Hospital admissions for lower respiratory tract infections among infants in the Canadian Arctic: a cohort study
Bibliographic record
Abstract
<h3>Background:</h3> It is unknown whether this burden of disease of lower respiratory tract infections is comparable across the Canadian Arctic. The objectives of this surveillance study were to compare the rates of hospital admission for lower respiratory tract infection and the severity of infection across Arctic Canada, and to describe the responsible viruses. <h3>Methods:</h3> We performed a prospective multicentre surveillance study of infants less than 1 year of age admitted in 2009 with lower respiratory tract infection to all hospitals (5 regional, 4 tertiary) in the Northwest Territories, Nunavut and Nunavik to assess for regional differences. Nasopharyngeal aspirates were processed by means of a polymerase chain reaction respiratory viral panel, testing for 20 respiratory viruses and influenza A (H1N1). The role of coinfection was assessed by means of regression analysis for length of stay (short: < 7 d; long: > 14 d). Outcomes compared included rates of lower respiratory tract infection, respiratory syncytial virus infection, transfer to tertiary hospital and severe lower respiratory tract infection (respiratory failure, intubation and mechanical ventilation, and/or cardiopulmonary resuscitation). <h3>Results:</h3> There were 348 admissions for lower respiratory tract infection in the population of interest in 2009. Rates of admission per 1000 live births varied significantly, from 39 in the Northwest Territories to 456 in Nunavik (<i>p</i> < 0.001). The rates of tertiary admissions and severe lower respiratory tract infection per 1000 live births in the Northwest Territories were 5.6 and 1.4, respectively, compared to 55.9 and 17.1, respectively, in Nunavut and 52.0 and 20.0, respectively, in Nunavik (<i>p</i> ≤ 0.001). Respiratory syncytial virus was the most common virus identified (124 cases [41.6% of those tested]), and coinfection was detected in 51 cases (41.1%) of infection with this virus. Longer length of stay was associated with coinfection (odds ratio [OR] 2.64) and underlying risk factors (OR 4.39). Length of stay decreased by 32.2% for every 30-day increase in age (OR 0.68). <h3>Interpretation:</h3> Nunavut and Nunavik have very elevated rates of lower respiratory tract infection, with severe outcomes. Respiratory syncytial virus was the most common virus identified, and coinfection was associated with longer length of stay. Targeted public health interventions are required to reduce the burden of disease for infants residing in these Arctic regions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".