Predicting cannabis abuse screening test (CAST) scores: a recursive partitioning analysis using survey data from Czech Republic, Italy, the Netherlands and Sweden.

<h4>Introduction</h4>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 a...

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Autores principales:	Matthijs Blankers, Tom Frijns, Vendula Belackova, Carla Rossi, Bengt Svensson, Franz Trautmann, Margriet van Laar
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9891c34032bd4c03bf3734431c0b63dd
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spelling	oai:doaj.org-article:9891c34032bd4c03bf3734431c0b63dd2021-11-25T05:58:57ZPredicting cannabis abuse screening test (CAST) scores: a recursive partitioning analysis using survey data from Czech Republic, Italy, the Netherlands and Sweden.1932-620310.1371/journal.pone.0108298https://doaj.org/article/9891c34032bd4c03bf3734431c0b63dd2014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0108298https://doaj.org/toc/1932-6203<h4>Introduction</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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).<h4>Conclusion</h4>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.Matthijs BlankersTom FrijnsVendula BelackovaCarla RossiBengt SvenssonFranz TrautmannMargriet van LaarPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e108298 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matthijs Blankers Tom Frijns Vendula Belackova Carla Rossi Bengt Svensson Franz Trautmann Margriet van Laar Predicting cannabis abuse screening test (CAST) scores: a recursive partitioning analysis using survey data from Czech Republic, Italy, the Netherlands and Sweden.
description	<h4>Introduction</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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).<h4>Conclusion</h4>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.
format	article
author	Matthijs Blankers Tom Frijns Vendula Belackova Carla Rossi Bengt Svensson Franz Trautmann Margriet van Laar
author_facet	Matthijs Blankers Tom Frijns Vendula Belackova Carla Rossi Bengt Svensson Franz Trautmann Margriet van Laar
author_sort	Matthijs Blankers
title	Predicting cannabis abuse screening test (CAST) scores: a recursive partitioning analysis using survey data from Czech Republic, Italy, the Netherlands and Sweden.
title_short	Predicting cannabis abuse screening test (CAST) scores: a recursive partitioning analysis using survey data from Czech Republic, Italy, the Netherlands and Sweden.
title_full	Predicting cannabis abuse screening test (CAST) scores: a recursive partitioning analysis using survey data from Czech Republic, Italy, the Netherlands and Sweden.
title_fullStr	Predicting cannabis abuse screening test (CAST) scores: a recursive partitioning analysis using survey data from Czech Republic, Italy, the Netherlands and Sweden.
title_full_unstemmed	Predicting cannabis abuse screening test (CAST) scores: a recursive partitioning analysis using survey data from Czech Republic, Italy, the Netherlands and Sweden.
title_sort	predicting cannabis abuse screening test (cast) scores: a recursive partitioning analysis using survey data from czech republic, italy, the netherlands and sweden.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/9891c34032bd4c03bf3734431c0b63dd
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Predicting cannabis abuse screening test (CAST) scores: a recursive partitioning analysis using survey data from Czech Republic, Italy, the Netherlands and Sweden.

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