Need for Invasive Meningococcal Disease Prevention Through Vaccination for Young Children in the Americas
Bibliographic record
Abstract
Background: Invasive meningococcal disease (IMD) is an uncommon but potentially life-threatening condition, resulting in life-long sequelae or death in up to 20% of cases. Most IMD cases are caused by Neisseria meningitidis serogroups (Men) A, B, C, W, X, and Y. The highest IMD incidence is among children < 5 years of age (YOA). We reviewed IMD epidemiology data and existing national immunization programs (NIP) in the Americas and identify unmet needs to decrease IMD burden in young children. Methods: Using national surveillance data and published literature from 2006 to 2024, we evaluated the IMD burden and national vaccination strategies for children < 5 YOA in the Americas, focusing on Canada, the United States, Brazil, Chile, Argentina. Results: The highest IMD incidence was among infants, followed by children 1–4 YOA, with MenB infections predominating in both age groups. Chile has both MenACWY (2014) and MenB (2023) infant vaccination in its NIP. Argentina and Brazil’s NIPs include MenACWY (2017) and MenC (2010) vaccinations for infants, respectively. In Canada, MenC (2002) vaccination is recommended at 1 YOA (replaced by MenACWY in 2024 in Manitoba); MenB vaccination is selectively recommended. In each country, the incidence of IMD caused by vaccine-preventable serogroups decreased following the introduction of the respective meningococcal vaccination in the NIP. Conclusions: Comprehensive meningococcal vaccination programs in the Americas have the potential to reduce the IMD burden in children < 5 YOA. National recommendations and NIPs could reduce IMD burden by offering equitable access to protection against IMD, aligning with the WHO roadmap to defeat meningitis by 2030.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".