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 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.

Neurobiological correlates of antisociality across adolescence and young adulthood: a multi-sample, multi-method study

“…Future studies may focus on relating these mechanisms to (preventive) interventions (e.g. see de Ruigh et al, 2021 ).…”

Evaluating sensitivity to classification uncertainty in latent subgroup effect analyses

“…Researchers in the behavioral, health and social sciences are increasingly interested in investigating how the causal effect of a treatment on an outcome differs among individuals in qualitatively different latent subgroups; see e.g., [1,2,3,4,5,6,7,8,9,10]. For example, the effect of a medical intervention, such as right heart catheterization, on the six-month mortality of critically ill patients [11] may differ across patients with different latent risk profiles that depend on their medical and health status.…”

Editorial: No short‐cuts – translating neurobiological knowledge to clinical practice requires moving away from stand‐alone biomarkers to an integrative biopsychosocial approach

“…However, it is of main importance to integrate neurobiological assessments with the mainly psychosocial diagnostic assessments that are currently used in clinical practice. Our previous work has shown that such a biopsychosocial approach including various neurobiological, psychological, and environmental factors in concert, can be used in defining more homogeneous clinical subsamples, in this case with different risks of criminal reoffending (de Ruigh et al., 2021). Through integrating structural biological assessments in clinical practice, data will become available that can be used to create individual diagnostic profiles in which both biological and environmental assessments are weighed.…”