IMPACT ON CARDIORESPIRATORY OUTCOMES OF HIGH VS. STANDARD DOSE INFLUENZA VACCINE IN U.S. NURSING HOMES
Bibliographic record
Abstract
Age and multiple morbidities increase susceptibility to influenza whilst responsiveness to vaccination declines. However, Fluzone® High-Dose, Influenza Vaccine (HD) reduces clinical influenza more than Fluzone vaccine standard dose (SD) among outpatient elderly. In 2013–2014 we randomized 823 nursing homes (NHs) to either SD or HD as their care standard. HD significantly reduced hospitalizations. We now report on diagnoses for hospitalization, using Medicare Fee-For-Service (FFS) discharge data. We compared primary and secondary discharge diagnoses for index hospitalizations for cardiac and respiratory illnesses using ICD-9 codes (acute myocardial infarction: 410.xx, 411.xx; heart failure: 428.x, 429.0, 429.1, 419.7; atrial fibrillation: 427.x; stroke: 433.xx-436.xx; and respiratory illness: 460–466, 480–488, 490–496, 500–518). We used marginal Poisson regression, accounting for clustering of NH residents and for pre-specified resident and facility baseline covariates: age and average age of NH residents, ADL and average ADL of NH residents, cognitive function, hospitalizations in prior year, and patients’ chronic heart failure. On 11/1/2013, of 38,256 FFS NH residents living in their NH >3 months, and ≥ 65 years old, 19,126 were offered HD and 19,129 were offered SD. From November through April, 7,297 FFS residents were hospitalized (3509 HD, 3788 SD, p=0.0110). The difference in all-cause hospitalization was accounted for largely by hospitalization for the combination of cardio-respiratory outcomes (unadjusted RR=0.918, 0.861–0.980 95% CI, p=0.0098; adjusted RR=0.908, 0.858–0.961 95% CI, p=0.0009). In our prospective cluster-RCT, HD provided better protection than SD against both respiratory and certain cardiac conditions that lead to hospitalization. Funding: Sanofi Pasteur, Swiftwater, PA
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".