ECONOMIC EVALUATION OF AN INFLUENZA IMMUNIZATION STRATEGY OF HEALTHY CHILDREN
Bibliographic record
Abstract
OBJECTIVES: Vaccinating healthy children is proposed as a strategy to produce a herd effect and protect vulnerable groups. The Hutterite Influenza Prevention Study investigated this strategy, comparing communities with or without childhood influenza immunization programs. There are costs associated with vaccination therefore there may be a trade-off between these costs and the benefits of avoiding influenza cases. This evaluation estimates the cost-effectiveness of immunizing only healthy children in preventing cases of influenza within entire communities. METHODS: Effect data and resource utilization were collected during the trial. Cost data were collected from payer, literature and Internet sources. A two-stage bootstrap (TSB) with shrinkage correction was used to estimate average costs and effects. The incremental cost effectiveness ratio (ICER) and sample uncertainty around this estimate were calculated from the TSB results. RESULTS: Mean costs per patient for the treatment and control arms were $69.07 and $32.66 (difference $36.41). Mean number of influenza cases for the treatment and control arms were 0.04 and 0.27 (difference 0.23). ICER was $164.12 ($28.38, $2767.75) per case of influenza averted. CONCLUSIONS: Immunizing healthy children for influenza is more costly, yet more effective than no immunization in preventing cases in the sample. At a cost of $164.12 to prevent a case of influenza, immunizing healthy children to protect all community members may be considered costeffective. Estimated results are conservative as the influenza season was mild and the sample population was healthy. In a more severe season with a less healthy population the ICER is expected to decrease.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".