Hospital admissions for acute respiratory tract infections among infants from Nunavut and the burden of respiratory syncytial virus: a 10-year review in regional and tertiary hospitals
Bibliographic record
Abstract
Abstract Background Nunavut is a northern Canadian territory in Inuit Nunangat (Inuit homeland in Canada). Approximately 85% of the population identifies as Inuit. A high proportion of infants in Nunavut are admitted to hospital with acute respiratory tract infection (ARI) but previous studies have been limited in regional and/or short duration of coverage. This study aimed to estimate the incidence rate, microbiology and outcomes of ARI hospitalizations in Nunavut infants. Methods We conducted chart reviews with a retrospective cohort of infants aged <1 year from Nunavut at six Canadian hospitals, including two regional and four tertiary pediatric hospitals January 1, 2010, to June 30, 2020. Descriptive statistics and multivariable logistic regression were performed. Results We identified 1189 ARI admissions of infants during the study period, with an incidence rate of 133.9 per 1000 infants per year (95% confidence interval (CI): 126.8, 141.3). Of these admissions, 56.0% (n=666) were to regional hospitals alone, 72.3% (n=860) involved hospitalization outside of Nunavut, 15.6% (n=185) were admitted into intensive care, and 9.2% (n=109) underwent mechanical ventilation. Of the 730 admissions with a pathogen identified, 45.8% had respiratory syncytial virus (RSV; n=334), for a yearly incidence rate of 37.8 hospitalizations per 1000 infants (95% CI: 33.9, 42.1). Among RSV hospitalizations, 41.1% (n=138) were infants 0-2 months of age and 32.1% (n=108) were > 6months. Interpretation Understanding the high burden of ARI among Nunavut infants can inform health policy and serve as a baseline for assessing the impact of any new interventions targeting infant ARIs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".