50 Risk factors for severe COVID-19 in hospitalized children in Canada: A national prospective study from March 2020–May 2021
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".