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Systematic Review and Meta-Analysis: Predictors of Relapsing, Recurrent, and Chronic Depression in Young People
Scott D Tagliaferri1, Laura K M Han2, Muskan Khetan1
1Orygen, Victoria, Australia; University of Melbourne, Victoria, Australia.
Summary
Identifying young people at risk for chronic depression is crucial. Female sex, higher symptom severity, lower functioning, suicidal behaviors, and longer sleep latency predict a poor depression course in youth.
Area of Science:
- Psychiatry
- Developmental Psychology
- Epidemiology
Background:
- Youth depression significantly impacts adult social and vocational transitions.
- Recurring or chronic depression episodes contribute substantially to the overall burden of the illness.
- Early identification of at-risk youth is critical for timely intervention and improved outcomes.
Purpose of the Study:
- To systematically review and meta-analyze prognostic factors for relapsing, recurrent, or chronic depression in young people.
- To identify demographic, clinical, and interpersonal factors associated with a poor depression trajectory in adolescence and young adulthood.
Main Methods:
- Comprehensive literature search across multiple databases up to March 2024.
- Inclusion of cohort studies and randomized trials with a minimum 3-month follow-up.
- Meta-analyses conducted using random-effects models; risk of bias and certainty of evidence assessed using QUIPS and GRADE.
Main Results:
- 388 prognostic factors were evaluated in 7,488 young people across 46 studies.
- Majority of studies (87%) had a high risk of bias.
- Factors predicting a poor depression course included: female sex, higher symptom severity, lower global functioning, suicidal thoughts/behaviors, and longer sleep-onset latency.
Conclusions:
- Demographic and clinical factors hold prognostic value for poor depression trajectories in youth.
- Relational/interpersonal factors show promise but require further research for validation.
- Future research should focus on large, diverse samples and incorporate genetic/neurobiological markers to improve risk prediction.
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