Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Aug 7.
Published in final edited form as: Subst Use Misuse. 2024 Jan 25;59(3):329–335. doi: 10.1080/10826084.2023.2269573

The Relationship between Vaping Cannabis and Frequency of Cannabis Use and Cannabis-Related Problems among Urban High School Students

Mary M Mitchell a, Shannon G Mitchell a, Jennifer H Brooks a, Olusegun G Akinwolere a, Kristi Dusek a, Kevin E O’Grady b, Robert P Schwartz a, Jan Gryczynski a
PMCID: PMC13445950  NIHMSID: NIHMS2194665  PMID: 38063201

Abstract

Background:

Vaping, including vaping cannabis, is increasing among adolescents. In this longitudinal study, we examined the relationship between vaping cannabis and frequency of cannabis use and related problems over 6months among adolescents.

Material and Methods:

Data were from 233 participants (46.8% male, 93.1% African American, mean age = 16.4years) reporting cannabis use. The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) assessed frequency of past 30-day cannabis use and cannabis-related problems at baseline, 3- and 6-months post-baseline. We used latent growth curve modeling to compare vaping to non-vaping adolescents on trends in cannabis use frequency and ASSIST cannabis scores.

Results:

Adolescents who vaped cannabis (11.7%) had higher past 30-day frequency (mean = 17.89 days, SD = 10.49) of cannabis use at baseline compared to adolescents who had not vaped (mean = 12.1days, SD = 10.93), but reported a significantly sharper decline in frequency of cannabis use (b = −0.34, p = 0.017). A significantly steeper decrease existed in the mean cannabis ASSIST scores for the vaping group than for the non-vaping group (b = −0.34, p = 0.014). Mean ASSIST scores on the cannabis subscale between the two groups were significantly different at 6-month follow-up (Vape mean = 6.00, SD = 8.12 vs. Non-vape mean = 9.6, SD = 9.39; p < 0.021).

Conclusions:

In a sample of cannabis-using adolescents, adolescents with experience vaping cannabis, compared to adolescents without vaping experience, on average reported sharper decreases in frequency of cannabis use and cannabis-related problems such as health or social problems.

Keywords: Cannabis vaping, adolescents, african american, ASSIST scores, frequency of cannabis use

Introduction

Although there are many ways to consume cannabis, using a vaporizer or “vaping” is a relatively new phenomenon, introduced to the US market in 2007 (Kenne et al., 2017). There are various reasons why individuals elect to vape, compared to traditional methods of consuming substances. For example, reasons include taste, pleasure, and curiosity (Patrick et al., 2016). Primarily, individuals report they enjoy vaping due to the pleasant taste the vape flavors provide and due to the pleasurable effect vaping brings (relaxation, socialization with peers, and relief from boredom) (Patrick et al., 2016). Additionally, individuals vape out of curiosity of the mechanism to explore its use with cannabis or other substances (Patrick et al., 2016). People who prefer vaping cannabis report they like this method because it produces less odor and is more discreet than smoking (Morean et al., 2015). The electronic vaporizer heats the cannabis to a temperature at which it is aerosolized but does not burn. Vaping occurs with dry cannabis as well as with e-cigarettes that use cannabis oil, which are produced in many flavors (Peters et al., 2018). Vaping cannabis can include using a variety of products including oils, dabs, and waxes, which can be 4 to 30 times more potent in THC concentration than dried leaves (Mehmedic et al., 2010; Morean et al., 2015).Vaping is an increasingly popular way to consume cannabis to achieve the effects of the substance (Russell et al., 2018), especially among youth (Pearson & Villanti, 2020).

In a survey of 8th, 10th and 12th grade students, the latter reported nearly double the prevalence of vaping cannabis between 2018 and 2019 (7.5% vs. 14%, respectively (Miech et al., 2020). For example, in a sample of 5 high schools (N = 3,847) in Connecticut, 5.4% of respondents reported ever vaping cannabis (Morean et al., 2015). Peters and colleagues (Peters et al., 2018) found that lifetime vaporized cannabis use was reported by 10.5% of tenth-grade students, while 4.9% reported past 30-day vaping use. Eggers and colleagues (Eggers et al., 2017) found that lifetime prevalence of cannabis use with electronic vaporizer pens was 3.4% among middle school students and 11.5% among high school students. Using the Monitoring the Future data from 2017 and 2019, Keyes and colleagues found faster increases in adolescents’ frequent cannabis vaping, defined as vaping cannabis at least six times in the previous 30 days, compared to rates among adolescents who vape cannabis occasionally or less than six times in the past 30 days (Keyes et al., 2022). Among high school seniors, Palamar and colleagues found that the incidence of frequent heavy use of vaping cannabis, defined as using cannabis at least 10 times in the previous 30 days, increased from 2.1% in 2018 to 4.9% in 2019 (Palamar, 2021).

Earlier research concluded that vaping was less harmful than smoking cannabis because the vaporized substance had fewer carcinogens (Earleywine & Barnwell, 2007; Lee et al., 2016). However, more recent studies have found mixed reviews on the level of toxins and chemicals that are present in vaporized cannabis (Russell et al., 2018; Varlet et al., 2016). With the legalization of cannabis in numerous states for both medical and recreational use, mixed messages on the potential harms of cannabis use in the media can give youth and adults misleading information regarding the potential for negative consequences of using cannabis. Potential harms include the development of cannabis use disorder (Apollonio et al., 2019; Budney et al., 2019) and excessive impairment (Russell et al., 2018). While harms specific to the vaping route of administration are relatively rare, e-cigarette or vaping associated lung injury (EVALI) has been documented by the CDC as resulting in 2,897 cases of which 68 cases have resulted in death (Harrell et al., 2022; Center for Disease Control and Prevention (CDC), 2020). Additional harms, especially among youth, may include impairments in brain development and difficulties with school achievement and social functioning (Borodovsky et al., 2017; Hall & Degenhardt, 2015; Volkow et al., 2014). Furthermore, high potency cannabis has been linked to mental health deterioration, including anxiety, psychosis, hallucinations, catatonia, and rapid onset of paranoid delusions (Di Forti et al., 2015; Whiting et al., 2015).

Although studies have reported on the prevalence and correlates of vaping cannabis among high school students, to our knowledge limited studies (Keyes et al., 2022; Palamar, 2021) have compared adolescents with and without vaping experience on their trajectories of cannabis use and cannabis-related problems. We would expect with the increased potency of vaping cannabis and cannabis products, the presence of a vaping history would be a signal for heavier or more frequent use. Also, with heavier and more frequent use, there are more opportunities for serious consequences of use. Therefore, we hypothesized that adolescents who vaped cannabis would report a greater frequency of cannabis use over time and more problems associated with cannabis use compared to those who smoked cannabis. To investigate these hypotheses in the present study, we examined changes in cannabis use and cannabis-related risks over a 6-month period of time among a sample of adolescents with risky cannabis use who received a brief intervention at a school-based health center.

Methods

Procedure

Parent study

The current study is a secondary analysis of data from a randomized trial of brief substance use intervention strategies. The parent study recruited a total of 300 adolescents ages 14–18 years from school-based health centers (SBHCs) in two urban high schools in Baltimore City, Maryland between 2014 and 2018. Adolescents were screened using the CRAFFT instrument (Knight et al., 2002), which is a 6-item series of questions designed to screen adolescents for substance use disorders. Adolescents were eligible if they had a positive CRAFFT screen (score of 2 or higher), with reported past year use of cannabis and/or alcohol, but no other drug use. Participants (N = 300) provided informed consent and were randomized to receive a brief intervention delivered by either an interactive computer program or a nurse practitioner. Self-report assessments using audio computer-assisted self-interviewing were conducted at baseline and again at 3- and 6-month follow-up. The study was reviewed by the Friends Research Institute Institutional Review Board, which approved the study with a waiver of parental consent, under the rationale that parental notification could bias the sample and participant candor, and because state law affords teens the ability to consent to confidential professional advice about substance use without parental notification. More details about the methods and findings have been reported elsewhere (Gryczynski et al., 2021). The main study found similar behavioral outcomes (including cannabis use) for the two brief intervention strategies, showing no significant differences between computer- and nurse practitioner-delivered interventions on the dependent variables of interest in the present manuscript (described below). Hence, all participants received a brief intervention (either via computer or nurse practitioner), but those interventions were not differentially effective with respect to reducing participants’ cannabis use. Therefore, we collapsed the data across arms in the main study.

Current analysis

The current study focused on youth with current cannabis use, or use within the past 30 days. Thus, the study examined data from a subgroup of 233 participants who reported using cannabis in the past 30 days at baseline. Approximately 10% of the sample was lost to follow-up at each subsequent time point, decreasing the sample size from 233 at baseline to 208 at 3-months to 182 at 6-months.

Measures

Outcome

The outcome variables of interest (for the two latent growth curve models, described below) were days of cannabis use in the previous 30 days and a cannabis involvement score on the ASSIST (Gryczynski et al., 2015; Humeniuk et al., 2008). Frequency of cannabis use was measured by the item, “In the past 30 days, on exactly how many days did you use cannabis?” with answers ranging from 0 to 30. The six cannabis-related items from the ASSIST were summed and used as a cannabis ASSIST score. Example items include, “In the past three months, how often have you used cannabis” with answer choices ranging from 0 = Never/none to 6 = Daily/Almost Daily and, “Has a friend or relative or anyone else ever expressed concern about your use of cannabis?” with answer choices including, “No, never,” “Yes, in the past three months,” and “Yes, but not in the past three months” (Gryczynski et al., 2015; Humeniuk et al., 2008). The cannabis involvement score is a measure of cannabis-related problems, with higher scores indicating higher risk. Thus, potential cannabis ASSIST scores had a possible range of 0 to 36.

Predictors

The main predictor of interest was whether or not the participant had ever vaped cannabis, which was measured by the yes/no question, “Have you ever used cannabis by an electronic vaporizer (also called ‘vaping’)?” Additional covariates were entered into the models including gender, age, and health center, which was an indicator variable for the recruitment venue where the assessments took place. Intervention condition was omitted from the model due to a nonsignificant effect of the intervention as reported in the primary outcomes article.

Data analyses

Frequencies and means were conducted with the outcome and predictor variables in SPSS 27.0 (IBM SPSS Statistics for Windows, 2020). Unconditional latent growth curve (LGC) models (Curran, 2000) were estimated to determine if the slopes of the main outcome variables were significant over the 6-month time-frame, and could therefore be used as outcomes (endogenous factors) in subsequent conditional LGCs. Unconditional models were also fit to test different distributional assumptions including linear, Poisson, and negative binomial models to compare model fit using the sample-size adjusted Bayesian Information Criterion (aBIC). The model with the lowest aBIC value was chosen as the best-fitting model for further analyses (Finch & Bronk, 2011). Subsequent latent growth curve models were fit with the intercept and slope factors regressed on cannabis vaping and additional covariates entered as control variables. All growth models were fit in Mplus 8.6 (Muthen & Muthen, 1998). Full information maximum likelihood (FIML) estimation was used with the total analysis sample of 233 participants.

Results

The analysis sample was primarily African American or Black (93.1%) and slightly over half were female (53.2%) (Table 1). The average age was 16.4 years (SD = 1.1). There were no significant differences in demographic characteristics (gender, age and race/ethnicity) between participants who had ever vaped cannabis and participants who had not. The total number of adolescents reporting having vaped cannabis was 27, which was 11.6% of the sample (Table 1).

Table 1.

Background characteristics of participants who had used cannabis in the past 3 months split by having ever vaped cannabis (N = 233).

Characteristics Total Sample (N = 233) Did not Vape Cannabis (n = 206) Vaped Cannabis (n = 27)
Gender (Female) [N (%)] 124 (53.2) 111 (53.9) 13 (48.1)
Race (Black/African American) [N (%)] 217 (93.1) 192 (93.2) 25 (92.6)
Ethnicity (Hispanic) [N (%)] 11 (4.7) 9 (4.4) 2 (7.4)
Age [Mean(SD)] 16.4 (1.1) 16.4 (1.1) 16.3 (1.1)
Open in a new tab

Note. Percentages are within vaping groups. SD = Standard Deviation.

Frequency of cannabis use

Mean days of cannabis use decreased over time from 12.8 (11.0) at baseline to 10.4 (11.7) at 6 months (Table 1), which was reflective of a significant negative mean slope in the unconditional latent growth model (slope b = −0.11, p = 0.013, 95% CI = [−0.03, −0.19]). Therefore, days of cannabis use for the sample as a whole decreased significantly over the course of the 6-month study.

Due to the positive skew in the cannabis use variable, models using three different distributions were tested for unconditional model fit. The Poisson model had the worst model fit with an aBIC of 5080.30, the linear model had the next worst fit with an aBIC of 4578.03, and the negative binomial had the best fit with an aBIC of 4170.84. Therefore, we fit the latent growth model assuming a negative binomial model with count data with overdispersion for the conditional model.

The conditional model regressed the intercept at baseline and slope latent variables on the variable indicating whether or not the participant had ever vaped, along with covariates (gender, age, and health center where recruitment and assessments took place). The significant effect of having ever vaped on the intercept (b = 0.46, [SE = 0.18], p = 0.010, IRR = 1.58, 95% Confidence Interval (CI) of IRR = [1.12, 2.25]) indicated that participants who had vaped cannabis started out at baseline using cannabis more frequently compared to the rest of the sample who did not vape (Figure 1). Also, the significant effect of vaping on slope (b = −0.34, [SE = 0.14], p < 0.017, IRR = 0.71, 95% CI of IRR = [0.54, 0.94]) indicated that the participants who vaped had a steeper decline in frequency of cannabis use compared to the rest of the non-vaping sample (Figure 1).

Figure 1.

Figure 1.

Open in a new tab

Mean days of cannabis use at baseline, 3-, and 6-month follow-up for the total sample and vaping and non-vaping group (N = 233).

Cannabis ASSIST score

A latent growth model was fit using the cannabis ASSIST score, with means decreasing from 11.6 (9.0) at baseline to 9.21 (9.3) at 6-month follow-up (Table 2). This result is reflective of the statistical test of the slope in the unconditional LGC model, where the slope factor was −0.12 (p ≡ 0.001, 95% CI = [−0.19, −0.05]). Therefore, the ASSIST scores in the total sample decreased significantly over the course of the study.

Table 2.

Means and standard deviations of number of days of cannabis use and total ASSIST scores for study participants at all three trime points (N = 233).

Variable Mean (Standard Deviation)
Baseline 3-mo follow-up 6-mo follow-up
Cannabis Use Days in Past 30 Days 12.79 (11.01) 10.78 (11.58) 10.40 (11.65)
Cannabis ASSIST Scores 11.60 (9.01) 10.52 (9.13) 9.21 (9.31)
Open in a new tab

As with the cannabis frequency scores, the cannabis ASSIST scores were also positively skewed and therefore entered into an unconditional growth model, which was tested under a variety of distributional assumptions. The Poisson model had an aBIC of 5045.40, while the linear model had an aBIC of 4384.29 and the negative binomial model had the lowest aBIC and therefore, the best model fit with an aBIC of 4177.64.

Subsequently, we fit a conditional latent growth model, regressing intercept and slope growth factors on having ever vaped cannabis, controlling for gender, age and health center where recruitment and assessments took place. In this model, vaping predicted a steeper negative slope over time (b = −0.34, [SE = 0.14], p = 0.014, IRR = 0.71, 95% CI = [0.54, 0.93]). However, the mean intercepts at baseline did not differ significantly between the group that vaped and the group who did not vape, which are depicted in Figure 2. Further tests revealed a significant difference in the means of cannabis ASSIST scores at 6-months with the intercept having a mean of 2.08 (p < 0.001), which indicates significant separation in the 6-month mean cannabis ASSIST scores. Given the parameterization of the LGC model, the intercept represents the mean difference between both 6-month cannabis ASSIST scores. Further analyses of the individual ASSIST items and how they might have been different between the two vaping status groups using chi-square tests of independence revealed no significant differences between the two groups on any of the individual items.

Figure 2.

Figure 2.

Open in a new tab

Mean cannabis assist scores at baseline, 3-, and 6-month follow-up for the total sample and vaping and non-vaping groups (N = 233).

Discussion

Vaping cannabis is a recently expanding research area of interest in the substance use literature. Extant literature has examined vaping cannabis among young adults (e.g. college students) (Cassidy et al., 2018; Frohe et al., 2018; Jones et al., 2016; Kenne et al., 2017), adults (Lee et al., 2016; Malouff et al., 2014; Russell et al., 2018), and adolescents (Borodovsky et al., 2017; Eggers et al., 2017; Knapp et al., 2019; Morean et al., 2015; Peters et al., 2018). Our study is one of the first to investigate cannabis vaping among a sample of primarily Black or African American adolescents. While many studies use data from a nationally representative sample, which includes relatively few minority students, we used data from our sample that was majority Black/African American. It is especially important to study cannabis vaping within this racial/ethnic subgroup because in recent years, cannabis use among African American students, compared to their White counterparts, has increased substantially with the difference in prevalence rates being more pronounced among eighth graders (Johnston et al., 2020). In addition, the gap in prevalence of cannabis use between African American and White students has narrowed for tenth and twelfth graders such that the former are catching up to the latter in cannabis usage (Johnston et al., 2020).

We found a relatively low prevalence of vaping cannabis (11.6%) in a sample of adolescents who all reported using cannabis. This lifetime prevalence is lower than the 13.6% prevalence found by Lim and colleagues in their meta-analysis that included all adolescents, rather than restricting the studies to report on only adolescents who indicate using cannabis (Lim et al., 2022). The low prevalence in our sample may be due to the sample being primarily African-American adolescents, who have been less likely to report vaping cannabis (Lee et al., 2016). In fact, younger white males have been found to report the heaviest use of vaped cannabis, a group which is largely absent from our sample. However, research has shown that African American youth have higher lifetime and 30-day prevalence rates of vaping with vaped liquid often containing cannabis, compared to peers who are White (Cambron, 2023). Therefore, we would expect the percentage of students who vaped in our sample of Black/African American youth to be higher than the comparison sample that included all adolescents.

Our failure to find sex or age as significant predictors may have been due to low statistical power given the small number of adolescents who reported vaping cannabis in our sample. Likely, the constricted age range is responsible for the lack of significant findings on this demographic variable.

In contrast to our proposed hypothesis and previous research (Jones et al., 2016), experience with vaping cannabis was not associated with increased frequency of cannabis use over time. Rather, we found that while participants who vaped cannabis began the study using more frequently than those who did not vape, by 6 months the former decreased their cannabis use to comparable levels as the latter. This suggests that those adolescents who vaped cannabis were not persistently heavier users than those who did not vape it.

Also surprising is that ASSIST scores, while comparable at baseline between those who vaped and those who did not vape, decreased significantly more for those who vaped, indicating they experienced a steeper reduction in problems associated with cannabis use. These results are in contrast to our expectations and the results in the extant literature. For example, a crossover trial (Spindle et al., 2018) found that vaping cannabis produced stronger effects compared to smoking. These would likely be associated with more problems, which would be expected to result in higher not lower scores on the ASSIST. These results are also contrary to previous research indicating that vaping cannabis is more highly associated with violent acts and property delinquency, such as theft, compared to youth who use other routes of administration (e.g., combustibles or edibles) (Jackson et al., 2019). Therefore, we would expect that adolescents in our sample who reported lifetime cannabis vaping would have more problems, such as legal ramifications of problem behaviors.

However, it is possible adolescents who were responding to the ASSIST questions may not have perceived risks or harms associated with vaping cannabis. Attitudes toward cannabis vaping are more positive than attitudes toward smoking cannabis. Reasons for these attitudes include that vaping is considered more discreet because it lacks the odor of cannabis smoke and comes with fewer resulting respiratory problems (Gieringer, 2001; Russell et al., 2018). Therefore, vaping cannabis may be seen as less problematic to adolescents and their peers and family. However, previous research has documented lung damage due to vaping, likely due to limited regulations governing the vaping products (Pearson & Villanti, 2020; Lung Injury Response Epidemiology/Surveillance Task Force, 2019).

Overall, compared to the students reporting no experience with vaping, the students who reported vaping decreased significantly in frequency of cannabis use and on reported problems associated with cannabis use on the ASSIST questionnaire. One possibility is that interventions used in the main study (clinician and computer-delivered brief intervention) conferred more benefits for youth with vaping experience than for youth who used cannabis by other routes. In other words, participants who vaped cannabis could have been especially receptive to intervention messaging. However, this is somewhat unexpected, as both the in-person and computer-delivered brief interventions were based on motivational interviewing and didactic content. The interventions would have been tailored based on reasons for use and personal goals, but not necessarily based on method of administration. Another possibility is that this subgroup of youth with risky cannabis use would have decreased their frequency of use and reported problems irrespective of intervention. Further research is warranted to replicate these findings and better explicate the reasons for the significant decline in use and ASSIST scores among those who vaped cannabis.

Limitations

There are several limitations associated with our study. First, the data are based exclusively on self-report. Therefore, responses may be subject to social desirability bias. Also, our findings lack generalizability because our sample was comprised of primarily African-American adolescents from urban high schools. However, this may also be a strength because most previous studies that have investigated cannabis vaping have been primarily among students who are White. In addition, our measure of frequency of cannabis use in the past 30 days and our measure of having ever vaped cannabis were coarse measures. For example, these measures did not account for route of administration (ROA), such as combustibles or edibles, or for type of cannabis (oils, waxes, dabs, dry leaves), which may impact the type and magnitude of drug effects experienced by the user. Identifying the type of cannabis used would enable clinicians to target adolescents more accurately who are at greater risk of experiencing acute and/or chronic problems due to cannabis use. In addition, we did not measure frequency of cannabis vaping, which would have been a stronger measure than a binary question measuring whether or not the participant reported ever vaping. Also, the small number of participants reporting having ever vaped cannabis may make our findings more preliminary as well as giving us limited power to find subgroup effects based on demographic characteristics. In addition, all adolescents in the study received a brief intervention that addressed cannabis use. Finally, it is possible that decreases in cannabis use frequency were part of longer-term seasonal trends that could not be captured by a 6-month study.

Conclusions

Despite our limitations, the current study adds to the scientific literature by examining the effects of having vaped cannabis on frequency of cannabis use and cannabis-related problems over a 6-month period. Our findings suggest that, among adolescents with risky cannabis use, prior experience with vaping cannabis is not associated with escalation of use or experience of excess problems compared to adolescents who use cannabis by other routes of administration. In contrast, it is possible that participants who vaped cannabis benefited more from a brief intervention, whether it was delivered by clinician or computer, compared to participants who had never vaped.

Funding

This work was supported by the National Institute of Drug Abuse (R01DA036604).

Footnotes

Disclosure statement

Dr. Schwartz reports providing consultation to Verily Life Sciences. No financial disclosures were reported by the other authors.

References

  1. Apollonio DE, Spetz J, Schmidt L, Jacobs L, Kaur M, & Ramo D (2019). Prevalence and correlates of simultaneous and separate 30-day use of Tobacco and Cannabis: Results from the California Adult Tobacco Survey. Substance Use & Misuse, 54(10), 1627–1632. 10.1080/10826084.2019.1597888 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Borodovsky JT, Lee DC, Crosier BS, Gabrielli JL, Sargent JD, & Budney AJ (2017). U.S. cannabis legalization and use of vaping and edible products among youth. Drug and Alcohol Dependence, 177, 299–306. 10.1016/j.drugalcdep.2017.02.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Budney AJ, Sofis MJ, & Borodovsky JT (2019). An update on cannabis use disorder with comment on the impact of policy related to therapeutic and recreational cannabis use. European Archives of Psychiatry and Clinical Neuroscience, 269(1), 73–86. 10.1007/s00406-018-0976-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Cambron C (2023). Racial/ethnic differences in vaping product use among youth: A state-level analysis. International Journal of Environmental Research and Public Health, 20(9), 5729. 10.3390/ijerph20095729 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cassidy RN, Meisel MK, DiGuiseppi G, Balestrieri S, & Barnett NP (2018). Initiation of vaporizing cannabis: Individual and social network predictors in a longitudinal study of young adults. Drug and Alcohol Dependence, 188, 334–340. 10.1016/j.drugalcdep.2018.04.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Center for Disease Control and Prevention (CDC). (2020). Outbreak of lung injury associated with the use of E-Cigarette, or Vaping, Products. Retrieved February 14, 2023. https://www.cdc.gov/tobacco/basic_information/e-cigarettes/severe-lung-disease.html. [Google Scholar]
  7. Curran PJ (2000). A latent curve framework for the study of developmental trajectories in adolescent substance use. In Multivariate applications in substance use research ((pp. 1–42)). Psychology Press. [Google Scholar]
  8. Di Forti M, Marconi A, Carra E, Fraietta S, Trotta A, Bonomo M, Bianconi F, Gardner-Sood P, O’Connor J, Russo M, Stilo SA, Marques TR, Mondelli V, Dazzan P, Pariante C, David AS, Gaughran F, Atakan Z, Iyegbe C, … Murray RM (2015). Proportion of patients in south London with first-episode psychosis attributable to use of high potency cannabis: A case-control study. The Lancet. Psychiatry, 2(3), 233–238. 10.1016/S2215-0366(14)00117-5 [DOI] [PubMed] [Google Scholar]
  9. Earleywine M, & Barnwell SS (2007). Decreased respiratory symptoms in cannabis users who vaporize. Harm Reduction Journal, 4(1), 11. 10.1186/1477-7517-4-11 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Eggers ME, Lee YO, Jackson K, Wiley JL, Porter L, & Nonnemaker JM (2017). Youth use of electronic vapor products and blunts for administering cannabis. Addictive Behaviors, 70, 79–82. 10.1016/j.addbeh.2017.02.020 [DOI] [PubMed] [Google Scholar]
  11. Etter JF (2015). Electronic cigarettes and cannabis: An exploratory study. European Addiction Research, 21(3), 124–130. 10.1159/000369791 [DOI] [PubMed] [Google Scholar]
  12. Finch WH, & Bronk KC (2011). Conducting confirmatory latent class analysis using Mplus. Structural Equation Modeling, 18(1), 132–151. 10.1080/10705511.2011.532732 [DOI] [Google Scholar]
  13. Frohe T, Leeman RF, Patock-Peckham J, Ecker A, Kraus S, & Foster DW (2018). Correlates of cannabis vape-pen use and knowledge among U.S. college students. Addictive Behaviors Reports, 7, 32–39. 10.1016/j.abrep.2017.11.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Gieringer DH (2001). Cannabis “Vaporization” a promising strategy for smoke harm reduction. Journal of Cannabis Therapeutics, 1(3–4), 153–170. 10.1300/J175v01n03_10 [DOI] [Google Scholar]
  15. Gryczynski J, Kelly SM, Mitchell SG, Kirk A, O’Grady KE, & Schwartz RP (2015). Validation and performance of the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) among adolescent primary care patients. Addiction, 110(2), 240–247. 10.1111/add.12767 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gryczynski J, Mitchell SG, Schwartz RP, Dusek K, O’Grady KE, Cowell AJ, Barbosa C, Barnosky A, & DiClemente CC (2021). Computer- vs. nurse practitioner-delivered brief intervention for adolescent marijuana, alcohol, and sex risk behaviors in school-based health centers. Drug and Alcohol Dependence, 218, 108423. 10.1016/j.drugalcdep.2020.108423 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hall W, & Degenhardt L (2015). High potency cannabis: A risk factor for dependence, poor, psychosocial outcomes, and psychosis. BMJ, 350(mar04 4), h1205–h1205. 10.1136/bmj.h1205 [DOI] [PubMed] [Google Scholar]
  18. Harrell MB, Clendennen SL, Sumbe A, Case KR, Mantey DS, & Swan S (2022). Cannabis vaping among youth and young adults: A scoping review. Current Addiction Reports, 9(3), 217–234. 10.1007/s40429-022-00413-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Humeniuk R, Ali R, Babor TF, Farrell M, Formigoni ML, Jittiwutikarn J, De Lacerda RB, Ling W, Marsden J, Monteiro M, Nhiwatiwa S, Pal H, Poznyak V, & Simon S (2008). Validation of the alcohol, smoking and substance involvement screening test (ASSIST). Addiction, 103(6), 1039–1047. 10.1111/j.1360-0443.2007.02114.x [DOI] [PubMed] [Google Scholar]
  20. IBM SPSS Statistics for Windows. (2020). Version 27.0 [software]. IBM Corp, [Google Scholar]
  21. Jackson DB, Boccio CM, Leal WE, & Vaughn MG (2019). It’s all the rage! Exploring the nuances in the link between vaping and adolescent delinquency. Journal of Criminal Justice, 63, 58–69. 10.1016/j.jcrimjus.2019.04.004 [DOI] [Google Scholar]
  22. Johnston L, Miech R, Patrick O, Bachman J, Schulenberg J, & Patrick M (2020). Monitoring the Future national survey results on drug use, 1975–2019: Overview, key findings on adolescent drug use. [Google Scholar]
  23. Jones CB, Hill ML, Pardini DA, & Meier MH (2016). Prevalence and correlates of vaping cannabis in a sample of young adults. Psychology of Addictive Behaviors, 30(8), 915–921. 10.1037/adb0000217 [DOI] [PubMed] [Google Scholar]
  24. Kenne DR, Fischbein RL, Tan ASL, & Banks M (2017). The use of substances other than nicotine in electronic cigarettes among college students. Substance Abuse, 11, 1178221817733736. 10.1177/1178221817733736 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Keyes KM, Kreski NT, Ankrum H, Cerdá M, Chen Q, Hasin DS, Martins SS, Olfson M, & Miech R (2022). Frequency of adolescent cannabis smoking and vaping in the United States: Trends, disparities and concurrent substance use, 2017–19. Addiction (Abingdon, England), 117(8), 2316–2324. 10.1111/add.15912 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Knapp AA, Lee DC, Borodovsky JT, Auty SG, Gabrielli J, & Budney AJ (2019). Emerging trends in cannabis administration among adolescent cannabis users. The Journal of Adolescent Health, 64(4), 487–493. 10.1016/j.jadohealth.2018.07.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Knight JR, Sherritt L, Shrier LA, Harris SK, & Chang G (2002). Validity of the CRAFFT substance abuse screening test among adolescent clinic patients. Archives of Pediatrics & Adolescent Medicine, 156(6), 607–614. 10.1001/archpedi.156.6.607 [DOI] [PubMed] [Google Scholar]
  28. Lee DC, Crosier BS, Borodovsky JT, Sargent JD, & Budney AJ (2016). Online survey characterizing vaporizer use among cannabis users. Drug and Alcohol Dependence, 159, 227–233. 10.1016/j.drugalcdep.2015.12.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lim CCW, Sun T, Leung J, Chung JYC, Gartner C, Connor J, Hall W, Chiu V, Stjepanovic D, & Chan GCK (2022). Prevalence of adolescent cannabis vaping: A systematic review and meta-analysis of US and Canadian Studies. JAMA Pediatrics, 176(1), 42. 10.1001/jamapediatrics.2021.4102 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lozier MJ, Wallace B, Anderson K, Ellington S, Jones CM, Rose D, Baldwin G, King BA, Briss P, Mikosz CA, & Force L (2019). Update: Demographic, product, and substance-use characteristics of hospitalized patients in a Nationwide outbreak of E-cigarette, or Vaping, product use–associated lung injuries – United States, December 2019. MMWR. Morbidity and Mortality Weekly Report, 68(49), 1142–1148. 10.15585/mmwr.mm6849e1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Malouff JM, Rooke SE, & Copeland J (2014). Experiences of marijuana-vaporizer users. Substance Abuse, 35(2), 127–128. 10.1080/08897077.2013.823902 [DOI] [PubMed] [Google Scholar]
  32. Mehmedic Z, Chandra S, Slade D, Denham H, Foster S, Patel AS, Ross SA, Khan IA, & ElSohly MA (2010). Potency trends of Delta9-THC and other cannabinoids in confiscated cannabis preparations from 1993 to 2008. Journal of Forensic Sciences, 55(5), 1209–1217. 10.1111/j.1556-4029.2010.01441.x [DOI] [PubMed] [Google Scholar]
  33. Miech RA, Patrick ME, O’Malley PM, Johnston LD, & Bachman JG (2020). Trends in reported Marijuana vaping among US adolescents, 2017–2019. JAMA, 323(5), 475–476. 10.1001/jama.2019.20185 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Morean ME, Kong G, Camenga DR, Cavallo DA, & Krishnan-Sarin S (2015). High school students’ use of electronic cigarettes to Vaporize Cannabis. Pediatrics, 136(4), 611–616. 10.1542/peds.2015-1727 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Muthen LK, & Muthen BO (1998). Mplus User’s Guide. Eight Edition [software]. Muthen & Muthen. 2017. [Google Scholar]
  36. Palamar JJ (2021). Increases in frequent vaping of cannabis among high school seniors in the United States, 2018–2019. The Journal of Adolescent Health, 69(1), 153–156. 10.1016/j.jadohealth.2021.03.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Patrick ME, Miech RA, Carlier C, O’Malley PM, Johnston LD, & Schulenberg JE (2016). Self-reported reasons for vaping among 8th, 10th, and 12th graders in the US: Nationally-representative results. Drug and Alcohol Dependence, 165, 275–278. 10.1016/j.drugalcdep.2016.05.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Pearson JL, & Villanti AC (2020). It is past time to consider cannabis in vaping research. Nicotine & Tobacco Research, 22(5), 597–598. 10.1093/ntr/ntaa012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Peters EN, Bae D, Barrington-Trimis JL, Jarvis BP, & Leventhal AM (2018). Prevalence and sociodemographic correlates of adolescent use and polyuse of combustible, vaporized, and edible cannabis products. JAMA Network Open, 1(5), e182765. 10.1001/jamanetworkopen.2018.2765 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Russell C, Rueda S, Room R, Tyndall M, & Fischer B (2018). Routes of administration for cannabis use – Basic prevalence and related health outcomes: A scoping review and synthesis. The International Journal on Drug Policy, 52, 87–96. 10.1016/j.drugpo.2017.11.008 [DOI] [PubMed] [Google Scholar]
  41. Spindle TR, Cone EJ, Schlienz NJ, Mitchell JM, Bigelow GE, Flegel R, Hayes E, & Vandrey R (2018). Acute effects of smoked and vaporized Cannabis in healthy adults who infrequently use Cannabis: A crossover trial. JAMA Network Open, 1(7), e184841. 10.1001/jamanetworkopen.2018.4841 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Varlet V, Concha-Lozano N, Berthet A, Plateel G, Favrat B, De Cesare M, Lauer E, Augsburger M, Thomas A, & Giroud C (2016). Drug vaping applied to cannabis: Is “Cannavaping” a therapeutic alternative to marijuana? Scientific Reports, 6(1), 25599. 10.1038/srep25599 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Volkow ND, Baler RD, Compton WM, & Weiss SRB (2014). Adverse health effects of Marijuana use. The New England Journal of Medicine, 370(23), 2219–2227. 10.1056/NEJMra1402309 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Whiting PF, Wolff RF, Deshpande S, Di Nisio M, Duffy S, Hernandez AV, Keurentjes JC, Lang S, Misso K, Ryder S, Schmidlkofer S, Westwood M, & Kleijnen J (2015). Cannabinoids for medical use: A systematic review and meta-analysis. JAMA, 313(24), 2456–2473. 10.1001/jama.2015.6358 [DOI] [PubMed] [Google Scholar]

RESOURCES