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A predictive model to allocate frequent service users of community-based Mental Health Services to different packages of care

Published online by Cambridge University Press:  11 April 2011

Laura Grigoletti*
Affiliation:
Department of Medicine and Public Health, Section of Psychiatry and Clinical Psychology, University of Verona, Verona (Italy)
Francesco Amaddeo
Affiliation:
Department of Medicine and Public Health, Section of Psychiatry and Clinical Psychology, University of Verona, Verona (Italy)
Aldrigo Grassi
Affiliation:
“Scalo” Mental Health Centre, Bologna (Italy)
Massimo Boldrini
Affiliation:
“Scalo” Mental Health Centre, Bologna (Italy)
Marco Chiappelli
Affiliation:
“Scalo” Mental Health Centre, Bologna (Italy)
Mauro Percudani
Affiliation:
Mental Health Department, Hospital of Legnano (Milan); present address: Mental Health Department, “G. Salvini” Hospital, Garbagnate Milanese (Italy)
Francesco Catapano
Affiliation:
Psychiatry Department, Second University of Naples, Naples (Italy)
Andrea Fiorillo
Affiliation:
Psychiatry Department, Second University of Naples, Naples (Italy)
Francesco Perris
Affiliation:
Psychiatry Department, Second University of Naples, Naples (Italy)
Maurizio Bacigalupi
Affiliation:
Mental Health Department ASL Rome B, Rome (Italy)
Paolo Albanese
Affiliation:
Mental Health Department ASL Rome B, Rome (Italy)
Simona Simonetti
Affiliation:
Mental Health Department ASL Rome B, Rome (Italy)
Paola De Agostini
Affiliation:
Economic Sciences Department, University of Verona, Verona (Italy)
Michele Tansella
Affiliation:
Department of Medicine and Public Health, Section of Psychiatry and Clinical Psychology, University of Verona, Verona (Italy)
*
Address for correspondence: Dr. L. Grigoletti, Department of Medicine and Public Health, Section of Psychiatry and Clinical Psychology, Ospedale Policlinico G.B. Rossi, P.le L.A. Scuro 10, 37134 Verona (Italy). Fax: +39-045-8124889 E-mail: laura.grigoletti@univr.it

Summary

Aim – To develop predictive models to allocate patients into frequent and low service users groups within the Italian Community-based Mental Health Services (CMHSs). To allocate frequent users to different packages of care, identifing the costs of these packages. Methods – Socio-demographic and clinical data and GAF scores at baseline were collected for 1250 users attending five CMHSs. All psychiatric contacts made by these patients during six months were recorded. A logistic regression identified frequent service users predictive variables. Multinomial logistic regression identified variables able to predict the most appropriate package of care. A cost function was utilised to estimate costs. Results – Frequent service users were 49%, using nearly 90% of all contacts. The model classified correctly 80% of users in the frequent and low users groups. Three packages of care were identified: Basic Community Treatment (4,133 Euro per six months); Intensive Community Treatment (6,180 Euro) and Rehabilitative Community Treatment (11,984 Euro) for 83%, 6% and 11% of frequent service users respectively. The model was found to be accurate for 85% of users. Conclusion – It is possible to develop predictive models to identify frequent service users and to assign them to pre-defined packages of care, and to use these models to inform the funding of psychiatric care.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2010

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