J Rehm1, J Manthey2, P Struzzo3, A Gual4, M Wojnar5. 1. Centre for Addiction and Mental Health, 33, Russell Street, Toronto, ON M5S 2S1, Canada; Addiction Policy, Dalla Lana School of Public Health, University of Toronto, 155, College Street, 6th floor, Toronto, ON M5T 3M7, Canada; Institute of Medical Science, University of Toronto, Faculty of Medicine, Medical Sciences Building, 1, King's College Circle, Room 2374, Toronto, ON M5S 1A8, Canada; Department of Psychiatry, University of Toronto, 250, College Street, 8th floor, Toronto, ON M5T 1R8, Canada; Institute of Clinical Psychology and Psychotherapy & Centre of Clinical Epidemiology and Longitudinal Studies (CELOS), Technische Universität Dresden, Chemnitzer Str. 46, 01187 Dresden, Germany. 2. Institute of Clinical Psychology and Psychotherapy & Centre of Clinical Epidemiology and Longitudinal Studies (CELOS), Technische Universität Dresden, Chemnitzer Str. 46, 01187 Dresden, Germany. Electronic address: jakobmanthey@snappyquest.org. 3. Regional Centre for the Training in Primary Care (Ceformed), Via Galvani 1, 34074 Monfalcone, GO, Italy; University of Trieste, Department of Life Sciences, Via Weiss 2, 34128 Trieste, Italy. 4. Addictions Unit, Psychiatry Department, Neurosciences Institute, Hospital Clinic, Carrer Villarroel 170, 08036 Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Carrer Rosselló 149, 08036 Barcelona, Catalonia, Spain; Red de Trastornos Adictivos (RTA-RETICS), Instituto de Salud Carlos III, Villarroel 170, 08036 Barcelona, Catalonia, Spain. 5. Department of Psychiatry, Medical University of Warsaw, Nowowiejska 27, 00-665 Warsaw, Poland; Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA.
Abstract
BACKGROUND: Alcohol use disorders (AUDs) are highly prevalent in Europe, but only a minority of those affected receive treatment. It is therefore important to identify factors that predict treatment in order to reframe strategies aimed at improving treatment rates. METHODS: Representative cross-sectional study with patients aged 18-64 from primary health care (PC, six European countries, n=8476, data collection 01/13-01/14) and from specialized health care (SC, eight European countries, n=1762, data collection 01/13-03/14). For descriptive purposes, six groups were distinguished, based on type of DSM-IV AUD and treatment setting. Treatment status (yes/no) for any treatment (model 1), and for SC treatment (model 2) were main outcome measures in logistic regression models. RESULTS: AUDs were prevalent in PC (12-month prevalence: 11.8%, 95% confidence interval (CI): 11.2-12.5%), with 17.6% receiving current treatment (95%CI: 15.3-19.9%). There were clear differences between the six groups regarding key variables from all five predictor domains. Prediction of any treatment (model 1) or SC treatment (model 2) was successful with high overall accuracy (both models: 95%), sufficient sensitivity (model 1: 79%/model 2: 76%) and high specificity (both models: 98%). The most predictive single variables were daily drinking level, anxiety, severity of mental distress, and number of inpatient nights during the last 6 months. CONCLUSIONS: Variables from four domains were highly predictive in identifying treatment for AUD, with SC treatment groups showing very high levels of social disintegration, drinking, comorbidity and functional losses. Earlier intervention and formal treatment for AUD in PC should be implemented to reduce these high levels of adverse outcomes.
BACKGROUND:Alcohol use disorders (AUDs) are highly prevalent in Europe, but only a minority of those affected receive treatment. It is therefore important to identify factors that predict treatment in order to reframe strategies aimed at improving treatment rates. METHODS: Representative cross-sectional study with patients aged 18-64 from primary health care (PC, six European countries, n=8476, data collection 01/13-01/14) and from specialized health care (SC, eight European countries, n=1762, data collection 01/13-03/14). For descriptive purposes, six groups were distinguished, based on type of DSM-IV AUD and treatment setting. Treatment status (yes/no) for any treatment (model 1), and for SC treatment (model 2) were main outcome measures in logistic regression models. RESULTS: AUDs were prevalent in PC (12-month prevalence: 11.8%, 95% confidence interval (CI): 11.2-12.5%), with 17.6% receiving current treatment (95%CI: 15.3-19.9%). There were clear differences between the six groups regarding key variables from all five predictor domains. Prediction of any treatment (model 1) or SC treatment (model 2) was successful with high overall accuracy (both models: 95%), sufficient sensitivity (model 1: 79%/model 2: 76%) and high specificity (both models: 98%). The most predictive single variables were daily drinking level, anxiety, severity of mental distress, and number of inpatient nights during the last 6 months. CONCLUSIONS: Variables from four domains were highly predictive in identifying treatment for AUD, with SC treatment groups showing very high levels of social disintegration, drinking, comorbidity and functional losses. Earlier intervention and formal treatment for AUD in PC should be implemented to reduce these high levels of adverse outcomes.