BACKGROUND: The growing movement in many European countries towards capitation-based systems for financing mental health care has generated increasing interest in developing appropriate models capitation formulae. The aims of the study were: to detect and compare any differences in service costs between patients with different diagnoses; and to analyse the associations between patient characteristics and service costs. METHODS: All patients in contact with the South-Verona Community Mental Health Service during the last quarter of 1996 were included in the study. Clinical and service-related variables were collected at first index contact; 3 months later, patients were interviewed using the Client Services Recipient Interview. For those who completed both the clinical assessments and the services receipt schedule (N = 339), 1-year psychiatric and non-psychiatric direct care costs were calculated. Weighted backward regression analyses were performed. RESULTS: The most significant variables associated with psychiatric costs were: admission to hospital in the previous year; intensity and duration of previous contacts with South-Verona CMHS; being unemployed; having a diagnosis of affective disorder; and, Global Assessment of Functioning score. The final model explained 66% of the variation in costs of psychiatric care and 13 % of variation in non-psychiatric medical costs. CONCLUSIONS: The model presented in this study explains a higher degree of cost variance than previously published studies. In community-based services more resources are targeted towards the most disabled patients. Previous psychiatric history (number of admissions in the previous year and intensity of psychiatric contacts lifetime) is strongly associated with psychiatric costs.
BACKGROUND: The growing movement in many European countries towards capitation-based systems for financing mental health care has generated increasing interest in developing appropriate models capitation formulae. The aims of the study were: to detect and compare any differences in service costs between patients with different diagnoses; and to analyse the associations between patient characteristics and service costs. METHODS: All patients in contact with the South-Verona Community Mental Health Service during the last quarter of 1996 were included in the study. Clinical and service-related variables were collected at first index contact; 3 months later, patients were interviewed using the Client Services Recipient Interview. For those who completed both the clinical assessments and the services receipt schedule (N = 339), 1-year psychiatric and non-psychiatric direct care costs were calculated. Weighted backward regression analyses were performed. RESULTS: The most significant variables associated with psychiatric costs were: admission to hospital in the previous year; intensity and duration of previous contacts with South-Verona CMHS; being unemployed; having a diagnosis of affective disorder; and, Global Assessment of Functioning score. The final model explained 66% of the variation in costs of psychiatric care and 13 % of variation in non-psychiatric medical costs. CONCLUSIONS: The model presented in this study explains a higher degree of cost variance than previously published studies. In community-based services more resources are targeted towards the most disabled patients. Previous psychiatric history (number of admissions in the previous year and intensity of psychiatric contacts lifetime) is strongly associated with psychiatric costs.