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The Cannabis Abuse Screening Test and the DSM‐5 in the general population: Optimal thresholds and underlying common structure using multiple factor analysis
Authors:Stéphane Legleye
Institution:1. Division recueil et traitement de l'information, Institut national de la statistique et des études économiques (Insee), Paris, France;2. CESP, Faculté de médecine, Université Paris Sud, Faculté de médecine UVSQ, INSERM, Université Paris‐Saclay, Villejuif, France
Abstract:The Cannabis Abuse Screening Test (CAST) aims at screening the problematic use of cannabis. It has never been validated against the Diagnostic and Statistical Manual of Mental Disorders (DSM)‐5 and its relationships with this latter have never been studied. We used a probabilistic telephone survey collected in 2014 (1351 past‐year cannabis users aged 15–64) implementing the CAST and a DSM‐5 adaptation of the Munich Composite International Diagnostic Interview assessing cannabis use disorders. Data were weighted, and CAST items were considered categorical. Factorial structures were assessed with confirmatory factor analyses; the relationships between the instruments were studied with multiple factor analysis (MFA). One factor for the DSM‐5 and two correlated factors for the CAST were the best confirmatory factor analyses solutions. The CAST thresholds for screening moderate/severe and severe cannabis use disorders were 5 (sensitivity = 78.2% and specificity = 79.6%) and 8 (sensitivity = 86.0% and specificity = 86.7%), respectively. The MFA identified two orthogonal dimensions: The first was equally shared by both instruments; the second was the second CAST dimension (extreme frequencies of use before midday and alone, memory problems, and reproaches from friends/family). T he CAST structure and screening properties were confirmed. The MFA explains its screening performances by its first dimension and identified the problematic patterns (the second dimension) that are not captured by the DSM‐5.
Keywords:cannabis problematic use  CAST  CFA  DSM‐5  multiple factor analysis  validation
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