Alcohol Abuse and Dependence Criteria as Predictors of a Chronic Course of Alcohol Use Disorders in the General Population

Bruijn, Carla de; Brink, Wim van den; Graaf, Ron de; Vollebergh, Wilma

doi:10.1093/alcalc/agh183

Cited by 23 publications

(27 citation statements)

References 47 publications

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“…The two cannabis abuse and dependence symptoms that relate to cannabis-related problems emerged as the ones that were most strongly (and independently) associated with persistence: ‘role impairment' (OR = 2.85) and ‘use despite problems' (OR = 2.34); a finding consistent with previous findings regarding the persistence of alcohol use disorder [19]. In contrast, ‘impaired control over use' was previously found to be the strongest predictor of persistent cannabis use and the onset of dependence [7,18].…”

Section: Discussionsupporting

confidence: 89%

“…Predictors of early remission (DDD + DDN vs. DNN + DND) and remission at three-year follow-up (DDD + DND vs. DDN + DNN) were assessed (e.g. [7,19]), with generally similar results. Also, stable trajectories DDD and DNN were compared, because measurement error in the dependence diagnosis may dilute findings, and latent Markov models recently showed that measurement error of the CIDI diagnosis of dependence is likely to be considerable [18].…”

Section: Resultsmentioning

confidence: 99%

“…An overall estimate of the predictive power of all predictors together is provided by McFadden's pseudo R 2 . The total number of the lifetime CUD symptoms is expected to be a strong predictor of the course of cannabis dependence [7,18,19], which may overshadow all other potential clinically important predictors. Therefore, the multivariable model was first tested with, and then without the lifetime number of CUD symptoms in the model.…”

Section: Methodsmentioning

confidence: 99%

“…Although age, years of cannabis use and the total quantity of cannabis use predicted dependence at follow-up only bivariately, the number of baseline cannabis abuse/dependence symptoms was the only independent and strongest predictor of ‘persistence'. The predictive power of individual cannabis abuse and dependence symptoms was not considered, while certain abuse/dependence symptoms (especially ‘impaired control over use') have been shown to be more predictive than others for persistent cannabis use [7], onset of cannabis dependence in frequent users [18] and persistence of alcohol use disorders [19]. Two retrospective studies showed higher remission rates in younger, female, highly educated and employed subjects, in subjects with a later onset of cannabis dependence, a shorter duration of cannabis dependence and in subjects without other mental disorders (conduct disorder (CD), personality disorder, alcohol dependence, cocaine dependence) [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Three-Year Course of Cannabis Dependence and Prediction of Persistence

et al. 2015

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Aims: To examine the course and the predictors of the persistence of cannabis dependence. Methods: Through cannabis outlets and chain referral, a prospective enriched community cohort of 207 young adults (aged 18-30) with DSM-IV cannabis dependence at baseline (T0) was formed and followed-up after 1.5 (T1) and 3 (T2) years. The presence of cannabis dependence, cannabis-related problems, functional impairment and treatment was assessed using the Composite International Diagnostic Interview (CIDI 3.0) and the Sheehan Disability Scale (SDS). Predictors of persistence were lifetime cannabis abuse and dependence symptoms, cannabis use characteristics, distant vulnerability factors (e.g. childhood adversity, family history of psychological/substance use problems, impulsivity, mental disorders), and proximal stress factors (recent life events, social support). Results: Four groups were distinguished: persistent dependent (DDD: 28.0%), stable non-persistent (DNN: 40.6%), late non-persistent (DDN: 17.9%) and recurrent dependent (DND: 13.5%). At T2, persisters (DDD) reported significantly more (heavy) cannabis use and cannabis problems than non-persisters (DNN/DDN/DND). Treatment seeking for cannabis-related problems was rare, even among persisters (15.5%). The number (OR = 1.23 (1.03-1.48)) and type (‘role impairment' OR = 2.85 (1.11-7.31), ‘use despite problems' OR = 2.34 (1.15-4.76)) of lifetime cannabis abuse/dependence symptoms were the only independent predictors of persistence with a total explained variance of 8.8%. Conclusions: Persistence of cannabis dependence in the community is low, difficult to predict, and associated with a negative outcome. The substantial proportion of stable non-persisters suggests that screening and monitoring or low-threshold brief interventions may suffice for many non-treatment-seeking cannabis-dependent people. However, those with many lifetime abuse/dependence symptoms may benefit from more intensive interventions.

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Section: Discussionsupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Three-Year Course of Cannabis Dependence and Prediction of Persistence

et al. 2015

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“…Craving itself represents a criterion for substance dependence in the current version of the International Statistical Classifi cation of Diseases and Related Health Problems (ICD-10;World Health Organization, 1992), and a longitudinal study found that alcohol craving was associated with the highest relative severity of all ICD-10 alcoholism symptoms (de Bruijn et al, 2005). Furthermore, craving has been adopted as a new symptom in the fi fth edition of the DSM from the American Psychiatric Association (2013).…”

mentioning

confidence: 99%

Craving and Subjective Responses to Alcohol Administration: Validation of the Desires for Alcohol Questionnaire in the Human Laboratory

Courtney

Ashenhurst

Bacio

et al. 2013

J. Stud. Alcohol Drugs

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ABSTRACT. Objective:The abbreviated Desires for Alcohol Questionnaire (DAQ) is a self-report assessment of craving comprising the following subscales: (a) strong desires/intentions to drink, (b) negative reinforcement, and (c) positive reinforcement and ability to control drinking. Although the DAQ is sensitive to changes in alcohol craving precipitated by alcohol administration and/or cue exposure, no studies to date have examined the relationship between DAQ scores and subjective responses to alcohol. This study addresses this gap in the literature by testing the relationship between subjective responses to alcohol during alcohol administration and DAQ scores assessed 1 month later. Method: Individuals with alcohol dependence (n = 32) completed a randomized, single-blinded, intravenous alcohol administration in the laboratory in which subjective responses to the alcohol were measured, followed by a visit to the laboratory 1 month later to complete the DAQ. Results: Analyses revealed robust associations between DAQ scores and alcohol craving during alcohol administration (partial correlations: r = .43-.50, ps < .01), with the exception of the positive reinforcement subscale (r = .20, p = .30). Subjective intoxication and sedation were only associated with the negative reinforcement subscale of the DAQ (r = .38, p < .05 and r = .33, p < .05, respectively). Conclusions: Craving, captured by the DAQ, is reliably and positively associated with alcohol-induced craving. The DAQ is also associated with specifi c dimensions of subjective responses to alcohol. These results support the clinical utility of the DAQ, particularly in large samples where experimental manipulations may not be feasible. (J. Stud. Alcohol Drugs, 74, 797-802, 2013)

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