Influenza outbreaks in Ontario hospitals, 2012–2016
Bibliographic record
Abstract
Background: Influenza outbreaks in hospital settings affect vulnerable patient populations and pose considerable risk of morbidity and mortality; however, key information regarding these outbreaks is limited.Objective: To describe surveillance data on influenza outbreaks in Ontario hospitals between 2012-13 and 2015-16 and compare H3N2-and H1N1-dominant influenza seasons.Methods: Hospital laboratory-confirmed influenza outbreaks occurring between September 1, 2012 and August 31, 2016 were analysed for indicators of outbreak duration and severity (case attack rate, pneumonia rate and fatality rate).Frequency, duration and severity of influenza A outbreaks were compared between H3N2-(2012-13, 2014-15) and H1N1-dominant seasons .Results: Over the four years, there were 256 hospital outbreaks involving 1,586 patients that included 91 cases of pneumonia and 40 deaths.The total number of outbreaks was lowest in the 2015-16 (n=36) and highest in the 2014-15 (n=117) influenza seasons.The 2014-15 season also had the highest number of influenza cases (n=753), pneumonia cases (n=46), fatalities (n=18) and hospital sites reporting ≥1 outbreak (n=72).Median outbreak duration ranged from 4.5 days in 2013-14 to 6.0 days in 2015-16.Comparisons of H3N2 and H1N1 seasons did not identify statistically significant differences in outbreak duration or severity; however, significantly more influenza A outbreaks than influenza B outbreaks were reported in H3N2 seasons compared with H1N1 seasons (p<0.05).Conclusion: While H3N2-dominant years contribute to influenza morbidity and mortality through an increased number of hospital outbreaks, the duration and severity of influenza A outbreaks are not significantly different in H3N2 and H1N1 seasons.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".