An overview of foodborne outbreaks in Canada reported through Outbreak Summaries: 2008-2014
Bibliographic record
Abstract
BACKGROUND: Enteric outbreak investigation in Canada is performed at the local, provincial/territorial (P/T) and federal levels. Historically, routine surveillance of outbreaks did not occur in all jurisdictions and so the Public Health Agency of Canada, in partnership with P/T public health authorities, developed a secure, web-based Outbreak Summaries (OS) Reporting System to address this gap. OBJECTIVE: This analysis summarizes the foodborne outbreak investigations reported to the OS Reporting System between 2008 and 2014. METHODS: Finalised reports of investigations between 2008 and 2014 for all participating jurisdictions in Canada were extracted and descriptive analysis was carried out for foodborne outbreaks on etiological agent, severity of illness, outbreak duration, exposure setting and outbreak source. RESULTS: was the most commonly reported cause of foodborne outbreak (40.9%) and Enteritidis was the most common serotype reported. Foodborne outbreaks accounted for 3,301 illnesses, 225 hospitalizations and 30 deaths. Overall, 38.3% of foodborne outbreaks were reported to have occurred in a community and 32.2% were associated with a food service establishment. Most foodborne outbreak investigations (63.5%) reported a specific food associated with the outbreak, most frequently meat. CONCLUSION: The OS Reporting System supports information sharing and collaboration among Canadian public health partners and offers an opportunity to obtain a national picture of foodborne outbreaks. This analysis has demonstrated the utility of the OS Reporting System data as an important and useful source of information to describe foodborne outbreak investigations in Canada.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".