BACKGROUND:

Cigarettes have been strongly associated with subsequent marijuana use among adolescents, but electronic cigarettes (e-cigarettes) are now rapidly replacing traditional cigarettes among youth. This study examines associations between youth e-cigarette use and subsequent marijuana use in a national sample.

METHODS:

Youth (aged 12–17 years) never marijuana users at wave 1 (n = 10 364; 2013–2014) from the Population Assessment of Tobacco and Health study were followed-up in 1 year (wave 2, 2014–2015). Multivariable logistic regressions were performed to evaluate associations between e-cigarette use at wave 1 and ever/heavy marijuana use in the past 12 months (P12M) and at wave 2.

RESULTS:

Among never marijuana users, e-cigarette ever use (versus never use) at wave 1 was associated with increased likelihood of marijuana P12M use (adjusted odds ratio [aOR] = 1.9; 95% confidence interval [CI]: 1.4–2.5) at wave 2. There was a significant interaction between e-cigarette use and age (P < .05) with aOR = 2.7 (95% CI: 1.7–4.3) for adolescents aged 12 to 14 and aOR = 1.6 (95% CI: 1.2–2.3) for adolescents aged 15 to 17. The association with heavy marijuana use was significant among younger adolescents (aOR = 2.5; 95% CI: 1.2–5.3) but was not among older adolescents. Heavier e-cigarette use at wave 1 yielded higher odds of P12M and heavy marijuana use at wave 2 for younger adolescents.

CONCLUSIONS:

E-cigarette use predicts subsequent marijuana use among youth, with a stronger associations among young adolescents. Reducing youth access to e-cigarettes may decrease downstream marijuana use.

What’s Known on This Subject:

Cigarettes have been strongly associated with subsequent marijuana use among adolescents, but electronic cigarettes (e-cigarettes) are now rapidly replacing traditional cigarettes among youth. A growing body of literature shows that e-cigarettes could increase the subsequent risk of cigarette smoking among youth.

What This Study Adds:

Youth e-cigarette use was associated with subsequent marijuana use, especially among young adolescents aged 12 to 14 years. Policies influencing the exposure of youth to e-cigarettes may have downstream effects on uptake of marijuana.

Although the prevalence of current cigarette smoking among youth declined from 28% in 1996 to 8% in 2016,1,2 electronic cigarette (e-cigarette) use is gaining popularity.3 Among teenagers, the use of e-cigarettes has outpaced the use of traditional cigarettes, especially among young adolescents.1,4 It is also indicated in studies that marijuana remains the substance with the highest prevalence of use among youth; current use among high school seniors is nearly double that of current cigarette use.4 Moreover, teenagers’ attitudes are moving toward greater acceptance of marijuana. For instance, the percentage of high school seniors who perceived the occasional use of marijuana as harmful dropped from 27.4% in 2009 to 17.1% in 2016.4 

In a growing body of longitudinal studies, it has been shown that youth who use e-cigarettes have higher odds of smoking cigarettes later,5,9 suggesting that e-cigarettes might increase the risk of future cigarette use among adolescents. Researchers have also systematically evaluated cigarette smoking and marijuana use among adolescents. Authors of most longitudinal studies that examined cigarette smoking before marijuana use generally supported the association between baseline cigarette use and progression to use of other addictive substances (eg, marijuana).10,12 Although e-cigarettes are marketed as less harmful alternatives to smoking,13 most youth are not using e-cigarettes to quit smoking. Instead, youth are attracted by the novelty of e-cigarettes and a wide variety of flavors specifically designed to appeal to the youth market.14 If youth e-cigarette use follows the same pattern as cigarette smoking, widespread use could expose youth to social environments that encourage substance use, thereby accelerating youths’ transitions to the use of other substances with more adverse health effects.15,16 

Researchers in a few cross-sectional studies also examined the drug use patterns among adolescents, including poly use of e-cigarettes, marijuana, and other substances.17,18 These studies revealed complex drug use patterns, and researchers identified higher risks of poly-drug use among e-cigarette users (versus nonusers).17,18 Authors of cross-sectional studies have identified a high prevalence of marijuana vaping in both youth and adult populations.19,21 Researchers in a longitudinal study of Hispanic young adults further reported that e-cigarette use increased the likelihood of transitioning from nonusers to users of cigarettes or marijuana over a 1-year period.15 However, studies are needed to evaluate the longitudinal associations of e-cigarette use and future marijuana use among adolescents in population-based samples.

Youth undergo multiple stages of development, and thus the role of e-cigarette use in an individual’s drug use patterns may differ by age.17 The age of e-cigarette initiation is similar to that of alcohol and marijuana, with a rapid increase after age 14 years and peaking around age 17 to 18 years.22 Because younger e-cigarette users were more likely to be nonusers of traditional drugs,17 and adolescents who initiate substance use at an earlier age have higher risks for addiction and adverse health outcomes,23,24 we hypothesize that there might be an interaction between e-cigarette use and age in association with subsequent marijuana use. The risk difference in association between e-cigarette use and subsequent marijuana transition by age group has not been assessed in published studies.

To address these gaps, we analyzed the Population Assessment of Tobacco and Health (PATH) survey to examine the associations of e-cigarette use (ever or number of e-cigarettes and/or cartridges used) at baseline (wave 1) and subsequent marijuana use (past 12 months [P12M] or heavy use) 1 year later (wave 2). We further stratified the analyses by age groups (12–14 and 15–17 years old) to enhance our understanding of this potential relationship.

Data on e-cigarette and marijuana use were obtained from wave 1 and wave 2 of the PATH study, a longitudinal cohort study of tobacco use behaviors, attitudes, and beliefs among a nationally representative sample of US civilian, noninstitutionalized individuals aged 12 years and older.25 A 4-stage, stratified probability sample design was used in the PATH study, and further details regarding the data collection, study design, and methods can be found elsewhere.25,26 In this study, we used the public-use files of young participants aged 12 to 17 years at wave 1, continuing young participants (those still aged <18 years), and those who had become adults (age ≥18 years; “aged-up adults”) at wave 2.

Wave 1 of the PATH study was collected from September 2013 to December 2014 with 13 651 youth and 32 320 adults. Wave 2 of PATH was collected from October 2014 to October 2015 with 12 172 youth and 28 362 adults. The weighted response rate for the wave 1 household screener was 54.0%. Among screened households, the overall weighted response rate for the wave 1 youth interview was 78.4%. The weighted retention rate for continuing youth at wave 2 was 88.4%, and the weighted recruitment rate for aged-up adults was 85.7%.25,26 Because the PATH research team provides publicly available deidentified data, this study was determined to be nonhuman subjects research by the Children’s Mercy Institutional Review Board.

E-cigarette Use at Wave 1

All participants were shown a brief description and pictures of e-cigarettes followed by a question, “Have you seen or heard of e-cigarettes before this study?” Those who responded positively were asked, “Have you ever used an e-cigarette, even 1 or 2 times?” Those who responded “Yes” were categorized as e-cigarette ever users at wave 1. For the dose-response analysis, the number of e-cigarettes and/or cartridges used in an entire life was measured by an ordinal variable ranging from 0 (no use) to 7 (100 or more).27 See Fig 1.

FIGURE 1

Flowchart for participants selected in the final study.

FIGURE 1

Flowchart for participants selected in the final study.

Marijuana Use at Wave 1 and Wave 2

Ever use of marijuana at wave 1 was defined by 2 items from the PATH study: “Have you ever used marijuana, hash, THC, grass, pot, or weed?” and “Have you ever smoked part of all of a cigar, cigarillo, or filter cigar with marijuana in it?” Those who responded “No” to both of these questions were categorized as marijuana never users at wave 1. Those who responded “Yes” to either of these questions were categorized as marijuana ever users at wave 1.

At wave 2, marijuana never users at wave 1 who reported using marijuana in the P12M were categorized as marijuana P12M users. On the basis of an additional item, “Which substances did you use weekly or more often?” we further categorized those who responded “marijuana, hash, THC, grass, pot, or weed” as marijuana heavy users at wave 2.

Other Substance Use at Wave 1

Cigarette ever users were defined by the following item: “Have you ever tried cigarette smoking, even 1 or 2 puffs?” Those who responded “Yes” were categorized as cigarette ever users. Alcohol ever users were participants who responded “Yes” to the following item: “Have you ever used alcohol at all, including sips of someone’s drink or your own drink?” Those who reported ever misuse of prescription drugs (ie, Ritalin, Adderall, painkillers, sedatives, or tranquilizers) were categorized as nonmedical ever users of prescription drugs. Those who reported ever using cocaine or crack, stimulants like methamphetamine or speed, heroin, inhalants, solvents, or hallucinogens were categorized as other illicit drug ever users.

Sensation Seeking

Sensation seeking was assessed by 3 items modified from the Brief Sensation Seeking Scale.28 Participants were asked to indicate the extent of agreement on the 5-point scale to the following: “I like new and exciting experiences, even if I have to break the rules”; “I like to do frightening things”; and “I prefer friends who are exciting and unpredictable.” Sensation seeking was calculated as the average response to these 3 items (Cronbach’s α = 0.76).28 

Covariates

Several covariates were included to control for potential confounding effects: age (12–14 or 15–17 years old), sex (male or female), race and/or ethnicity (non-Hispanic [NH] white, NH African American, Hispanic, or NH other), grade performance (“mostly A’s” was classified as “A,” “A’s and B’s or mostly B’s” was classified as “B,” and “B’s and C’s or mostly C’s or below” was classified as “C or below”), parental education (less than high school, high school graduate, some college, or bachelor’s degree or greater), and region (Northeast, South, Midwest, West).

Statistical Methods

Weighted estimates of demographic characteristics and substance use at wave 1 were calculated for the overall sample and stratified by e-cigarette ever use status. Balanced repeated replication method with Fay’s adjustment = 0.3 was used to increase stability in variance estimation.29,30 Confidence intervals (CIs) at the 95% level were calculated by using Wilson’s method. For continuing youth, wave 2 sampling weights in youth data were used; for aged-up adults, wave 2 sampling weights in adult data were used.27 Separate multivariable logistic regressions were used to examine the associations of e-cigarette use (ever, number of e-cigarettes/cartridges used) at wave 1 on subsequent marijuana use (P12M or heavy use) at wave 2 among marijuana never users at wave 1. Stratified analyses were conducted by age group (12–14 and 15–17 years old at wave 1). Adjusted odds ratios (aORs) were calculated in the multivariable analysis, in which all risk factors and covariates were included. Statistical analyses were performed by using SAS 9.4 (SAS Institute, Inc, Cary, NC), and P values <.05 were considered statistically significant.

A total of 11 996 young participants aged 12 to 17 years completed both the wave 1 and wave 2 surveys. Ever users of marijuana (n = 1605; 13.4%) and subjects with missing marijuana ever use status (n = 27) at wave 1 were excluded. The final analysis included 10 364 never marijuana users at wave 1 (Fig 1). As compared with ever marijuana users, never marijuana users tend to be younger and less likely to report using cigarettes, e-cigarettes, and other substances (Supplemental Table 5).

Overall, 44.4% of never marijuana users were aged 15 to 17 years, and 48.8% were female. NH white users accounted for 55.1% of respondents, followed by Hispanic users (21.7%), NH African American users (13.9%), and NH other users (9.4%) (Table 1). In terms of substance use, 5.1% of adolescents reported ever use of e-cigarettes, 6.0% reported ever smoking cigarettes, 31.0% reported ever drinking alcohol, 7.3% reported ever nonmedical use of prescription drugs, and 0.2% reported ever use of other illicit drugs.

TABLE 1

Sample Characteristics and Substance Use Prevalence, Overall and Stratified by E-cigarette Use Status Among Never Marijuana Users at Wave 1, PATH Study 2013–2015

Marijuana Never Users at Wave 1TotalWave 1 E-cigarette Ever Use
n = 10 364No, n = 9820Yes, n = 511Pa
CharacteristicsnWeighted % (95% CI)bnWeighted % (95% CI)bnWeighted % (95% CI)b
Age, y       <.0001 
 12–14 5901 55.6 (54.7–56.6) 5703 56.9 (55.9–57.9) 177 31.7 (27.8–35.8) 
 15–17 4463 44.4 (43.4–45.3) 4117 43.1 (42.1–44.1) 334 68.3 (64.2–72.2) 
Sex       <.0001 
 Male 5298 51.2 (50.2–52.1) 4971 50.6 (49.7–51.6) 307 60.2 (55.9–64.4) 
 Female 5066 48.8 (47.9–49.8) 4849 49.4 (48.4–50.3) 204 39.8 (35.6–44.1) 
Race and/or ethnicity       .0205 
 NH white 5079 55.1 (54.1–56.0) 4783 54.7 (53.7–55.7) 284 61.5 (57.3–65.7) 
 NH African American 1418 13.9 (13.2–14.5) 1361 14.0 (13.3–14.7) 50 11.4 (8.8–14.5) 
 Hispanic 2932 21.7 (20.9–22.5) 2790 21.8 (21.0–22.6) 129 19.5 (16.3–23.1) 
 NH others 935 9.4 (8.8–9.9) 886 9.5 (8.9–10.1) 48 7.6 (5.6–10.3) 
Grade performance       <.0001 
 A 2807 29.2 (28.1–30.4) 2739 30.0 (28.9–31.2) 65 14.7 (11.7–18.3) 
 B 4528 43.4 (42.2–44.5) 4306 43.5 (42.4–44.6) 213 42.3 (37.7–46.9) 
 C or below 2986 27.4 (26.3–28.6) 2733 26.5 (25.4–27.6) 232 43.1 (38.7–47.6) 
Parental education       .0110 
 Less than high school 2097 17.5 (16.3–18.8) 1985 17.4 (16.2–18.7) 100 17.5 (14.1–21.6) 
 High school graduate 1874 17.4 (16.3–18.6) 1752 17.2 (16.0–18.4) 115 21.5 (17.6–26.0) 
 Some college 3256 31.5 (30.0–33.0) 3064 31.3 (29.7–32.8) 183 36.1 (31.8–40.6) 
 Bachelor’s degree or greater 3068 33.6 (31.4–35.9) 2953 34.1 (31.8–36.5) 110 24.9 (20.2–30.3) 
Region       .0110 
 Northeast 1482 16.5 (15.8–17.2) 1422 16.7 (16.0–17.5) 59 13.4 (10.7–16.8) 
 South 2314 21.8 (21.1–22.7) 2193 21.9 (21.0–22.7) 114 21.1 (17.7–25.0) 
 Midwest 3932 38.1 (37.2–39.0) 3699 37.7 (36.8–38.7) 218 44.8 (40.1–49.5) 
 West 2636 23.6 (22.8–24.4) 2506 23.7 (22.9–24.6) 120 20.6 (17.4–24.4) 
E-cigarette ever use       — 
 No 9820 94.9 (94.5–95.4) 9820 100.0 — — 
 Yes 511 5.1 (4.6–5.5) — — 511 100.0 
Cigarette ever use       <.0001 
 No 9732 94.0 (93.3–94.6) 9416 96 (95.6–96.5) 285 55.7 (51.1–60.3) 
 Yes 619 6.0 (5.4–6.7) 393 4.0 (3.5–4.4) 225 44.3 (39.7–48.9) 
Alcohol ever use       <.0001 
 No 7226 69.0 (67.6–70.4) 7013 70.7(69.3–72.1) 185 36.1 (31.5–40.9) 
 Yes 3088 31.0 (29.6–32.4) 2759 29.3 (27.9–30.7) 325 63.9 (59.1–68.5) 
Nonmedical use of prescription drugsc       .0011 
 No 9467 92.7 (92.1–93.3) 8992 92.9 (92.3–93.5) 446 89.2 (86.2–91.6) 
 Yes 743 7.3 (6.7–7.9) 684 7.1 (6.5–7.7) 57 10.8 (8.4–13.8) 
Other illicit drug ever used       <.0001 
 No 10 255 99.8 (99.7–99.8) 9725 99.8 (99.7–99.9) 500 99.0 (97.8–99.6) 
 Yes 29 0.2 (0.2–0.3) 22 0.2 (0.1–0.3) 1.0 (0.4–2.2) 
Sensation seeking (mean and SE)e 10 164 2.54 ± 0.01 9633 2.51 ± 0.01 502 3.08 ± 0.05 <.0001 
Marijuana Never Users at Wave 1TotalWave 1 E-cigarette Ever Use
n = 10 364No, n = 9820Yes, n = 511Pa
CharacteristicsnWeighted % (95% CI)bnWeighted % (95% CI)bnWeighted % (95% CI)b
Age, y       <.0001 
 12–14 5901 55.6 (54.7–56.6) 5703 56.9 (55.9–57.9) 177 31.7 (27.8–35.8) 
 15–17 4463 44.4 (43.4–45.3) 4117 43.1 (42.1–44.1) 334 68.3 (64.2–72.2) 
Sex       <.0001 
 Male 5298 51.2 (50.2–52.1) 4971 50.6 (49.7–51.6) 307 60.2 (55.9–64.4) 
 Female 5066 48.8 (47.9–49.8) 4849 49.4 (48.4–50.3) 204 39.8 (35.6–44.1) 
Race and/or ethnicity       .0205 
 NH white 5079 55.1 (54.1–56.0) 4783 54.7 (53.7–55.7) 284 61.5 (57.3–65.7) 
 NH African American 1418 13.9 (13.2–14.5) 1361 14.0 (13.3–14.7) 50 11.4 (8.8–14.5) 
 Hispanic 2932 21.7 (20.9–22.5) 2790 21.8 (21.0–22.6) 129 19.5 (16.3–23.1) 
 NH others 935 9.4 (8.8–9.9) 886 9.5 (8.9–10.1) 48 7.6 (5.6–10.3) 
Grade performance       <.0001 
 A 2807 29.2 (28.1–30.4) 2739 30.0 (28.9–31.2) 65 14.7 (11.7–18.3) 
 B 4528 43.4 (42.2–44.5) 4306 43.5 (42.4–44.6) 213 42.3 (37.7–46.9) 
 C or below 2986 27.4 (26.3–28.6) 2733 26.5 (25.4–27.6) 232 43.1 (38.7–47.6) 
Parental education       .0110 
 Less than high school 2097 17.5 (16.3–18.8) 1985 17.4 (16.2–18.7) 100 17.5 (14.1–21.6) 
 High school graduate 1874 17.4 (16.3–18.6) 1752 17.2 (16.0–18.4) 115 21.5 (17.6–26.0) 
 Some college 3256 31.5 (30.0–33.0) 3064 31.3 (29.7–32.8) 183 36.1 (31.8–40.6) 
 Bachelor’s degree or greater 3068 33.6 (31.4–35.9) 2953 34.1 (31.8–36.5) 110 24.9 (20.2–30.3) 
Region       .0110 
 Northeast 1482 16.5 (15.8–17.2) 1422 16.7 (16.0–17.5) 59 13.4 (10.7–16.8) 
 South 2314 21.8 (21.1–22.7) 2193 21.9 (21.0–22.7) 114 21.1 (17.7–25.0) 
 Midwest 3932 38.1 (37.2–39.0) 3699 37.7 (36.8–38.7) 218 44.8 (40.1–49.5) 
 West 2636 23.6 (22.8–24.4) 2506 23.7 (22.9–24.6) 120 20.6 (17.4–24.4) 
E-cigarette ever use       — 
 No 9820 94.9 (94.5–95.4) 9820 100.0 — — 
 Yes 511 5.1 (4.6–5.5) — — 511 100.0 
Cigarette ever use       <.0001 
 No 9732 94.0 (93.3–94.6) 9416 96 (95.6–96.5) 285 55.7 (51.1–60.3) 
 Yes 619 6.0 (5.4–6.7) 393 4.0 (3.5–4.4) 225 44.3 (39.7–48.9) 
Alcohol ever use       <.0001 
 No 7226 69.0 (67.6–70.4) 7013 70.7(69.3–72.1) 185 36.1 (31.5–40.9) 
 Yes 3088 31.0 (29.6–32.4) 2759 29.3 (27.9–30.7) 325 63.9 (59.1–68.5) 
Nonmedical use of prescription drugsc       .0011 
 No 9467 92.7 (92.1–93.3) 8992 92.9 (92.3–93.5) 446 89.2 (86.2–91.6) 
 Yes 743 7.3 (6.7–7.9) 684 7.1 (6.5–7.7) 57 10.8 (8.4–13.8) 
Other illicit drug ever used       <.0001 
 No 10 255 99.8 (99.7–99.8) 9725 99.8 (99.7–99.9) 500 99.0 (97.8–99.6) 
 Yes 29 0.2 (0.2–0.3) 22 0.2 (0.1–0.3) 1.0 (0.4–2.2) 
Sensation seeking (mean and SE)e 10 164 2.54 ± 0.01 9633 2.51 ± 0.01 502 3.08 ± 0.05 <.0001 

—, not applicable.

a

Rao-Scott χ2 test was performed to compare the distribution of sample characteristics and substance use by e-cigarette use status at wave 1.

b

Researchers of the PATH study oversampled adult tobacco users, young adults aged 18–24 y, and African Americans, so weighted estimates were calculated to reflect the prevalence in population.

c

Prescription drugs include Ritalin, Adderall, painkillers, sedatives, or tranquilizers.

d

Other illicit drugs include cocaine or crack, stimulants like methamphetamine or speed, heroin, inhalants, solvents, or hallucinogens.

e

Sensation seeking was based on 5-point scales, labeled “strongly disagree,” “disagree,” “neither agree nor disagree,” “agree,” and “strongly agree.”

Significant differences between e-cigarette never and ever users were observed. E-cigarette ever users were more likely than e-cigarette never users to be older, male, white, and have poorer grade performance. They were also more likely to report sensation seeking, smoking cigarettes, drinking, and ever nonmedical use of prescription drugs and using other illicit drugs.

Overall, 8.7% of never marijuana users at wave 1 reported use of marijuana at wave 2. More than 1 in 4 (26.6%) adolescents who ever used e-cigarettes at wave 1 reported subsequent marijuana use at wave 2, as compared with 7.7% of adolescents who never used e-cigarettes at wave 1 (P < .05) (Table 2). After adjusting for demographic factors and other substance use, e-cigarette ever users at wave 1 were more likely to report subsequent P12M marijuana use at wave 2 (aOR = 1.9; CI: 1.4–2.5). Older age, being female, being African American, and having lower grade performance all had significantly elevated adjusted odds for wave 2 marijuana P12M use. In addition, wave 1 sensation seeking and cigarette and alcohol ever use had significantly elevated odds for wave 2 marijuana use.

TABLE 2

Temporal Association of E-cigarette Use and Covariates at Wave 1 With Marijuana Use at Wave 2 Among Baseline Marijuana Never Users, PATH Study, 2013–2015

Marijuana Never Users at Wave 1Marijuana P12M Use at Wave 2Marijuana Heavy Use at Wave 2
nnWeighted % (95% CI)aORanWeighted % (95% CI)aORa
Total 10 364 897 8.7 (8.1–9.3) — 286 2.8 (2.4–3.2) — 
E-cigarette ever use        
 No 9820 759 7.7 (7.1–8.4) Reference 245 2.5 (2.2–3.0) Reference 
 Yes 511 137 26.6 (22.6–31.0) 1.9 (1.4–2.5)*** 41 7.9 (5.6–10.9) 1.3 (0.8–2.1)b 
Age, y        
 12–14 5901 373 6.2 (5.6–6.9) Reference 128 2.2 (1.8–2.7) Reference 
 15–17 4463 524 11.8 (10.7–13.0) 1.4 (1.2–1.7)*** 158 3.6 (3–4.4.0) 1.1 (0.8–1.6) 
Male 5298 431 8.2 (7.5–9.1) Reference 118 2.2 (1.8–2.8) Reference 
Female 5066 466 9.1 (8.3–10.1) 1.2 (1–1.4)* 168 3.4 (2.9–4.0) 1.6 (1.2–2.2)** 
Race and/or ethnicity        
 NH white 5079 418 8.5 (7.6–9.4) Reference 147 2.9 (2.5–3.5) Reference 
 NH African American 1418 156 11.2 (9.4–13.4) 1.8 (1.3–2.4)*** 39 2.9 (2.0–4.2) 1.2 (0.8–2.0) 
 Hispanic 2932 240 8.3 (7.3–9.3) 1.0 (0.8–1.2) 79 2.8 (2.2–3.6) 1.0 (0.7–1.5) 
 NH others 935 83 7.1 (5.5–9.1) 0.9 (0.7–1.3) 21 1.9 (1.1–3.3) 0.7 (0.4–1.2) 
Grade performance        
 A 2807 161 5.6 (4.7–6.6) Reference 47 1.7 (1.2–2.3) Reference 
 B 4528 384 8.8 (8.0–9.6) 1.4 (1.1–1.7)** 113 2.7 (2.2–3.3) 1.4 (0.9–2.3) 
 C or below 2986 349 11.9 (10.5–13.4) 1.8 (1.4–2.3)*** 125 4.2 (3.5–5.1) 2.2 (1.4–3.6)** 
Parental education        
 Less than high school 2097 190 9.3 (8.2–10.6) Reference 61 2.9 (2.2–3.7) Reference 
 High school graduate 1874 174 9.2 (8.0–10.6) 0.9 (0.7–1.1) 60 3.3 (2.6–4.3) 1.1 (0.8–1.6) 
 Some college 3256 302 9.3 (8.2–10.4) 0.9 (0.7–1.1) 97 3 (2.4–3.7.0) 0.9 (0.6–1.3) 
 Bachelor’s degree or greater 3068 221 7.4 (6.4–8.5) 0.9 (0.7–1.1) 64 2.3 (1.7–3.0) 0.9 (0.6–1.3) 
Region        
 Northeast 1482 141 9.9 (8.2–11.8) Reference 50 3.5 (2.5–5.0) Reference 
 South 2314 213 9.1 (8.0–10.4) 0.8 (0.7–1.1) 65 2.8 (2.2–3.6) 0.7 (0.5–1.2) 
 Midwest 3932 318 8.0 (7.0–9.1) 0.7 (0.5–0.9)* 100 2.5 (2.0–3.1) 0.6 (0.4–1.0) 
 West 2636 225 8.5 (7.2–10.0) 0.9 (0.7–1.2) 71 2.8 (2.0–4.0) 0.8 (0.5–1.4) 
Cigarette ever use        
 No 9732 732 7.5 (6.9–8.2) Reference 227 2.4 (2.1–2.8) Reference 
 Yes 619 163 26.5 (22.6–30.9) 2.0 (1.5–2.7)*** 59 9.4 (7.1–12.3) 2.1 (1.4–3.2)*** 
Alcohol ever use        
 No 7226 392 5.3 (4.8–5.9) Reference 108 1.6 (1.2–1.9) Reference 
 Yes 3088 499 16.2 (14.8–17.8) 2.3 (1.9–2.8)*** 177 5.6 (4.8–6.6) 2.4 (1.8–3.3)*** 
Nonmedical use of prescription drugs        
 No 9467 784 8.3 (7.7–9.0) Reference 247 2.6 (2.3–3.0) Reference 
 Yes 743 101 13.1 (10.4–16.4) 1.2 (0.9–1.5) 37 5.0 (3.4–7.3) 1.2 (0.8–1.8) 
Other illicit drug ever use        
 No 10 255 883 8.7 (8.0–9.3) Reference 282 2.8 (2.4–3.2) Reference 
 Yes 29 17.4 (7.5–35.1) 1.0 (0.3–3.4) 12.0 (4.3–29) 2.4 (0.7–8.7) 
Sensation seekingc — — — 1.6 (1.5–1.7)*** — — 1.7 (1.5–2)*** 
Marijuana Never Users at Wave 1Marijuana P12M Use at Wave 2Marijuana Heavy Use at Wave 2
nnWeighted % (95% CI)aORanWeighted % (95% CI)aORa
Total 10 364 897 8.7 (8.1–9.3) — 286 2.8 (2.4–3.2) — 
E-cigarette ever use        
 No 9820 759 7.7 (7.1–8.4) Reference 245 2.5 (2.2–3.0) Reference 
 Yes 511 137 26.6 (22.6–31.0) 1.9 (1.4–2.5)*** 41 7.9 (5.6–10.9) 1.3 (0.8–2.1)b 
Age, y        
 12–14 5901 373 6.2 (5.6–6.9) Reference 128 2.2 (1.8–2.7) Reference 
 15–17 4463 524 11.8 (10.7–13.0) 1.4 (1.2–1.7)*** 158 3.6 (3–4.4.0) 1.1 (0.8–1.6) 
Male 5298 431 8.2 (7.5–9.1) Reference 118 2.2 (1.8–2.8) Reference 
Female 5066 466 9.1 (8.3–10.1) 1.2 (1–1.4)* 168 3.4 (2.9–4.0) 1.6 (1.2–2.2)** 
Race and/or ethnicity        
 NH white 5079 418 8.5 (7.6–9.4) Reference 147 2.9 (2.5–3.5) Reference 
 NH African American 1418 156 11.2 (9.4–13.4) 1.8 (1.3–2.4)*** 39 2.9 (2.0–4.2) 1.2 (0.8–2.0) 
 Hispanic 2932 240 8.3 (7.3–9.3) 1.0 (0.8–1.2) 79 2.8 (2.2–3.6) 1.0 (0.7–1.5) 
 NH others 935 83 7.1 (5.5–9.1) 0.9 (0.7–1.3) 21 1.9 (1.1–3.3) 0.7 (0.4–1.2) 
Grade performance        
 A 2807 161 5.6 (4.7–6.6) Reference 47 1.7 (1.2–2.3) Reference 
 B 4528 384 8.8 (8.0–9.6) 1.4 (1.1–1.7)** 113 2.7 (2.2–3.3) 1.4 (0.9–2.3) 
 C or below 2986 349 11.9 (10.5–13.4) 1.8 (1.4–2.3)*** 125 4.2 (3.5–5.1) 2.2 (1.4–3.6)** 
Parental education        
 Less than high school 2097 190 9.3 (8.2–10.6) Reference 61 2.9 (2.2–3.7) Reference 
 High school graduate 1874 174 9.2 (8.0–10.6) 0.9 (0.7–1.1) 60 3.3 (2.6–4.3) 1.1 (0.8–1.6) 
 Some college 3256 302 9.3 (8.2–10.4) 0.9 (0.7–1.1) 97 3 (2.4–3.7.0) 0.9 (0.6–1.3) 
 Bachelor’s degree or greater 3068 221 7.4 (6.4–8.5) 0.9 (0.7–1.1) 64 2.3 (1.7–3.0) 0.9 (0.6–1.3) 
Region        
 Northeast 1482 141 9.9 (8.2–11.8) Reference 50 3.5 (2.5–5.0) Reference 
 South 2314 213 9.1 (8.0–10.4) 0.8 (0.7–1.1) 65 2.8 (2.2–3.6) 0.7 (0.5–1.2) 
 Midwest 3932 318 8.0 (7.0–9.1) 0.7 (0.5–0.9)* 100 2.5 (2.0–3.1) 0.6 (0.4–1.0) 
 West 2636 225 8.5 (7.2–10.0) 0.9 (0.7–1.2) 71 2.8 (2.0–4.0) 0.8 (0.5–1.4) 
Cigarette ever use        
 No 9732 732 7.5 (6.9–8.2) Reference 227 2.4 (2.1–2.8) Reference 
 Yes 619 163 26.5 (22.6–30.9) 2.0 (1.5–2.7)*** 59 9.4 (7.1–12.3) 2.1 (1.4–3.2)*** 
Alcohol ever use        
 No 7226 392 5.3 (4.8–5.9) Reference 108 1.6 (1.2–1.9) Reference 
 Yes 3088 499 16.2 (14.8–17.8) 2.3 (1.9–2.8)*** 177 5.6 (4.8–6.6) 2.4 (1.8–3.3)*** 
Nonmedical use of prescription drugs        
 No 9467 784 8.3 (7.7–9.0) Reference 247 2.6 (2.3–3.0) Reference 
 Yes 743 101 13.1 (10.4–16.4) 1.2 (0.9–1.5) 37 5.0 (3.4–7.3) 1.2 (0.8–1.8) 
Other illicit drug ever use        
 No 10 255 883 8.7 (8.0–9.3) Reference 282 2.8 (2.4–3.2) Reference 
 Yes 29 17.4 (7.5–35.1) 1.0 (0.3–3.4) 12.0 (4.3–29) 2.4 (0.7–8.7) 
Sensation seekingc — — — 1.6 (1.5–1.7)*** — — 1.7 (1.5–2)*** 

—, not applicable.

a

aOR was calculated in the multivariable logistic regression, in which the explanatory variables included all listed variables (demographics, substance ever use, and sensation seeking) at wave 1.

b

There were significant interactions between age and e-cigarette use. See Table 3.

c

aOR corresponds to 1 U increase in the risk of sensation seeking (5-point scale).

*

P < .05;

**

P < .01;

***

P < .001.

Overall, 2.8% of marijuana never users at wave 1 reported heavy use of marijuana at wave 2. Female participants (versus male participants) and adolescents with a grade performance of “C or below” (versus those with “A”) were more likely to report heavy use of marijuana at wave 2. Sensation seeking, cigarette ever use, and alcohol ever use at wave 1 were associated with higher odds of reporting heavy use of marijuana at wave 2.

There were significant interactions between age group (12–14 years and 15–17 years) and marijuana P12M (P < .05) and heavy (P < .05) use (Table 3, Supplemental Fig 2). The association between baseline e-cigarette use and P12M marijuana use at wave 2 was significant among both younger adolescents aged 12 to 14 years (29.2% vs 5.5%; aOR = 2.7; CI: 1.7–4.3) and older adolescents aged 15 to 17 years (25.3% vs 10.6%; aOR = 1.6; CI: 1.2–2.3). Moreover, the association between baseline e-cigarette use and subsequent heavy marijuana use was significant among young adolescents (12.0% vs 1.9%; aOR = 2.5; CI: 1.2–5.3) but was not significant among older adolescents.

TABLE 3

Age-Stratified Analysis of the Temporal Association Between E-cigarette Ever Use at Wave 1 and Marijuana Use at Wave 2 Among Baseline Marijuana Never Users, PATH Study, 2013–2015

Marijuana Never Users at Wave 1E-cigarette Ever Use at Wave 1Marijuana P12M Use at Wave 2Marijuana Heavy Use at Wave 2
nWeighted % (95% CI)aORanWeighted % (95% CI)aORa
Aged 12–14 (n = 5901)b No (n = 5703) 321 5.5 (4.9–6.2) REF 107 1.9 (1.5–2.3) REF 
Yes (n = 177) 52 29.2 (23.0–36.2) 2.7 (1.7–43)*** 21 12.0 (7.6–18.5) 2.5 (1.2-–5.3)* 
Aged 15–17 (n = 4463)b No (n = 4117) 438 10.6 (9.5–11.9) REF 138 3.4 (2.8–4.2) REF 
Yes (n = 334) 85 25.3 (21.0–30.3) 1.6 (1.2–2.3)** 20 5.9 (3.8–9.1) 0.9 (0.5–1.5) 
Interaction between age group and e-cigarette ever usec — — — 2.1 (1.3–3.3)** — — 3.1 (1.5–6.4)** 
Marijuana Never Users at Wave 1E-cigarette Ever Use at Wave 1Marijuana P12M Use at Wave 2Marijuana Heavy Use at Wave 2
nWeighted % (95% CI)aORanWeighted % (95% CI)aORa
Aged 12–14 (n = 5901)b No (n = 5703) 321 5.5 (4.9–6.2) REF 107 1.9 (1.5–2.3) REF 
Yes (n = 177) 52 29.2 (23.0–36.2) 2.7 (1.7–43)*** 21 12.0 (7.6–18.5) 2.5 (1.2-–5.3)* 
Aged 15–17 (n = 4463)b No (n = 4117) 438 10.6 (9.5–11.9) REF 138 3.4 (2.8–4.2) REF 
Yes (n = 334) 85 25.3 (21.0–30.3) 1.6 (1.2–2.3)** 20 5.9 (3.8–9.1) 0.9 (0.5–1.5) 
Interaction between age group and e-cigarette ever usec — — — 2.1 (1.3–3.3)** — — 3.1 (1.5–6.4)** 

—, not applicable.

a

For all models, aOR was adjusted by sensation seeking, ever use of cigarettes, drinking, nonmedical use of prescription drugs and other illicit drugs, and demographics (sex, race and/or ethnicity, grade performance, parental education, and region) at wave 1.

b

Separate multivariable logistic regression models were performed for youth aged 12–14 years and youth aged 15–17 years, respectively.

c

A joint model was constructed to evaluate the main effects of age and e-cigarette use, along with an interaction between age and e-cigarette use. Interaction aOR indicates that e-cigarette use (versus no use) at baseline was associated with higher risks of marijuana use at wave 2 among younger adolescents than among older adolescents.

*

P < .05;

**

P < .01;

***

P < .001.

We tested a dose-response relationship between the amount of e-cigarettes used and subsequent marijuana use in Table 4. Reporting a larger number of e-cigarettes/cartridges used in a lifetime at wave 1 was associated with higher odds of P12M (aOR = 1.7; CI: 1.3–2.0) and heavy (aOR = 1.6; CI: 1.2–2.2) marijuana use for younger adolescents.

TABLE 4

Age-Stratified Analysis of the Temporal Association Between the Number of E-cigarettes and/or Cartridges Used at Wave 1 and Marijuana Use at Wave 2 Among Baseline Marijuana Never Users, PATH Study, 2013–2015

Marijuana Never Users at Wave 1Marijuana P12M Use at Wave 2Marijuana Heavy Use at Wave 2
No. E-cigarettes and/or Cartridges Used at Wave 1anaORbnaORb
All adolescents, n = 10 364 897 1.3 (1.1–1.5)*** 286 1.2 (1.0–1.5) 
Aged 12–14 y (n = 5901) 373 1.7 (1.3–2.0)*** 128 1.6 (1.2–2.2)** 
Aged 15–17 y (n = 4463) 524 1.2 (1.0–1.4) 158 0.9 (0.7–1.3) 
Interaction between e-cigarette use and age groupc — 1.5 (1.2–1.9)** — 1.8 (1.2–2.6)** 
Marijuana Never Users at Wave 1Marijuana P12M Use at Wave 2Marijuana Heavy Use at Wave 2
No. E-cigarettes and/or Cartridges Used at Wave 1anaORbnaORb
All adolescents, n = 10 364 897 1.3 (1.1–1.5)*** 286 1.2 (1.0–1.5) 
Aged 12–14 y (n = 5901) 373 1.7 (1.3–2.0)*** 128 1.6 (1.2–2.2)** 
Aged 15–17 y (n = 4463) 524 1.2 (1.0–1.4) 158 0.9 (0.7–1.3) 
Interaction between e-cigarette use and age groupc — 1.5 (1.2–1.9)** — 1.8 (1.2–2.6)** 

—, not applicable.

a

A dose-response analysis was conducted by treating the number of e-cigarette cartridges used as an ordinal variable, and aOR corresponds to 1 U increase in the scale.

b

For all models, aOR was adjusted by sensation seeking, ever use of cigarettes, drinking, nonmedical use of prescription drugs and other illicit drugs, and demographics (sex, race and/or ethnicity, grade performance, parental education, and region) at wave 1.

c

Significant interaction indicates that that number of e-cigarettes used at baseline was associated with higher risks of marijuana use at wave 2 among younger adolescents than among older adolescents.

**

P < .01;

***

P < .001.

It is suggested in the results from this longitudinal study that baseline e-cigarette use independently predicts subsequent marijuana use among youth after controlling for social-demographic factors, sensation seeking, and other substance use. The overall prevalence of ever e-cigarette use among adolescents was 10.6% in the PATH 2013–2014 study (Supplemental Table 5), which is aligned with the prevalence reported by the National Youth Tobacco Survey (8.1% in 2013).3 At wave 1, never marijuana users had a significantly lower prevalence of e-cigarette ever use as compared with marijuana users (5.1% vs 46.4%). To avoid confounding effects, baseline marijuana users were excluded from analysis, which led to a low prevalence of e-cigarette use at baseline in this study. For associations between e-cigarette and subsequent marijuana use, the aORs were lower than the crude ratios. As shown in Supplemental Table 6, the reduction was most affected by ever use of cigarettes, followed by drinking alcohol and sensation seeking, and slightly affected by age and grade performance. There are several possible reasons why e-cigarette use might be associated with subsequent marijuana initiation. On the one hand, e-cigarettes may simply be a marker of risk-taking behavior; e-cigarette users are more likely to smoke cigarettes and drink alcohol, which are also associated with marijuana use.31,32 Alternatively, because the brain is still developing during the teenage years, nicotine exposure might lead to changes in the central nervous system that predispose teenagers to dependence on other drugs of abuse.33 Experimenting with e-cigarettes might also increase a youth’s curiosity about marijuana, reduce perceived harm of marijuana use, and increase the social access to marijuana from peers and friends. As a result of marketing and social media promotion,34,35 vaping cannabis is gaining popularity.19,20 Youth who experiment with e-cigarettes may use the same device or switch to newer generation devices for vaping marijuana,20,36 which could lead to use of substance with stronger addictive effects.37 

With these findings, we highlight the importance of policy and education to reduce risks for youth. Currently, e-cigarette and marijuana are the 2 most commonly used substances by teenagers. The FDA has extended its authority to regulate e-cigarettes, and the first round of these new regulations, including mandatory age and photo identification checks to prevent illegal sales of newly regulated tobacco products to minors, went into effect on August 8, 201638 (after data collected for this study). Data from this study may have implications for state authorities that presently vary greatly in their enforcement of e-cigarette sales and legal access to marijuana.39,41 It is important for health providers and educators to advise youth about the risks of e-cigarette use, including the propensity of progression to marijuana use after e-cigarette use. Policies to prevent youth use of e-cigarettes and reduce youth access to e-cigarettes, such as expanding smoke-free policies and Tobacco 21 policies, should be considered as well.

In research on the developmental trajectory of substance use and addiction, the critical role of nicotine initiation at an early age has been highlighted. For instance, smoking at a young age increases the likelihood of becoming an addicted daily smoker.2,24,42,43 Our study revealed heterogeneity in the associations of e-cigarette use on subsequent marijuana use by age group. The temporal association between baseline e-cigarette use and initiation of marijuana use was larger among younger adolescents aged 12 to 14 years than among older adolescents aged 15 to 17 years. This finding is consistent with previous cross-sectional studies in which it was indicated that younger high school students were more likely to use e-cigarettes to vape cannabis, and that e-cigarettes may have made inroads among younger users who have low risks of using traditional substances.17,20 As youth start to initiate e-cigarettes as early as 7 years old,44 the interaction between age and subsequent marijuana use underscores the importance of starting prevention efforts on e-cigarette and other substance use at an earlier age because these youth have the most to gain. In addition, we found that youth who reported a larger number of e-cigarettes and/or cartridges used in a lifetime at baseline were more likely to be subsequent marijuana heavy users. Because the regular use of marijuana during adolescence is of particular concern for adverse health effects,45 we add to the existing literature by identifying the quantity of e-cigarette use as a risk factor for marijuana heavy use.

This study is subject to several limitations. First, both e-cigarette and marijuana use were self-reported. Thus, reporting and recall biases might have occurred, especially for younger respondents.46 Second, not all youth included in wave 1 of the PATH study responded to the wave 2 survey. However, attrition was not associated with a previous history of substance use (Supplemental Table 5). Finally, the investigators of the PATH study did not ask respondents about what substances youth vaped in their e-cigarettes, which could include flavoring only, nicotine, or marijuana derivatives.47 Therefore, we were not able to ascertain which specific vaped substances had effects on subsequent marijuana use.

Future studies are needed to investigate the underlying mechanism of substance transition and evaluate long-term impacts of e-cigarette use. Our study revealed that e-cigarette use was associated with an increased risk of subsequent marijuana use among youth, with a stronger temporal association among younger adolescents. With these findings, we suggest that the widespread use of e-cigarettes among youth may have implications for the uptake of other drugs of abuse beyond nicotine and tobacco products.

     
  • aOR

    adjusted odds ratio

  •  
  • CI

    confidence interval

  •  
  • e-cigarette

    electronic cigarette

  •  
  • NH

    non-Hispanic

  •  
  • PATH

    Population Assessment of Tobacco and Health

  •  
  • P12M

    past 12 months

Dr Dai conceptualized the study, performed analyses, drafted the initial manuscript, and critically revised the manuscript; Drs Catley, Richter, and Goggin assisted in data analysis and result interpretation and critically reviewed and revised the manuscript; Dr Ellerbeck contributed to the manuscript design, assisted in data presentation and result interpretation, and critically reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: No external funding.

We thank Dr Adam Leventhal from the University of Southern California for his helpful comments and suggestions on the manuscript.

1
Jamal
A
,
Gentzke
A
,
Hu
SS
, et al
.
Tobacco use among middle and high school students - United States, 2011-2016.
MMWR Morb Mortal Wkly Rep
.
2017
;
66
(
23
):
597
603
[PubMed]
2
U.S. Department of Health and Human Services
.
Preventing Tobacco Use Among Youth and Young Adults: A Report of the Surgeon General
.
Atlanta, GA
:
US Department of Health and Human Services
,
Centers for Disease Control and Prevention
,
National Center for Chronic Disease Prevention and Health Promotion
,
Office on Smoking and Health
;
2012
3
U.S. Department of Health and Human Services
.
E-Cigarette Use Among Youth and Young Adults: A Report of the Surgeon General
.
Atlanta, GA
:
US Department of Health and Human Services
,
Centers for Disease Control and Prevention
,
National Center for Chronic Disease Prevention and Health Promotion
,
Office on Smoking and Health
;
2016
4
Johnston
LD
,
O’Malley
PM
,
Miech
RA
,
Bachman
JG
,
Schulenberg
JE
.
Monitoring the Future National Survey Results on Drug Use, 1975-2016: Overview, Key Findings on Adolescent Drug Use
.
Ann Arbor, MI
:
Institute for Social Research, The University of Michigan
;
2017
5
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
[PubMed]
6
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
[PubMed]
7
Barrington-Trimis
JL
,
Urman
R
,
Berhane
K
, et al
.
E-cigarettes and future cigarette use.
Pediatrics
.
2016
;
138
(
1
):
e20160379
[PubMed]
8
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.
JAMA Pediatr
.
2017
;
171
(
8
):
788
797
[PubMed]
9
Miech
R
,
Patrick
ME
,
O’Malley
PM
,
Johnston
LD
.
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
[PubMed]
10
Ramo
DE
,
Liu
H
,
Prochaska
JJ
.
Tobacco and marijuana use among adolescents and young adults: a systematic review of their co-use.
Clin Psychol Rev
.
2012
;
32
(
2
):
105
121
[PubMed]
11
Kandel
DB
,
Yamaguchi
K
,
Chen
K
.
Stages of progression in drug involvement from adolescence to adulthood: further evidence for the gateway theory.
J Stud Alcohol
.
1992
;
53
(
5
):
447
457
[PubMed]
12
Lynskey
MT
,
Heath
AC
,
Bucholz
KK
, et al
.
Escalation of drug use in early-onset cannabis users vs co-twin controls.
JAMA
.
2003
;
289
(
4
):
427
433
[PubMed]
13
Farsalinos
KE
,
Polosa
R
.
Safety evaluation and risk assessment of electronic cigarettes as tobacco cigarette substitutes: a systematic review.
Ther Adv Drug Saf
.
2014
;
5
(
2
):
67
86
[PubMed]
14
Wadley
J
. Most youth use e-cigarettes for novelty, flavors–not to quit smoking. Michigan News. December 16, 2015. Available at: www.ns.umich.edu/new/releases/23371-most-youth-use-e-cigarettes-for-novelty-flavors-not-to-quit-smoking. Accessed February 11, 2017
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
[PubMed]
16
Kandel
DB
. Examining the gateway hypothesis: stages and pathways of drug involvement. In:
Kandel
DB
, ed.
Stages and Pathways of Drug Involvement: Examining the Gateway Hypothesis.
New York, NY
:
Cambridge University Press
;
2002
:
3
15
17
Miech
RA
,
O’Malley
PM
,
Johnston
LD
,
Patrick
ME
.
E-cigarettes and the drug use patterns of adolescents.
Nicotine Tob Res
.
2016
;
18
(
5
):
654
659
[PubMed]
18
Morean
ME
,
Kong
G
,
Camenga
DR
,
Cavallo
DA
,
Simon
P
,
Krishnan-Sarin
S
.
Latent class analysis of current e-cigarette and other substance use in high school students.
Drug Alcohol Depend
.
2016
;
161
:
292
297
[PubMed]
19
Lee
DC
,
Crosier
BS
,
Borodovsky
JT
,
Sargent
JD
,
Budney
AJ
.
Online survey characterizing vaporizer use among cannabis users.
Drug Alcohol Depend
.
2016
;
159
:
227
233
[PubMed]
20
Morean
ME
,
Kong
G
,
Camenga
DR
,
Cavallo
DA
,
Krishnan-Sarin
S
.
High school students’ use of electronic cigarettes to vaporize cannabis.
Pediatrics
.
2015
;
136
(
4
):
611
616
[PubMed]
21
Mammen
G
,
Rehm
J
,
Rueda
S
.
Vaporizing cannabis through e-cigarettes: prevalence and socio-demographic correlates among Ontario high school students.
Can J Public Health
.
2016
;
107
(
3
):
e337
e338
[PubMed]
22
Agrawal
A
,
Grant
JD
,
Waldron
M
, et al
.
Risk for initiation of substance use as a function of age of onset of cigarette, alcohol and cannabis use: findings in a Midwestern female twin cohort.
Prev Med
.
2006
;
43
(
2
):
125
128
[PubMed]
23
Mack
AH
. Adolescent substance use: America’s # 1 public health problem. In
Talbott
JA
,
Ballenger
JC
,
Buckley
PF
,
Frances
RJ
,
Krupnick
JL
,
Mack
AH
, eds.
Year Book of Psychiatry and Applied Mental Health.
New York, NY
:
Elsevier Health Sciences
;
2012
:
39
40
24
Taioli
E
,
Wynder
EL
.
Effect of the age at which smoking begins on frequency of smoking in adulthood.
N Engl J Med
.
1991
;
325
(
13
):
968
969
[PubMed]
25
United States Department of Health and Human Services
;
National Institutes of Health
;
National Institute on Drug Abuse, and United States Department of Health and Human Services
;
US Food and Drug Administration
;
Center for Tobacco Products
.
Population Assessment of Tobacco and Health (PATH) Study [United States] Restricted-Use Files
.
Ann Arbor, MI
:
Inter-university Consortium for Political and Social Research
;
2017
26
Hyland
A
,
Travers
MJ
,
Cummings
KM
,
Bauer
J
,
Alford
T
,
Wieczorek
WF
.
Tobacco outlet density and demographics in Erie County, New York.
Am J Public Health
.
2003
;
93
(
7
):
1075
1076
[PubMed]
27
Inter-university Consortium for Political and Social Research
. Population assessment of tobacco and health (PATH) study [United States] restricted-use files: user guide. Available at: . Accessed February 20, 2018
28
Hoyle
RH
,
Stephenson
MT
,
Palmgreen
P
,
Lorch
EP
,
Donohew
RL
.
Reliability and validity of a brief measure of sensation seeking.
Pers Individ Dif
.
2002
;
32
(
3
):
401
414
29
McCarthy
PJ
.
Pseudoreplication: further evaluation and applications of the balanced half-sample technique.
Vital Health Stat 2
.
1969
;(
31
):
1
24
[PubMed]
30
Judkins
DR
.
Fay’s method for variance estimation.
J Off Stat
.
1990
;
6
(
3
):
223
239
31
Miech
R
,
Johnston
L
,
O’Malley
PM
.
Prevalence and attitudes regarding marijuana use among adolescents over the past decade.
Pediatrics
.
2017
;
140
(
6
):
e20170982
[PubMed]
32
Cohn
A
,
Villanti
A
,
Richardson
A
, et al
.
The association between alcohol, marijuana use, and new and emerging tobacco products in a young adult population.
Addict Behav
.
2015
;
48
:
79
88
[PubMed]
33
Kandel
ER
,
Kandel
DB
.
Shattuck lecture. A molecular basis for nicotine as a gateway drug.
N Engl J Med
.
2014
;
371
(
10
):
932
943
[PubMed]
34
Cavazos-Rehg
PA
,
Krauss
M
,
Fisher
SL
,
Salyer
P
,
Grucza
RA
,
Bierut
LJ
.
Twitter chatter about marijuana.
J Adolesc Health
.
2015
;
56
(
2
):
139
145
[PubMed]
35
Cavazos-Rehg
PA
,
Krauss
MJ
,
Sowles
SJ
,
Bierut
LJ
.
Marijuana-related posts on Instagram.
Prev Sci
.
2016
;
17
(
6
):
710
720
36
Miech
R
,
Patrick
ME
,
O’Malley
PM
,
Johnston
LD
.
What are kids vaping? Results from a national survey of US adolescents.
Tob Control
.
2017
;
26
(
4
):
386
391
[PubMed]
37
Vanyukov
MM
,
Tarter
RE
,
Kirillova
GP
, et al
.
Common liability to addiction and “gateway hypothesis”: theoretical, empirical and evolutionary perspective.
Drug Alcohol Depend
.
2012
;
123
(
suppl 1
):
S3
S17
[PubMed]
38
Food and Drug Administration, HHS
.
Deeming tobacco products to be subject to the federal food, drug, and cosmetic act, as amended by the family smoking prevention and tobacco control act; restrictions on the sale and distribution of tobacco products and required warning statements for tobacco products. Final rule.
Fed Regist
.
2016
;
81
(
90
):
28973
29106
[PubMed]
39
Tobacco Control Legal Consortium
. U.S. e-cigarette regulations–50 state review. 2017. Available at: www.publichealthlawcenter.org/resources/us-e-cigarette-regulations-50-state-review. Accessed June 2, 2017
40
National Conference of State Legislatures
. State medical marijuana laws. 2017. Available at: www.ncsl.org/research/health/state-medical-marijuana-laws.aspx. Accessed December 28, 2017
41
Governing the States and Localities
. State marijuana laws in 2017 map. 2017. Available at: www.governing.com/gov-data/state-marijuana-laws-map-medical-recreational.html. Accessed December 28, 2017
42
US Department of Health and Human Services
.
The Health Consequences of Smoking–50 Years of Progress: A Report of the Surgeon General
.
Atlanta, GA
:
US Department of Health and Human Services
,
Centers for Disease Control and Prevention
,
National Center for Chronic Disease Prevention and Health Promotion
,
Office on Smoking and Health
;
2014
43
Campaign for Tobacco-Free Kids
. The path to tobacco addiction starts at very young ages. 2017. Available at: https://www.tobaccofreekids.org/research/factsheets/pdf/0127.pdf. Accessed December 23, 2017
44
Chen
X
,
Yu
B
,
Wang
Y
.
Initiation of electronic cigarette use by age among youth in the US
Am J Prev Med
.
2017
;
53
(
3
):
396
399
45
Volkow
ND
,
Baler
RD
,
Compton
WM
,
Weiss
SR
.
Adverse health effects of marijuana use.
N Engl J Med
.
2014
;
370
(
23
):
2219
2227
[PubMed]
46
Brener
ND
,
Billy
JO
,
Grady
WR
.
Assessment of factors affecting the validity of self-reported health-risk behavior among adolescents: evidence from the scientific literature.
J Adolesc Health
.
2003
;
33
(
6
):
436
457
[PubMed]
47
Johnston
LD
,
Bachman
JG
,
O’Malley
PM
,
Schulenberg
JE
,
Miech
RA
.
Monitoring the Future: A Continuing Study of American Youth (12th-Grade Survey), 2015.
Ann Arbor, MI
:
Inter-university Consortium for Political and Social Research
;
2016

Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data