Changes in Pediatric RSV Hospitalizations after the COVID-19 Pandemic, 2022-2023, Canada: An Active Surveillance Study
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic impacted RSV epidemiology. We describe Canadian pediatric tertiary care RSV hospitalizations in 2022-2023 and assess pandemic-related changes. Methods Active surveillance of hospitalized children aged 0-16 years was conducted at 13 Immunization Monitoring Program, Active (IMPACT) centres. RSV hospitalizations in 2022-2023 were compared to those in the pre-pandemic period (2017-2018 through 2019-2020). Province-specific and age-stratified proportions of all-cause hospitalizations with RSV detection and age-stratified proportions of RSV-associated intensive care unit (ICU) admission were calculated. Changes in seasonality were assessed using Seasonal Autoregressive Integrated Moving Average (SARIMA) modelling. Results In 2022-2023, there were 5362 RSV-associated hospitalizations including 1260 (23.5%) ICU admissions, both more than double pre-pandemic yearly averages. Overall, median (IQR) age increased from 6 months (1-20) to 9 months (2-27; P<0.001). The proportion of RSV hospitalizations among all-cause hospitalizations increased by 3.5 percentage points (95% CI 3.3-3.7 percentage points), to 6.8% (95% CI 6.6%-7.0%). While 41.5% of RSV hospitalizations were among children <6 months old, they accounted for 62% of ICU admissions. Overall, ICU proportion remained constant; however, odds of ICU admission among infants <6 months old increased (adjusted OR 1.35, 95% CI 1.2-1.52) compared to the pre-pandemic period. National weekly incidence in 2022-2023 peaked earlier, higher and persisted longer than expected by SARIMA. Interpretations In 2022-2023, the number of RSV hospitalizations and ICU admissions increased dramatically in Canadian pediatric hospitals. Despite an older age distribution, the greatest burden remained in children <6 months old. RSV immunization strategies for young infants will likely have substantial potential public health impact.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".