Use of quality-adjusted life years for the estimation of effectiveness of health care: A systematic literature review
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to identify, in a systematic literature review, published studies having used quality-adjusted life years (QALYs) based on actual measurements of patients' health-related quality of life (HRQoL) and to determine which HRQoL instruments have been used to calculate QALYs. Furthermore, the aims were to characterize studies with regard to medical specialty, intervention studied, results obtained, quality, country of origin, QALY gain observed, and interpretation of results regarding cost-effectiveness. METHODS: Systematic searches of the literature were made using the MEDLINE, Embase, CINAHL, SCI, and Cochrane Library electronic databases. Initial screening of identified articles was based on abstracts read independently by two of the authors; full-text articles were again evaluated by two authors, who made the final decision on which articles should be included. RESULTS: The search identified 3,882 articles; 624 were obtained for closer review. Of the reviewed full-text articles, seventy reported QALYs based on actual before-after measurements using a valid HRQoL instrument. The most frequently used instrument was EuroQol HRQoL instrument (EQ-5D, 47.5 percent). Other instruments used were Health Utilities Index (HUI, 8.8 percent), the Rosser-Kind Index (6.3 percent), Quality of Well-Being (QWB, 6.3 percent), Short Form-6D (SF-6D, 5.0 percent), and 15D (2.5 percent). The rest (23.8 percent) used a direct valuation method: Time Trade-Off (10.0 percent), Standard Gamble (5.0 percent), visual analogue scale (5.0 percent), or rating scale (3.8 percent). The most frequently studied medical specialties were orthopedics (15.5 percent), pulmonary diseases (12.7 percent), and cardiology (9.9 percent). Ninety percent of the studies came from four countries: United Kingdom, United States, Canada, the Netherlands. Approximately half of the papers were methodologically high quality randomized trials. Forty-nine percent of the studied interventions were viewed by the authors of the original studies as being cost-effective; only 13 percent of interventions were deemed not to be cost-effective. CONCLUSIONS: Although QALYs gained are considered an important measure of effectiveness of health care, the number of studies in which QALYs are based on actual measurements of patients' HRQoL is still fairly limited.
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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 | 0.067 | 0.205 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.019 |
| Bibliometrics | 0.036 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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; 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".