Economic Implications of Influenza and Influenza Vaccine
Bibliographic record
Abstract
The objective of this chapter is to review and summarize the current economic estimates of influenza and the cost-effectiveness of its vaccines. We reviewed the published assessments of the economic costs of human seasonal and pandemic influenza internationally. Seasonal influenza costs Germany, France, and the USA between $4 and $87 billion annually. Depending upon the intensity of transmission and severity of disease, pandemic influenza may cause as many as 350 million deaths and result in economic losses topping $1 trillion – an impact great enough to create a worldwide recession. We then reviewed 100 papers primarily from more than a dozen countries which studied the cost-effectiveness of influenza vaccine in children, adults, and the elderly. These studies demonstrate that influenza vaccination is quite cost-effective among children 6 months to 18 years old, in health care workers and pregnant women, and in high-risk individuals. Remarkably, compared with the other recently introduced vaccines for children, such as rotavirus and pneumococcal polysaccharide, vaccinating children and school attendees results in societal cost savings because it obviates lost productivity and wages among infected individuals and their caretakers. Vaccination for children is recommended in the USA and in Canada, but public health policy makers in Europe have undervalued this vaccine and not recommended it so widely. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".