Comparing the impact of high-dose versus standard dose influenza vaccines on hospitalization cost for cardiovascular and respiratory diseases: Economic assessment in the US Veteran population during 5 respiratory seasons using an instrumental variable method
Bibliographic record
Abstract
OBJECTIVES: Cost savings associated with high-dose (HD) as compared to standard-dose (SD) influenza vaccination in the United States (US) Veteran's Health Administration (VHA) population have been attributed to better protection against hospitalization for cardiac and respiratory diseases. The relative contribution of each of these disease categories to the reported savings remains to be explored. METHODS: During a recently completed study of HD versus SD vaccine effectiveness (conducted in the VHA over five respiratory seasons from 2010/11 through 2014/15), we collected cost data for all healthcare services provided at both VHA and Medicare-funded facilities. In that analysis, we compared the costs of vaccination and hospital care for patients admitted with either cardiovascular or respiratory disease. Treatment selection bias and other confounding factors were adjusted using an instrumental variable (IV) method. In this brief report we use the same study cohort and methods to stratify the results by patients admitted for cardiovascular disease (CVD) and those admitted for respiratory disease. RESULTS: We analyzed 3.5 million SD and 0.16 million HD person-seasons. The IV-adjusted rVEs were 14% (7-20%) against hospitalizations for CVD and 15% (5-25%) against respiratory hospitalizations. Net cost savings per HD recipient were $138 ($66-$200) for CVD related hospitalizations and $62 ($10-$107) for respiratory disease related hospitalizations. CONCLUSIONS: In the US VHA population, the reduction in hospitalizations for CVD over five respiratory seasons contributed twice the cost savings (per HD recipient) of the reduction in hospitalizations for respiratory disease.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".