Estimating Influenza Vaccine Effectiveness in Community-Dwelling Elderly Patients Using the Instrumental Variable Analysis Method
Bibliographic record
Abstract
BACKGROUND: Estimates of influenza vaccine effectiveness in elderly individuals are largely from observational studies, which are susceptible to bias. Instrumental variable (IV) methods control for overt and hidden biases in observational studies. METHODS: We used linked health administrative databases in Ontario to examine the association between influenza vaccination and all-cause mortality among community-dwelling individuals older than 65 years for 9 influenza seasons (2000-2001 to 2008-2009). We examined the composite of hospitalization for pneumonia and influenza and all-cause mortality as a secondary outcome. We used logistic regression modeling and IV analysis to remove the effect of selection bias. RESULTS: We included 12 621 806 person-influenza seasons of observation. Logistic regression produced adjusted odds ratios of 0.67 (95% CI, 0.62-0.72) for all-cause mortality during influenza seasons and 0.85 (0.83-0.86) during post-influenza seasons when influenza is not circulating, suggesting the presence of bias. In contrast, IV analysis yielded adjusted odds ratios of 0.94 (95% CI, 0.84-1.03) during influenza seasons and 1.13 (1.07-1.19) during post-influenza seasons. For the composite of hospitalization for pneumonia and influenza and death, logistic regression produced adjusted odds ratios of 0.74 (95% CI, 0.70-0.78) during influenza seasons and 0.88 (0.87-0.90) during post-influenza seasons, whereas IV analysis produced adjusted odds ratios of 0.86 (95% CI, 0.79-0.92) and 1.02 (0.97-1.06), respectively. CONCLUSIONS: Influenza vaccination is associated with reductions in the composite of hospitalizations for pneumonia and influenza and all-cause mortality during the influenza season but not mortality alone. Compared with standard modeling, IV analysis appears to produce less-biased estimates of vaccine effectiveness.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".