24 Characteristics and Outcomes of Hospitalized Children by SARS-Cov-2 Lineage: An IMPACT Surveillance Network Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».