Cost-effectiveness of an indicated preventive intervention for depression in adolescents: a model to support decision making
Bibliographic record
Abstract
BACKGROUND: Adolescent depression has negative health and economic outcomes in the short- and long-term. Indicated preventive interventions, in particular group based cognitive behavioural therapy (GB-CBT), are effective in preventing depression in adolescents with subsyndromal depression. However, little is known about the cost-effectiveness of these interventions. METHODS: A Markov cohort model was used to conduct cost-effectiveness analyses comparing a GB-CBT indicated preventive intervention for depression, to a no-intervention option in a Swedish setting. Taking a time horizon of 5- and 10 years, incremental differences in societal costs and health benefits expressed as differences in the proportion of cases of depression prevented, and as quality adjusted life years (QALYs) gained were estimated. Through univariate and probabilistic sensitivity analyses, the robustness of the results was explored. Costs, presented in 2018 USD, and effects were discounted at a yearly rate of 3%. RESULTS: The base-case analysis showed that GB-CBT indicated preventive intervention incurred lower costs, prevented a larger proportion of cases of depression and generated higher QALYs compared to the no-intervention option for both time horizons. Offering the intervention was even a cost saving strategy and demonstrated a probability of being cost-effective of over 95%. In the sensitivity analyses, these results were robust to the modelling assumptions. LIMITATIONS: The study considered a homogeneous cohort and assumed a constant annual decay rate of the relative treatment effect. CONCLUSIONS: GB-CBT indicated preventive interventions for depression in adolescence can generate good value for money compared to leaving adolescents with subsyndromal depression untreated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".