50 Risk factors for severe COVID-19 in hospitalized children in Canada: A national prospective study from March 2020–May 2021
Notice bibliographique
Résumé
Abstract Background Children living with chronic comorbid conditions are at increased risk for severe COVID-19 disease, though there is limited evidence regarding the risks associated with specific conditions and which children may benefit from targeted COVID-19 therapies. Age-specific baseline indicators of COVID-19 severity are also needed to evaluate the effectiveness of SARS-CoV-2 vaccination strategies in the paediatric population. Objectives In this study, we aimed to 1) identify factors associated with severe COVID-19 in children, and 2) describe rates of hospitalization, intensive care unit (ICU) admission, and severe COVID-19 within specific pediatric age groups. Design/Methods We conducted a national prospective study on hospitalized children with microbiologically confirmed SARS-CoV-2 infection via the Canadian Paediatric Surveillance Program from March 2020–May 2021. Cases were reported voluntarily by a network of >2800 paediatricians and paediatric subspecialists. SARS-CoV-2 hospitalizations were classified as COVID-19-related, incidental infection, or infection control/social admissions. Severe disease was defined as intensive care, ventilatory or hemodynamic requirements, select organ system complications, or death. Outcomes were described among children aged <6 months, 6–23 months, 2–4 years, 5–11 years, and 12–17 years. Risk factors for severe disease were identified using multivariable Poisson regression, adjusting for child age and sex, coinfections, and timing of hospitalization. Results We identified 541 children hospitalized with SARS-CoV-2 infection, including 329 (60.8%) with COVID-19-related disease. Median age at admission was 2.8 years (IQR 0.3-13.5) and 42.9% (n=232) had at least one comorbidity. Among COVID-19-related hospitalizations, severe disease occurred in 29.5% of children (n=97/329), including a higher proportion of children aged 2–4 years (48.7%) and 12–17 years (41.3%) (Table 1). Comorbidities associated with severe disease are described in Figure 1, and included technology dependence (adjusted risk ratio [aRR] 1.96, 95% confidence interval [CI] 1.31-2.95), neurologic conditions (e.g. epilepsy and chromosomal/genetic conditions) (aRR 1.87, 95% CI 1.34-2.61), and pulmonary conditions (e.g. bronchopulmonary dysplasia and uncontrolled asthma) (aRR 1.66, 95% CI 1.13-2.42). Conclusion While severe outcomes were detected at all ages and among patients with and without comorbidities, neurologic and pulmonary conditions as well as technology dependence were associated with increased risk of severe COVID-19. Children aged 2–4 years more commonly experienced severe COVID-19 in this study, which was conducted at a time when no children were eligible for SARS-CoV-2 vaccines. Notably, this high-risk group remains without access to approved vaccines. These findings may help guide vaccination programs and prioritize targeted COVID-19 therapies for children.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».