Historique
Article de Périodique
A validation of the Cannabis Abuse Screening Test (CAST) using a latent class analysis of the DSM-IV among adolescents (2013)
Auteur(s) :
S. LEGLEYE ;
D. PIONTEK ;
L. KRAUS ;
E. MORAND ;
B. FALISSARD
Article en page(s) :
16-26
Domaine :
Drogues illicites / Illicit drugs
Langue(s) :
Anglais
Discipline :
PRO (Produits, mode d'action, méthode de dépistage / Substances, action mode, screening methods)
Thésaurus géographique
FRANCE
Thésaurus mots-clés
CAST
;
ADOLESCENT
;
DEPISTAGE
;
VALIDITE
;
CANNABIS
;
DSM (III,IV,5)
;
TEST
;
ESCAPAD
Résumé :
This paper explored the latent class structure of the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) (assessed with the Munich Composite International Diagnostic Interview). Secondly, the screening properties of the Cannabis Abuse Screening Test (CAST) in adolescents were assessed with classical test theory using the latent class structure as empirical gold standard. The sample comprised 3266 French cannabis users aged 17 to 19 from the general population. Three latent classes of cannabis users were identified reflecting a continuum of problem severity: non-symptomatic, moderate and severe. Gender-specific analyses showed the best model fit, although results were almost identical in the total sample. The latent classes were good predictors of daily cannabis use, number of joints per day and age of first experimentation. The CAST showed good screening properties for the moderate/severe class (area under receiver operating characteristic curve>0.85) and very good for the severe class (0.90). It was more sensitive for boys, more specific for girls. Although structural equivalence across gender was rejected, results suggest small gender differences in the latent structure of the DSM-IV. The performance of the CAST in screening for the latent class structure was good and superior to those obtained with the classical DSM-IV diagnoses. Copyright (c) 2013 John Wiley & Sons, Ltd.
Affiliation :
Institut national des études démographiques (INED), Paris, France