Influenza Vaccination in Patients With Congenital Heart Disease in the Pre-COVID-19 Era: Coverage Rate, Patient Characteristics, and Outcomes
Bibliographic record
Abstract
BACKGROUND: Influenza vaccination is the most commonly recommended immune prevention strategy. However, data on influenza vaccination in patients with congenital heart disease (CHD) are scarce. In this study, our goals were to: (1) measure vaccination coverage rates (VCRs) for influenza in a large cohort of children, adolescents, and adults with CHD; (2) identify patient characteristics as predictors for vaccination; and (3) investigate the effect of influenza vaccination on hospitalization. METHODS: A nationwide cohort study in Belgium included 16,778 patients, representing 134,782 vaccination years, from the Belgian Congenital Heart Disease Database Combining Administrative and Clinical Data (BELCODAC). Data over 9 vaccination years (2006-2015) were used, and patients were stratified into 5 age cohorts: 6 months to 4 years; 5-17 years; 18-49 years; 50-64 years; and 65 years and older. RESULTS: In the respective age cohorts, the VCR was estimated to be 6.6%, 8.0%, 23.9%, 46.6%, and 72.8%. There was a steep increase in VCRs as of the age of 40 years. Multivariable logistic regression showed that higher anatomical complexity of CHD, older age, presence of genetic syndromes, and previous cardiac interventions were associated with significantly higher VCRs. Among adults, men had lower and pregnant women had higher VCRs. The association between influenza vaccination and all-cause hospitalization was not significant in this study. CONCLUSIONS: The influenza VCR in people with CHD is low, especially in children and adolescents. Older patients, particularly those with complex CHD, are well covered. Our findings should inform vaccination promotion strategies in populations with CHD.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".