Flu Vaccine and Mortality in Hypertension: A Nationwide Cohort Study
Bibliographic record
Abstract
Background Influenza infection may increase the risk of stroke and acute myocardial infarction (AMI). Whether influenza vaccination may reduce mortality in patients with hypertension is currently unknown. Methods and Results We performed a nationwide cohort study including all patients with hypertension in Denmark during 9 consecutive influenza seasons in the period 2007 to 2016 who were prescribed at least 2 different classes of antihypertensive medication (renin‐angiotensin system inhibitors, diuretics, calcium antagonists, or beta‐blockers). We excluded patients who were aged <18 years, >100 years, had ischemic heart disease, heart failure, chronic obstructive lung disease, cancer, or cerebrovascular disease. The exposure to influenza vaccination was assessed before each influenza season. The end points were defined as death from all‐causes, from cardiovascular causes, or from stroke or AMI. For each influenza season, patients were followed from December 1 until April 1 the next year. We included a total of 608 452 patients. The median follow‐up was 5 seasons (interquartile range, 2–8 seasons) resulting in a total follow‐up time of 975 902 person‐years. Vaccine coverage ranged from 26% to 36% during the study seasons. During follow‐up 21 571 patients died of all‐causes (3.5%), 12 270 patients died of cardiovascular causes (2.0%), and 3846 patients died of AMI/stroke (0.6%). After adjusting for confounders, vaccination was significantly associated with reduced risks of all‐cause death (HR, 0.82; P <0.001), cardiovascular death (HR, 0.84; P <0.001), and death from AMI/stroke (HR, 0.90; P =0.017). Conclusions Influenza vaccination was significantly associated with reduced risks of death from all‐causes, cardiovascular causes, and AMI/stroke in patients with hypertension. Influenza vaccination might improve outcome in hypertension.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".