Healthcare utilization in Canadian children and young adults with asthma during the COVID-19 pandemic
Bibliographic record
Abstract
Literature is limited regarding the COVID-19 pandemic's impact on health services use in younger Canadian populations with asthma. We utilized health administrative databases from January 2019-December 2021 for a population-based cross-sectional study to identify Ontario residents 0-25 years old with physician-diagnosed asthma and calculate rates of healthcare use. Multivariable negative binomial regression analysis was used to adjust for confounders. We included 716,690 children and young adults ≤25 years. There was a sharp increase of ICS and SABA prescription rates at the start of the pandemic (March 2020) of 61.7% and 54.6%, respectively. Monthly virtual physician visit rates increased from zero to 0.23 per 100 asthma population during the pandemic. After adjusting for potential confounders, rate ratios (RR) with 95% confidence intervals (CI) showed that the pandemic was associated with significant decrease in hospital admissions (RR = 0.21, 95% CI: 0.18-0.24), emergency department visits (RR = 0.35, 95% CI: 0.34-0.37), and physician visits (RR = 0.61, 95% CI: 0.60-0.61). ICS and SABA prescriptions filled also significantly decreased during the pandemic (RR = 0.58, 95% CI: 0.57-0.60 and RR = 0.47, 95% CI: 0.46-0.48, respectively). This Canadian population-based asthma study demonstrated a dramatic decline in physician and emergency department visits, hospitalizations, and medication prescriptions filled during the COVID-19 pandemic. An extensive evaluation of the factors contributing to an 80% reduction in the risk of hospitalization may inform post-pandemic asthma management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".