Interventions for people at <scp>ultra‐high</scp> risk for psychosis: A systematic review of economic evaluations
Bibliographic record
Abstract
AIM: Psychotic disorders have long-term negative consequences for functioning and quality of life. Ultra-high risk (UHR) programs aim to identify and treat people during the prodromal period before their first psychotic episode. Though studies on the clinical effectiveness of treating prodromal symptoms in people at UHR for psychosis exist, no review has exclusively and comprehensively evaluated the economic impact of UHR programs. Our objective was to systematically review the literature on economic evaluations of UHR programs. METHODS: We searched the Cochrane, EMBASE, MEDLINE, and PsycInfo electronic databases, in addition to grey literature, from inception to March 2020 to identify economic evaluations of UHR programs. We included all cost and cost-effectiveness studies of interventions for people at UHR. The data were synthesized qualitatively, and a risk of bias assessment was performed. RESULTS: Of the 1916 articles retrieved, six studies met our inclusion criteria. These included three cost analysis studies and three cost-effectiveness studies. Five studies were conducted from the health system perspective and the time horizon varied between six months and ten years. Only two reported quality-adjusted life-years (QALYs) as their outcome. Overall, all cost-effectiveness studies and one cost analysis suggested that UHR programs were cost-effective and cost saving, respectively. The risk of bias assessment suggested moderate levels of bias across all studies. CONCLUSION: Economic evaluations of UHR programs varied in terms of outcomes and length of follow-up; however, most studies found them to be cost-effective. Future studies would benefit from long-term evaluations of UHR programs and consistent valuation of outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 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".