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

Abstract 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 not yet integrated in this context. Moreover, translation group differences individual assessment has proven difficult. We aimed identify associated with differential youth offending outcomes, and characteristics, test whether the resulting model can be for cases. Methods A 223 detained juveniles from justice institutions was studied. Latent class regression analysis detect outcome (recidivism at 12 month follow-up). As proof principle, it tested separate 76 participants cases could assigned identified subgroups, using prototype ‘tool’ calculating membership. Results Three were identified: ‘high risk—externalizing’ subgroup, ‘medium risk—adverse environment’ ‘low risk—psychopathic traits’ subgroup. Within these both autonomic nervous system neuroendocrinological measures added differentially prediction subtypes reoffending (no, non-violent, violent). The membership correctly 92.1% risk. Conclusions LCRA approach appears useful integrate different re-offending within institutions. This may development biopsychosocial tool eventually help clinicians assign individuals those subsequently tailor intervention their