Severity Weighting of Quality-Adjusted Life-Years for Economic Evaluation: What, How, and Where Next?
Bibliographic record
Abstract
Some health technology assessment (HTA) agencies incorporate severity weighting in health economic evaluations to prioritize interventions for severe diseases. Measures like absolute and proportional shortfall are used to quantify disease severity; decision modifiers are then applied to increase the value of quality-adjusted life-year gains for severe conditions. This paper reviews studies that measure societal preferences for prioritizing severity, as well as current methods used by HTA agencies to incorporate severity weighting into economic evaluations. The review of the literature highlights preferences to reduce unfair health variations, with most studies indicating a willingness to prioritize the severely ill. A review of approaches conducted by HTA agencies found variation in how severity weighting was incorporated and valued. Further research is needed to better understand societal values regarding severity and the appropriate weighting of disease severity for economic evaluations within Canada.
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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 | 0.173 | 0.378 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| 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".