A comparison of national vaccination policies to prevent serogroup B meningococcal disease
Bibliographic record
Abstract
OBJECTIVES: To understand the global landscape of prevention and control efforts targeting serogroup B meningococcal (MenB) disease and to identify the key challenges and gaps yet to be addressed. METHODS: We conducted a comprehensive review of policies and practices for the use of protein-based MenB vaccines (Bexsero® [GlaxoSmithKline] and Trumenba® [Pfizer]) in all countries (n = 58) where either or both vaccine is authorized for use. We searched the literature (PubMed) and websites of health ministries and other relevant agencies to identify policy documents and plans and collect information about implementation timelines, target groups, vaccines being used, recommended schedules, and coverage data. Experts in the field were contacted for additional details andclarifications, as needed. RESULTS: We found evidence of a national MenB vaccination policy in 24 out of 58 countries where one or both protein-based MenB vaccines are authorized. Of these, 15 countries have included MenB vaccination in their immunization plans for at least one age-based risk group (mostly infants), 21 have issued recommendations for various risk groups based on underlying medical conditions (e.g. asplenia), and 13 have done so for select groups at increased risk of exposure (e.g. laboratory staff). Recommended vaccination schedules and number of doses, where available, varied widely. Vaccination coverage data for age-based risk groups were not obtained for most countries. CONCLUSIONS: Our findings highlighted the significant heterogeneity in recommendations for MenB vaccination across countries. Greater transparency in reporting MenB vaccination recommendations and more robust data on implementation and the impact of vaccination would better facilitate optimizing MenB prevention strategies.
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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 | 0.035 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".