Vaccine Effectiveness of non-adjuvanted and adjuvanted trivalent inactivated influenza vaccines in the prevention of influenza-related hospitalization in older adults: A pooled analysis from the Serious Outcomes Surveillance (SOS) Network of the Canadian Immunization Research Network (CIRN)
Bibliographic record
Abstract
BACKGROUND: Influenza vaccines prevent influenza-related morbidity and mortality; however, suboptimal vaccine effectiveness (VE) of non-adjuvanted trivalent inactivated influenza vaccine (naTIV) or quadrivalent formulations in older adults prompted the use of enhanced products such as adjuvanted TIV (aTIV). Here, the VE of aTIV is compared to naTIV for preventing influenza-associated hospitalization among older adults. METHODS: A test-negative design study was used with pooled data from the 2012 to 2015 influenza seasons. An inverse probability of treatment (IPT)-weighted logistic regression estimated the Odds Ratio (OR) for laboratory-confirmed influenza-associated hospitalization. VE was calculated as (1-OR)*100% with accompanying 95% confidence intervals (CI). RESULTS: Of 7,101 adults aged ≥ 65, 3,364 received naTIV and 526 received aTIV. The overall VE against influenza hospitalization was 45.9% (95% CI: 40.2%-51.1%) for naTIV and 53.5% (42.8%-62.3%) for aTIV. No statistically significant differences in VE were found between aTIV and naTIV by age group or influenza season, though a trend favoring aTIV over naTIV was noted. Frailty may have impacted VE in aTIV recipients compared to those receiving naTIV, according to an exploratory analysis; VE adjusted by frailty was 59.1% (49.6%-66.8%) for aTIV and 44.8% (39.1%-50.0%) for naTIV. The overall relative VE of aTIV to naTIV against laboratory-confirmed influenza hospital admission was 25% (OR 0.75; 0.61-0.92), demonstrating statistically significant benefit favoring aTIV. CONCLUSIONS: Adjusting for frailty, aTIV showed statistically significantly better protection than naTIV against influenza-associated hospitalizations in older adults. In future studies, it is important to consider frailty as a significant confounder of VE.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.031 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".