1211. Incidence of All-Cause Community-Acquired Pneumonia in Ontario and British Columbia, Canada, 2002-2018; a Canadian Immunization Research Network (CIRN) study
Bibliographic record
Abstract
Abstract Background Community-acquired pneumonia (CAP) causes substantial morbidity and mortality. There is a lack of data on the comprehensive burden of CAP across the life span in Canada. We estimated the incidence of all-cause CAP in all age groups in Ontario and British Columbia (BC), Canada. Methods We identified hospitalized and outpatient CAP episodes from the Discharge Abstract Database (DAD) and physician billing claims databases (Ontario Health Insurance Plan in Ontario and Medical Services Plan in BC) in both provinces. The National Ambulatory Care Reporting System was used to identify CAP episodes from emergency department visits in Ontario. CAP recorded with a primary or secondary diagnosis was identified using International Classification of Diseases 9 (480–486, 510, 513) and 10 (J10.0, J11.0, J12–J18, J86.9, J85.1) codes. We estimated the age and sex adjusted annual incidence of CAP overall, and by age groups (0–4, 5–17, 18–39, 40–64, 65–74, 75–84 and ≥85 years) according to routine childhood pneumococcal conjugate vaccine (PCV) immunization periods from 2005–2018 in Ontario and from 2002–2018 in BC. Poisson regression models were fitted with population denominators from Statistics Canada to estimate the incidence rates. Results Ontario had 3,607,186 CAP episodes from 2005–2015 with a mean annual incidence of 2,801 (95% confidence interval [CI]: 2,748, 2,854) per 100,000 population; incidence declined from 3,077/100,000 in 2005 to 2,604/100,000 in 2010 before increasing to 2,843/100,000 in 2018. BC had 1,146,172 CAP episodes from 2002–2008, with a mean annual incidence of 2,146 (95% CI: 2105, 2189); the incidence increased from 2,005 /100,000 in 2002 to 2,199/100,000 in 2018. A high incidence of CAP was observed in children aged 0–4 years and older adults, particularly in adults aged ≥85 years in both provinces across all PCV program periods (Figure 1). Figure 1: Age group-specific incidence of all-cause community-acquired pneumonia according to childhood pneumococcal conjugate vaccine (PCV) program periods in Ontario (PCV7 [1 Jan 2005–30 Sep 2009]), PCV10 [1 Oct 2009–31 Oct 2010] and PCV13 [1 Nov 2010–31 Dec 2018]) and British Columbia (PCV7 [1 Sep 2003–31 May 2010] and PCV13 [1 Jun 2010–31 Dec 2018]), Canada Conclusion CAP continues to be a public health burden in Canada despite publicly funded pneumococcal vaccination programs. Ontario seems to have higher CAP burden than British Columbia that warrants further investigation. The youngest cohort of children and older adults contribute significantly to the CAP burden. Disclosures Manish Sadarangani, BM BCh, DPhil, GlaxoSmithKline (Grant/Research Support)Merck (Grant/Research Support)Pfizer (Grant/Research Support)Sanofi Pasteur (Grant/Research Support)Seqirus (Grant/Research Support)Symvivo (Grant/Research Support)VBI Vaccines (Research Grant or Support) Allison McGeer, MSc,MD,FRCPC,FSHEA, GlaxoSmithKline (Advisor or Review Panel member)Merck (Advisor or Review Panel member, Research Grant or Support)Pfizer (Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member) James D. Kellner, MD, FRCPC, FIDSA, Pfizer, Merck, GSK, Moderna (Grant/Research Support) Shaun Morris, MD, MPH, DTM&H, FRCPC, FAAP, GSK (Speaker’s Bureau)Pfizer (Advisor or Review Panel member)Pfizer (Grant/Research Support) Shaza A. Fadel, PhD MPH, Merck (Other Financial or Material Support, Salary is paid by the University of Toronto via a donation by Merck to the Centre for Vaccine Preventable Diseases to support educational and operational activities.) Fawziah Marra, BSc(Pharm), PharmD, Pfizer Canada (Research Grant or Support)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".