The present study identified three latent classes of frequent cannabis users characterized by consumption intensity. Age, education, number of years of use, and buying cannabis in a club emerged as correlates of class membership. No significant intergroup differences were observed regarding other factors—gender, unemployment, mode of administration, motives, and other substance use (except cocaine). The prevalence of CUD and dependence increased through the classes.
At least two previous LCA-based studies have shown that cannabis users are a varied population, with DND users comprising three main groups or categories. Pearson et al (24) conducted LCA on data collected from a sample of college students who had used cannabis in the past 30 days. They identified four latent classes using three variables related to the intensity of consumption and one more about cannabis-related problems. The largest class consisted of infrequent marijuana users; the other three revealed increasingly frequent use and more negative consequences. They concluded that people using cannabis a few times monthly were distinct from DND users. Manning et al. (25) discovered five latent classes among a sample of 374 cannabis-using adults. The three variables used for the LCA were cannabis use frequency, quantity, and problems experienced. Three classes reported more heavy use patterns associated with increased adverse outcomes. Our study confirms that heavy users can be classified into three groups according to cannabis exposure and cannabis-related problems increase among DND users in parallel with use intensity. As such, use frequency may not be the key category for distinguishing heavy users. The number of daily doses (joints in our research) and whether users consumed cannabis throughout the waking day resulted in clear, distinct patterns of use and consequences. The most heavy users may be intoxicated throughout the waking day. However, for some DND users, especially if they consume lower doses at specific times, intoxication may not interfere drastically with their quotidian life.
In our study, the most heavy users were older, had more years of cannabis use, and were less educated. Older users may have had more time to develop more heavy use patterns since there were no significant intergroup differences in terms of the age of cannabis use onset. Former research has interpreted early-onset and prolonged cannabis use as predictors of poorer educational outcomes and unemployment(37–40). We only observed intergroup differences regarding education. Future studies should examine correlates between the three groups and other demographics such as gender or employment in larger samples of heavy users. For instance, in our research, 80% of the most heavy users were male, but the intergroup gender differences were not significant.
Many studies have reported associations between intensity (frequency and/or quantity) of cannabis use and cannabis-related problems (10, 24, 25, 41–43). Our participants did not widely acknowledge cannabis-related problems. A third reported psychological and financial difficulties and only 10% health damage. In contrast, the average CAST score was high (M = 10, SD = 4.4), and notable intergroup differences emerged. The proportion of individuals with scores ≥ 9 (indicating CUD) in the very heavy group was more than two times higher than in the moderately heavy group. This difference increased to more than three times when considering scores ≥ 12 (denoting dependence). These findings are consistent with former research, indicating that frequency of consumption is the most significant predictor of CUD, even when controlling for different products and modes of use (22, 44). Additionally, nearly one in three frequent cannabis users develops dependence (45).
Consistent with previous research (24, 25, 46, 47), enhancement, coping, expansion, and social motives were the most prevalent cannabis use reasons across classes. We did not observe significant intergroup differences regarding reasons for use (including medical). These findings are consistent with those of Pearson et al. (24). In Spain, cannabis is not recognized for medical use by the public health system, which may explain our sample’s small proportion of medical users.
Concurrent use of other substances, except opioids, was common in our sample. Tobacco and alcohol use in the past month was prevalent across classes, and more than half reported having used at least one illicit drug other than cannabis in the past year (most commonly cocaine, followed by ecstasy and amphetamines). The prevalence of cocaine use in the past year increased significantly through the classes. Other studies have observed this polydrug use in daily users (48). According to the literature, tobacco and cannabis seem to be complementary (49). It is less clear if alcohol is a substitute for cannabis (50) Therefore, more research is needed to clarify the relationships between cannabis and other substances.
Spanish cannabis clubs may have become a principal supply source for the heaviest users in Spain. We found significant and broad intergroup differences regarding accessing cannabis clubs to obtain and use cannabis on their premises. Two previous studies of Spanish cannabis clubs (51, 52) found 77% and 68% of their members were daily users, respectively (samples N = 458 and N = 155). Most members were long-term cannabis users, and they did not change their use pattern after joining the club.
Some authors have pointed out that cannabis clubs could play a relevant role in implementing harm reduction practices (53, 54). The preference of the heaviest users for this method of supply might support this proposal. However, in general, cannabis clubs have to fill some crucial gaps to implement a harm reduction policy: providing information on risks and harms, offering health support services for members, performing lab tests on the cannabis they supply, etc. (55). Additionally, cannabis clubs must reconsider the maximum quantity of cannabis distributed monthly to each member—currently between 60 and 90 g (54)—, conduct follow-ups with frequent users, advise them to reduce their doses and frequency of use, and help problematic users access treatment and health advisory services. Based on our findings, clinical treatment interventions should also pay special attention to the use patterns of heavy cannabis users since they are diverse and related to CUD. Further research will need to identify more correlates of class membership, which will enable more specific interventions for each heavy user class.
Limitations
The study has several limitations. Firstly, we cannot know the representativeness of the sample. Network sampling is widely used to reduce biases in gathering samples of hard-to-reach and hidden populations, such as heavy drug users (56). Secondly, all data were self-reported measures, which must be considered when interpreting the results. Although self-report is an accepted method for obtaining population behavior information, individual bias and memory issues can compromise data accuracy (57). Nonetheless, we have confidence in the validity of our main findings, which are consistent with former studies. We believe the set of questions assessing the quantity of cannabis used is reliable. We have more reservations about the results related to use of other substances. Future research will benefit from combining interview assessments, biological controls of cannabis use, and behavioral tasks to assess more accurate constructs. Thirdly, we could not measure THC content and other cannabinoids in the cannabis herb and resin used by participants, which are paramount to assessing the intensity of consumption and its consequences. However, a previous study (28) did not find large average differences in the potencies of these products in Spain. Future research will need to identify the potency of cannabis products to have better control of the study variables.