Cost-utility analysis of COVID-19 vaccination strategies for endemic SARS-CoV-2 circulation in Canada
Bibliographic record
Abstract
ABSTRACT Background With shifting epidemiology and changes in the vaccine funding landscape, resource use considerations for ongoing COVID-19 vaccination programs are increasingly important. We assessed the cost-effectiveness of COVID-19 vaccination programs, where eligibility is defined by combinations of age and chronic medical conditions, including a strategy similar to current Canadian recommendations, from the health system and societal perspectives. Methods We used a static, individual-based probabilistic model simulating medically attended COVID-19 in a population of 1 million people followed over a 15-month time period to estimate costs in 2023 Canadian dollars, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs), discounted at 1.5%. COVID-19 epidemiology, vaccine characteristics, and costs were based on the most recently available data. Results Annual vaccination for adults aged 65 years and older consistently emerged as a cost-effective intervention, with ICERs less than $50,000 per QALY compared to no vaccination for a range of model assumptions. Adding a second dose for adults aged 65 years and older or expanding programs to include vaccination for younger age groups, including those at higher risk of COVID-19 due to chronic medical conditions, generally resulted in ICERs of greater than $50,000 per QALY. Shifting timing of vaccination programs to better align with periods of high COVID-19 case occurrence could result in biannual vaccination for those aged 65 and older being a cost-effective strategy. Conclusions COVID-19 vaccination programs may be cost-effective when focused on groups at higher risk of disease. Optimal timing of vaccination could improve the cost-effectiveness of various strategies.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".