Few Differences in the Externalizing and Criminal History of Young Violent Offenders With and Without Autism Spectrum Disorders.

Autism spectrum disorders (ASDs) are known to be associated with an increased risk of aggression and challenging behavior. In this study, we have mapped the externalizing history of a nationally representative cohort of young violent offenders with ASD, compared with offenders without ASD. Two hundred and sixty-nine violent offenders were assessed for prevalence of ASD, and participated in a thorough assessment of previous externalizing problems and criminal history. Twenty-six offenders met consensus clinical DSM-IV criteria for ASD and they were compared to offenders without ASD from the same cohort. Overall, we found a very high prevalence of externalizing and antisocial behaviors in the history of these offenders and there were few differences between the groups. Placements in foster homes were overrepresented in the ASD group and the ASD-offenders had significantly more often been diagnosed with a neurodevelopmental disorder (i.e. ASD or ADHD) by a clinician before the study. At index conviction, ASD offenders were overrepresented in sex crimes with a child victim. Though offenders without ASD had more previous convictions, in particular drug crimes, we found no difference in terms of total number of prosecuted crimes. Substance use disorders were more common among offenders without ASD. The ASD offenders scored higher compared to the non-ASD offenders on the Affective facet of the Psychopathy Checklist-Revised (PCL-R) but there were no differences in terms of total PCL-R scores. Our results provide important knowledge of the developmental history of offenders with ASD. Though this is a small and atypical phenotype it poses significant challenges to the criminal justice system and we need to understand more of it to be able to prevent these individuals from committing crimes but also to provide a fair judicial treatment, to assess exculpatory factors and improve our forensic treatment models.