PP07 Vaccine Decision-making In Canada: Processes And Guidelines For Using Economic Evidence
Bibliographic record
Abstract
Introduction Canada’s National Advisory Committee on Immunization (NACI) makes recommendations on the use of human vaccines. Provinces and territories subsequently use the advice to make decisions on public funding and program implementation. Traditionally, NACI reviewed vaccine characteristics and burden of illness. With its recent expanded mandate, NACI now considers cost-effectiveness via economic evaluations, among other decision determinants. As such, new processes and guidelines were needed to formalize the incorporation of economic evidence into federal vaccine decision-making. Methods Two task groups were convened respectively to develop NACI’s “Economic Process” and “Guidelines for the Economic Evaluation of Vaccination Programs in Canada”. The groups conducted environmental scans to inform their work, as well as engaged with government partners, decision-makers, academics, national immunization technical advisory groups from other countries, health technology assessment agencies, industry, patient groups, among others. Results The Economic Process outlines when and how NACI incorporates economic evidence for vaccine recommendation. For instance, it describes how policy questions are prioritized given institutional capacity constraints for generating economic evidence. It also describes how policy questions are assessed to determine the appropriate type of economic evidence required (i.e., systematic review, economic evaluation, multi-model comparison of external models). The Economic Guidelines provide recommendations in 15 chapters on how to conduct economic evaluations (i.e., from defining the decision problem to reporting). Unlike other health technologies, vaccines have the potential to affect both vaccinated and unvaccinated individuals. Hence, the Guidelines consider population-level impacts such as externalities (e.g., herd immunity, age-shifting of disease) and spillover effects. They also discuss equity considerations and non-health impacts of vaccines such as to productivity, consumption and education. Conclusions The Economic Process and Economic Guidelines promote the generation and use of credible and standardized economic evidence. They advocate for transparency, allowing evidence to be used across jurisdictions beyond Canada. Next steps include documentation of user feedback, incorporation of Indigenous considerations, and formal evaluations.
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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 | 0.221 | 0.416 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.025 | 0.034 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.015 | 0.010 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".