Impact of Frailty on Influenza Vaccine Effectiveness and Clinical Outcomes: Experience From the Canadian Immunization Research Network (CIRN) Serious Outcomes Surveillance (SOS) Network 2011/12 Season
Bibliographic record
Abstract
Background. Health impact of influenza is traditionally considered only in acute terms. There is increasing evidence that influenza may have lasting health implications, particularly for frail older adults. We studied vaccine effectiveness (VE) and outcomes of influenza-related hospitalization in relation to frailty & functional status. Methods. The SOS Network conducted active surveillance for influenza in Canadian hospitals for the 2011/12 influenza season. VE for prevention of influenza-hospitalization was assessed using a matched test-negative case-control analysis. Special attention was paid to frailty and functional status of patients ≥65 years at baseline (2 weeks prior to onset of symptoms) and follow up (30 days post-discharge). Admission swabs were tested by PCR to identify influenza cases (positive) and controls (negative). VE was calculated as 1 minus the odds ratio of vaccination in cases versus controls × 100. VE estimates were adjusted using conditional multivariate logistic regression with age, antiviral use, frailty and a stepwise backward selection of covariates with p < 0.1 by univariate analysis. Frailty was assessed using a validated 39-item frailty index (FI) and function was assessed using the Barthel Index (BI). Results. SOS enrolled 320 cases and 564 controls. Unadjusted VE for patients ≥65 years against influenza-hospitalization due to any strain was 45.0% (95% CI: 25.7–59.3); adjusted VE was 58.0% (95% CI: 34.2–73.2). Adjusting for frailty on top of fixed covariates alone very closely approximated the final fully adjusted model. On average, all older adults experienced functional loss during hospitalization. A total of 15.1% experienced persistent catastrophic disability (≥20 point decline on the BI between baseline and follow up); older patients with influenza were more likely to experience this decline than controls in the same age segment (p = 0.047). Conclusion. VE was moderate for prevention of influenza-related hospitalization in elderly people. Not accounting for frailty may underestimate VE due to a frailty bias; frailty is the most important confounder to take into account in adults 65+. Persistent functional decline is an important adverse outcome of influenza-related hospitalization and reducing this burden represents an important public health goal. Disclosures. M. K. Andrew, GSK: Investigator, Research support; G. Boivin, Biocryst: Investigator, Research grant. Merck: Investigator, Research grant; W. Bowie, GSK: Investigator, Research grant; A. Chit, Sanofi Pasteur: Employee, Salary; G. Dos Santos, Business and Decision Life Sciences: Consultant, Salary; T. Hatchette, GSK: Investigator, Research grant; F. Haguinet, GSK Vaccines: Employee, Salary; S. A. Halperin, GSK: Consultant, Grant Investigator and Research Contractor, Consulting fee and Grant recipient; B. Ibarguchi, GSK: Employee, Salary; P. Lagace-Wiens, Merck: Scientific Advisor, Consulting fee and Speaker honorarium; J. M. Langley, Sanofi Pasteur: Investigator, Research grant. GSK: Investigator, Research grant. PREVENT: Investigator, Research grant; A. E. Mccarthy, GSK: Investigator, Research support; J. E. Mcelhaney, GSK: Scientific Advisor, Research support and Speaker honorarium. Sanofi Pasteur: Scientific Advisor, Speaker honorarium; A. Poirier, Actelion: Investigator, Research grant. Genetech: Investigator, Research grant. Sanofi Pasteur: Investigator, Research grant. Vertex: Investigator, Research grant; J. Powis, GSK: Investigator, Research support; V. Shinde, GSK: Employee, Salary; S. A. Mcneil, GSK: Grant Investigator, Research grant and Research support. Pfizer: Grant Investigator, Consulting fee, Research grant, Research support and Speaker honorarium. Merck: Consultant and Investigator, Consulting fee, Research support and Speaker honorarium
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".