Cost of care of patients with personality disorders in forensic psychiatric hospitals in the Netherlands.

BACKGROUND: Ter beschikking stelling (TBS) clinics form the mainstay of forensic psychiatric services in the Netherlands. Their costs are rising, but little is known about how these costs are distributed. AIM: To determine the distribution of service costs for patients with personality disorders in TBS medium security units in the Netherlands. METHOD: Data on service use were extracted retrospectively from the case files of 55 people with personality disorder who are residents in six medium security units within two TBS centres during 2006 (De Rooyse Wissel and Pompestichting). Standard unit costs were obtained for each service, and multiplied by frequency of service use to obtain the total cost of service per patient. A modified version of the Secure Facilities Service Use Schedule was completed. RESULTS: The average daily cost of a bed in a TBS hospital in 2006 was 388 Euros (402 (SD 37) Euros in De Rooyse Wissel; 375 (SD 48) Euros in the Pompestichting). Over half of this was spent on non-treatment fixed costs (overheads). There was considerable difference between patients and between unit variations in the other costs, but about one-third went on costs of staying in department (sociotherapists), and less than 10% each on specific therapeutic interventions or daily activities. About 3% of the budget overall was spent on other costs, but, as these included escorted leaves, at times these costs accounted for a much higher proportion of the per patient expenditure. CONCLUSION: Our results may provide a baseline measurement, with which future costs of TBS treatment can be compared as the services expand and develop. More attention to costs in this way may help to contain budget increases. New studies could examine more specific aspects of treatment or other specific patient groups.

Static and dynamic patient characteristics as predictors of criminal recidivism: A prospective study in a Dutch forensic psychiatric sample.

If clinicians in forensic psychiatry want to reduce risk of reoffending in their patients, they require insight into dynamic risk factors, and evidence that these add predictive power to static risk indicators. Predictors need to be evaluated under clinically realistic circumstances. This study aimed to validate dynamic and static variables as predictors of reconviction in a naturalistic outcome study. Data on static and dynamic risk factors were collected for 151 patients discharged from Dutch forensic psychiatric hospitals. Community follow-up was prospective, with a 5.5 year minimum. A prediction model was developed using Cox regression analysis. The magnitude of the predictive power of this model was estimated using receiver operating characteristic (ROC) analysis. The final prediction model contained four static and no dynamic predictors. The model's ROC area under the curve was .79 (95% CI .69-.89). Clinical risk ratings were non-predictive. Post hoc analyses exploring the influence of subgroups of patients did not yield better models. It is concluded that a small set of static predictors yielded a good estimate of future reconvictions; inclusion of dynamic predictors did not add predictive power.

The structural coherence of clinically derived dynamic indicators of reoffending risk.

BACKGROUND: Forensic psychiatrists aim to reduce the risk of reoffending through treatment. With few exceptions, research evidence tends to favour risk assessment aids reliant on fixed historical rather than clinical data, but transparency in the making of clinical judgements is lacking. AIMS: To evaluate further a clinically derived 47-item dynamic risk assessment checklist; specifically to test first whether it has a meaningful dimensional structure and, second, the extent to which items and underlying dimensions are associated with a separate, direct clinical judgement of risk of reoffending. METHODS: Data sets from four previous studies on the reliability and validity of the Clinical Inventory of Dynamic Reoffending Risk Indicators (CIDRRI) were merged, yielding 370 cases. The resulting data set was analysed using principal axis (common) factor analysis, with orthogonal (varimax) rotation. In addition, receiver operating characteristic (ROC) curves were calculated. RESULTS: The model of best fit depended on treatment stage; for those in residential treatment it was a six- factor model (responsibility, self-reliance, antisocial/narcissistic traits, treatment compliance, goal attainment and avoidance); for those back in the community a five-actor model in which, effectively, the compliance and goal attainment factors became one, provided a better fit. These dimensions and some individual scale items were associated with clinical judgement of risk of reoffending. CONCLUSIONS AND CLINICAL IMPLICATIONS: This study provides evidence that the CIDRRI is an adequate operationalization of underpinnings of clinical risk assessment, and that these underpinnings are part of meaningful higher-order dimensions. The CIDRRI is a viable instrument for clinical use, taking only 15 minutes to complete and identifying dynamic risk factors, the validity of which needs to be further established.