Automated Feedback After Internet-Based Depression Screening: Cost-Effectiveness Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
Background: The clinical and cost-related consequences of internet-based depression screening, in combination with automated feedback, have been rarely investigated. We aimed to conduct a cost-effectiveness analysis of DISCOVER, a 3-armed, observer-masked, randomized controlled trial that focused on 2 versions of automated feedback interventions after internet-based depression screening. Objective: This study aimed to evaluate the cost-effectiveness of automated nontailored and tailored feedback interventions after internet-based depression screening from a societal perspective. Methods: Participants were recruited from the general population via traditional and social media. Participants who were undiagnosed but screened positive for depression on an online version of the Patient Health Questionnaire-9 (≥10 points) were randomized to automatically receive no feedback, nontailored feedback, or tailored feedback. The feedback interventions included the depression screening result, a recommendation to seek professional advice, and brief general information about depression. The tailored feedback was additionally framed according to the participants' symptom profiles, treatment preferences, health insurance plans, and local residency. The time horizon was 6 months. The main outcome was the incremental cost-effectiveness ratio (ICER) from a societal perspective using quality-adjusted life years (QALY) based on the EuroQol-5D-5L. Cost-effectiveness acceptability curves were constructed. Furthermore, several sensitivity analyses and explorative subgroup analyses were conducted. Results: A total of 1012 participants (no feedback: n=343, 33.9%; nontailored feedback: n=338, 33.4%; and tailored feedback: n=331, 32.7%) were included. Differences in costs and effects were not statistically significant. However, ICER results indicated that both no feedback and tailored feedback exhibited dominance over nontailored feedback. The ICER of tailored feedback compared to no feedback was €109,730 per QALY (a currency exchange rate of €1=US $1.02 was applicable as of December 31, 2022), whereas both costs and QALYs were lower in tailored feedback. The cost-effectiveness probability of tailored feedback compared to no feedback ranged between 41% and 80%. Sensitivity analyses exhibited similar trends. Conclusions: Six months postintervention, feedback interventions had no statistically significant effect on costs from a societal perspective or on QALYs. Tailored feedback was associated with moderate cost-effectiveness probabilities compared to no feedback. Explorative subgroup analyses revealed subpopulations for which the interventions might be cost-effective.
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".