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Record W4283734370 · doi:10.1136/bmjresp-2022-001218

Estimating population-based incidence of community-acquired pneumonia and acute otitis media in children and adults in Ontario and British Columbia using health administrative data, 2005–2018: a Canadian Immunisation Research Network (CIRN) study

2022· article· en· W4283734370 on OpenAlexafffundabout
Sharifa Nasreen, Jun Wang, Manish Sadarangani, Jeffrey C. Kwong, Caroline Quach, Natasha S. Crowcroft, Sarah E. Wilson, Allison McGeer, Shaun K. Morris, James D. Kellner, Beate Sander, Julianne V. Kus, Linda Hoang, Fawziah Marra, Shaza A. Fadel

Bibliographic record

VenueBMJ Open Respiratory Research · 2022
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsBC Centre for Disease ControlAlberta Health ServicesSinai Health SystemUniversité de MontréalHospital for Sick ChildrenPublic Health OntarioUniversity of British ColumbiaUniversity of CalgaryInstitute for Clinical Evaluative SciencesUniversity Health NetworkBC Children's HospitalUniversity of Toronto
FundersDepartment of Family and Community Medicine, University of TorontoCanadian Immunization Research NetworkBC Children's HospitalMichael Smith Health Research BCUniversity of TorontoCanadian Child Health Clinician Scientist ProgramChildren's Hospital Foundation
KeywordsMedicineIncidence (geometry)Community-acquired pneumoniaPneumoniaPopulationAcute otitis mediaPneumococcal pneumoniaPediatricsOtitisStreptococcus pneumoniaeRate ratioDemographyInternal medicineSurgeryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: There is a paucity of data on the burden of the full spectrum of community-acquired pneumonia (CAP) and acute otitis media (AOM) from outpatient and inpatient settings across the age spectrum. METHODS: We conducted a population-based retrospective study in Ontario and British Columbia (BC), Canada, to estimate the incidence rate of CAP and AOM in children and adults over a 14-year period using health administrative databases. CAP and AOM cases were identified from outpatient physician consultation and hospitalisation data in both provinces, and from emergency department visit data in Ontario. RESULTS: During 2005-2018, Ontario had 3 607 124 CAP, 172 290 bacterial CAP, 7814 pneumococcal pneumonia, and 8 026 971 AOM cases. The incidence rate of CAP declined from 3077/100 000 in 2005 to 2604/100 000 in 2010 before increasing to 2843/100 000 in 2018; bacterial CAP incidence rate also declined from 178/100 000 in 2005 to 112/100 000 in 2010 before increasing to 149/100 000 in 2018. The incidence rate of AOM decreased from 4192/100 000 in 2005 to 3178/100 000 in 2018. BC had 970 455 CAP, 317 913 bacterial CAP, 35 287 pneumococcal pneumonia and 2 022 871 AOM cases. The incidence rate of CAP in BC decreased from 2214/100 000 in 2005 to 1964/100 000 in 2010 before increasing to 2176/100 000 in 2018; bacterial CAP incidence rate increased from 442/100 000 in 2005 to 981/100 000 in 2018. The incidence rate of AOM decreased from 3684/100 000 in 2005 to 2398/100 000 in 2018. The incidence rate of bacterial CAP increased with age in older adults (≥65 years) with the highest burden in the oldest cohort aged ≥85 years both before and after 13-valent pneumococcal conjugate vaccine (PCV13) programme in both provinces. Hospitalised pneumococcal pneumonia decreased slightly but non-hospitalised pneumococcal pneumonia increased in BC during PCV13 period. No consistent direct benefit of PCV13 on CAP was observed in the paediatric population. CONCLUSIONS: There is a substantial burden of CAP and AOM in Ontario and BC. Indirect benefits from childhood PCV vaccination and polysaccharide vaccination of older adults have not substantially decreased the burden of pneumococcal pneumonia in older adults.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.051
Threshold uncertainty score0.373

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.010
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0010.001
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 head0.286
GPT teacher head0.483
Teacher spread0.197 · 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

Citations13
Published2022
Admission routes3
Has abstractyes

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