Susceptibility | ||||||
| Yes | 1.54 (1.31, 1.82) | ---- | 2.05 (1.52, 2.78) | ---- | ||
| No | Reference | Reference | ||||
Notes: Confidence intervals are weighted estimates.
Age categorization was not available for young adults (ages 18–24) from the PATH public use data files.
Associations between E-cigarette Marketing Exposure and E-cigarette Experimentation
Between Waves 2 and 3, 4.9% and 4.5% of the overall youth and young adult never tobacco users experimented with e-cigarettes, respectively. Among youth, 5.7% and 3.0% of those who reported and did not report exposure to e-cigarette marketing at Wave 2 experimented with e-cigarettes at Wave 3, respectively. These percentages were 5.4% and 2.0% for young adults.
Table 4 shows that in separate multivariable regression models for overall youth, e-cigarette marketing exposure (AOR=1.53, 95% CI=1.07–2.17) and a higher reported number of places for marketing exposure (AOR=1.17, 95% CI=1.09–1.25) were associated with e-cigarette experimentation. For the susceptible youth, e-cigarette marketing exposure was not associated with e-cigarette experimentation, whereas an increase in the number of places for marketing exposure increased the odds of using e-cigarettes (AOR=1.17, 95% CI=1.04–1.32). For non-susceptible youth, exposure to e-cigarette marketing (AOR=1.68, 95% CI=1.07–2.64) and each additional place (AOR=1.16, 95% CI=1.06–1.26) increased the odds of e-cigarette experimentation. Reported exposure to each place of e-cigarette marketing was associated with subsequent e-cigarette experimentation among youth in general as well as non-susceptible youth.
Table 4.
Logistic regressions for e-cigarette experimentation at Wave 3 by e-cigarette use susceptibility at Wave 2, PATH Study (Wave 2 and 3 interviews) 2014–2016
| E-cigarette Experimentation at Wave 3 | ||||||
|---|---|---|---|---|---|---|
| Youth (ages 12–17) | Young Adults (ages 18–24) | |||||
| Overall | Susceptible to E-cigarette Use | Not Susceptible to E-cigarette Use | Overall | Susceptible to E-cigarette Use | Not Susceptible to E-cigarette Use | |
| AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | |
| Model 1 | ||||||
| E-cigarette Marketing Exposure to Any Places at Wave 2 | ||||||
| No Exposure | Reference | Reference | Reference | Reference | Reference | Reference |
| Exposure | 1.53 (1.07, 2.17) | 1.25 (0.73, 2.16) | 1.68 (1.07, 2.64) | 2.73 (1.16, 6.42) | 7.74 (1.63, 36.80) | 1.95 (0.71, 5.36) |
| Model 2 | ||||||
| Number of Places for E-cigarette Marketing Exposure at Wave 2 | ||||||
| 1.17 (1.09, 1.25) | 1.17 (1.04, 1.32) | 1.16 (1.06, 1.26) | 1.13 (0.99, 1.28) | 1.14 (0.92, 1.40) | 1.12 (0.93, 1.34) | |
| Model 3 | ||||||
| E-cigarette Marketing Exposure to Individual Place at Wave 2 | ||||||
| Television | ||||||
| No | Reference | Reference | Reference | Reference | Reference | Reference |
| Yes | 1.62 (1.11, 2.38) | 1.54 (0.86, 2.77) | 1.67 (1.02, 2.74) | 2.54 (1.04, 6.18) | 9.22 (1.96, 43.36) | 1.47 (0.47, 4.63) |
| Posters or billboards | ||||||
| No | Reference | Reference | Reference | Reference | Reference | Reference |
| Yes | 1.65 (1.14, 2.40) | 1.37 (0.78, 2.43) | 1.83 (1.13, 2.95) | 2.59 (1.09, 6.13) | 7.00 (1.43, 34.43) | 1.81 (0.64, 5.11) |
| Websites or social media sites | ||||||
| No | Reference | Reference | Reference | Reference | Reference | Reference |
| Yes | 1.81 (1.24, 2.63) | 1.41 (0.80, 2.50) | 2.05 (1.28, 3.27) | 3.30 (1.40, 7.78) | 8.52 (1.69, 42.97) | 2.48 (0.90, 6.87) |
| Newspapers or magazines | ||||||
| No | Reference | Reference | Reference | Reference | Reference | Reference |
| Yes | 1.60 (1.09, 2.34) | 1.38 (0.78, 2.44) | 1.76 (1.06, 2.94) | 2.66 (1.06, 6.64) | 6.11 (1.21, 30.89) | 2.13 (0.66, 6.85) |
| Radio | ||||||
| No | Reference | Reference | Reference | Reference | Reference | Reference |
| Yes | 1.89 (1.23, 2.89) | 1.72 (0.86, 3.44) | 1.92 (1.08, 3.41) | 3.01 (1.38, 6.59) | 6.36 (1.57, 25.66) | 1.77 (0.62, 5.08) |
| Event like fairs and festivals | ||||||
| No | Reference | Reference | Reference | Reference | Reference | Reference |
| Yes | 1.72 (1.09, 2.73) | 0.96 (0.56, 1.63) | 2.18 (1.25, 3.78) | 4.21 (1.49, 11.96) | 9.98 (1.44, 69.17) | 3.04 (0.79, 11.71) |
Notes: The logistic regression models controlled for age (youth only), gender identity, race/ethnicity, annual household income, highest educational attainment, past-month internalizing problems, past-month externalizing problem, and e-cigarette use susceptibility (for the overall models only). Confidence intervals are weighted estimates.
In the multivariable regression models for overall young adults, e-cigarette marketing exposure (AOR=2.73, 95% CI=1.16–6.42) was associated with subsequent e-cigarette use. Susceptible young adults exposed to e-cigarette marketing were about eight times as likely (AOR=7.74, 95% CI=1.63–36.80) to have experimented with e-cigarettes than those not exposed. No association, however, was found for non-susceptible young adults. An increased number of places of marketing exposure was not associated with e-cigarette experimentation among young adults regardless of their baseline susceptibility. Reported exposure to each place of e-cigarette marketing was associated with subsequent e-cigarette experimentation among young adults in general as well as susceptible young adults.
DISCUSSION
This study adds to the body of evidence consistent with the hypothesis that exposure to e-cigarette marketing may promote subsequent e-cigarette experimentation among U.S. youth and young adult never tobacco users. Youth and young adults reported pervasive exposure to e-cigarette marketing despite having limited tobacco use experience: more than 70% of youth and young adults in our sample (equivalent about 18 million of the U.S. population) reported exposure to e-cigarette marketing in the past month. Combined, these results signify the need for the FDA to reconsider the regulatory structures surrounding e-cigarette marketing. The minimal restrictions currently in place do not adequately prevent tobacco-naïve youth and young adults from frequently encountering e-cigarette marketing, and this exposure may indeed be consequential for uptake of e-cigarettes among otherwise non-tobacco users.35
Overall, e-cigarette marketing exposure was associated with increased odds of e-cigarette use among youth never tobacco users. This result was driven by non-susceptible youth who initially reported no interest in trying the product. This finding suggests that exposure to e-cigarette marketing may lead non-susceptible youth to develop e-cigarette use susceptibility within a one-year period or prompt this group to make non-rational decisions of trying e-cigarette products on impulse. Previous research has found a positive relationship between e-cigarette advertising exposure and increased e-cigarette use susceptibility among U.S. youth24 and identified links between exposure to tobacco advertising and impulse purchasing of tobacco products.36 Our results also showed that exposure to e-cigarette marketing, however, was not associated with subsequent e-cigarette experimentation among susceptible youth, though this finding may be attributable to a lack of statistical power—only 15% of the youth in our sample (N=1,218) were susceptible to e-cigarette use. Regardless, susceptible youth reported greater exposure to e-cigarette marketing across multiple places, suggesting the possibility that this exposure may play a role in increasing their susceptibility to use the product in the future.
As for young adults, although marketing exposure increased the odds of e-cigarette experimentation among the overall sample, the association did not remain significant among those who were not susceptible to e-cigarettes. In contrast, among susceptible young adults, e-cigarette marketing exposure was associated with an almost eight times greater odds of trying e-cigarettes. This finding suggests that e-cigarette marketing may serve as a final propelling force that pushes susceptible young adults to try the product, and identifies susceptible young adults as an important target for public health prevention efforts aimed at alleviating the influence of e-cigarette marketing. Additionally, since exposure to additional marketing channels did not further increase the odds of e-cigarette use among susceptible young adults, eliminating the presence of e-cigarette marketing—rather than just diminishing the breadth of its presence across many channels—may be necessary for e-cigarette use prevention among this group. Additionally, our results show that exposure to e-cigarette marketing through events like music festivals puts young adults at heightened risks of e-cigarette experimentation, signifying the need to develop and enforce regulations on experiential tobacco marketing strategies often used to attract young adults.37 Lastly, investigating the factors that protect the non-susceptible young adults from trying e-cigarettes may help inform counter-marketing messages geared towards the young adult population more generally.
This study also identified varying risks of e-cigarette marketing exposure by race and ethnicity. Non-Hispanic black youth were more likely to report exposure to e-cigarette marketing than their non-Hispanic white youth, whereas we observed an inverse relationship for young adults. We can only speculate that this pattern of results could be attributable to the media preferences,38 specific channels of marketing exposure,39 and tobacco marketing exposure recall and engagement40 that are different by race/ethnicity and age. Nevertheless, additional research is needed to understand the reasons for racial/ethnic differences in exposure to e-cigarette marketing, especially among youth never tobacco users, in order to mitigate potential health disparities experienced by racial/ethnic minority populations.
This study should be viewed with the following limitations. First, due to the unavailability of survey data, this study may not have accounted for all the confounders that explain the examined relationships. For example, sensation-seeking tendencies, which were not included in PATH Wave 2 surveys, may be related to both e-cigarette marketing exposure and experimentation. Second, the study results relied on respondents’ self-report of e-cigarette use and recalled e-cigarette marketing exposure without biochemical or observed validation. Recalled exposure to tobacco marketing may be closely linked to favorable responses (e.g., attention and liking) to tobacco marketing and/or tobacco products,41 and thus introducing potential bias to these results. Lastly, this study may not have fully captured all e-cigarette advertising seen by young people. For example, young people are likely to see e-cigarette advertising from tobacco retail settings15 which is not included in the PATH study survey. This may result in an underestimate of the prevalence of e-cigarette marketing exposure among the target population and potentially weaken the investigated association between e-cigarette marketing exposure and e-cigarette use behavior given the long-established strong relationship between tobacco marketing exposure in retail settings and tobacco use among young people.42–44 Additionally, online e-cigarette promotion messages disseminated through social media influencers and brand ambassadors45 may not be perceived as e-cigarette advertising by young people.
Further research is also warranted to investigate the specific features of e-cigarette marketing strategies that may influence a young viewer’s positive perceptions about e-cigarette use, which may consequently lead to e-cigarette experimentation. Based on prior work,17,46–48 we suspect that e-cigarette marketing may shape viewers’ behavior by introducing attractive flavors, reinforcing the alluring social benefits of vaping, illuminating the lifestyles of celebrities and young models, and offering price promotions and direct access to e-cigarette retail websites. Counter-marketing messages designed to address particularly impactful marketing features may help reduce the influence of e-cigarette marketing among the target populations.
What’s known on the subject:
E-cigarette marketing exposure is associated with e-cigarette use among youth in cross-sectional studies. It is yet unknown whether exposure to e-cigarette marketing prospectively predicts e-cigarette use experimentation among nationally representative samples of tobacco-naïve youth and young adults.
What this study adds:
This study provides the first prospective longitudinal evidence indicating that exposure to e-cigarette marketing through various marketing channels is positively associated with subsequent e-cigarette experimentation among U.S. youth and young adult never tobacco users.
Funding Source:
Efforts of JCC and KC were supported by the National Institute on Minority Health and Health Disparities Division of Intramural Research.
Abbreviations:
- CI
Confidence intervals
- AOR
adjusted odds ratios
- PATH Study
The Population Assessment of Tobacco and Health (PATH) Study
Footnotes
Financial Disclosure: The authors have no financial relationships relevant to this article to disclose.
Conflict of Interest: The authors have no conflicts of interest relevant to this article to disclose.
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