Using the biopsychosocial model for identifying subgroups of detained juveniles at different risk of re-offending in practice: a latent class regression analysis approach.

BACKGROUND: Juvenile delinquents constitute a heterogeneous group, which complicates decision-making based on risk assessment. Various psychosocial factors have been used to define clinically relevant subgroups of juvenile offenders, while neurobiological variables have not yet been integrated in this context. Moreover, translation of neurobiological group differences to individual risk assessment has proven difficult. We aimed to identify clinically relevant subgroups associated with differential youth offending outcomes, based on psychosocial and neurobiological characteristics, and to test whether the resulting model can be used for risk assessment of individual cases. METHODS: A group of 223 detained juveniles from juvenile justice institutions was studied. Latent class regression analysis was used to detect subgroups associated with differential offending outcome (recidivism at 12 month follow-up). As a proof of principle, it was tested in a separate group of 76 participants whether individual cases could be assigned to the identified subgroups, using a prototype 'tool' for calculating class membership. RESULTS: Three subgroups were identified: a 'high risk-externalizing' subgroup, a 'medium risk-adverse environment' subgroup, and a 'low risk-psychopathic traits' subgroup. Within these subgroups, both autonomic nervous system and neuroendocrinological measures added differentially to the prediction of subtypes of reoffending (no, non-violent, violent). The 'tool' for calculating class membership correctly assigned 92.1% of participants to a class and reoffending risk. CONCLUSIONS: The LCRA approach appears to be a useful approach to integrate neurobiological and psychosocial risk factors to identify subgroups with different re-offending risk within juvenile justice institutions. This approach may be useful in the development of a biopsychosocial assessment tool and may eventually help clinicians to assign individuals to those subgroups and subsequently tailor intervention based on their re-offending risk.

Predicting quality of life during and post detention in incarcerated juveniles.

PURPOSE: Besides reducing recidivism, juvenile justice institutions aim to rehabilitate juvenile delinquents, in order for them to reintegrate in society. As such, improving quality of life (QoL), especially post detention, is an important treatment goal. However, research is primarily focused on recidivism as an outcome measure for juvenile detention. The aim of the current study is therefore to describe and predict QoL of detained young offenders up to 1 year after an initial assessment, and to examine whether QoL differs between youth who are still detained versus released. METHODS: A sample of 186 juveniles admitted to juvenile justice institutions in the Netherlands was assessed within the institution (initial assessment/T0), using psychosocial and neurobiological factors as predictors (self-control, treatment motivation, trauma, mental health problems, respiratory sinus arrhythmia). QoL (MANSA), as well as substance use (alcohol, cannabis) and daily activities (education, work) were assessed at first, second, and third follow-up (respectively 2.5 months, 4.5 months, and 12 months after T0). RESULTS: QoL increased from first to third follow-up, and was higher for individuals who were no longer detained. The model that best predicted higher QoL upon follow-up consisted of lower trauma and stronger parasympathetic nervous system reactivity. The effects of the predictors did not differ between the various follow-ups, nor between individuals who were or were not detained. CONCLUSION: Methods incorporating trauma-sensitive focus and relaxation techniques in treatment protocols in juvenile justice institutions may be of added value in improving the general functioning of these individuals.