Influenza Vaccine Effectiveness by A(H3N2) Phylogenetic Subcluster and Prior Vaccination History: 2016–2017 and 2017–2018 Epidemics in Canada
Bibliographic record
Abstract
BACKGROUND: The influenza A(H3N2) vaccine was updated from clade 3C.3a in 2015-2016 to 3C.2a for 2016-2017 and 2017-2018. Circulating 3C.2a viruses showed considerable hemagglutinin glycoprotein diversification and the egg-adapted vaccine also bore mutations. METHODS: Vaccine effectiveness (VE) in 2016-2017 and 2017-2018 was assessed by test-negative design, explored by A(H3N2) phylogenetic subcluster and prior season's vaccination history. RESULTS: In 2016-2017, A(H3N2) VE was 36% (95% confidence interval [CI], 18%-50%), comparable with (43%; 95% CI, 24%-58%) or without (33%; 95% CI, -21% to 62%) prior season's vaccination. In 2017-2018, VE was 14% (95% CI, -8% to 31%), lower with (9%; 95% CI, -18% to 30%) versus without (45%; 95% CI, -7% to 71%) prior season's vaccination. In 2016-2017, VE against predominant clade 3C.2a1 viruses was 33% (95% CI, 11%-50%): 18% (95% CI, -40% to 52%) for 3C.2a1a defined by a pivotal T135K loss of glycosylation; 60% (95% CI, 19%-81%) for 3C.2a1b (without T135K); and 31% (95% CI, 2%-51%) for other 3C.2a1 variants (with/without T135K). VE against 3C.2a2 viruses was 45% (95% CI, 2%-70%) in 2016-2017 but 15% (95% CI, -7% to 33%) in 2017-2018 when 3C.2a2 predominated. VE against 3C.2a1b in 2017-2018 was 37% (95% CI, -57% to 75%), lower at 12% (95% CI, -129% to 67%) for a new 3C.2a1b subcluster (n = 28) also bearing T135K. CONCLUSIONS: Exploring VE by phylogenetic subcluster and prior vaccination history reveals informative heterogeneity. Pivotal mutations affecting glycosylation sites, and repeat vaccination using unchanged antigen, may reduce VE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".