Estimates of Foodborne Illness–Related Hospitalizations and Deaths in Canada for 30 Specified Pathogens and Unspecified Agents
Bibliographic record
Abstract
Foodborne illness estimates help to set food safety priorities and create public health policies. The Public Health Agency of Canada estimates that 4 million episodes of foodborne illness occur each year in Canada due to 30 known pathogens and unspecified agents. The main objective of this study was to estimate the number of domestically acquired foodborne illness-related hospitalizations and deaths. Using the estimates of foodborne illness for Canada along with data from the Canadian Hospitalization Morbidity Database (for years 2000-2010) and relevant international literature, the number of hospitalizations and deaths for 30 pathogens and unspecified agents were calculated. Analysis accounted for under-reporting and underdiagnosis. Estimates of the proportion foodborne and the proportion travel-related were incorporated for each pathogen. Monte Carlo simulations were performed to account for uncertainty generating mean estimates and 90% probability intervals. It is estimated that each year there are 4000 hospitalizations (range 3200-4800) and 105 (range 75-139) deaths associated with domestically acquired foodborne illness related to 30 known pathogens and 7600 (range 5900-9650) hospitalizations and 133 (range 77-192) deaths associated with unspecified agents, for a total estimate of 11,600 (range 9250-14,150) hospitalizations and 238 (range 155-323) deaths associated with domestically acquired foodborne illness in Canada. Key pathogens associated with these hospitalizations or deaths include norovirus, nontyphoidal Salmonella spp., Campylobacter spp., VTEC O157 and Listeria monocytogenes. This is the first time Canada has established pathogen-specific estimates of domestically acquired foodborne illness-related hospitalizations and deaths. This information illustrates the substantial burden of foodborne illness in Canada.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".