The Impact of Prior Season Vaccination on Subsequent Influenza Vaccine Effectiveness to Prevent Influenza-related Hospitalizations Over 4 Influenza Seasons in Canada
Bibliographic record
Abstract
BACKGROUND: Recent studies have demonstrated the possibility of negative associations between prior influenza vaccines and subsequent influenza vaccine effectiveness (VE), depending on season and strain. We investigated this association over 4 consecutive influenza seasons (2011-2012 through 2014-2015) in Canada. METHODS: Using a matched test-negative design, laboratory-confirmed influenza cases and matched test-negative controls admitted to hospitals were enrolled. Patients were stratified into 4 groups according to influenza vaccine history (not vaccinated current and prior season [referent], vaccinated prior season only, vaccinated current season only, and vaccinated both current and prior season). Conditional logistic regression was used to estimate VE; prior vaccine impact was assessed each season for overall effect and effect stratified by age (<65 years, ≥65 years) and type/subtype (A/H1N1, A/H3N2, influenza B). RESULTS: Overall, mainly nonsignificant associations were observed. Trends of nonsignificant decreased VE among patients repeatedly vaccinated in both prior and current season relative to the current season only were observed in the A/H3N2-dominant seasons of 2012-2013 and 2014-2015. Conversely, in 2011-2012, during which B viruses circulated, and in 2013-2014, when A/H1N1 circulated, being vaccinated in both seasons tended to result in a high VE in the current season against the dominant circulating subtype. CONCLUSIONS: Prior vaccine impact on subsequent VE among Canadian inpatients was mainly nonsignificant. Even in circumstances where we observed a trend of negative impact, being repeatedly vaccinated was still more effective than not receiving the current season's vaccine. These findings favor continuation of annual influenza vaccination recommendations, particularly in older adults. CLINICAL TRIALS REGISTRATION: NCT01517191.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".