Evaluating the Impact of Statin Use on Influenza Vaccine Effectiveness and Influenza Infection in Older Adults
Bibliographic record
Abstract
BACKGROUND: Older adults are recommended to receive influenza vaccination annually, and many use statins. Statins have immunomodulatory properties that might modify influenza vaccine effectiveness (VE) and alter influenza infection risk. METHODS: Using the test-negative design and linked laboratory and health administrative databases in Ontario, Canada, we estimated VE against laboratory-confirmed influenza among community-dwelling statin users and nonusers aged ≥66 years during the 2010-2011 to 2018-2019 influenza seasons. We also estimated the odds ratio for influenza infection comparing statin users and nonusers by vaccination status. RESULTS: Among persons tested for influenza across the 9 seasons, 54 243 had continuous statin exposure before testing and 48 469 were deemed unexposed. The VE against laboratory-confirmed influenza was similar between statin users and nonusers (17% [95% confidence interval, 13%-20%] and 17% [13%-21%] respectively; test for interaction, P = .87). In both vaccinated and unvaccinated persons, statin users had higher odds of laboratory-confirmed influenza than nonusers (odds ratios for vaccinated and unvaccinated persons 1.15 [95% confidence interval, 1.10-1.21] and 1.15 [1.10-1.20], respectively). These findings were consistent by mean daily dose and statin type. VE did not differ between users and nonusers of other cardiovascular drugs, except for β-blockers. We did not observe that vaccinated and unvaccinated users of these drugs had increased odds of influenza, except for unvaccinated β-blocker users. CONCLUSIONS: Influenza VE did not differ between statin users and nonusers. Statin use was associated with increased odds of laboratory-confirmed influenza in vaccinated and unvaccinated persons, but these associations might be affected by residual confounding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".