Estimated Numbers of Community Cases of Illness Due to Salmonella, Campylobacter and Verotoxigenic Escherichia Coli: Pathogen-specific Community Rates
Bibliographic record
Abstract
OBJECTIVE: To estimate the annual number of cases of illness due to verotoxigenic Escherichia coli (VTEC), Salmonella and Campylobacter in the Canadian population, using data from the National Notifiable Disease registry (NND), estimates of under-reporting derived from several National Studies on Acute Gastrointestinal Illness, and the literature. METHODS: For each of the three pathogens (VTEC, Salmonella and Campylobacter), data were used to estimate the percentage of cases reported at each step in the surveillance system. The number of reported cases in the NND for each pathogen was then divided by these percentages. In cases where the pathogen-specific estimates were unavailable, data on acute gastrointestinal illness were used, accounting for differences between those with bloody and nonbloody diarrhea. RESULTS: For every case of VTEC, Salmonella and Campylobacter infection reported in the NND, there were an estimated 10 to 47, 13 to 37, and 23 to 49 cases annually in the Canadian population, respectively. CONCLUSIONS: The authors estimate that a significant number of infections due to VTEC, Salmonella and Campylobacter occur each year in Canada, highlighting the fact that these enteric pathogens still pose a significant health burden. Recognizing the significant amount of under-reporting is essential to designing appropriate interventions and assessing the impact of these pathogens in the population.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".