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Record W4406874344 · doi:10.1016/j.jinf.2025.106426

Global impact of 10- and 13-valent pneumococcal conjugate vaccines on pneumococcal meningitis in all ages: The PSERENADE project

2025· article· en· W4406874344 on OpenAlexaff
Yangyupei Yang, Maria Deloria Knoll, Carly Herbert, Julia C. Bennett, Daniel R. Feikin, Maria Garcia Quesada, Marissa K. Hetrich, Scott L. Zeger, E. Wangeci Kagucia, Melody Xiao, Adam L. Cohen, Mark van der Linden, Mignon du Plessis, İnci Yıldırım, Brita Askeland Winje, Emmanuelle Varon, Marı́a Teresa Valenzuela, Palle Valentiner‐Branth, Anneke Steens, J. Anthony G. Scott, Larisa Savrasova, Juan Carlos Sanz, Aalisha Sahu Khan, Kazunori Oishi, Néhémie Nzoyikorera, J. Pekka Nuorti, Jolita Mereckiene, Kimberley McMahon, Allison McGeer, Grant Mackenzie, Laura MacDonald, Shamez Ladhani, Karl G. Kristinsson, Jackie Kleynhans, James D. Kellner, Sanjay Jayasinghe, Pak‐Leung Ho, Markus Hilty, Laura L. Hammitt, Marcela Guevara, Charlotte Gilkison, Ryan Gierke, Stefanie Desmet, Philippe De Wals, Edoardo Colzani, Pilar Ciruela, Urtnasan Chuluunbat, Guanhao Chan, Romina Camilli, Michael G. Bruce, Maria-Cristina C Brandileone, Krow Ampofo, Katherine L. O’Brien, Kyla Hayford

Bibliographic record

VenueJournal of Infection · 2025
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversité LavalCalgary Laboratory ServicesUniversity of TorontoAlberta Health ServicesUniversity Health Network
FundersPan American Health OrganizationEuropean Centre for Disease Prevention and ControlNational Institute for Health and Care ResearchWellcome TrustNational Institute of Allergy and Infectious DiseasesBill and Melinda Gates FoundationPfizerJohns Hopkins UniversityWorld Health OrganizationMedical Research CouncilMerck
KeywordsMeningitisMedicineStreptococcus pneumoniaePneumococcal conjugate vaccinePneumococcal diseasePneumococcal infectionsConjugateBacterial meningitisVirologyImmunologyPediatricsMicrobiologyAntibioticsBiologyMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Pneumococcal conjugate vaccines (PCVs) introduced in childhood national immunization programs lowered vaccine-type invasive pneumococcal disease (IPD), but replacement with non-vaccine-types persisted throughout the PCV10/13 follow-up period. We assessed PCV10/13 impact on pneumococcal meningitis incidence globally. METHODS: The number of cases with serotyped pneumococci detected in cerebrospinal fluid and population denominators were obtained from surveillance sites globally. Site-specific meningitis incidence rate ratios (IRRs) comparing pre-PCV incidence to each year post-PCV10/13 were estimated by age (<5, 5-17 and ≥18 years) using Bayesian multi-level mixed effects Poisson regression, accounting for pre-PCV trends. All-site weighted average IRRs were estimated using linear mixed-effects regression stratified by age, product (PCV10 or PCV13) and prior PCV7 impact (none, moderate, or substantial). Changes in pneumococcal meningitis incidence were estimated overall and for product-specific vaccine-types and non-PCV13-types. RESULTS: Analyses included 10,168 cases <5 y from PCV13 sites and 2849 from PCV10 sites, 3711 and 1549 for 5-17 y and 29,187 and 5653 for ≥18 y from 42 surveillance sites (30 PCV13, 12 PCV10, 2 PCV10/13) in 30 countries, primarily high-income (84%). Six years after PCV10/PCV13 introduction, pneumococcal meningitis declined 48-74% across products and PCV7 impact strata for children <5 y, 35-62% for 5-17 y and 0-36% for ≥18 y. Impact against PCV10-types at PCV10 sites, and PCV13-types at PCV13 sites was high for all age groups (<5 y: 96-100%; 5-17 y: 77-85%; ≥18 y: 73-85%). After switching from PCV7 to PCV10/13, increases in non-PCV13-types were generally low to none for all age groups. CONCLUSION: Pneumococcal meningitis declined in all age groups following PCV10/PCV13 introduction. Plateaus in non-PCV13-type meningitis suggest less replacement than for all IPD. Data from meningitis belt and high-burden settings were limited.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.373
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2025
Admission routes1
Has abstractyes

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