A Perfect Storm: Impact of Genomic Variation and Serial Vaccination on Low Influenza Vaccine Effectiveness During the 2014–2015 Season
Bibliographic record
Abstract
BACKGROUND: The 2014-2015 influenza season was distinguished by an epidemic of antigenically-drifted A(H3N2) viruses and vaccine components identical to 2013-2014. We report 2014-2015 vaccine effectiveness (VE) from Canada and explore contributing agent-host factors. METHODS: VE against laboratory-confirmed influenza was derived using a test-negative design among outpatients with influenza-like illness. Sequencing identified amino acid mutations at key antigenic sites of the viral hemagglutinin protein. RESULTS: Overall, 815/1930 (42%) patients tested influenza-positive: 590 (72%) influenza A and 226 (28%) influenza B. Most influenza A viruses with known subtype were A(H3N2) (570/577; 99%); 409/460 (89%) sequenced viruses belonged to genetic clade 3C.2a and 39/460 (8%) to clade 3C.3b. Dominant clade 3C.2a viruses bore the pivotal mutations F159Y (a cluster-transition position) and K160T (a predicted gain of glycosylation) compared to the mismatched clade 3C.1 vaccine. VE against A(H3N2) was -17% (95% confidence interval [CI], -50% to 9%) overall with clade-specific VE of -13% (95% CI, -51% to 15%) for clade 3C.2a but 52% (95% CI, -17% to 80%) for clade 3C.3b. VE against A(H3N2) was 53% (95% CI, 10% to 75%) for patients vaccinated in 2014-2015 only, significantly lower at -32% (95% CI, -75% to 0%) if also vaccinated in 2013-2014 and -54% (95% CI, -108% to -14%) if vaccinated each year since 2012-2013. VE against clade-mismatched B(Yamagata) viruses was 42% (95% CI, 10% to 62%) with less-pronounced reduction from prior vaccination compared to A(H3N2). CONCLUSIONS: Variation in the viral genome and negative effects of serial vaccination likely contributed to poor influenza vaccine performance in 2014-2015.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".