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Journal Article

Citation

Nagaoka D, Uno A, Usami S, Tanaka R, Minami R, Sawai Y, Okuma A, Yamasaki S, Miyashita M, Nishida A, Kasai K, Ando S. Lancet Reg. Health West. Pac. 2024; 43: e100979.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.lanwpc.2023.100979

PMID

38456092

PMCID

PMC10920037

Abstract

BACKGROUND: Psychopathological and behavioral problems in adolescence are highly comorbid, making their developmental trajectories complex and unclear partly due to technical limitations. We aimed to classify these trajectories using deep learning and identify predictors of cluster membership.

METHODS: We conducted a population-based cohort study on 3171 adolescents from three Tokyo municipalities, with 2344 pairs of adolescents and caregivers participating at all four timepoints (ages 10, 12, 14, and 16) from 2012 to 2021. Adolescent psychopathological and behavioral problems were assessed by using self-report questionnaires. Both adolescents and caregivers assessed depression/anxiety and psychotic-like experiences. Caregivers assessed obsession/compulsion, dissociation, sociality problem, hyperactivity/inattention, conduct problem, somatic symptom, and withdrawal. Adolescents assessed desire for slimness, self-harm, and suicidal ideation. These trajectories were clustered with variational deep embedding with recurrence, and predictors were explored using multinomial logistic regression.

FINDINGS: Five clusters were identified: unaffected (60.5%), minimal problems; internalizing (16.2%), persistent or worsening internalizing problems; discrepant (9.9%), subjective problems overlooked by caregivers; externalizing (9.6%), persistent externalizing problems; and severe (3.9%), chronic severe problems across symptoms. Stronger autistic traits and experience of bullying victimization commonly predicted the four "affected" clusters. The discrepant cluster, showing the highest risks for self-harm and suicidal ideation, was predicted by avoiding help-seeking for depression. The severe cluster predictors included maternal smoking during pregnancy, not bullying others, caregiver's psychological distress, and adolescent's dissatisfaction with family.

INTERPRETATION: Approximately 40% of adolescents were classified as "affected" clusters. Proactive societal attention is warranted toward adolescents in the discrepant cluster whose suicidality is overlooked and who have difficulty seeking help. FUNDING: Japan Ministry of Health, Labor and Welfare, Japan Agency for Medical Research and Development, and Japan Science and Technology Agency.


Language: en

Keywords

Adolescent; Clustering; Cohort study; Comorbidity; Deep learning; Longitudinal; Psychobehavioral problems; Psychopathological and behavioral problems; Psychopathology; Trajectory

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