Population-based incidence of invasive pneumococcal disease in children and adults in Ontario and British Columbia, 2002–2018: A Canadian Immunization Research Network (CIRN) study
Bibliographic record
Abstract
BACKGROUND: Invasive pneumococcal disease (IPD) burden, evaluated in Canada using reported confirmed cases in surveillance systems, is likely underestimated due to underreporting. We estimated the burden of IPD in Ontario and British Columbia (BC) by combining surveillance data with health administrative databases. METHODS: We established a cohort of 27,525 individuals in Ontario and BC. Laboratory-confirmed IPD cases were identified from Ontario's integrated Public Health Information System and the BC Centre for Disease Control Public Health Laboratory. Possible IPD cases were identified from hospitalization data in both provinces, and from emergency department visit data in Ontario. We estimated the age and sex adjusted annual incidence of IPD and pneumococcal conjugate/polysaccharide vaccine (PCV/PPV) serotype-specific IPD using Poisson regression models. RESULTS: In Ontario, 20,205 overall IPD cases, including 15,299 laboratory-confirmed cases, were identified with relatively stable age- and sex-adjusted annual incidence rates ranging from 13.7/100,000 (2005) to 13.6/100,000 (2018). In BC, 7,320 overall IPD cases, including 5,932 laboratory-confirmed cases were identified; annual incidence rates increased from 10.9/100,000 (2002) to 13.2/100,000 (2018). Older adults aged ≥ 85 years had the highest incidence rates. During 2007-2018 the incidence of PCV7 serotypes and additional PCV13 serotypes decreased while the incidence of unique PPV23 and non-vaccine serotypes increased in both provinces. CONCLUSIONS: IPD continues to cause a substantial public health burden in Canada despite publicly funded pneumococcal vaccination programs, resulting in part from an increase in unique PPV23 and non-vaccine serotypes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".