Expected cost effectiveness of high-dose trivalent influenza vaccine in US seniors
Bibliographic record
Abstract
OBJECTIVES: Seniors are particularly vulnerable to complications resulting from influenza infection. Numerous influenza vaccines are available to immunize US seniors, and practitioners must decide which product to use. Options include trivalent and quadrivalent standard-dose inactivated influenza vaccines (IIV3 and IIV4 respectively), as well as a high-dose IIV3 (HD). Our research examines the public health impact, budget impact, and cost-utility of HD versus IIV3 and IIV4 for immunization of US seniors 65 years of age and older. METHODS: Our model was based on US influenza-related health outcome data. Health care costs and vaccine prices were obtained from the Centers for Medicare and Medicaid Services. Efficacies of IIV3 and IIV4 were estimated from various meta-analyses of IIV3 efficacy. The results of a head-to-head randomized controlled trial of HD vs. IIV3 were used to estimate relative efficacy of HD. Conservatively, herd protection was not considered. RESULTS: Compared to IIV3, HD would avert 195,958 cases of influenza, 22,567 influenza-related hospitalizations, and 5423 influenza-related deaths among US seniors. HD generates 29,023 more Quality Adjusted Life Years (QALYs) and a net societal budget impact of $154 million. The Incremental Cost Effectiveness Ratio (ICER) for this comparison is $5299/QALY. 71% of the probabilistic sensitivity analysis (PSA) simulations were <$100,000/QALY. Compared to IIV4, HD would avert 169,257 cases of influenza, 21,222 hospitalizations and 5212 deaths. HD generates 27,718 more QALYs and a net societal budget impact of -$17 million and as such dominates IIV4. For this comparison, 81% of PSA simulations were <$100,000/QALY. CONCLUSIONS: HD is expected to achieve significant reductions in influenza-related morbidity and mortality. Further, HD is a cost effective alternative to both IIV3 and IIV4 in seniors. Our conclusions were robust in the face of sensitivity analyses.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".