Patterns of acute care visits for lower respiratory tract infections in children across Canada
Bibliographic record
Abstract
BackgroundAcute lower respiratory tract infections (LRTI), including bronchiolitis, bronchitis, and pneumonia, are the most common and costly causes of hospitalisations in Canadian children, representing a major burden for healthcare systems.The COVID-19 pandemic severely impacted the presentation and seasonality of these hospitalisations.We sought to gain a better understanding of the patterns and factors of admissions for pediatric LRTIs. ObjectivesThe study aimed to [1] estimate the incidence rate of pediatric LRTI hospitalisations across Canada, before and after the onset of the COVID-19 pandemic, [2] evaluate the factors associated with LRTI tertiary care pediatric and intensive care unit (ICU) admissions. Methods This Canadian population-based longitudinal study used health administrative data of hospitalisations provided by the Discharge Abstract Database (DAD) of the Canadian Institute forHealth Information (CIHI).We included hospitalisations between April 1, 2016, and March 31, 2023 (divided into pre-and post-onset COVID-19 pandemic periods, 2016-20 and 2020-23) for patients between 0 and 17 years of age with a primary diagnosis of LRTI.Seasons were defined from April to March of the following year.Annual incidence rates per 100,000 population and incidence rate ratios (IRR) or annual proportions per 1,000 hospitalisations were computed.Seasonal auto-regressive moving average (SARIMA) models, stratified by sex and diagnosis subgroups, were used to quantify the weekly incidence and seasonality of LRTI hospitalisations.Secondary analyses used multivariate logistic regressions to identify factors associated with ICU and pediatric center admissions. ResultsAmong the 94,391 hospitalisations for LRTIs, 33,463 were admitted to a pediatric tertiary care center and 9,827 to the ICU.Altogether, 45% of hospitalised patients were admitted with a primary diagnosis of bronchiolitis, 43% with a diagnosis of pneumonia, and 12% with a diagnosis of other LRTIs.Compared to the pre-pandemic average, the annual incidence of hospitalisations decreased by 92% in 2020-21 (incidence rate ratio (IRR) = 0.086, 95% confidence interval (CI) 0.08-0.09)and by 51% in 2021-22 (IRR 0.49, 95% CI 0.48-0.50); in 2022-23, it increased by 48% (IRR 1.48, 95% CI 1.46-1.51).Seasonal patterns shifted after the onset of the pandemic.The typical seasonality was disrupted in 2020, with no clear pattern observed, and an outlying peak occurred in June 2022, deviating from the previously established winter seasonality.However, by the 2022-23 season, the patterns appeared to be returning to pre-pandemic trends.The annual proportion of admissions to pediatric centers and to the ICU increased between 2016-17 and 2022-23, from 256.6 to 378.5 admissions per 1,000 LRTI hospitalisations for pediatric Contributions of Authors
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".