24 Characteristics and Outcomes of Hospitalized Children by SARS-Cov-2 Lineage: An IMPACT Surveillance Network Analysis
Bibliographic record
Abstract
Abstract Introduction/Background Changes in relative virulence of SARS-CoV-2 lineages among children remain poorly understood, yet are important considerations for vaccination and health resource management. Further evidence is needed to assess the burden of severe paediatric COVID-19 due to the Omicron variant. Objectives In this study, we aimed to compare presenting features and disease severity among hospitalized children with COVID-19 in Canada by SARS-CoV-2 lineage. Design/Methods Data were collected during two national surveillance studies: the Canadian Paediatric Surveillance Program (April 2020–May 2021) and the Canadian Immunization Monitoring Program, ACTive (June 2021–May 2022). Cases were included if children were <17 years old and hospitalized for COVID-19 (excluding incidental SARS-CoV-2) at one of thirteen sentinel paediatric hospitals. SARS-CoV-2 lineages were classified as “Ancestral”, “pre-Delta” (Alpha, Beta, or Gamma), “Delta”, or “Omicron” based on genetic sequencing or the predominant lineage across Canada at the time of hospitalization. Severe disease was defined as intensive care, ventilatory or hemodynamic support requirements, organ system complications, or death. Results We identified 1357 children hospitalized with COVID-19, including 254 (18.7%) ancestral, 105 (7.7%) pre-Delta, 175 (12.9%) Delta, and 823 (60.6%) Omicron (Table). Median age at hospitalization was highest for Delta (3.0 years, interquartile range 0.2–11.1) and lowest for Omicron (1.3 years, interquartile range 0.3–5.4; p<0.001). The proportion of children with comorbid conditions did not differ significantly by lineage, ranging from 44.9% (ancestral) to 54.3% (Delta, p=0.149). COVID-19 vaccination (≥1 doses) was received by <5 pre-Delta cases (<4.8%), six Delta cases (3.4%), and 98 Omicron cases (11.9%). Among unvaccinated patients (n=1251), severe COVID-19 was most common among Delta lineages (34.3%) versus other lineages (24.0% ancestral, 22.3 pre-Delta, 24.8% Omicron; p=0.049). Conclusion Our results show more children were hospitalized during Omicron waves than all other waves combined, though Delta was relatively more severe. These findings underscore the substantial burden of Omicron in children. Potential competing interests Jesse Papenburg received consultant fees/honoraria from Astra-Zeneca, Merck, and Seegene, and is site PI for industry trials by Astra-Zeneca, MedImmune, Merck, and Sanofi (all outside current work). Rupeena Purewal is a consultant for Verity Pharmaceuticals. Manish Sadarangani has been an investigator on projects funded by GlaxoSmithKline, Merck, Moderna, Pfizer, Sanofi-Pasteur, Seqirus, Symvivo, and VBI Vaccines (all outside current work). Fatima Kakkar received salary support from the FRQS Chercheur Boursieurs Program, and honoraria from the Association des Pédiatres du Québec. Shaun Morris has received honoraria for lectures from GlaxoSmithKline and was a member of ad hoc advisory boards for Pfizer Canada and Sanofi Pasteur (all outside current work). No other competing interests are declared.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".