992. 2016–2017 Influenza Burden of Disease and End-of-Season Influenza Vaccine Effectiveness (VE) Estimates for Preventing Influenza-Related Hospitalization Among Canadian Adults: An Analysis From the Canadian Immunization Research Network (CIRN) Serious Outcomes Surveillance (SOS) Network
Bibliographic record
Abstract
Abstract Background To inform public health decision making around influenza prevention and treatment, ongoing surveillance of the influenza burden of disease and assessment of influenza vaccine effectiveness (VE) is critical. The Canadian Immunization Research Network (CIRN) Serious Outcomes Surveillance (SOS) Network conducts active surveillance each influenza season to characterize the burden of influenza disease and to provide estimates of influenza VE to prevent influenza-related hospitalization in Canadian adults (≥16 years of age). Methods Active surveillance for influenza was conducted at 13 hospitals in four provinces beginning on November 15, 2016 and ending April 30, 2017. Patients admitted with any respiratory diagnosis or symptom were eligible for enrolment. Eligible patients had a nasopharyngeal swab collected and tested for influenza using polymerase chain reaction (PCR). Patients who tested positive for influenza were considered cases; patients who tested negative for influenza were eligible to become matched controls. Detailed demographic and medical information were obtained from the medical record. Influenza VE was estimated as 1 − odds ratio (OR) of influenza in vaccinated vs. unvaccinated patients × 100% using conditional logistic regression, with corresponding 95% confidence intervals (CIs). Results A total of 1,431 influenza cases were enrolled; the majority were influenza A (n = 1,299) and 100% of patients with known influenza A subtype were A/H3N2. Among all influenza cases, 144 (10.1%) patients were admitted to the intensive care unit (ICU) and 91 (6.4%) patients died within 30 days of discharge. Overall adjusted influenza VE for prevention of influenza-related hospitalization in all ages was 23.3% (95% CI: 2.9–39.4%), with slightly lower VE observed in patients ≥65 years (VE: 19.4%; 95% CI: −7.8–39.8%) and higher VE observed in patients <65 years (VE: 47.9%; 95% CI: 9.9–69.9%). Conclusion Overall, influenza VE was low but effective (VE: 23%) for preventing influenza-related hospitalization during the 2016–2017 season in Canada. Given the low influenza VE observed, continued assessment of influenza VE is crucial to inform immunization policy in Canada and to emphasize the importance of the development and utilization of improved influenza vaccines. Disclosures M. K. Andrew, GSK: Grant Investigator, Research grant. Pfizer: Grant Investigator, Research grant. sanofi pasteur: Grant Investigator, Research grant. T. F. Hatchette, GSK: Grant Investigator, Research grant. Pfizer: Grant Investigator, Research grant. Abbvie: Consultant, Speaker honorarium. J. McElhaney, GSK: Scientific Advisor, Speaker honorarium. sanofi pasteur: Scientific Advisor, Speaker honorarium. A. McGeer, GSK: Grant Investigator, Research grant. Hoffman La Roche: Grant Investigator, Research grant. sanofi pasteur: Grant Investigator, Research grant. A. Poirier, sanofi pasteur: Investigator, Research grant. Actelion: Grant Investigator, Research grant. J. Powis, GSK: Grant Investigator, Research grant. Merck: Grant Investigator, Research grant. Roche: Grant Investigator, Research grant. Synthetic Biologics: Investigator, Grant recipient. M. Semret, GSK: Grant Investigator, Research grant. Pfizer: Grant Investigator, Research grant. S. Trottier, CIHR: Grant Investigator, Research grant. S. A. McNeil, GSK: Grant Investigator, Research grant. Pfizer: Grant Investigator, Research grant. Merck: Collaborator and Consultant, Contract clinical trials and Speaker honorarium. Novartis: Collaborator, Contract clinical trials. sanofi pasteur: Collaborator, Contract clinical trials.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.003 | 0.001 |
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".