Did the H1N1 Vaccine Reduce the Risk of Admission with Influenza and Pneumonia during the Pandemic?
Bibliographic record
Abstract
BACKGROUND: The extent to which A(H1N1)pdm09 influenza vaccines prevented hospital admissions with pneumonia and influenza (P&I) during the 2009 pandemic remains poorly understood. We evaluated the effectiveness of the A(H1N1)pdm09 and seasonal influenza vaccines (TIV) used during the 2009 mass vaccination campaign in Manitoba (Canada) in preventing P&I hospitalization. METHODS: A population-based record-linkage nested case-control study. Cases (N = 1,812) were persons hospitalized with influenza (ICD-10:J09-J11) or pneumonia (ICD-10:J12-J18) during the study period. Age-, gender- and area of residence-matched controls (N = 7,915) were randomly sampled from Manitoba's Population Registry. Information on receipt of A(H1N1)pdm09 vaccine and TIV was obtained from the Manitoba Immunization Monitoring System, a province-wide vaccine registry. RESULTS: Overall, the adjuvanted A(H1N1)pdm09 vaccine was 27% (95%CI 13-39%) effective against P&I hospitalization ≥ 14 days following administration. Effectiveness seemed lower among older (≥ 65 years) adults (10%; -16-30%), particularly when compared to under-5 children (58%; 30-75%). The number-needed-to-vaccinate to prevent 1 P&I admission was lowest among <4 year-olds (928) and ≥65 years (1,721). VE against hospitalization with laboratory-confirmed A(H1N1)pdm09 was 70% (39-85%) overall and (91%; 62-98%) ≥ 14 days following vaccination. DISCUSSION: Our data suggest that the adjuvanted A(H1N1)pdm09 vaccine was effective in preventing about 55-60% of P&I hospitalizations among children and younger adults who were at much higher risk of infection. Unfortunately, the vaccine was less effective among 65 or older adults. Despite that the vaccine still had a significant population-based impact especially among the very young (<5) and the older (≥ 65 years).
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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 | 0.003 | 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 | 0.001 | 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".