Longitudinal Trajectories of Depression Symptoms in Adolescence: Psychosocial Risk Factors and Outcomes

Rachel E.R. Ellis, Marc L. Seal, Julian G. Simmons, Sarah Whittle, Orli S. Schwartz, Michelle L. Byrne, Nicholas B. Allen

Research output: Contribution to journalArticleResearchpeer-review

33 Citations (Scopus)

Abstract

Variations in symptom trajectories within a population may represent distinct groups with different etiologies and outcomes. This study aimed to identify subgroups of depression symptom trajectories in a sample of adolescents, and to describe psychosocial attributes of the different groups. In a longitudinal study, 243 adolescents (121 males and 122 females), were assessed using a battery of measures of temperament, psychopathology, and psychological and behavioral functioning. Four phases of data collection over 7 years spanned average ages of the participants from 12 to 18 years old. Depressive symptoms from each phase were used to model latent class growth trajectories. A 4-group solution was selected as the best-fitting model: (1) ongoing stable low levels of depression; (2) very high depressive symptoms initially, but a steep decrease in symptoms over time; (3) moderately high depressive symptoms initially, but symptoms decreased over time; and (4) initially low levels of symptoms that increased over time. Trajectory group membership was associated with a range of psychosocial variables including temperament, childhood maltreatment, and young adult quality of life. Characterising these subgroups allows for a better understanding of how the interaction of risk factors increases the likelihood of depression and other poor outcomes, and highlights the importance of early interventions to prevent and treat adolescent depression.

Original languageEnglish
Pages (from-to)554-571
Number of pages18
JournalChild Psychiatry and Human Development
Volume48
Issue number4
DOIs
Publication statusPublished - Aug 2017
Externally publishedYes

Keywords

  • Adolescence
  • Depression
  • Growth mixture modeling
  • Longitudinal studies
  • Risk factors

Cite this