Trends in SARS-CoV-2-related pediatric hospitalizations reported to the Canadian Nosocomial Infection Surveillance Program, March 2020 to December 2022
Bibliographic record
Abstract
Abstract Objective: This study describes trends in COVID-19 hospitalizations and healthcare-associated (HA) COVID-19 in Canada among pediatric (age <18 years) patients during pre-Omicron and Omicron-dominant periods. Design: Prospective surveillance for COVID-19 infection Setting: The Canadian Nosocomial Infection Surveillance Program is a sentinel surveillance system with 45 hospitals providing COVID-19 data on pediatric patients, including all 13 pediatric tertiary care facilities in Canada. Patients: Pediatric patients hospitalized with laboratory-confirmed COVID-19 at a participating hospital between March 1, 2020, and December 31, 2022. Methods: Analyzed case-level data on pediatric patients with COVID-19, including demographics, acquisition source, and outcomes. Results: Among 5,143 pediatric cases, the majority (81%) were reported during the Omicron-dominant period (beginning December 26, 2021). However, a lower proportion required intensive care during the Omicron wave (11% vs 14%, P < 0.05); no difference in mortality was observed. Of those patients admitted to hospital due to COVID-19 (n = 2,700), 45% had at least one pre-existing comorbidity. The majority (90%) of patients with HA-COVID-19 were reported during the Omicron period. There was no difference in mortality between patients with HA and community-associated (CA) infection, although a greater proportion of CA infections led to intensive care unit admission (6% vs 13%, P < 0.01). Conclusions: Surveillance findings indicate that both HA- and CA-COVID-19 hospitalizations in Canada increased among pediatric patients following the emergence of the Omicron variant, although disease severity decreased. Pre-existing health conditions were common among pediatric patients hospitalized with COVID-19, highlighting the importance of preventing severe illness in this sub-population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".