Differences in the use of involuntary admission across the Veneto Region: which role for individual and contextual variables?

• Published in: Epidemiology and psychiatric sciences 2016-02, Vol.25 (1), p.49-57
• Main Authors: Donisi, V., Tedeschi, F., Salazzari, D., Amaddeo, F.
• Format: Article
• Language: English
• Subjects: Adult; Commitment of Mentally Ill; Female; Humans; Italy - epidemiology; Male; Mental Disorders - epidemiology; Mental Disorders - therapy; Mental health care; Mental Health Services; Original; Original Articles; Patient admissions; Prevalence
• ISSN: 2045-7960; 2045-7979
• DOI: 10.1017/S2045796014000663

The first aim of this study is to compare involuntary admissions across the Veneto Region in Italy. The second aim is to explore the relation between mental health services provision, characteristics of population, individual factors and involuntary admissions. For 21 Mental Health Departments (MHDs) in the Veneto Region (Italy), the average population prevalence rate of involuntary admissions between 2000 and 2007 and the percentage of involuntary admissions were calculated. Chi-square tests for equality of proportions were used to test hypotheses. Variables at the individual, contextual and organisational levels were used in multiple regressions, with the involuntary admission data as dependent variables. The average prevalence rate of involuntary commitment was 12.75 ranging from 1.96 to 27.59 across MHDs . About 75% of the involuntary admissions referred to psychotic patients, and almost half of patients were aged 25-44. Significant differences among MHDs emerged; higher percentages of involuntary admissions were generally found in densely populated areas. Higher ageing indices and rates of social workers were found as predictors of the prevalence rate. In the multilevel regression, being males and psychotic significantly increased involuntary admissions, while the percentage of singles in population decreased it. This study contributes to define the specific contribution of each factor predicting the use of involuntary admission, even within areas under the same legislation. It shows how the inclusion of both individual and contextual factors may lead to better predictions and provides precious data for the services improvement.