TY - JOUR
T1 - Using the biopsychosocial model for identifying subgroups of detained juveniles at different risk of re-offending in practice: a latent class regression analysis approach
AU - de Ruigh, E. L.
AU - Bouwmeester, S.
AU - Popma, A.
AU - Vermeiren, R. R. J. M.
AU - van Domburgh, L.
AU - Jansen, L. M. C.
N1 - Funding Information: This work was supported by the Academic Workplace Risk Youth (AWRJ). We thank the juveniles and staff of the participating institutions; H.S. van der Baan, R. W. Wiers, A. Collot D?Escury, B. Verschuere, and M.D. de Boer, department of Developmental Psychology, University of Amsterdam, for their cooperation in data collection; Laboratory of Clinical Chemistry & Haematology (LKCH), University Medical Centre Utrecht, for performing hormone analyses. The authors report no potential conflicts of interest. Funding Information: This work was supported by the Academic Workplace Risk Youth (AWRJ). We thank the juveniles and staff of the participating institutions; H.S. van der Baan, R. W. Wiers, A. Collot D’Escury, B. Verschuere, and M.D. de Boer, department of Developmental Psychology, University of Amsterdam, for their cooperation in data collection; Laboratory of Clinical Chemistry & Haematology (LKCH), University Medical Centre Utrecht, for performing hormone analyses. The authors report no potential conflicts of interest. Funding Information: This study was funded by the Ministry of Justice and Security of the Netherlands. Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - 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.
AB - 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.
KW - Juvenile offenders
KW - Latent class regression
KW - Neurobiology
KW - Reoffending
KW - Risk assessment
KW - Subgroups
UR - http://www.scopus.com/inward/record.url?scp=85111529762&partnerID=8YFLogxK
U2 - https://doi.org/10.1186/s13034-021-00379-1
DO - https://doi.org/10.1186/s13034-021-00379-1
M3 - Article
C2 - 34158097
SN - 1753-2000
VL - 15
JO - Child and Adolescent Psychiatry and Mental Health
JF - Child and Adolescent Psychiatry and Mental Health
IS - 1
M1 - 33
ER -