Cost-Effectiveness of Internet-Delivered Emotion Regulation Therapy for Adolescents With Nonsuicidal Self-Injury: Within-Trial Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
Background: Nonsuicidal self-injury (NSSI) is common among adolescents and is associated with adverse clinical outcomes, as well as suicidal behavior. Current treatments are resource-intensive and may not be accessible to all adolescents with NSSI. Internet-delivered emotion regulation individual therapy for adolescents (IERITA) with NSSI disorder is a promising treatment option, but its cost-effectiveness is unknown. Objective: This study aims to evaluate the cost-effectiveness of IERITA for adolescents with NSSI disorder. Methods: Within-trial cost-effectiveness analysis of a randomized controlled trial at three child and adolescent mental health services in Sweden (n=166). A total of 12 weeks of IERITA plus treatment as usual (TAU) versus TAU only were compared. The primary outcome was the frequency of NSSI at 1-month posttreatment. Secondary outcomes were NSSI remission and quality-adjusted life years (QALYs). Results: IERITA led to reductions in NSSI frequency, a higher proportion of participants with NSSI remission, and more QALYs at 1-month posttreatment, at additional health care costs of US $3663 (95% CI US $2182-$5002) and societal costs of US $4458 (95% CI US $-577 to $9509). The incremental cost of one additional NSSI remission at 1-month posttreatment was US $18,677, and the incremental cost per QALY gained was US $792,244 for IERITA+TAU relative to TAU. IERITA had an 8% probability of being cost-effective at a societal willingness-to-pay threshold of US $84,000 for one QALY at 1-month posttreatment, which increased to 18% at 3-months posttreatment. Conclusions: IERITA delivered adjunctive to TAU led to improvements in NSSI frequency, remission, and QALYs, at additional costs compared to TAU only. This study provides an estimate of the additional cost of delivering IERITA; however, future studies should include longer follow-up periods to better assess the magnitude of the effects on QALYs and societal costs.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".