Impact of Prior Season Influenza Vaccination on Seasonal Influenza Vaccine Effectiveness: An Analysis over Four Consecutive Seasons from the Serious Outcomes Surveillance (SOS) Network of the Canadian Immunization Research Network (CIRN)
Bibliographic record
Abstract
Recent controversy has arisen from observational studies suggesting a potential negative association between prior influenza vaccination and subsequent influenza vaccine effectiveness (VE). As immunologic theories suggest this impact could vary by season/strain, we investigated this association over 4 influenza seasons in Canada. The CIRN SOS Network prospectively enrolled laboratory-confirmed influenza cases and influenza-negative controls admitted to participating hospitals. Using a test-negative control design, matched conditional logistic regression modeling stratifying participants into 4 groups (not vaccinated current or prior season [referent], vaccinated prior season only, vaccinated current season only, and vaccinated both current and prior season) was used to calculate odds ratios (OR) to estimate the effect of vaccination status on influenza-related hospitalization (VE= 1-OR x100). We assessed VE overall and stratified by strain (A/H3N2, A/H1N1, and influenza B) for 4 influenza seasons in Canada: 2011/2012–2014/2015. Although impact of prior vaccination varied, the largest strain-specific impacts were observed in the H3N2 dominant seasons 2012/2013 and 2014/2015, seasons where the H3N2 vaccine component was matched, and mismatched, respectively, to the circulating strain. In 12/13, adjusted VE against influenza H3N2 hospitalization was 58.6% (95% Confidence Interval [CI]: 32.5–74.7%) for patients vaccinated in current season only, relative to 30.9% (10.8–46.5%) among those vaccinated in both prior and current season; VE against influenza B hospitalization in 12/13 was 83.2% (18.9–96.5%) in current season only vaccinees and 54.1% (-6.0–80.2%) in both seasons vaccinees. In 14/15, H3N2 VE was 35.3% (-32.6–68.5%) in current season only vaccinees and -8.3% (-56.7–25.1%) in both seasons vaccinees. While our findings support a possible negative association between prior influenza vaccination and subsequent season VE against some strains in certain seasons, non-statistically significant reductions in VE were observed. Future prospective studies, using varying methodology to examine this association and to explore contributing biological/immunological mechanisms, are critical to inform immunization policy. M. K. Andrew, GSK: Grant Investigator, Research grant; Pfizer: Grant Investigator, Research grant; Sanofi-Pasteur: Grant Investigator, Research grant; T. Hatchette, GSK: Grant Investigator, Grant recipient; Pfizer: Grant Investigator, Grant recipient; Abbvie: Speaker for a talk on biologics and risk of TB reactivation, Speaker honorarium; G. Dos Santos, GSK: Employee, Salary; Business and Decision Life Sciences (Contractor for GSK Vaccines): Independent Contractor, Salary; M. Elsherif, Canadian Institutes of Health Research: Investigator, Research grant; Public Health Agency of Canada: Investigator, Research grant; GSK: Investigator, Research grant; F. Haguinet, GSK: Employee, Salary; J. Mcelhaney, GSK: Scientific Advisor, Honorarium to institution; sanofi pasteur: Scientific Advisor, Honorarium to institution; A. Mcgeer, Hoffman La Roche: Investigator, Research grant; GSK: Investigator, Research grant; sanofi pasteur: Investigator, Research grant; J. Powis, Merck: Grant Investigator, Research grant; GSK: Grant Investigator, Research grant; Roche: Grant Investigator, Research grant; Synthetic Biologicals: Investigator, Research grant; M. Semret, GSK: Investigator, Research grant; Pfizer: Investigator, Research grant; R. Sharma, GSK: Employee and Shareholder, Salary; V. Shinde, Novavax: Employee, Salary; GSK: Shareholder, Stocks; GSK: Employee, Salary; S. Trottier, Canadian Institutes of Health Research: Investigator, Research grant; S. McNeil, GSK: Contract Clinical Trials and Grant Investigator, Research grant; Merck: Contract Clinical Trials and Speaker’s Bureau, Speaker honorarium; Novartis: Contract Clinical Trials, No personal renumeration; sanofi pasteur: Contract Clinical Trials, No personal renumeration
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".