Beyond flu: Trends in respiratory infection outbreaks in Ontario healthcare settings from 2007 to 2017, and implications for non-influenza outbreak management
Bibliographic record
Abstract
BACKGROUND: Outbreaks cause significant morbidity and mortality in healthcare settings. Current testing methods can identify specific viral respiratory pathogens, yet the approach to outbreak management remains general. OBJECTIVES: Our aim was to examine pathogen-specific trends in respiratory outbreaks, including how attack rates, case fatality rates and outbreak duration differ by pathogen between hospitals and long-term care (LTC) and retirement homes (RH) in Ontario. METHODS: Confirmed respiratory outbreaks in Ontario hospitals and LTC/RH reported between September 1, 2007, and August 31, 2017, were extracted from the integrated Public Health Information System (iPHIS). Median attack rates and outbreak duration and overall case fatality rates of pathogen-specific outbreaks were compared in both settings. RESULTS: Over the 10-year surveillance period, 9,870 confirmed respiratory outbreaks were reported in Ontario hospitals and LTC/RH. Influenza was responsible for most outbreaks (32% in LTC/RH, 51% in hospitals), but these outbreaks were shorter and had lower attack rates than most non-influenza outbreaks in either setting. Human metapneumovirus, while uncommon (<4% of outbreaks) had high case fatality rates in both settings. CONCLUSION: Attack rates and case fatality rates varied by pathogen, as did outbreak duration. Development of specific outbreak management guidance that takes into account pathogen and healthcare setting may be useful to limit the burden of respiratory outbreaks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".