MétaCan
Menu
Back to cohort
Record W4366463600 · doi:10.1007/s40121-023-00798-x

Historical Population-Level Impact of Infant 13-Valent Pneumococcal Conjugate Vaccine (PCV13) National Immunization Programs on Invasive Pneumococcal Disease in Australia, Canada, England and Wales, Israel, and the United States

2023· article· en· W4366463600 on OpenAlexaboutno aff
Johnna Perdrizet, Emily K. Horn, Kyla Hayford, Lindsay R. Grant, Rachid Barry, Liping Huang, Cheryl McDade, Michele Wilson

Bibliographic record

VenueInfectious Diseases and Therapy · 2023
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsnot available
FundersPfizer
KeywordsPneumococcal conjugate vaccineIncidence (geometry)MedicineSerotypePopulationPneumococcal diseaseDemographyPediatricsVaccinationStreptococcus pneumoniaeImmunologyEnvironmental healthBiologyAntibioticsMicrobiology

Abstract

fetched live from OpenAlex

INTRODUCTION: This study estimates the annual population-level impact of 13-valent pneumococcal conjugate vaccine (PCV13) infant national immunization programs (NIPs) on vaccine-type and non-vaccine type invasive pneumococcal disease (IPD) incidence across all ages using national surveillance data. METHODS: We identified countries (Australia, Canada, England and Wales, Israel, and the US) with national IPD active surveillance data that introduced the seven-valent PCV (PCV7) followed by PCV13, which also reported annual serotype- and age group-specific incidence. We extracted IPD incidence by serotype groupings [PCV13 minus PCV7 (PCV13-7) serotypes; PCV13-7 serotypes excluding serotype 3; non-PCV13 serotypes; and the 20-valent (PCV20) minus PCV13 (PCV20-13) serotypes] and by age groups (< 2 years, 2-4 years, 5-17 years, 18-34 years, 35-49 years, 50-64 years, and ≥ 65 years). For each country, we calculated the annual relative change in IPD incidence (percent change), and the corresponding incidence rate ratio (IRR), for 7 years post introduction compared to the year prior to PCV13 program initiation. RESULTS: PCV13-7 vaccine-type IPD incidence consistently decreased over time following introduction of PCV13 across countries, reaching an approximate steady state after 3-4 years in ages < 5 years, with roughly 60-90% decrease (IRRs = 0.1-0.4) and after 4-5 years in ages ≥ 65 years with approximately 60-80% decrease (IRRs = 0.2-0.4). Incidence declines were more substantial for the PCV13-7 grouping when excluding serotype 3. Non-PCV13 serotype incidence was variable by country and age group, ranging from virtually no serotype replacement compared to the PCV7 period across ages in the US to increases for other countries ranging from 10 to 204% (IRRs = 1.10-3.04) in children < 5 years and 41% to 123% (IRRs = 1.41-2.23) in ages ≥ 65 years. CONCLUSIONS: Countries with longstanding PCV13 infant NIPs have observed substantial direct and indirect benefits, which are demonstrated in this study by the reduction in PCV13-7 IPD incidence compared to PCV7 period in all age groups. Over time, non-PCV13 serotypes have emerged in response to the reduction of incidence of PCV13-unique serotypes. Higher-valent PCVs are needed to address this emerging pneumococcal disease burden as well as the direct vaccination of both pediatric and adult populations against the most prevalent circulating serotypes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.702
Threshold uncertainty score0.592

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.292
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), 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

Citations34
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInfectious Diseases and TherapySame topicPneumonia and Respiratory InfectionsFrench-language works237,207