Impact of influenza vaccination on respiratory illness rates in children attending private boarding schools in England, 2013–2014: a cohort study
Bibliographic record
Abstract
Several private boarding schools in England have established universal influenza vaccination programmes for their pupils. We evaluated the impact of these programmes on the burden of respiratory illnesses in boarders. Between November 2013 and May 2014, age-specific respiratory disease incidence rates in boarders were compared between schools offering and not offering influenza vaccine to healthy boarders. We adjusted for age, sex, school size and week using negative binomial regression. Forty-three schools comprising 14 776 boarders participated. Almost all boarders (99%) were aged 11-17 years. Nineteen (44%) schools vaccinated healthy boarders against influenza, with a mean uptake of 48·5% (range 14·2-88·5%). Over the study period, 1468 respiratory illnesses were reported in boarders (5·66/1000 boarder-weeks); of these, 33 were influenza-like illnesses (ILIs, 0·26/1000 boarder-weeks) in vaccinating schools and 95 were ILIs (0·74/1000 boarder-weeks) in non-vaccinating schools. The impact of vaccinating healthy boarders was a 54% reduction in ILI in all boarders [rate ratio (RR) 0·46, 95% confidence interval (CI) 0·28-0·76]. Disease rates were also reduced for upper respiratory tract infections (RR 0·72, 95% CI 0·61-0·85) and chest infections (RR 0·18, 95% CI 0·09-0·36). These findings demonstrate a significant impact of influenza vaccination on ILI and other clinical endpoints in secondary-school boarders. Additional research is needed to investigate the impact of influenza vaccination in non-boarding secondary-school settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".