Analysis of relative effectiveness of high-dose versus standard-dose influenza vaccines using an instrumental variable method
Bibliographic record
Abstract
BACKGROUND: Observational studies of the relative effectiveness of influenza vaccines are essential for public health decision making. Their estimates, however, are subject to bias due to unmeasured confounders. Instrumental variable (IV) methods can control for observed and unobserved confounders. METHODS: We used linked electronic medical record databases in the Veterans Health Administration (VHA) as well as Medicare administrative files to examine the relative vaccine effectiveness (rVE) of high-dose influenza vaccine (HD) versus standard-dose influenza vaccines (SD) in preventing hospitalizations among VHA-enrolled Veterans ≥65 years of age during 5 influenza seasons (2010-2011 through 2014-2015). Using multivariable IV Poisson regression modeling to address unmeasured confounding and bias, we analyzed the data by each season and through longitudinal analysis of all five seasons. FINDINGS: We included 3,638,924 person-influenza seasons of observation where 158,636 (4%) were among HD vaccine recipients and 3,480,288 (96%) were among SD vaccine recipients. Of the 1,728,562 Veterans, 1,702,824 (98.5%) were male and 1,299,412 (75%) were non-Hispanic white. Based on the longitudinal analysis of all five seasons, the IV-adjusted rVE estimate of HD vs. SD was 10% (95% CI, 8-12%) against all-cause hospitalization; 18% (95% CI, 15-21%) against cardiorespiratory-associated hospitalization; and 14% (95% CI, 6-22%) against influenza/pneumonia-associated hospitalization. The findings by season were similar. INTERPRETATION: Our analysis of VHA clinical data collected from approximately 1.7 million Veterans 65 years and older during five seasons demonstrates that high-dose influenza vaccine is more effective than standard-dose influenza vaccines in preventing influenza- or pneumonia-associated hospitalizations, cardiorespiratory hospitalizations, and all-cause hospitalizations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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 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".