Cost effectiveness of guided Internet-based interventions for depression in comparison with control conditions: An individual-participant data meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is limited evidence on the cost effectiveness of Internet-based treatments for depression. The aim was to evaluate the cost effectiveness of guided Internet-based interventions for depression compared to controls. METHODS: Individual-participant data from five randomized controlled trials (RCT), including 1,426 participants, were combined. Cost-effectiveness analyses were conducted at 8 weeks, 6 months, and 12 months follow-up. RESULTS: The guided Internet-based interventions were more costly than the controls, but not statistically significant (12 months mean difference = €406, 95% CI: - 611 to 1,444). The mean differences in clinical effects were not statistically significant (12 months mean difference = 1.75, 95% CI: - .09 to 3.60 in Center for Epidemiologic Studies Depression Scale [CES-D] score, .06, 95% CI: - .02 to .13 in response rate, and .00, 95% CI: - .03 to .03 in quality-adjusted life-years [QALYs]). Cost-effectiveness acceptability curves indicated that high investments are needed to reach an acceptable probability that the intervention is cost effective compared to control for CES-D and response to treatment (e.g., at 12-month follow-up the probability of being cost effective was .95 at a ceiling ratio of 2,000 €/point of improvement in CES-D score). For QALYs, the intervention's probability of being cost effective compared to control was low at the commonly accepted willingness-to-pay threshold (e.g., at 12-month follow-up the probability was .29 and. 31 at a ceiling ratio of 24,000 and 35,000 €/QALY, respectively). CONCLUSIONS: Based on the present findings, guided Internet-based interventions for depression are not considered cost effective compared to controls. However, only a minority of RCTs investigating the clinical effectiveness of guided Internet-based interventions also assessed cost effectiveness and were included in this individual-participant data meta-analysis.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.066 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".