Predictors of mental health-related acute service utilisation and treatment costs in the 12 months following an acute psychiatric admission.

Abstract

A key step in informing mental health resource allocation is to identify the predictors of service utilisation and costs. This project aims to identify the predictors of mental health-related acute service utilisation and treatment costs in the year following an acute public psychiatric hospital admission. A dataset containing administrative and routinely measured outcome data for 1 year before and after an acute psychiatric admission for 1757 public mental health patients was analysed. Multivariate regression models were developed to identify patient- and treatment-related predictors of four measures of service utilisation or cost: (a) duration of index admission; and, in the year after discharge from the index admission (b) acute psychiatric inpatient bed-days; (c) emergency department (ED) presentations; and (d) total acute mental health service costs. Split-sample cross-validation was used. A diagnosis of psychosis, problems with living conditions and prior acute psychiatric inpatient bed-days predicted a longer duration of index admission, while prior ED presentations and self-harm predicted a shorter duration. A greater number of acute psychiatric inpatient bed-days in the year post-discharge were predicted by psychosis diagnosis, problems with living conditions and prior acute psychiatric inpatient admissions. The number of future ED presentations was predicted by past ED presentations. For total acute care costs, diagnosis of psychosis was the strongest predictor. Illness acuity and prior acute psychiatric inpatient admission also predicted higher costs, while self-harm predicted lower costs. The development of effective models for predicting acute mental health treatment costs using existing administrative data is an essential step towards a workable activity-based funding model for mental health. Future studies would benefit from the inclusion of a wider range of variables, including ethnicity, clinical complexity, cognition, mental health legal status, electroconvulsive therapy, problems with activities of daily living and community contacts.