The acute care burden of asthma in children was profoundly reduced during the COVID-19 pandemic: A multi-centre Canadian retrospective study
Bibliographic record
Abstract
Objectives: Asthma is a chronic lung condition that can be exacerbated when triggered by viruses. Pandemic public health restrictions aimed to reduce COVID-19 transmission indirectly effected other circulating viruses. This study assessed the impact of the pandemic and associated public health measures on acute paediatric asthma across four tertiary sites in three Canadian provinces. We queried whether pandemic-related changes would impair preventive care and delay presentation to care, increasing asthma exacerbation severity. Methods: This retrospective study compared the frequency of acute care access and severity of presentation to emergency departments (ED) for acute asthma to four tertiary care children's hospitals during the COVID-19 pandemic (from March 17, 2020 to June 30, 2021) to a pre-lockdown control period (July 1, 2018 to March 16, 2020). Data was subjected to interrupted time series and Chi-square analysis. Results: Our study included 26,316 acute asthma visits to ED. Sites experienced a 63% to 89% reduction in acute asthma visits during the pandemic, compared with pre-lockdown controls, and a 17% to 85% reduction in asthma, that is out of proportion as a fraction of all-cause ED visits. For asthma, there was no difference in severity measured by rate of ward admission or rate of Paediatric Intensive Care Unit (PICU) admission. Conclusions: Public health measures appear to have resulted in a specific protective association on acute asthma with reduced acute care utilization over and above the reduction in all-cause presentations, without an increase in severity upon presentation. Our study indicates an importance to antiviral public health and engineering strategies to reduce viral transmission and thereby asthma morbidity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".