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Record W4414866895 · doi:10.1016/j.lana.2025.101257

Healthcare costs and resource utilization for acute respiratory syncytial virus pediatric hospitalizations in Canada: a population-based study

2025· article· en· W4414866895 on OpenAlexafffundabout
Nirma Khatri Vadlamudi, Kyle Gomes, Malou Bourdeau, Joanne Embreé, Scott A. Halperin, Taj Jadavji, Kescha Kazmi, Joanne M. Langley, Nicole Le Saux, Dorothy Moore, Shaun K. Morris, Jeffrey M. Pernica, J. Ben Robinson, Manish Sadarangani, Julie A. Bettinger, Natalie Bridger, Jared Bullard, Catherine Burton, Jeanette Comeau, Marc Lebel, Jesse Papenburg, Rupeena Purewal, Roseline Thibeault, Karina A. Top

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2025
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsUniversity of AlbertaMcMaster UniversityMcMaster Children's HospitalAlberta Children's HospitalNova Scotia Health AuthorityUniversity of ManitobaSickKids FoundationUniversity of TorontoBC Children's HospitalChildren's Hospital of Eastern OntarioMcGill UniversityHospital for Sick ChildrenUniversity of British Columbia
FundersInstitut canadien d'information sur la santéSanofi PasteurChildren's Hospital FoundationPublic Health Agency of CanadaSociété Canadienne de PédiatrieMerckMichael Smith Health Research BCGlaxoSmithKlineAstraZeneca
KeywordsHealth careAgency (philosophy)Public healthImmunizationPalivizumabPublic health surveillancePneumovirinaeImmunization programPandemic

Abstract

fetched live from OpenAlex

Background: Respiratory syncytial virus (RSV) is a major cause of bronchiolitis and pneumonia in pediatric populations, especially in infancy. This study aims to assess overall and age-specific incidence of RSV-associated hospitalization and healthcare resource use throughout childhood in Canada. Methods: Data were retrieved from a national administrative dataset, which captured hospitalizations with International Classification of Diseases (ICD) codes, tenth revision from participating Canadian hospitals (Canadian Institute for Health Information, CIHI), and from an active surveillance program in tertiary care pediatric hospitals (the Canadian Immunization Program ACTive, IMPACT). Children aged 0-16 years with RSV in November 2017 through April 2023 were eligible. We estimated overall and age-specific RSV hospitalization incidence, healthcare resource use (length of stay, mechanical ventilation use), and costs by age group (0-5, 6-11, and 12-23 months, and 2-4 years, 5-9 years, 10-16 years). Costs were adjusted to 2022 Canadian dollars (CAD). The population denominator for age-specific RSV incidence estimates was retrieved from Statistics Canada. Findings: An estimated 29,277 RSV hospitalizations occurred, with an average of 5831 cases/year in pre-pandemic years (November 2017-June 2020). Infants aged <6 months old accounted for 13,055 (44.6%) of cases for study duration (November 2017-April 2023). RSV incidence among infants aged <6 months increased from 1250 per 100,000 in November 2017-June 2018 to 2393 per 100,000 in July 2022-April 2023. The average annual cost of RSV was $66,267,950 CAD. Infants <6 months old accounted for 49.0% ($32,471,296 CAD) of annual average RSV healthcare costs. Interpretation: Although RSV occurs throughout childhood, the high RSV hospitalization incidence in infants younger than 6 months of age highlights an urgent need for prevention strategies in this population to alleviate the health burden in this population and economic burden on healthcare systems. Funding: This surveillance activity is conducted as part of the Canadian Immunization Monitoring Program Active (IMPACT), a national surveillance initiative managed by the Canadian Paediatric Society (CPS) and conducted by the IMPACT network of pediatric investigators. Funding for RSV surveillance was provided by the Public Health Agency of Canada; funding for RSV economic burden analyses was provided by Sanofi and AstraZeneca.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.039
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.007
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.121
GPT teacher head0.453
Teacher spread0.331 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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