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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{"title"=>"Predicting Cannabis Abuse Screening Test (CAST) Scores: A recursive partitioning analysis using survey data from Czech Republic, Italy, the Netherlands and Sweden", "type"=>"journal", "authors"=>[{"first_name"=>"Matthijs", "last_name"=>"Blankers", "scopus_author_id"=>"25652964700"}, {"first_name"=>"Tom", "last_name"=>"Frijns", "scopus_author_id"=>"23008411900"}, {"first_name"=>"Vendula", "last_name"=>"Belackova", "scopus_author_id"=>"37060435300"}, {"first_name"=>"Carla", "last_name"=>"Rossi", "scopus_author_id"=>"7202652822"}, {"first_name"=>"Bengt", "last_name"=>"Svensson", "scopus_author_id"=>"36094774800"}, {"first_name"=>"Franz", "last_name"=>"Trautmann", "scopus_author_id"=>"36888300400"}, {"first_name"=>"Margriet", "last_name"=>"Van Laar", "scopus_author_id"=>"24068215200"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84907484132", "doi"=>"10.1371/journal.pone.0108298", "sgr"=>"84907484132", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"25264894", "issn"=>"19326203", "pui"=>"600074460"}, "id"=>"0945525a-951c-3dd0-8b5d-9a74f8daf8e5", "abstract"=>"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.", "link"=>"http://www.mendeley.com/research/predicting-cannabis-abuse-screening-test-cast-scores-recursive-partitioning-analysis-using-survey-da", "reader_count"=>27, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Librarian"=>1, "Student > Doctoral Student"=>1, "Researcher"=>5, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Other"=>3, "Student > Master"=>3, "Student > Bachelor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Librarian"=>1, "Student > Doctoral Student"=>1, "Researcher"=>5, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Other"=>3, "Student > Master"=>3, "Student > Bachelor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Biochemistry, Genetics and Molecular Biology"=>2, "Nursing and Health Professions"=>1, "Medicine and Dentistry"=>4, "Agricultural and Biological Sciences"=>1, "Arts and Humanities"=>1, "Neuroscience"=>1, "Psychology"=>6, "Social Sciences"=>7}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Neuroscience"=>{"Neuroscience"=>1}, "Social Sciences"=>{"Social Sciences"=>7}, "Psychology"=>{"Psychology"=>6}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Unspecified"=>{"Unspecified"=>4}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Brazil"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1695858"], "description"=>"<p>Note: Generic classification model based on training data (n = 2074). The variables in the numbered boxes indicate the splitting variables identified in the recursive partitioning analysis. The cut-off value for each split, and the number of participants involved in each split is indicated next to the arrows diverting participants from the splitting variable. The six grey area's in the bottom of the lowest boxes (“terminal nodes”), and the “p” in these boxes indicates the <i>proportion</i> of participants in each partitioned area with scores of 7 or higher on the CAST. “n” in the lowest boxes indicates the number of participants in each of the terminal nodes. For each of the 5 splits, <i>p</i><0.001.</p>", "links"=>[], "tags"=>["Cannabis Abuse Screening Test", "risk cannabis users", "12 months", "drug market experiences", "cannabis use days", "cannabis use disorders", "cannabis use", "Czech Republic", "users.Methods 59 variables", "cannabis users", "cannabis use disorder", "Recursive Partitioning Analysis", "model"], "article_id"=>1186540, "categories"=>["Biological Sciences"], "users"=>["Matthijs Blankers", "Tom Frijns", "Vendula Belackova", "Carla Rossi", "Bengt Svensson", "Franz Trautmann", "Margriet van Laar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0108298.g001", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Recursive_partitioning_classification_tree_analysis_of_probability_CAST_8805_7_for_four_countries_/1186540", "title"=>"Recursive partitioning classification tree analysis of probability CAST ≥7 for four countries.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-29 03:19:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1695861"], "description"=>"<p>Note: Percentages are based on available cases (missing values omitted) Regular use is defined as use at least once per month; Number of use days is an aggregated version of the number of use days predictor used in the analysis, which has 12 levels; Lifetime use refers to any use/incidence of use; Cannabis use on weekdays refers to the question whether participants use cannabis in the weekends only, or also (or mainly) on working days; Presented in the proportion indicating that they use cannabis also/mainly on weekdays; Cannabis use any time of day refers to the question whether participants use cannabis on a specific time of the day or not; Presented is the proportion indicating that they use cannabis on any time of the day; Sample sizes of the four countries (and combined) are lower than the indicated number for some variables due to item missingness. F tests and chi-squared tests were performed to test the difference between the four countries. Samples are not representative of the general population in any of the four countries.</p><p>Demographic statistics of the participants from the four countries.</p>", "links"=>[], "tags"=>["Cannabis Abuse Screening Test", "risk cannabis users", "12 months", "drug market experiences", "cannabis use days", "cannabis use disorders", "cannabis use", "Czech Republic", "users.Methods 59 variables", "cannabis users", "cannabis use disorder", "Recursive Partitioning Analysis", "model"], "article_id"=>1186543, "categories"=>["Biological Sciences"], "users"=>["Matthijs Blankers", "Tom Frijns", "Vendula Belackova", "Carla Rossi", "Bengt Svensson", "Franz Trautmann", "Margriet van Laar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0108298.t001", "stats"=>{"downloads"=>4, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Demographic_statistics_of_the_participants_from_the_four_countries_/1186543", "title"=>"Demographic statistics of the participants from the four countries.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-29 03:19:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1695863"], "description"=>"<p>Note: Variables correspond to those described in the section ‘Bivariate analysis’. AIC is Akaike Information Criterion, Nagelkerke R<sup>2</sup> provides a goodness-of-fit index between 0–1.</p><p>Ten models with optimal fit.</p>", "links"=>[], "tags"=>["Cannabis Abuse Screening Test", "risk cannabis users", "12 months", "drug market experiences", "cannabis use days", "cannabis use disorders", "cannabis use", "Czech Republic", "users.Methods 59 variables", "cannabis users", "cannabis use disorder", "Recursive Partitioning Analysis", "model"], "article_id"=>1186544, "categories"=>["Biological Sciences"], "users"=>["Matthijs Blankers", "Tom Frijns", "Vendula Belackova", "Carla Rossi", "Bengt Svensson", "Franz Trautmann", "Margriet van Laar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0108298.t002", "stats"=>{"downloads"=>3, "page_views"=>37, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ten_models_with_optimal_fit_/1186544", "title"=>"Ten models with optimal fit.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-29 03:19:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1695860"], "description"=>"<p>Note: Country specific classification tree models for the Czech Republic (top-left), Italy (top-right), Netherlands (bottom-left) and Sweden (bottom-right). The variables in the numbered boxes indicate the splitting variables identified in the recursive partitioning analysis. The cut-off value for each split, and the number of participants involved in each split is indicated next to the arrows diverting participants from the splitting variable. The six grey area's in the bottom of the lowest boxes (“terminal nodes”), and the “p” in these boxes indicates the proportion of participants in each partitioned area with scores of 7 or higher on the CAST. “n” in the lowest boxes indicates the number of participants in each of the terminal nodes.</p>", "links"=>[], "tags"=>["Cannabis Abuse Screening Test", "risk cannabis users", "12 months", "drug market experiences", "cannabis use days", "cannabis use disorders", "cannabis use", "Czech Republic", "users.Methods 59 variables", "cannabis users", "cannabis use disorder", "Recursive Partitioning Analysis", "model"], "article_id"=>1186542, "categories"=>["Biological Sciences"], "users"=>["Matthijs Blankers", "Tom Frijns", "Vendula Belackova", "Carla Rossi", "Bengt Svensson", "Franz Trautmann", "Margriet van Laar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0108298.g002", "stats"=>{"downloads"=>9, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Country_specific_classification_tree_models_/1186542", "title"=>"Country specific classification tree models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-29 03:19:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1695866"], "description"=>"<p>Note: Country Specific Model refers to the classification trees presented in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108298#pone-0108298-g002\" target=\"_blank\">Figure 2</a>; Generic Model refers to the overarching model presented in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108298#pone-0108298-g001\" target=\"_blank\">Figure 1</a>; Accuracy indicates the proportion of correctly classified cases, with associated confidence interval; Difference in accuracy between Country Specific Model and Generic Model was tested using a 2-sided Chi-Square test; Sensitivity = [number of correctly classified CAST ≥7]/[number of CAST ≥7 in sample]; Specificity = [number of correctly classified CAST <7]/[number of CAST <7 in sample]; Positive predictive value = [number of correctly classified CAST ≥7]/[all classified CAST ≥7]; Negative predictive value = [number of correctly classified CAST <7]/[all classified CAST <7]. The country datasets contain both participants from the training and the validation dataset.</p><p>Country specific classification tree models compared to the generic tree model.</p>", "links"=>[], "tags"=>["Cannabis Abuse Screening Test", "risk cannabis users", "12 months", "drug market experiences", "cannabis use days", "cannabis use disorders", "cannabis use", "Czech Republic", "users.Methods 59 variables", "cannabis users", "cannabis use disorder", "Recursive Partitioning Analysis", "model"], "article_id"=>1186547, "categories"=>["Biological Sciences"], "users"=>["Matthijs Blankers", "Tom Frijns", "Vendula Belackova", "Carla Rossi", "Bengt Svensson", "Franz Trautmann", "Margriet van Laar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0108298.t004", "stats"=>{"downloads"=>4, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Country_specific_classification_tree_models_compared_to_the_generic_tree_model_/1186547", "title"=>"Country specific classification tree models compared to the generic tree model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-29 03:19:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1695865"], "description"=>"<p>Note: Accuracy indicates the proportion of correctly classified cases, with associated confidence interval; No information rate (NIR) is 1-[proportion of CAST≥7] in the sample; Difference between model accuracy and NIR tested using a one-sided binomial test; Sensitivity = [number of correctly classified CAST ≥7]/[number of CAST ≥7 in sample]; Specificity = [number of correctly classified CAST <7]/[number of CAST <7 in sample]; Positive predictive value = [number of correctly classified CAST ≥7]/[all classified CAST ≥7]; Negative predictive value = [number of correctly classified CAST <7]/[all classified CAST <7]. The country datasets contain both participants from the training and the validation dataset.</p><p>Performance statistics of the generic classification tree model.</p>", "links"=>[], "tags"=>["Cannabis Abuse Screening Test", "risk cannabis users", "12 months", "drug market experiences", "cannabis use days", "cannabis use disorders", "cannabis use", "Czech Republic", "users.Methods 59 variables", "cannabis users", "cannabis use disorder", "Recursive Partitioning Analysis", "model"], "article_id"=>1186546, "categories"=>["Biological Sciences"], "users"=>["Matthijs Blankers", "Tom Frijns", "Vendula Belackova", "Carla Rossi", "Bengt Svensson", "Franz Trautmann", "Margriet van Laar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0108298.t003", "stats"=>{"downloads"=>4, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_statistics_of_the_generic_classification_tree_model_/1186546", "title"=>"Performance statistics of the generic classification tree model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-29 03:19:53"}

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