Cost-Utility Analysis of COVID-19 Vaccination Strategies for Endemic SARS-CoV-2
Bibliographic record
Abstract
Importance: With shifting epidemiology and changes in the vaccine funding landscape, resource use considerations for COVID-19 vaccination programs are increasingly important. Objective: To assess 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. Design, Setting, and Participants: Static, individual-based, probabilistic cost-utility model economic evaluation parameterized with recent data describing COVID-19 epidemiology, vaccine characteristics, and costs. The analysis used a 15-month time horizon from July 2024 to September 2025 and a modeled cohort of 1 million people with characteristics based on the Canadian population, stratified by age group and presence or absence of at least 1 chronic medical condition. Exposure: Annual or biannual COVID-19 vaccination strategies offered to different age and medical risk groups, with annual vaccination occurring in October and November in the primary analysis. Main Outcomes and Measures: Medically attended SARS-CoV-2 infections treated in outpatient and inpatient settings, including post-COVID condition cases and deaths. Costs in 2023 Canadian dollars, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs), discounted at 1.5% for the health system and societal perspectives. Results: Among 1 million simulated individuals, annual vaccination for adults aged 65 years and older consistently emerged as a cost-effective intervention, with ICERs less than CAD $50 000 per QALY compared with 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 greater than $50 000 per QALY. Shifting timing of vaccination programs to better align with periods of high COVID-19 case occurrence resulted in biannual vaccination for those aged 65 years and older being cost effective. Conclusions and Relevance: In this economic evaluation of COVID-19 vaccination strategies, programs were observed to be cost effective when focused on groups at higher risk of disease. Optimal timing of programs improved the cost effectiveness of vaccination strategies. As COVID-19 transitioned to an endemic disease with high levels of population immunity, many jurisdictions revisited COVID-19 vaccination recommendations; these results identified COVID-19 vaccination programs that may provide good value for money.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".