The Importance of Frailty in the Assessment of Influenza Vaccine Effectiveness Against Influenza-Related Hospitalization in Elderly People
Bibliographic record
Abstract
Background: Influenza is an important cause of morbidity and mortality among older adults. Even so, effectiveness of influenza vaccine for older adults has been reported to be lower than for younger adults, and the impact of frailty on vaccine effectiveness (VE) and outcomes is uncertain. We aimed to study VE against influenza hospitalization in older adults, focusing on the impact of frailty. Methods: We report VE of trivalent influenza vaccine (TIV) in people ≥65 years of age hospitalized during the 2011-2012 influenza season using a multicenter, prospective, test-negative case-control design. A validated frailty index (FI) was used to measure frailty. Results: Three hundred twenty cases and 564 controls (mean age, 80.6 and 78.7 years, respectively) were enrolled. Cases had higher baseline frailty than controls (P = .006). In the fully adjusted model, VE against influenza hospitalization was 58.0% (95% confidence interval [CI], 34.2%-73.2%). The contribution of frailty was important; adjusting for frailty alone yielded a VE estimate of 58.7% (95% CI, 36.2%-73.2%). VE was 77.6% among nonfrail older adults and declined as frailty increased. Conclusions: Despite commonly held views that VE is poor in older adults, we found that TIV provided good protection against influenza hospitalization in older adults who were not frail, though VE diminished as frailty increased. Clinical Trials Registration: NCT01517191.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".