Influenza Vaccine Effectiveness in Preventing Hospitalizations in Older Patients With Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
BACKGROUND: Annual influenza immunization is recommended for people with chronic obstructive pulmonary disease (COPD) by all major COPD clinical practice guidelines. We sought to determine the seasonal influenza vaccine effectiveness (VE) against laboratory-confirmed influenza-associated hospitalizations among older adults with COPD. METHODS: We conducted a test-negative study of influenza VE in community-dwelling older adults with COPD in Ontario, Canada using health administrative data and respiratory specimens collected from patients tested for influenza during the 2010-11 to 2015-16 influenza seasons. Influenza vaccination was ascertained from physician and pharmacist billing claims. Multivariable logistic regression was used to estimate the adjusted odds ratio of influenza vaccination in people with, compared to those without, laboratory-confirmed influenza. RESULTS: Receipt of seasonal influenza vaccine was associated with an adjusted 22% (95% confidence interval [CI], 15%-27%) reduction in laboratory-confirmed influenza-associated hospitalization. Adjustment for potential misclassification of vaccination status increased this to 43% (95% CI, 35%-52%). Vaccine effectiveness was not found to vary by patient- or influenza-related variables. CONCLUSIONS: During the studied influenza seasons, influenza vaccination was at least modestly effective in reducing laboratory-confirmed influenza-associated hospitalizations in people with COPD. The imperfect effectiveness emphasizes the need for better influenza vaccines and other preventive strategies.
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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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".