Willingness-to-pay and parametric trends in cost-effectiveness and cost-utility studies in ophthalmology
Bibliographic record
Abstract
OBJECTIVE: To evaluate the frequencies of input parameters in cost-effectiveness analyses (CEA) within ophthalmology, particularly in willingness-to-pay (WTP), and to assess trends over time in studies conducted in the United States. METHODS AND ANALYSIS: A cross-sectional analysis of CEAs from the Tufts Medical Center CEA Registry spanning 1993 to 2022 was conducted, including all studies evaluating diseases of the eye and adnexa. The primary outcomes measured included trends in WTP thresholds, funding sources, types of interventions and disease classifications. RESULTS: A total of 82 US-based CEAs met the inclusion criteria. All studies assessed outcomes in quality-adjusted life years (QALYs). WTP thresholds of US$50 000 (41%) and US$100 000 (39%) were most frequently reported, with US$150 000 emerging in 9% of studies since 2019. Discounting at 3.0% for costs and QALYs was universally applied. Government (33%), nonprofit (29%) and pharmaceutical (17%) funding predominated. Pharmaceutical-funded studies often employed higher WTP thresholds of US$100 000 (29%) and US$150 000 (29%). The most common intervention types were surgical (40%) and pharmaceutical (40%), whereas diseases of the choroid and retina (43%) were most frequently studied. Healthcare perspectives (17 studies) were more commonly reported than societal perspectives (6 studies). CONCLUSIONS: US-based ophthalmology CEAs commonly use US$50 000-$100 000 WTP thresholds and a 3.0% discount rate, with higher thresholds emerging recently. Public and nonprofit funding predominates, focusing on retinal diseases and surgical or pharmaceutical interventions. Reassessing fixed WTP thresholds and incorporating societal perspectives could improve CEAs' relevance, ensuring alignment with evolving economic and healthcare landscapes.
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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 | 0.206 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".