Serial Vaccination and the Antigenic Distance Hypothesis: Effects on Influenza Vaccine Effectiveness During A(H3N2) Epidemics in Canada, 2010–2011 to 2014–2015
Bibliographic record
Abstract
Background: The antigenic distance hypothesis (ADH) predicts that negative interference from prior season's influenza vaccine (v1) on the current season's vaccine (v2) protection may occur when the antigenic distance is small between v1 and v2 (v1 ≈ v2) but large between v1 and the current epidemic (e) strain (v1 ≠ e). Methods: Vaccine effectiveness (VE) against medically attended, laboratory-confirmed influenza A(H3N2) illness was estimated by test-negative design during 3 A(H3N2) epidemics (2010-2011, 2012-2013, 2014-2015) in Canada. Vaccine effectiveness was derived with covariate adjustment across v2 and/or v1 categories relative to no vaccine receipt among outpatients aged ≥9 years. Prior vaccination effects were interpreted within the ADH framework. Results: Prior vaccination effects varied significantly by season, consistent with the ADH. There was no interference by v1 in 2010-2011 when v1 ≠ v2 and v1 ≠ e, with comparable VE for v2 alone or v2 + v1: 34% (95% confidence interval [CI] = -51% to 71%) versus 34% (95% CI = -5% to 58%). Negative interference by v1 was suggested in 2012-2013 with nonsignificant reduction in VE when v1 ≈ v2 and v1 ≠ e: 49% (95% CI = -47% to 83%) versus 28% (95% CI = -12% to 54%). Negative effects of prior vaccination were pronounced and statistically significant in 2014-2015 when v1 ≡ v2 and v1 ≠ e: 65% (95% CI = 25% to 83%) versus -33% (95% CI = -78% to 1%). Conclusions: Effects of repeat influenza vaccination were consistent with the ADH and may have contributed to findings of low VE across recent A(H3N2) epidemics since 2010 in Canada.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".