Abstract
IntroductionCannabis is Europe's most commonly used illicit drug. Some users do not develop dependence or other problems, whereas others do. Many factors are associated with the occurrence of cannabis-related disorders. This makes it difficult to identify key risk factors and markers to profile at-risk cannabis users using traditional hypothesis-driven approaches. Therefore, the use of a data-mining technique called binary recursive partitioning is demonstrated in this study by creating a classification tree to profile at-risk users.Methods59 variables on cannabis use and drug market experiences were extracted from an internet-based survey dataset collected in four European countries (Czech Republic, Italy, Netherlands and Sweden), n = 2617. These 59 potential predictors of problematic cannabis use were used to partition individual respondents into subgroups with low and high risk of having a cannabis use disorder, based on their responses on the Cannabis Abuse Screening Test. Both a generic model for the four countries combined and four country-specific models were constructed.ResultsOf the 59 variables included in the first analysis step, only three variables were required to construct a generic partitioning model to classify high risk cannabis users with 65–73% accuracy. Based on the generic model for the four countries combined, the highest risk for cannabis use disorder is seen in participants reporting a cannabis use on more than 200 days in the last 12 months. In comparison to the generic model, the country-specific models led to modest, non-significant improvements in classification accuracy, with an exception for Italy (p = 0.01).ConclusionUsing recursive partitioning, it is feasible to construct classification trees based on only a few variables with acceptable performance to classify cannabis users into groups with low or high risk of meeting criteria for cannabis use disorder. The number of cannabis use days in the last 12 months is the most relevant variable. The identified variables may be considered for use in future screeners for cannabis use disorders.
Highlights
Cannabis is Europe’s most commonly used illicit drug
Participants who did not complete the Cannabis Abuse Screening Test (CAST) section of the survey, or who did not provide responses on at least one of the other variables included in the analyses were removed as their responses would not be usable for the planned analyses
Main conclusions Of the 59 variables included in step 1 of the presented analysis, three variables may be sufficient to construct a generic model to classify cannabis users from Czech Republic, Italy, the Netherlands and Sweden with 65–73% accuracy in groups of above or below a CAST cut-off score, indicative of meeting the criteria for cannabis use disorder formulated in DSM-V
Summary
Cannabis is Europe’s most commonly used illicit drug. Some users do not develop dependence or other problems, whereas others do. In a recent study, living alone, coping motives for cannabis use, recent negative life events, and cannabis use disorder symptoms were found to predict first incidence of cannabis dependence [2] Against this backdrop, two important public health challenges can be raised: (1) to develop, validate and implement screening tools to identify those at-risk to develop cannabis use disorders, and (2) to identify patterns of risk factors or markers associated with at-risk use, in order to target drug policy efforts to individuals manifesting these patterns [53]. Annaheim [11] provides a recent review in which she identified 44 potentially useful cannabis problems screening tools She found the CAST to be among three of the most appropriate instruments [11]
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