Are economic evaluations an important tool in vaccine policy decisions?
Bibliographic record
Abstract
In the 1980s, drug prices began rising considerably worldwide, and in the 1990s, countries began incorporating health economics into the scientific review process. Rising prices in vaccines began around the year 2000 and national bodies began to use health economics to review vaccines in the next decade. Health economics is a discipline that evaluates alternative interventions, balancing costs and health outcomes. There are characteristics of infectious diseases that differ from other illnesses, most notably the herd effect. We reviewed the role of economics in conducting vaccine scientific reviews. We conclude that health economics can move some of the considerations in vaccine policy decision-making from the political to the scientific arena, but there are still many unresolved issues. Health economists will continue to address these issues in the coming years, but there will always be a need for a separate policy review.
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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 | 0.293 | 0.605 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".