Using Health Economics to Inform Immunization Policy Across All Levels of Government
Bibliographic record
Abstract
Publicly funded immunization programs have grown in both complexity and scope, resulting in increased costs and more complex programmatic decision making. Economic evaluations can provide crucial information to support informed decision making. While very few countries have National Immunization Technical Advisory Groups that analyze economic information, many have started to develop processes for this purpose. Since these guidelines are being developed at the national level, we propose that regional jurisdictions, especially those responsible for healthcare (e.g., provinces, territories, states), need clear processes for incorporating this information into their immunization decision making and program implementation. We interviewed Canadian vaccine experts involved in provincial vaccine policy decision making to identify current practices, perceptions, and recommendations around incorporating economic analysis into that process. Based on these interviews, we make five recommendations: (1) economic evidence should be routinely incorporated into the decision making process; (2) economic experts should sit on, or be available to, regional advisory committees; (3) efforts should be made to build on regional expertise by increasing educational opportunities on economic evaluation; (4) processes should include guidelines for when economic analysis is not required; and (5) clarification on the role of regional advisory groups in economic analysis is needed in relation to national expertise. The information presented here provides a starting point for regional health policy experts and decision makers to work collaboratively with national partners to create transparent and effective approaches to incorporating economic analysis into vaccine decision making.
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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 | 0.085 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".