Immunization and Cardiovascular Disease in Latin America. The CorVacc Study: Results
Bibliographic record
Abstract
Introduction: Immunization rates against influenza and pneumococcus in Latin America remain lower than expected, particularly in Andean region, Central America, Mexico, and Caribbean region. An incremental correlation between economic strata and educational level and vaccines uptake has been observed. This highlights the need for more comprehensive data to accurately characterize the current health landscape and develop strategies for improvement. Methods and Design: The Inter-American Registry of Influenza and Pneumococcal Vaccination (CorVacc Study) is a cross-sectional survey of the general population conducted across 19 Latin American countries. Adults aged 18 years and older completed a 34-question online survey. The pool was validated within the first 1000 responses. Data were grouped into seven categories: demographics, socioeconomic and educational level, cardiometabolic profile, cardiovascular interventions, medical follow-up and treatments, and COVID-19 vaccination status. Results: A total of 21,389 responses were obtained, distributed as follows: 8915 from the North, Central, and Caribbean region; 7492 from the Andean region; and 4801 from the Southern Cone region. Influenza vaccination rates were lower in the Andean region (OR: 0.62; 95% CI: 0.50-0.78), the Caribbean (OR: 0.30; 95% CI: 0.23-0.39), and Central America (OR: 0.59; 95% CI: 0.46-0.76) compared with the Southern Cone. Residing in Central America (OR: 3.06; 95% CI: 1.62-5.77) was associated with greater pneumococcal vaccination. The probability of being vaccinated against influenza was higher in men (OR: 1.3; 95% CI: 1.1-1.6) and in individuals with obesity (OR: 1.26; 95% CI: 1.13-1.40). COPD was associated with a lower probability of pneumococcal vaccination (OR: 0.51; 95% CI: 0.33-0.79). Conclusions: This study highlights the importance of targeted vaccination campaigns to improve coverage, particularly in regions with lower rates. It also underscores the need for enhanced education and awareness of the benefits of vaccination. Tackling barriers such as vaccine hesitancy and misinformation will be essential for raising vaccination rates and, ultimately, for reducing the burden of cardiovascular disease.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".