Influenza and Pneumococcal Vaccination in Non-Infected Cardiometabolic Patients from the Americas during the COVID-19 Pandemic. A Sub-Analysis of the CorCOVID-LATAM Study
Bibliographic record
Abstract
Background: Influenza vaccination (IV) and Pneumococcus vaccination (PV) are recommended for patients with cardiometabolic diseases. This study aimed to evaluate the immunization rate of ambulatory cardiometabolic patients during the COVID-19 pandemic in the Americas. Methods: Electronic surveys were collected from 13 Spanish speaking countries between 15 June and 15 July 2020. Results: 4216 patients were analyzed. Mean age 60 (±15) years and 49% females. Global IV rate was 46.5% and PV 24.6%. Vaccinated patients were older (IV = 63 vs. 58 years; PV = 68 vs. 59, p < 0.01) but without gender difference. Vaccination rates were greater in higher-risk groups (65+, diabetics, heart failure), but not in coronary artery disease patients. In the Southern cone, the rate of IV and PV was approximately double that in the tropical regions of the Americas. In a multivariate model, geographic zone (IV = OR 2.02, PV = OR 2.42, p < 0.001), age (IV = OR 1.023, PV = OR 1.035, p < 0.001), and incomes (IV = OR 1.28, PV = OR 1.58, p < 0.001) were predictors for vaccination. Conclusions: During the COVID-19 pandemic, ambulatory patients with cardiometabolic diseases from the Americas with no evidence of COVID-19 infection had lower-than-expected rates of IV and PV. Geographic, social, and cultural differences were found, and they should be explored in depth.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".