Effect of influenza vaccination in patients with asthma
Bibliographic record
Abstract
BACKGROUND: Although annual influenza vaccination is recommended for persons with asthma, its effectiveness in this patient population is not well described. We evaluated the effect of influenza vaccination in the current and previous seasons in preventing influenza among people with asthma. METHODS: Using population health data from the Navarre region of Spain for the 2015/16 to 2019/20 influenza seasons, we conducted a test-negative case-control study to assess the effect of influenza vaccination in the current and 5 previous seasons. From patients presenting to hospitals and primary health care centres with influenza-like illness who underwent testing for influenza, we estimated the effects of influenza vaccination among patients with asthma overall and between those presenting as inpatients or outpatients, as well as between patients with and without asthma. RESULTS: Of 1032 patients who had asthma and were tested, we confirmed that 421 had influenza and the remaining 611 were test-negative controls. We found that the average effect of influenza vaccination was 43% (adjusted odds ratio [OR] 0.57, 95% confidence interval [CI] 0.40 to 0.80) for current-season vaccination regardless of previous doses, and 38% (adjusted OR 0.62, 95% CI 0.39 to 0.96) for vaccination in previous seasons only. Effects were similar for outpatients and inpatients. Among patients with asthma and confirmed influenza, current-season vaccination did not reduce the odds of hospital admission (adjusted OR 1.05, 95% CI 0.51 to 2.18). Influenza vaccination effects were similar for patients with and without asthma. INTERPRETATION: We estimated that, on average, current or previous influenza vaccination of people with asthma prevented almost half of influenza cases. These results support recommendations that people with asthma receive influenza vaccination.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".