Severe Respiratory Disease Among Children With and Without Medical Complexity During the COVID-19 Pandemic
Bibliographic record
Abstract
Importance: Severe respiratory disease declined during the COVID-19 pandemic, partially due to decreased circulation of respiratory pathogens. However, the outcomes of children with higher risk have not been described using population-based data. Objective: To compare respiratory-related hospitalizations, intensive care unit (ICU) admissions, and mortality during the pandemic vs prepandemic, among children with medical complexity (CMC) and without medical complexity (non-CMC). Design, Setting, and Participants: This population-based repeated cross-sectional study used Canadian health administrative data of children aged younger than 18 years in community and pediatric hospitals during a pandemic period (April 1, 2020, to February 28, 2022) compared with a 3-year prepandemic period (April 1, 2017, to March 31, 2020). The pandemic period was analyzed separately for year 1 (April 1, 2020, to March 31, 2021) and year 2 (April 1, 2021, to February 28, 2022). Statistical analysis was performed from October 2022 to April 2023. Main Outcomes and Measures: Respiratory-related hospitalizations, ICU admissions, and mortality before and during the pandemic among CMC and non-CMC. Results: A total of 139 078 respiratory hospitalizations (29 461 respiratory hospitalizations for CMC and 109 617 for non-CMC) occurred during the study period. Among CMC, there were fewer respiratory hospitalizations in both 2020 (rate ratio [RR], 0.44 [95% CI, 0.42-0.46]) and 2021 (RR, 0.55 [95% CI, 0.51-0.62]) compared with the prepandemic period. Among non-CMC, there was an even larger relative reduction in respiratory hospitalizations in 2020 (RR, 0.18 [95% CI, 0.17-0.19]) and a similar reduction in 2021 (RR, 0.55 [95% CI, 0.54-0.56]), compared with the prepandemic period. Reductions in ICU admissions for respiratory illness followed a similar pattern for CMC (2020: RR, 0.56 [95% CI, 0.53-0.59]; 2021: RR, 0.66 [95% CI, 0.63-0.70]) and non-CMC (2020: RR, 0.22 [95% CI, 0.20-0.24]; RR, 0.65 [95% CI, 0.61-0.69]). In-hospital mortality for these conditions decreased among CMC in both 2020 (RR, 0.63 [95% CI, 0.51-0.77]) and 2021 (RR, 0.72 [95% CI, 0.59-0.87]). Conclusions and Relevance: This cross-sectional study found a substantial decrease in severe respiratory disease resulting in hospitalizations, ICU admissions, and mortality during the first 2 years of the pandemic compared with the 3 prepandemic years. These findings suggest that future evaluations of the effect of public health interventions aimed at reducing circulating respiratory pathogens during nonpandemic periods of increased respiratory illness may be warranted.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".