The Potential Economic and Public Health Impact of MDMA‐Assisted Group Therapy for PTSD in Ukraine
Bibliographic record
Abstract
ABSTRACT The war in Ukraine has led to widespread trauma, with 6.4 million people suffering from severe, chronic posttraumatic stress disorder (PTSD). This study evaluates the cost‐effectiveness and societal impact of implementing modified group MDMA‐assisted therapy (MAT), with supplemental individual therapy for PTSD treatment in Ukraine. Using a decision analysis model, we estimated clinical benefits, costs, and cost‐effectiveness of MAT for 1000 PTSD patients in Ukraine. The model incorporates PTSD severity, mortality rates, healthcare costs, productivity effects, and caregiver costs. We analyzed outcomes from healthcare payer and societal perspectives over 1‐, 3‐, and 5‐year horizons, projecting scaled‐up impacts for 25%, 50%, and 75% of eligible patients over 10 years. Assuming 3 years of MAT efficacy, treating 1000 patients would cost $1.1 million, avert 19.2 deaths and gain 717 quality‐adjusted life years (QALYs). From a healthcare payer's perspective, MAT is cost‐effective with an incremental cost‐effectiveness ratio of $1537 per QALY gained and a net monetary benefit of $2843. From a partial societal perspective, MAT generates net savings of $2.6 million. Scaled to 50% of eligible patients over 10 years, MAT could save 48,000 lives and gain 1.5 million QALYs, with net societal savings of $5.6 billion. Making MAT available for PTSD treatment in Ukraine is likely to be cost‐effective or cost‐saving, while substantially improving health outcomes. These findings support consideration of MAT as part of Ukraine's strategy to address widespread mental health needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".