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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.

Previous studies have attempted to forecast the costs of mental health care, using clinical and individual variables; the inclusion of ecological measures could improve the knowledge of predictors of psychiatric service utilisation and costs to support clinical and strategic decision-making.

Using a Psychiatric Case Register (PCR), all patients with an ICD-10 psychiatric diagnosis, who had at least one contact with community-based psychiatric services in the Verona Health District, Northern Italy, were included in the study (N = 4558). For each patient, one year's total cost of care was calculated by merging service contact data with unit cost estimates and clinical and socio-demographic variables were collected. A socio-economic status (SES) index was developed, as a proxy of deprivation, using census data. Multilevel multiple regression models, considering socio-demographic and clinical characteristics of patients as well as socioeconomic local characteristics, were estimated to predict costs.

The mean annual cost for all patients was 2,606.11 Euros; patients with an ongoing episode of care and with psychosis presented higher mean costs. Previous psychiatric history represented the most significant predictor of cost (36.99% R2 increase) and diagnosis was also a significant predictor but explained only 4.96% of cost variance. Psychiatric costs were uniform throughout the Verona Health District and SES characteristics alone contributed towards less than 1% of the cost variance.

For all patients of community-based psychiatric services, a comprehensive model, including both patients' individual characteristics and socioeconomic local status, was able to predict 43% of variance in costs of care.