Invasive Pneumococcal Disease Epidemiology and Serotype Replacement After the Introduction of the 13-Valent Pneumococcal Conjugate Vaccine in Ontario, Canada, 2007–2022
Bibliographic record
Abstract
Background: New vaccine products were recently authorized for protection against invasive pneumococcal disease (IPD) in Canada. Our aim was to determine age- and serotype-specific trends in IPD incidence and severity in Canada's largest province, Ontario. Methods: We included all confirmed IPD cases reported in Ontario and defined the pre-pneumococcal 13-valent conjugate vaccine (PCV13) era (01/2007 to 12/2010), post-PCV13 era (01/2011 to 12/2019), and coronavirus disease 2019 (COVID-19) pandemic era (01/2020 to 12/2022). We estimated incidence, hospitalization, and case fatality rate (CFR) by age. We grouped IPD cases by vaccine-specific serotypes (PCV13; PCV15-non-PCV13; PCV20-non-PCV13; PCV20-non-PCV15; polysaccharide 23-valent vaccine-non-PCV20; and non-vaccine-preventable [NVP]). We then compared incidence rates by age and serotype group in the pre- and post-PCV13 eras by calculating rate ratios (RRs) and their 95% CIs. Results: Incidence and hospitalizations declined from the pre- to post-PCV13 era in children aged <5 years (RR, 0.7; 95% CI, 0.6-0.8; and RR, 0.8; 95% CI, 0.7-0.9, respectively), but the CFR increased (1.4% to 2.3%). Other age groups saw smaller declines or more stable incidence rates across the years; hospitalizations increased in adults aged 50-64 years (RR, 1.2; 95% CI, 1.1-1.4) and ≥65 years (RR, 1.1; 95% CI, 1.0-1.1). For all ages, IPD cases and hospitalizations attributable to PCV13 serotypes declined, and those attributable to PCV15-non-PCV13, PCV20-non-PCV13, and NVP serotypes increased. IPD incidence declined during the COVID-19 era. Conclusions: IPD incidence and hospitalizations due to PCV13 serotypes decreased after PCV13 introduction but increased for other serotypes. Continued surveillance is required to evaluate changes to pneumococcal vaccination programs and ongoing changes to the distribution of IPD-causing serotypes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".