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Journal Article

Citation

Peck A, Hutchinson M, Provost S. Front. Child Adolesc. Psychiatry 2023; 2: e1074861.

Copyright

(Copyright © 2023, Frontiers Media)

DOI

10.3389/frcha.2023.1074861

PMID

unavailable

Abstract

AIM: To discover developmental risk trajectories for emerging mental health problems among a sample of adolescent family violence offenders to inform service delivery focused on early preventative interventions with children and their families.

Design: A retrospective case-series design employing data linkage.

Setting: An Australian regional location.

Participants: Adolescents (born between 1994 and 2006) issued a legal action by the NSW Police Force for an adolescent-to-parent family violence offense (n = 775).

Procedure: Discrete routinely collected episode data in police and health service electronic records for children, and police data for parents, were linked and transformed into longitudinal person-based records from birth to 19 years to identify trajectories for mental health problems.

Results: Sixty-three percent (n = 489) of adolescents had contact with a mental health service before age 19. The majority of these adolescents received a diagnosis for a stress or anxiety disorder (n = 200). Trajectory analysis found childhood exposure to parental intimate partner violence and parental drug and/or alcohol use were dominant events in the pathway to receiving a mental health diagnosis. Being a victim of a sexual offense was found to increase the odds of adolescents having a diagnosis for each of the main mental health categories (with the exception of drug or alcohol disorders).

Conclusions: Pathways to mental health problems were characterized by inter-related adverse childhood events and poly-victimization for many adolescents. Early identification of at-risk children must be a continued focus of child health services in order to reduce and identify early emerging mental health problems.


Language: en

Keywords

Anxiety Disorders; adolescent-to-parent violence; Child and Adolescent Psychiatry; depression and mood disorders; Process mining; Statistics and research methods

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