Priority setting in practice: what is the best way to compare costs and benefits?
Bibliographic record
Abstract
Prioritizing candidates for health-care expenditure using cost per Quality-Adjusted Life Year (QALY) is a helpful but insufficient means of ranking alternative uses for scarce health-care funds at the local level. This is because QALYs do not by themselves capture all criteria decision makers need to take into account. Other criteria such as reducing inequalities, meeting national and local priorities and public acceptability also feature in the decision maker's utility function. Programme budgeting and marginal analysis (PBMA) is an established framework for systematic priority setting in which a 'weighted benefit score' for each option is calculated based on all relevant decision-making criteria. Ranking options as a ratio of cost to benefit is desirable and necessary to ensure efficiency. In this paper we review a number of approaches to scoring costs and benefits of options in a PBMA context. Several approaches rank by benefit score alone, rather than efficiency (cost per unit of benefit). Of those that do rank by efficiency, we discuss the benefits and drawbacks. The optimal approach is far from clear, with each technique having its own strengths and weaknesses. A deliberative approach using summaries of costs and benefits of options as a basis for discussion may be preferable.
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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 | 0.265 | 0.556 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".