P-636. Medical Need for Meningococcal Vaccination in Young Children from the Americas
Bibliographic record
Abstract
Abstract Background Invasive meningococcal disease (IMD), caused mainly by Neisseria meningitidis serogroups (Men) A, B, C, W, X, and Y, is an uncommon but serious condition that can lead to life-long sequelae and is fatal in up to 20% of cases even with treatment. IMD incidence is highest in children <5 years. We present the current epidemiology of IMD in the Americas and unmet needs to reduce IMD burden in young children. Methods We reviewed the literature and available surveillance data from 2015 to 2024 to evaluate the IMD burden and national vaccination strategies for children < 5 years of age in the Americas. Results Overall, across countries, the incidence is highest in children aged <1 year, followed by children 1–4 years of age. MenB is the predominant serogroup in children < 5 years. In most countries with available data, IMD incidence decreased sharply during the COVID-19 pandemic but increased again after nonpharmaceutical interventions were lifted. Most countries do not have recommendations for routine meningococcal vaccination in children <5 years (Figure). Chile is the only country in the Americas that has routine vaccination against 5 serogroups, by including both MenACWY (2014) and MenB (2023) vaccinations in its national immunization program (NIP). The Argentinian, Brazilian, and Cuban NIPs include MenACWY (2017), MenC (2010), and MenBC (1991) vaccinations in infants, respectively. In Canada, MenC vaccination has been recommended for all infants since 2002, with one province (Manitoba) introducing MenACWY in 2024; MenB vaccination is recommended on an individual basis. Following introduction in the NIP/national recommendations, the incidence of IMD caused by serogroups covered by the vaccines has decreased in these countries. In the United States, MenACWY vaccination is recommended in children at high risk of IMD, but not for routine vaccination in <5-year olds. Conclusion Considering the current incidence and burden of IMD in infants < 1 and children < 5 years of age across the region, especially MenB-IMD, comprehensive IMD vaccination programs could reduce the overall burden in this population. NIPs/national recommendations would facilitate equitable access to protection against IMD, aligned to the World Health Organization roadmap to defeat meningitis by 2030. Funding: GSK Disclosures Gaurav Mathur, MD, GSK: Employee|GSK: Stocks/Bonds (Private Company)|OpenHealth: Writing support Maria Gabriela Graña, MD, GSK: employment|GSK: Stocks/Bonds (Private Company) Reena Ladak, MS, GSK: Employee|GSK: Stocks/Bonds (Private Company) Joanne M. Langley, MD, GSK: Grant/Research Support|Inventprise: Grant/Research Support|Merck: Grant/Research Support|Moderna: Grant/Research Support|Pfizer: Grant/Research Support|VBI: Grant/Research Support|VIDO: Grant/Research Support Oluwatosin Olaiya, MBChB, MSc, GSK: employment (current)|Merck: Previous employer Alysa Pompeo, BPharm, GSK: employment Laura Taddei, M.Sc, GSK: GSK employment|GSK: Stocks/Bonds (Public Company) Rodolfo Villena, MD, GSK: Advisor/Consultant|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".