Risk factors for severe COVID-19 in hospitalized children in Canada: A national prospective study from March 2020–May 2021
Bibliographic record
Abstract
Background: Children living with chronic comorbid conditions are at increased risk for severe COVID-19, though there is limited evidence regarding the risks associated with specific conditions and which children may benefit from targeted COVID-19 therapies. The objective of this study was to identify factors associated with severe disease among hospitalized children with COVID-19 in Canada. Methods: We conducted a national prospective study on hospitalized children with microbiologically confirmed SARS-CoV-2 infection via the Canadian Paediatric Surveillance Program (CPSP) from April 2020-May 2021. Cases were reported voluntarily by a network of >2800 paediatricians. Hospitalizations were classified as COVID-19-related, incidental infection, or infection control/social admissions. Severe disease (among COVID-19-related hospitalizations only) was defined as disease requiring intensive care, ventilatory or hemodynamic support, select organ system complications, or death. Risk factors for severe disease were identified using multivariable Poisson regression, adjusting for age, sex, concomitant infections, and timing of hospitalization. Findings: We identified 544 children hospitalized with SARS-CoV-2 infection, including 60·7% with COVID-19-related disease and 39·3% with incidental infection or infection control/social admissions. Among COVID-19-related hospitalizations (n=330), the median age was 1·9 years (IQR 0·1-13·3) and 43·0% had chronic comorbid conditions. Severe disease occurred in 29·7% of COVID-19-related hospitalizations (n=98/330 including 60 admitted to intensive care), most frequently among children aged 2-4 years (48·7%) and 12-17 years (41·3%). Comorbid conditions associated with severe disease included pre-existing technology dependence requirements (adjusted risk ratio [aRR] 2·01, 95% confidence interval [CI] 1·37-2·95), body mass index Z-scores ≥3 (aRR 1·90, 95% CI 1·10-3·28), neurologic conditions (e.g. epilepsy and select chromosomal/genetic conditions) (aRR 1·84, 95% CI 1·32-2·57), and pulmonary conditions (e.g. bronchopulmonary dysplasia and uncontrolled asthma) (aRR 1·63, 95% CI 1·12-2·39). Interpretation: 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. These findings may help guide vaccination programs and prioritize targeted COVID-19 therapies for children. Funding: Financial support for the CPSP was received from the Public Health Agency of Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".