Cost-Effectiveness of Antiviral Drugs to Prevent or Treat Influenza A, Influenza B, or Zoonotic Influenza
Bibliographic record
Abstract
What Is the Issue? Influenza is a major public health concern, causing significant illness, death, economic challenges, and pandemic potential. This underscores the need for effective prevention (also known as prophylaxis), treatment, and pandemic preparedness strategies. Antiviral medications such as baloxavir marboxil, oseltamivir, peramivir, zanamivir are recommended for the treatment of influenza; however, their economic value remains unclear. What Did We Do? We conducted a rapid review to identify and summarize evidence on the cost-effectiveness of antivirals for preventing and treating influenza A, influenza B, and zoonotic influenza, as well as the cost-effectiveness of antiviral stockpiling. We searched electronic databases and key online sources for economic evaluation studies published in English from January 1, 2019, to December 13, 2024. Additionally, we examined the cost-effectiveness of stockpiling from studies published from January 1, 2020, to April 4, 2025. One researcher screened citations, selected studies, and narratively summarized the study findings. What Did We Find? We identified 9 economic evaluations: 8 examining the treatment of influenza and 1 examining both post-exposure prophylaxis and treatment. We did not identify any studies on the cost-effectiveness of antivirals for the prophylaxis or treatment of zoonotic influenza or any studies that evaluated the cost-effectiveness of stockpiling antiviral drugs. Economic studies suggest that certain antivirals may be cost-effective for treating influenza compared to standard of care, particularly in high-risk populations. However, it is unclear how cost-effective antivirals are for post-exposure prophylaxis, as there is limited evidence from just 1 study. What Does It Mean? Economic evaluations suggest that oseltamivir or baloxavir marboxil are cost-effective treatments for influenza, particularly in high-risk populations. Baloxavir marboxil may be a valuable alternative in cases of oseltamivir resistance to ensure optimal resource allocation and long-term sustainability of antiviral treatments. However, its higher cost requires careful consideration. The generalizability of existing economic evaluations may be limited due to variability in influenza strains, health care systems, and cost structures that differ from Canada.
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".