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Record W4389030131 · doi:10.1093/ofid/ofad500.2211

2596. Title: The Dynamics Of Invasive Pneumococcal Disease (IPD) due to <i>Streptococcus pneumoniae</i> and Impact Of Pneumococcal Conjugate Vaccines (PCVs) In Canada From 2000-2019

2023· article· en· W4389030131 on OpenAlexaffabout
Bernice Ramos, Nirma Khatri Vadlamudi, Irene Martín, Greg Tyrrell, Nicholas Brousseau, Averil Griffith, Manish Sadarangani

Bibliographic record

VenueOpen Forum Infectious Diseases · 2023
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsInstitut National de Santé Publique du QuébecUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsPneumococcal diseaseMedicineSerotypePneumococcal conjugate vaccineStreptococcus pneumoniaeIncidence (geometry)PopulationPediatricsPneumococcal infectionsEpidemiologyDemographyVirologyInternal medicineEnvironmental healthBiologyMicrobiologyAntibiotics

Abstract

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Abstract Background IPD remains a major cause of morbidity and mortality globally despite the implementation of PCVs in childhood immunization programs, due to the emergence of non-vaccine serotypes (NVTs) and persisting vaccine types. Newly approved vaccines such as PCV15 and PCV20 may address some of these issues. This study aimed to describe the epidemiology of IPD in Canada from 2000-2019 after PCV7 and PCV13 approval in 2001 and 2010, respectively, and evaluate the potential future impact of PCV15 and PCV20. Methods Data were collected from IPD cases in children and adults captured by the Pneumococcal Reference Laboratory in Alberta and the National Microbiology Lab during 2000-2019; this includes the majority of isolates from IPD across Canada, excluding Quebec. We calculated annual age-, and serotype-specific incidence rates and serotype distribution using population estimates from Statistics Canada. Results A total of 37,921 isolates from IPD cases were included in the analyses. After PCV7 introduction, IPD due to PCV7 serotypes decreased by 64% (2.2 to 0.8 cases per 100,000 population per year) from 2001 to 2010, while PCV13/non-PCV7 serotypes increased between 2004 and 2010 from 1.0 to 3.8 per 100,000 per year. After PCV13 introduction, IPD due to PCV13/non-PCV7 serotypes decreased by 53% (3.8 to 1.8 per 100,000 per year) between 2010 and 2019. The overall rate initially decreased from 8.7 to 7.6 per 100,000 between 2010-2014 but increased to 9.6 per 100,000 in 2019. From 2010 to 2019, IPD incidence increased by 63% (0.8 to 1.3 per 100,000 per year) for PCV15/non-PCV13 serotypes and by 33% (1.2 to 1.6 per 100,000) for PCV20/non-PCV15 serotypes. The proportion of serotypes in 2019 covered by PCV15 and PCV20 was 32% and 45% in infants aged &amp;lt; 2 years and 43% and 56% in adults aged ≥65 years, respectively. There was an increased IPD burden observed for some PCV13 serotypes: serotype 3 increased from 0.7 to 1.1 per 100,000 per year between 2010 and 2019 and serotype 4 increased from 0.2 to 0.8 per 100,000 per year between 2014 and 2019. Conclusion IPD rates in Canada continue to increase despite the widespread use of PCV13. While PCV15 and PCV20 hold promise in addressing prevalent recently circulating serotypes, there remains a need to develop vaccines with broader coverage considering the constant emergence of NVTs. Disclosures Nirma Khatri Vadlamudi, PhD, MPH, Pfizer Canada: Grant/Research Support Greg Tyrrell, PhD, Merck Canada: Advisor/Consultant|Merck Canada: Grant/Research Support Manish Sadarangani, BM BCh, FRCPC, DPhil, GlaxoSmithKline: Grant/Research Support|Merck: Grant/Research Support|Moderna: Grant/Research Support|Pfizer: Grant/Research Support|Sanofi Pasteur: Grant/Research Support|Seqirus: Grant/Research Support|Symvivo: Grant/Research Support|VBI Vaccines: Grant/Research Support

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 score0.299
Threshold uncertainty score0.804

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 head0.008
GPT teacher head0.268
Teacher spread0.260 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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