https://orcid.org/0000-0001-8538-432X
Abstract
Background: Historically substance use literature has focused on smoking as the main mode of cannabis consumption, so there are knowledge gaps surrounding current understanding of edibles and vaping. These alternative modes of cannabis use are already common among Canadian youth; however, little is known about how these cannabis use patterns change over time. Methods: This study examined the mode (smoking, eating/drinking, vaping) and frequency of cannabis use among a large sample of Canadian youth who participated in 2017–2018 and 2018–2019 data collection waves of the COMPASS study. Using latent transition analysis, this sample consisting of 18,824 youth in grades 9–12 were categorized into cannabis use classes stratified by sex, and their transition between these classes over the one-year period was examined. Results: Three cannabis use classes were identified (occasional multimode, regular multimode, and smoking) alongside one nonuse class. Among youth who reported cannabis use at baseline, transitioning to a multimode group, and/or increasing frequency of multimode use was likely over the one-year period. Conclusions: These findings may highlight a key leverage point for harm-reduction strategies which aim to prevent cannabis related harms associated with high frequency use.
1. Introduction
Canada legalized combustible forms of cannabis in October 2018, and alternative forms of cannabis (e.g., edibles, concentrates) were legalized the following year (October 2019). Since then, a wealth of new research has begun to delve into cannabis edible and concentrate use. This is in contrast to historical cannabis use literature, which has mostly focused on exposure through smoking, or neglected to examine the modes in which people used cannabis. This shift toward recognizing the alternative modes of cannabis consumption, sometimes referred to as “routes of administration” in the literature, is necessary, as cannabis consumption via edibles and vaping has been gaining popularity over time (Russell et al., Citation2018). Recent data suggest that about 40% of Canadians who use cannabis report using at least one other mode of consumption aside from smoking the dried/leaf version of the product (Rotermann, Citation2019).
A primary concern for the research and policy of alternative modes of cannabis use is the popularity of these products among youth. Youth cannabis use prevalence in Canada has remained relatively high over time in comparison to other countries (United Nations Children’s Fund, 2013; World Health Organization, Citation2016). Canadian youth and young adults aged 15–24 use cannabis more than any other age group; as of 2017, the prevalence of past year use was 19% among 15- to 19-year old, 33% among 20- to 24-year old, and only 13% for those 25 or older (Canadian Tobacco, Alcohol & Drugs Survey, 2017). Prevalence of youth cannabis use in Canada is similar to the United States, but both countries have higher rates compared to England, which may stem from differences in regulation and perceptions of perceived harm among youth (Wadsworth & Hammond, Citation2019). While substance use experimentation is to be expected in adolescents (Casey & Jones, Citation2010; Griffin & Botvin, Citation2010), the prevention of problematic substance use, such as high-frequency or high-potency substance use, is a public health priority in Canada as it has been associated with a variety of negative outcomes for youth, including detriments to brain development and cognitive function (Battistella et al., Citation2014; Lisdahl et al., Citation2013), poorer mental health (Ganguli et al., Citation2002; Meier et al., Citation2015), and propensity for future problematic substance use (Degenhardt et al., Citation2010). Alternative modes of cannabis use, such as edibles and vaping, have become additional public health concerns, as it is known that these products are especially appealing to youth (Friese et al., Citation2016; Jones et al., Citation2016), and are already fairly common in this age group (Doggett et al., in press; Knapp et al., Citation2019). Previous Canadian research indicates that among youth who had used cannabis in the last year, 23% reported having vaped cannabis during that time, and 36% reported having ate/drank cannabis (Doggett et al., in press). Research has also suggested that youth who use multiple modes of cannabis are more likely to use in higher frequencies (Knapp et al., Citation2019).
Alternative modes of cannabis use carry additional concerns compared to consumption through smoking. Cannabis edibles and concentrates (used for vaping) have greater potential for unintentional cannabis poisoning as they contain higher doses of tetrahydrocannabinol (THC), are easier to overconsume, and have delayed psychoactive effects compared to smoking the dried product (Langrand et al., Citation2019). Potency of alternative cannabis products has already been identified as a critical component to manage (White et al., Citation2020), and the federal government in Canada controls food safety aspects of cannabis products, including maximum THC content in a single product. However, research has highlighted that consumer literacy surrounding THC content labels on cannabis products use is low among young Canadians, which may pose consumption risks (Leos-Toro et al., Citation2020). Of course, regulation of THC content and labeling only apply to the legal drug supply, and little is known about homemade products. However, one study suggested that lack of consumer knowledge extends beyond packaging; among a small sample of young Canadians, nearly a third of those reporting current cannabis use were either unsure or underestimated the time for edibles to take effect (Paphitis et al., Citation2019). Low literacy for alternative cannabis products represents a public health concern given the potential for adverse harms and increased availability of these products that comes with legalization.
Although cannabis vaping is becoming nearly as common as edible use among youth (Doggett et al., in press; Knapp et al., Citation2019), little is known about the potential health implications of vaping or related risky behaviors (Pearson & Villanti, Citation2020; Spindle et al., Citation2019). While the mechanism of action is unclear, a large scale study of US adolescents found that those who reported vaping cannabis were worse off for all of the risk and protective factors measured, including propensity to use other substances (Meier et al., Citation2019). Youth have reported perceiving cannabis vaping as a “healthier” way to use substances (Chadi et al., Citation2020), which is generally in-line with low risk cannabis use guidelines suggesting that individuals avoid combustion-based methods of consumption (Fischer et al., Citation2017). However, it is unclear whether the increasing popularity of alternatives modes of cannabis represents replacement of combustion-based consumption. Moreover, it is important that low-risk guidelines are properly understood, as it may be problematic if alternative modes of use are perceived as zero harm. Unfortunately, research indicates that the proportion of US young people who perceived edibles as harmless has increased in recent years (Reboussin et al., Citation2019).
Given the existing popularity of alternative cannabis products among youth in Canada, it is a public health priority to direct research and policy efforts toward understanding youth consumption patterns in effort to protect from potential harms. While research has begun to examine the use of cannabis edibles and vaping products among youth, literature in this area remains sparse. Notably, research based on longitudinal studies is extremely limited, and as such there is a lack of understanding surrounding changes in the modes of cannabis use among youth over time. Temporal analysis is important in this area of research, as experimenting with substances is common during adolescence and relying on prevalence rates alone may mask important patterns of change in consumption over time. The present study aims to fill this information gap by using linked longitudinal data from the COMPASS study, a prospective cohort study consisting of a large sample of Canadian youth. The objective of this study is to use latent transition analysis (LTA) to group youth into classes based on their cannabis use frequency and modes of use at baseline, and to examine their probabilities of switching into a different distinct class of cannabis use at follow up.
2. Methods
2.1. COMPASS study
This research uses data from the COMPASS study, a longitudinal cohort study of youth in Canada which collects yearly self-report data. COMPASS uses purposive sampling at the school-level and employs an active-information passive-consent protocol, an important research method to limit self-selection bias, particularly for self-report of substance use (Rojas et al., Citation2008; White et al., Citation2004). A yearly paper-based questionnaire is administered to all students at participating schools during class time and includes a variety of demographic and health behavior questions pertaining to diet, physical activity, substance use, and mental health. Notably, data collection occurs during school year cycles (Sept–May), and as such “2017/2018–2018/2019” represents a one-year time frame. Participating schools typically collect data at approximately the same time each year. The process of linking participating student responses across time is described in detail elsewhere (Battista et al., Citation2019). The active-information passive-consent protocol operates on an opt-in default, such that all students participate in the survey unless parents have opted them out, or the student themselves refuse to participate on data collection day. This approach not only limits selection biases, but also yields high response rates; the response rate for both years in the present study was 79%. COMPASS collects data in Ontario, Alberta, British Columbia, and Québec. However, Québec data was excluded from the present research as the 2017/2018 data collection year did not include the modes of cannabis use question.
2.2. Measures
Cannabis use frequency was determined through responses to the question “In the last 12 months, how often did you use marijuana or cannabis? (a joint, pot, weed, hash),” where the response options included “I have never used marijuana,” “I have used marijuana but not in the last 12 months,” “Less than once a month,” “Once a month,” “2 or 3 times a month,” “Once a week,” “2 or 3 times a week,” “4 to 6 times a week,” and “Every day.” The “less than once a month” and the “1–3 times per month” categories were left as is, whereas the “weekly or more often” categories were aggregated. “No use” consisted of students who reported never having used cannabis, or not having used cannabis in the last 12 months.
Mode of cannabis use was determined through responses to the question “If you have used marijuana or cannabis in the last 12 months, how did you use it?” where the response options included “I have used it by smoking it (e.g., in a joint, a pipe, a bong),” “I have used it by vaping it,” “I have used it by eating or drinking it (e.g., in brownies, cookies, candies, tea),” and “I have not used marijuana or cannabis in the last 12 months.” Students were asked to select all that applied. Several baseline covariates were identified for this analysis, including sex (male, female), grade (Battistella et al., Citation2014; Ganguli et al., Citation2002; Lisdahl et al., Citation2013), ethnicity racialized [Black, Asian, Indigenous (First Nations, Métis, Inuit), Latin American/Hispanic, Other/Mixed], nonracialized), weekly spending money (zero, $1–$40, $41–$100, >$100, I don’t know), and binary indicators of three other substance use behaviors: smoking, e-cigarette use, and binge drinking.
2.3. Sample
The analysis used data from 18,824 students in grades 9–11 who participated in Year 6 of the COMPASS study (2017–2018) in Ontario, Alberta, and British Columbia, and who were subsequently followed across Year 7 (2018–2019). A total of 80 schools participated in both years. Students with contradictory responses to the cannabis measures (e.g., reporting no cannabis use frequency but selecting a response for the modes of use question) (n = 1319), or with missing values for covariates (n = 344) were removed, leaving a final complete case sample of 17,161 students. Students with missing values for cannabis use frequency were included in the analysis, since LTA methods account for missing outcome data through full information maximum likelihood estimation (Collins & Lanza, Citation2010). An attrition analysis of students who were linked compared to those who could not be linked is provided in of the Appendix; nonlinked participants were more likely to have report high-frequency cannabis use at baseline. A full technical report detailing the linking procedure and how linked data from the COMPASS study may differ from unlinked data is available online (Qian et al., Citation2015). A missing data analysis comparing those who were removed due to missing covariates is available in of the Appendix; while there were some significant differences observed, the magnitude of differences considered alongside the overall low amount of missing covariate data increases confidence that removing these individuals did not greatly impact results presented.
2.4. Analysis
Mode of cannabis use included four indicators: frequency, smoked, vaped, and ate/drank. Sample descriptive statistics were calculated and chi-square tests were used to examine differences between males and females. Following the process outlined by Ryoo et al. (Citation2018), initial latent class analysis (LCA) models were run at each time point to examine the optimal number of latent classes. Measurement invariance testing was used to examine differences in class prevalence and item response probabilities by sex. Latent transition analysis (LTA) described by Collins and Lanza (Citation2010) was used to examine distinct patterns of cannabis use over time. LTA models included measures for frequency and mode of use, which were used to group students based on similar frequency and modes of cannabis use. LTA is a useful approach to examine substance use patterns, as multiple characteristics are considered to examine specific substance use profiles over time (Lanza et al., Citation2010).
Given established differences in substance use patterns between males and females (Hemsing & Greaves, Citation2020; Leatherdale & Burkhalter, Citation2012) and results of initial invariance testing (see in the Appendix), all models were stratified by sex. First, sequential models without covariates were run to determine the optimal number of latent classes. Model fit was assessed using the Bayesian Information Criterion (BIC), with a lower BIC indicating better fit. Only properly identified models (i.e., those with positive degrees of freedom) were compared, and model parsimony was considered in the fit assessment to optimize balance. The entropy statistic was used to measure the quality of class separation (Ramaswamy et al., Citation1993). Additional parameter constraints were applied to specify a single distinct “nonuse” class with no constraints on the number or type of “use” classes, thus, allowing for additional degrees of freedom.
Once the optimal LTA models were determined, covariates were included to predict class membership. Grade was included as an important covariate, as substance use patterns are known to change during adolescents; typically, older students show greater prevalence of overall substance use as well as higher-frequency substance use (Gardner & Steinberg, Citation2005; Leatherdale & Burkhalter, Citation2012). Spending money was also considered an important covariate to examine because ability to purchase has been identified as a factor which contributes to youth cannabis use (Zuckermann et al., Citation2021). Other substance use behaviors, including binge drinking, smoking, and e-cigarette use were considered as important covariates given established patterns of clustering of substance use behaviors among youth populations (Costello et al., Citation2012; Leatherdale & Ahmed, Citation2010; Williams et al., Citation2021). Multinomial logistic regression models were used with time 1 class membership as the outcome and all covariates as predictors. The influence of covariates on transition probabilities were also examined, however, model estimation issues due to sparseness prevented further analysis. All models were run in Mplus Version 8.4.
3. Results
3.1. Identifying cannabis use patterns
Consistent with differences seen in the sample descriptive statistics in , testing for measurement invariance by sex revealed significant differences in item response probabilities, resulting in the need to stratify models by sex. Initial LCA model testing is shown in . Model fit statistics provided in show that the four-class models had the lowest BIC values for most groups, while models containing more than five classes were not well estimated due to cell sparseness. As such, the four-class model was identified as the optimal model at baseline and follow-up for females and males. Model fit statistics for the four-class LTA models showed entropy estimates were 0.93 for females and 0.94 for males, suggesting good class separation.
Table 1. Descriptive statistics of the COMPASS study sample, 2017/2018 to 2018/2019.
Table 2. Model fit statistics for LCA models (COMPASS Study 2017/2018–2018/2019).
and present LTA model results; with the exception of the prevalence rates, all values presented in these tables represent probabilities. Although models were stratified to examine male and female cannabis use patterns separately, cannabis use patterns within the classes were generally similar between the two groups, and therefore the labels chosen to describe the classes are almost identical. The one exception is that the “occasional smoking” cannabis-use class observed among females (see ) was just labeled “smoking” among males (see ) because the frequency patterns appeared more spread out for males rather than concentrated on occasional use for females.
Table 3. LTA model results among females in the COMPASS 2018/2019 sample.
Table 4. LTA model results among males in the COMPASS 2018/2019 sample.
3.1.1. Females
The results of the LTA for females can be found in . The four patterns of cannabis use identified for females in this sample were: “regular multimode,” “occasional smoking,” “occasional multimode,” and “nonuse.” The regular multimode class included 3.4% of the sample at baseline, and 7.4% at follow-up. This class was labeled “regular” because it was most likely to include youth who reported using cannabis weekly or more often (0.66). Youth in this class were almost guaranteed to have reported smoking cannabis (0.99), and also had reasonable likelihood to have reported eating/drinking (0.67) and vaping (0.44) cannabis.
The occasional smoking class was the most prevalent cannabis-use class, including 8.7% of females at baseline, and 10.9% at follow-up. This class was most likely to include females who reported cannabis use less than once a month (0.65) and who were guaranteed to report smoking (1.00). The probability of females in this class reporting vaping or eating/drinking cannabis was very low (0.06 and 0.07, respectively).
The occasional multimode class showed the largest increase in prevalence between years, including only 3.0% of the sample at baseline, but rising to 10.0% of the sample at follow-up. This class was similar in likelihood to the regular multimode class to engage in vaping and eating/drinking but were moderately less likely to smoke cannabis (0.75). However, the occasional multimode class is distinct from the regular multimode class and labeled “occasional” because females in this class were most likely to report a cannabis use frequency less than once a month (0.70). The nonuse class was the most prevalent class in the sample, with 84.9% of females in this class at baseline, and 71.1% at follow-up. As previously stated in the methods section, this class was constrained to only include individuals who reported no cannabis use, in any mode, in the previous 12 months.
3.1.2. Males
The results of the LTA for males can be found in . The four patterns of cannabis use identified for males in this sample were “regular multimode,” “smoking,” “occasional multimode,” and “nonuse.” These classes mostly align with the classes identified for females, but the “occasional” label was removed from the “smoking” class because for males, the likelihoods were more evenly distributed across use frequencies (0.49 for less than once a month, 0.27 for 1–3 times per months, 0.24 for weekly or more often). Class prevalence rates at baseline and follow-up were nearly identical to those among females. In the regular multimode class, males were more likely than females to have ate/drank cannabis (0.79), and also more likely to have vaped cannabis (0.68). Compared to females, males in the occasional multimode class were slightly less likely to smoke (0.68 compared to 0.75 for females), and slightly more likely to vape cannabis (0.40 compared to 0.35 for females).
3.2. Transition probabilities of cannabis use patterns
3.2.1. Females
Transition probabilities for females can be found in the lower half of and are visualized in . Females in the regular multimode class were highly likely to remain in that class (0.94). Females in the occasional smoking class were more likely to switch classes than to remain in that class (0.43). The likelihood of switching from occasional smoking to one of the other use classes was about equal (0.24 likely to switch to regular multimode, 0.21 likely to switch to occasional multimode), and switching to nonuse was not very likely (0.12). Females in the occasional multimode class were most likely to remain there (0.63), but it was also moderately probable for them to switch to occasional smoking (0.21). Females in the nonuse class were most likely to remain as nonuse (0.83). Switching from the nonuse class to either of the occasional-use classes was equally likely (0.08 for both) and switching to the regular multimode class was highly unlikely (0.02).
Figure 1. Visual depiction of transition probabilities for different modes of cannabis use among females in the COMPASS Study (2017/2018–2018/2019). In this Sankey diagram, the size of the colored bars corresponds to the magnitude of the transition probabilities in , by which thicker bars represent greater probability.
3.2.2. Males
Transition probabilities for males can be found in the lower half of and are visualized in . Similar to females, males in the regular multimode class were most likely to remain there (0.94). Also similar to females, males were unlikely to remain in the smoking class (0.43). However, unlike females, males who started in the smoking class were more likely to switch to regular multimode use (0.35) than they were to switch to occasional multimode use (0.11), or nonuse (0.08). Males in the occasional multimode class were about equally as likely to remain in that class as they were to switch (0.49), which differs from the female sample. The most likely transition was to the regular multimode class (0.31), followed by occasional smoking (0.13), and then, nonuse (0.08). Similar to the female sample, switching from the nonuse class was unlikely (0.83 likely to remain).
Figure 2. Visual depiction of transition probabilities for different modes of cannabis use among males in the COMPASS Study (2017/2018–2018/2019). In this Sankey diagram, the size of the colored bars corresponds to the magnitude of the transition probabilities in , by which thicker bars represent greater probability.
3.3. Predictors of cannabis use patterns
The results of the multinomial logistic regression models predicting class memberships can be found in and for females and males, respectively. Current use of the measured substance use behaviors (cigarette smoking, e-cigarette use, binge drinking) were all significantly associated with being in one of the cannabis use classes compared to being in the nonuse class. This was true and consistent for both males and females, with the exception that current cigarette smoking was not significantly associated with being in the occasional multimode class compared to nonuse for males. Having at least some spending money (>$0) was significantly associated with being in a cannabis use class compared to the nonuse class among females and males for most of the class comparisons.
Table 5. Multinominal logistic regression model predictors of baseline class prevalence among females in the 2017/2018 COMPASS sample.
Table 6. Multinominal logistic regression model predictors of baseline class prevalence among males in the 2017/2018 COMPASS sample.
4. Discussion
This research examined how the patterns of cannabis use among youth changed over a one-year period. The present study identified three cannabis use classes: regular multimode use, smoking (occasional smoking for females), and occasional multimode use. There was also one nonuse class among this sample. Notable differences between males and females are discussed in Subsection 4.3 but given the high degree of similarity for much of the findings, the following sections leading up to Subsection 4.3 will discuss the results of males and females collectively.
4.1. Transitions
Latent transition analyses estimated the likelihood of remaining in a particular group, or switching to a different group, over the one-year period measured. Youth who used multiple modes of cannabis and reported higher frequency cannabis use (i.e., those in the regular multimode group) were unlikely to switch to another group. This suggests that youth in this group were unlikely to reduce their frequency of use, or cease use of certain modes of use, over the measurement period. In contrast, youth in the cannabis smoking class were more likely to transition to either the occasional or regular multimode group over time. Recalling that both multimode classes also showed high propensity for smoking cannabis in addition to the other modes, this study may indicate that youth are more likely to add, rather than replace, different modes of cannabis use over time, which is consistent with what has been suggested in previous research (Doggett et al., in press).
The high propensity for youth to switch to multimode use, or increase frequency of cannabis use if already engaging in multimode use, is unsurprising considering that youth report many appealing aspects of alternative cannabis products compared to smoking cannabis. Youth have indicated that stronger/better effects, ease of concealment, and perception of being “healthier” (Friese et al., Citation2016; Lee et al., Citation2016; Morean et al., Citation2017) are all perceived benefits of alternative cannabis products. The perception of alternative cannabis products being “healthier” is interesting in the context of the present study, considering there was no class that avoided smoking entirely and all classes showed a high propensity to smoke cannabis. Notably, this study was unable to assess whether youth decreased, increased, or maintained their smoking (or other mode of use) frequency over the transition periods, as frequency was not measured for each mode of use. However, one previous large national study from the United States indicated that only 14% of those who reported cannabis use indicated that they reduced the amount that they smoked cannabis since initiating vaping (Lee et al., Citation2016). Moreover, increased likelihood of multimode usage over time may carry forward beyond adolescence, as a recent Canadian study among a small sample of undergraduate students revealed that 88% of those who reported cannabis use were multimode users (Swan et al., Citation2021).
Youth in this sample were likely to increase their frequency of cannabis use over time. It is generally known that youth propensity for substance use behaviors increases as youth get older (Gardner & Steinberg, Citation2005; Leatherdale & Burkhalter, Citation2012), and this study indicates that cannabis use via edibles or vaping are no exception. Although substance use is common among Canadian adolescents, preventing aforementioned cannabis-related harms associated with high-frequency or high-potency use remains a public health priority. From a harm-reduction standpoint, the regular multimode cannabis use class is of most concern in the present study, as those in this group report higher frequency cannabis use, being most likely to use cannabis weekly or more often. It is concerning then that this regular multimode class was the most probable group for youth to transition into over the one-year period. Moreover, those who started in the regular multimode group were unlikely to transition out of it. These findings may highlight a leverage point for intervention; if programming can reduce the amount of youth who transition to this regular multimode pattern of cannabis use, it may help reduce the overall risk of cannabis-related harms associated with high-frequency/high-potency use among this Canadian youth population. Considering that existing research indicates that cannabis literacy surrounding edibles is low (Leos-Toro et al., Citation2020; Paphitis et al., Citation2019), and that youth have indicated that alternative cannabis modes are appealing because they perceive them as “healthier” options (Gardner & Steinberg, Citation2005; Leatherdale & Burkhalter, Citation2012), there is likely room for improvement in substance use education and programming. Such programming should include an evidence-based approach to informing youth of the different levels of risks related to the different cannabis products.
Switching from nonuse to any of the cannabis use classes was unlikely, as was the reverse, however the present research is limited in its ability to capture this type of transition, since there was only a year between data collections and the measurements used refer to the previous 12 months. However, those who did initiate cannabis use over the time period in this study were equally likely to transition either to the occasional smoking or the occasional multimode group, suggesting there was not one identified class that appealed more to initiating youth. Future research should further examine the probability of transitioning from nonuse to use but leave a year or more period in-between such that there is more opportunity to observe such transitions.
4.2. Notable sex differences
Stratified models yielded interesting differences between male and females. For all cannabis use classes, males showed higher probabilities of weekly use compared to females, and males were more likely than females to transition from occasional to regular multimode cannabis use at follow-up. These differences imply that males tend toward higher frequency cannabis use compared to females, which is consistent with the existing literature (Hemsing & Greaves, Citation2020). Although some differences were observed between the male and female classes, it is interesting that similar classes were identified between the groups. Given established differences between male and female youth substance use patterns (Hemsing & Greaves, Citation2020; Leatherdale & Burkhalter, Citation2012), one might expect to see more substantial differences between the classes identified for each group.
4.3. Covariates
Examining the associations between several demographic characteristics and group membership demonstrates consistency with what is known from previous research. Other substance use behaviors, including smoking, e-cigarette use, and binge drinking, were all more likely among the cannabis use classes compared to the nonuse class. This is consistent with previous research demonstrating that among youth, substance use tends to co-occur (Leatherdale, Citation2015). Although there are some differences between categories of spending money and across males and females, generally having some amount of spending money was associated with being in one of the cannabis use classes compared to nonuse. As such, although youth cannot legally purchase cannabis directly, the findings in this study suggest that personal spending money still impacts the modes and frequency at which youth use (or do not use) cannabis. This is consistent with previous research indicating that greater income is associated with greater likelihood for cannabis use among youth (Patrick et al., Citation2012; Zuckermann et al., Citation2019)
4.4. Strengths and limitations
This study has some limitations to note. First, it used purposive sampling, and as such results are not generalizable to all Canadian youth. Second, the one-year follow-up period may have been too short to capture some transitions, and future research using LTA to examine youth cannabis use should consider analyzing longer follow-up periods to examine the impact this may have on findings. Future research may also want to conduct similar LTA analyses stratified by grade to examine differences in transition probabilities across age levels. Lastly, the self-report nature of this study means there is potential for response bias. However, this study also has several strengths. This study used an active information passive consent protocol, which is an important factor to limit participation bias in youth substance use research (Rojas et al., Citation2008; White et al., Citation2004). Further, temporal analysis is a key priority for substance use research, and the large longitudinal sample used in this study allowed for robust examination of changes in cannabis use over time.
5. Conclusion
This study examined how cannabis use patterns, including frequency and mode of use (smoking, eating/drinking, vaping) changed over time among a large sample of Canadian youth. Youth were likely to increase their frequency of cannabis use over time, and those who mainly reported smoking cannabis at baseline were likely to transition to multimode use. It is clear that alternative cannabis products are popular among youth, and the prevalence of youth use must continue to be closely monitored in Canada. There may room for improvement in existing substance use programing to educate youth specifically on alternative cannabis products using an evidence-based approach.
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The authors report no conflict of interest.
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References
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Appendix:
Supplemental analyses
Table A1. Attrition analysis comparing baseline cannabis use between participants who were linked vs. not linked.
Table A2. Missing data analysis comparing included vs. excluded participants due to missing covariates (COMPASS 2017/2018–2018/2019).
Table A3. Invariance testing for differences by sex.