A comparison of scoring models for computerised mental health screening for federal prison inmates

Background: There are high rates of mental disorder in correctional environments, so effective mental health screening is needed. Implementation of the computerised mental health screen of the Correctional Service of Canada has led to improved identification of offenders with mental health needs but with high rates of false positives.

Aims: The goal of this study is to evaluate the use of an iterative classification tree (ICT) approach to mental health screening compared with a simple binary approach using cut-off scores on screening tools.

Methods: A total of 504 consecutive admissions to federal prison completed the screen and were also interviewed by a mental health professional. Relationships between screening results and more extended assessment and clinical team discussion were tested.

Results: The ICT was more parsimonious in identifying probable 'cases' than standard binary screening. ICT was also highly accurate at detecting mental health needs (AUC=0.87, 95% CI 0.84-0.90). The model identified 118 (23.4%) offenders as likely to need further assessment or treatment, 87% of whom were confirmed cases at clinical interview. Of the 244 (48.4%) offenders who were screened out, only 9% were clinically assessed as requiring further assessment or treatment. Standard binary screening was characterised by more false positives and a comparable false negative rate.

Conclusions: The use of ICTs to interpret screening data on the mental health of prisoners needs further evaluation in independent samples in Canada and elsewhere. This first evaluation of the application of such an approach offers the prospect of more effective and efficient use of the scarce resource of mental health services in prisons. Although not required, the use of computers can increase the ease of implementing an ICT model.