Multi-state statistical modelling to investigate the association between serious mental illness and offending behaviour in longitudinally linked data

Abstract

Background:Mental illness and offending behaviour are complex issues for both the community and the justice health system. Thedemand for special treatment and for mental health programs and services for reducing the reoffending rate amongindividuals with mental illness has been a critical focus for those working in the criminal justice system.Aims: This thesis aims to apply various statistical methods—survival analysis, competing risks and multi-state models—for analysing complex time-to-event data to examine the relationship between psychosis, mental health treatment andoffending behaviour.Methods:First, the factors associated with reoffending (violent or non-violent) were identified using Cox regression models.Second, competing risks models were used to identify the predictors of reoffending after categorising the first offence aseither violent or non-violent. Third, multi-state models were employed to analyse the time to reoffending amongindividuals who had a serious mental health diagnosis (psychosis). In this subpopulation, the factors associated with timeto reoffending were investigated using a three-state modelling scenario. The transition probabilities for reoffence werederived for those who continued mental health treatment and those who did not. Lastly, the variables used in this thesiswere evaluated for their ability to predict reoffence in a risk prediction model.Results:Individuals diverted into a treatment order were less likely to reoffend than those who received a punitive sanction fortheir first offence. The probability of non-violent reoffence was more than that of violent reoffence, and there was a higherprobability observed for the risk of reoffending for those who disengaged from treatment compared with those whoremained in treatment. The lack of treatment for a mental health illness indicated a higher contribution to the risk ofreoffence.Conclusion:The thesis demonstrates that using the survival analysis, the competing risks, the risk prediction and the multi-statestatistical models provides novel insights and increases the understanding of the role of mental health treatment inpreventing reoffending. In particular, receiving treatment following an offence reduces reoffending, and diversion intomental health treatment by the courts is important in managing those with serious mental illness to prevent reoffending.The thesis outcomes can facilitate developing policies around treatment and interventions for reoffences associated withthose diagnosed with severe mental illness. Further, these findings may assist with developing guidelines to support areduction in reoffending as well improvement in treatment strategies, counseling and discussion for those diagnosed withpsychosis. Indeed, when this thesis was being written, the NSW Attorney General presented draft legislation to the NSWParliament, parts of which are based on this research