Using Published Health Utilities in Cost-Utility Analyses: Discrepancies and Issues in Cardiovascular Disease
Bibliographic record
Abstract
BACKGROUND: Health utilities are commonly used as quality weights to calculate quality-adjusted life years in cost-utility analysis (CUA). However, if published health utilities are not properly used, the credibility of CUA could be affected. OBJECTIVES: To identify discrepancies in using published health utilities in CUAs for cardiovascular disease (CVD). METHODS: CVD CUAs in the Tufts Cost-Effectiveness Analysis Registry that reported health utilities were included in the analysis. References cited for health utilities in these CUAs were reviewed to identify the original health utility studies. The description and value of health utilities used in the CUA were compared with those reported in the original utility studies. Logistic regression was used to identify the factors that can predict the discrepancy. RESULTS: A total of 585 eligible CUAs published between 1977 and 2016 were identified and reviewed. Of these studies, 74.5% were published between 2007 and 2016. 442 CUAs that used a total of 2235 health utilities published in 203 original utility studies were included for the comparison. As compared with those utilities originally reported, only 596 (26.7%) health utilities had the same description and value, whereas 991 health utilities (44.3%) differed in both description and value. Of 1290 health utilities with a different description, 69.1% were due to different severity or disease. No explanation or justification was provided for 1171 (87.4%) of 1340 health utilities with different value. CONCLUSIONS: There are concerning discrepancies in using published health utilities for CVD CUAs. Given the important role health utilities play in CUAs, authors of CUAs should always refer to the original studies for health utilities and be transparent about how published health utilities are selected and incorporated into CUAs.
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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 | 0.424 | 0.712 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.022 | 0.033 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".