INTRODUCTION: Cannabis 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. METHODS: 59 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. RESULTS: Of 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). CONCLUSION: Using 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.

Centre for Biostatistics and Bioinformatics University Rome Tor Vergata Rome Italy

Department of Addictology 1st Faculty of Medicine Charles University and General University Hospital Prague Czech Republic

Department of Drug Monitoring Trimbos Institute Utrecht the Netherlands

Department of Drug Monitoring Trimbos Institute Utrecht the Netherlands; Department of Psychiatry Academic Medical Centre University of Amsterdam Amsterdam the Netherlands; Department of Research Arkin Amsterdam the Netherlands

Department of Social Work Malmö University Malmö Sweden

See more in PubMed

European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) (2013) Perspectives on Drugs: Characteristics of frequent and high-risk cannabis users. Lisbon: EMCDDA.

van der Pol P, Liebregts N, de Graaf R, Korf DJ, van den Brink W, et al. (2013) Predicting the transition from frequent cannabis use to cannabis dependence: A three-year prospective study. Drug Alcohol Depend 133: 352–359. PubMed

Hammersley R, Leon V (2006) Patterns of cannabis use and positive and negative experiences of use amongst university students. Addict Res Theory 14: 189–205.

Coffey C, Carlin JB, Lynskey M, Li N, Patton GC (2003) Adolescent precursors of cannabis dependence: findings from the Victorian Adolescent Health Cohort Study. Br J Psychiatry 182: 330–336. PubMed

Legleye S, Piontek D, Kraus L, Morand E, Falissard B (2013) A validation of the Cannabis Abuse Screening Test (CAST) using a latent class analysis of the DSM-IV among adolescents. Int J Methods Psychiatr Res 22: 16–26. PubMed PMC

Gossop M, Darke S, Griffiths P, Hando J, Powis B, et al. (1995) The Severity of Dependence Scale (SDS): psychometric properties of the SDS in English and Australian samples of heroin, cocaine and amphetamine users. Addiction 90: 607–614. PubMed

Martin G, Copeland J, Gates P, Gilmour S (2006) The Severity of Dependence Scale (SDS) in an adolescent population of cannabis users: reliability, validity and diagnostic cut-off. Drug Alcohol Depend 83: 90–93. PubMed

Adamson SJ, Sellman JD (2003) A prototype screening instrument for cannabis use disorder: the Cannabis Use Disorders Identification Test (CUDIT) in an alcohol dependent clinical sample. Drug Alcohol Rev 22: 309–315. PubMed

Annaheim B, Rehm J, Gmel G (2008) How to screen for problematic cannabis use in population surveys: an evaluation of the Cannabis Use Disorders Identification Test (CUDIT) in a Swiss sample of adolescents and young Adults. Eur Addict Res 14: 190–197. PubMed

Legleye S, Karila L, Beck F, Reynaud M (2007) Validation of the CAST, a general population Cannabis Abuse Screening Test. Journal of substance use 12: 233–242.

Annaheim B (2013) Who is smoking pot for fun and who is not? An overview of instruments to screen for cannabis-related problems in general population surveys. Addict Res Theory 21: 410–428.

Hibell B, Guttormsson U, Ahlström S, Balakireva O, Bjarnason T, et al... (2012) The 2011 ESPAD Report: Substance Use Among Students in 36 European Countries. Stockholm: The Swedish Council for Information on Alcohol and Other Drugs.

Bastiani L, Siciliano V, Curzio O, LuppiC, Gori M, et al. (2013) Optimal scaling of the CAST and of SDS Scale in a national sample of adolescents. Addict Behav 38: 2060–2067. PubMed

Cuenca-Royo AM, Sánchez-Niubó A, Forero CG, Torrens M, Suelves JM, et al. (2012) Psychometric properties of the CAST and SDS scales in young adult cannabis users. Addict Behav 37: 709–715. PubMed

Thanki D, Domingo-Salvany A, Anta GB, Mañez AS, Aleixandre NL, et al. (2013) The Choice of Screening Instrument Matters: The Case of Problematic Cannabis Use Screening in Spanish Population of Adolescents. ISRN Addict 2013: 723131. PubMed PMC

Legleye S, Piontek D, Kraus L (2011) Psychometric properties of the Cannabis Abuse Screening Test (CAST) in a French sample of adolescents. Drug Alcohol Depend 113: 229–235. PubMed

Verweij KJ, Zietsch BP, Lynskey MT, Medland SE, Neale MC, et al. (2010) Genetic and environmental influences on cannabis use initiation and problematic use: a meta-analysis of twin studies. Addiction 105: 417–430. PubMed PMC

Hyman SM, Sinha R (2009) Stress-related factors in cannabis use and misuse: implications for prevention and treatment. J Subst Abuse Treat 36: 400–413. PubMed PMC

Tziraki S (2012) Mental disorders and neuropsychological impairment related to chronic use of cannabis. Rev Neurol 54: 750–760. PubMed

Rey JM, Martin A, Krabman P (2004) Is the party over? Cannabis and juvenile psychiatric disorder: the past 10 years. J Am Acad Child Adolesc Psychiatry 43: 1194–1205. PubMed

Large M, Sharma S, Compton MT, Slade T, Nielssen O (2011) Cannabis use and earlier onset of psychosis: a systematic meta-analysis. Arch Gen Psychiatry 68: 555–561. PubMed

Fernandez-Espejo E, Viveros MP, Núñez L, Ellenbroek BA, Rodriguez de Fonseca F (2009) Role of cannabis and endocannabinoids in the genesis of schizophrenia. Psychopharmacology (Berl) 206: 531–549. PubMed

Martín-Santos R, Fagundo AB, Crippa JA, Atakan Z, Bhattacharyya S, et al. (2010) Neuroimaging in cannabis use: a systematic review of the literature. Psychol Med 40: 383–398. PubMed

Verdejo-García A, Pérez-García M, Sánchez-Barrera M, Rodriguez-Fernández A, Gómez-Río M (2007) Neuroimaging and drug addiction: Neuroanatomical correlates of cocaine, opiates, cannabis and ecstasy abuse. Rev Neurol 44: 432–439. PubMed

Daniel JZ, Hickman M, Macleod J, Wiles N, Lingford-Hughes A, et al. (2009) Is socioeconomic status in early life associated with drug use? A systematic review of the evidence. Drug Alcohol Rev 28: 142–153. PubMed

Chen CY, Storr CL, Anthony JC (2009) Early-onset drug use and risk for drug dependence problems. Addict Behav 34: 319–322. PubMed PMC

Looby A, Earleywine M (2007) Negative consequences associated with dependence in daily cannabis users. Subst Abuse Treat Prev Policy 2: 3. PubMed PMC

Moss HB, Chen CM, Yi HY (2012) Measures of substance consumption among substance users, DSM-IV abusers, and those with DSM-IV dependence disorders in a nationally representative sample. J Stud Alcohol Drugs 73: 820–828. PubMed PMC

Walden N, Earleywine M (2008) How high: quantity as a predictor of cannabis-related problems. Harm Reduct J 5: 20. PubMed PMC

Zeisser C, Thompson K, Stockwell T, Duff C, Chow C, et al. (2011) A ‘standard joint’? The role of quantity in predicting cannabis-related problems. Addict Res Theory 20: 82–92.

van der Pol P, Liebregts N, de Graaf R, Ten Have M, Korf DJ, et al. (2013) Mental health differences between frequent cannabis users with and without dependence and the general population. Addiction 108: 1459–1469. PubMed

van den Bree MB, Pickworth WB (2005) Risk factors predicting changes in marijuana involvement in teenagers. Arch Gen Psychiatry 62: 311–319. PubMed

Kerr WT, Lau EP, Owens GE, Trefler A (2012) The future of medical diagnostics: large digitized databases. Yale J Biol Med 85: 363–377. PubMed PMC

Mello MM, Messing NA (2011) Restrictions on the use of prescribing data for drug promotion. N Engl J Med 365: 1248–1254. PubMed

Hellemann G, Conner B, Anglin MD, Longshore D (2009) Seeing the trees despite the forest: Applying recursive partitioning to the evaluation of drug treatment retention. J Subst Abuse Treat 36: 59–64. PubMed

Yonkers KA, Gotman N, Kershaw T, Forray A, Howell HB, et al. (2010) Screening for Prenatal Substance Use: Development of the Substance Use Risk Profile-Pregnancy Scale. Obstet Gynecol 116: 827–833. PubMed PMC

Blankers M, Koeter MWJ, Schippers GM (2013) Baseline predictors of treatment outcome in Internet-based alcohol interventions: a recursive partitioning analysis alongside a randomized trial. BMC Public Health 13: 455. PubMed PMC

Huang Y, Britton J, Hubbard R, Lewis S (2013) Who receives prescriptions for smoking cessation medications? An association rule mining analysis using a large primary care database. Tob Control 22: 274–279. PubMed

Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and Regression Trees. Belmont (CA): Wadsworth.

Hawkins DM (1997) FIRM: Formal Inference-based Recursive Modeling, PC Version, Release 2.1 (Technical Report 546). Minnesota: University of Minnesota, School of Statistics.

Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods 14: 323–348. PubMed PMC

Trautmann F, Kilmer B, Turnbull P (Eds) (2013) Further insights into aspects of the EU illicit drugs market. Luxembourg: Publications Office of the European Union.

Swan GE, Javitz HS, Jack LM, Curry SJ, McAfee T (2004) Heterogeneity in 12-month outcome among female and male smokers. Addiction 99: 237–250. PubMed

Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78: 691–692.

Zhang H, Singer B (1999) Recursive partitioning in the health sciences. New York: Springer Verlag.

Hothorn T, Hornik K, Strobl C, Zeileis A (2013) party: A Laboratory for Recursive Partytioning. R package version 1.0-10 Available: http://cran.r-project.org/web/packages/party/index.html. Accessed 2013 November 28..

R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria Available: http://www.R-project.org/. Accessed 2013 November 28..

Therneau TM, Atkinson EJ (1997) An introduction to recursive partitioning using the RPART routines. Minnesota: Mayo Clinic.

UNODC (2007) Sweden's successful drug policy: A review of the evidence. Available: http://www.unodc.org/pdf/research/Swedish_drug_control.pdf. Accessed 2013 November 28.

Bendel RB, Afifi AA (1977) Comparison of stopping rules in forward regression. J Am Stat Ass 72: 46–53.

Rehm J, Anderson P, Gual A, Kraus L, et al. (2014) The tangible common denominator of substance use disorders: a reply to commentaries to Rehm et al. (2013a) [Letter to the Editor]. Alcohol Alcohol 49: 118–122. PubMed

Rehm J, Marmet S, Anderson P, Gual A, Kraus L, et al. (2013) Defining substance use disorders: do we really need more than heavy use? Alcohol and Alcohol 48: 633–640. PubMed

EMCDDA (2008) A cannabis reader: global issues and local experiences, Monograph series 8, Volume 2, European Monitoring Centre for Drugs and Drug Addiction, Lisbon. p. 44.

American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association, Arlington, VA