Assessing the Impact of EQ-5D Country-specific Value Sets on Cost-utility Outcomes
Bibliographic record
Abstract
PURPOSE: To assess the impact of EQ-5D country-specific value sets on cost-utility outcomes. METHODS: Data from 2 randomized controlled trials on low back pain (LBP) and depression were used. 3L value sets were identified from the EuroQol Web site. A nonparametric crosswalk was employed for each tariff to obtain the likely 5L values. Differences in quality-adjusted life years (QALYs) between countries were tested using paired t tests, with United Kingdom as reference. Cost-utility outcomes were estimated for both studies and both EQ-5D versions, including differences in QALYs and cost-effectiveness acceptability curves. RESULTS: For the 3L, QALYs ranged between 0.650 (Taiwan) and 0.892 (United States) in the LBP study and between 0.619 (Taiwan) and 0.879 (United States) in the depression study. In both studies, most country-specific QALY estimates differed statistically significantly from that of the United Kingdom. Incremental cost-effectiveness ratios ranged between &OV0556;2044/QALY (Taiwan) and &OV0556;5897/QALY (Zimbabwe) in the LBP study and between &OV0556;38,287/QALY (Singapore) and &OV0556;96,550/QALY (Japan) in the depression study. At the NICE threshold of &OV0556;23,300/QALY (≈£20,000/QALY), the intervention's probability of being cost-effective versus control ranged between 0.751 (Zimbabwe) and 0.952 (Taiwan) and between 0.230 (Canada) and 0.396 (Singapore) in the LBP study and depression study, respectively. Similar results were found for the 5L, with extensive differences in ICERs and moderate differences in the probability of cost-effectiveness. CONCLUSIONS: This study indicates that the use of different EQ-5D country-specific value sets impacts on cost-utility outcomes. Therefore, to account for the fact that health state preferences are affected by sociocultural differences, relevant country-specific value sets should be used.
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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 | 0.077 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".