Escherichia coli O103 outbreak associated with minced celery among hospitalized individuals in Victoria, British Columbia, 2021
Bibliographic record
Abstract
Background: In April 2021, a Shiga toxin-producing Escherichia coli (E. coli) (STEC) O103 outbreak was identified among patients at two hospitals in Victoria, British Columbia (BC). The objective of this study is to describe this outbreak investigation and identify issues of food safety for high-risk products prepared for vulnerable populations. Methods: Confirmed cases of E. coli O103 were reported to the Island Health communicable disease unit. The provincial public health laboratory conducted whole genome sequencing on confirmed case isolates, as per routine practice for STEC in BC. Exposure information was obtained through case interviews and review of hospital menus. Federal and local public health authorities conducted an inspection of the processing plant for the suspect source. Results: Six confirmed cases of E. coli O103 were identified, all related by whole genome sequencing. The majority of cases were female (67%) and the median age was 61 years (range 24–87 years). All confirmed cases were inpatients or outpatients at two hospitals and were exposed to raw minced celery within prepared sandwiches provided by hospital food services. A local processor supplied the minced celery exclusively to the two hospitals. Testing of product at the processor was infrequent, and chlorine rinse occurred before mincing. The spread of residual E. coli contamination through the mincing process, in addition to temperature abuse at the hospitals, are thought to have contributed to this outbreak. Conclusion: Raw vegetables, such as celery, are a potential source of STEC and present a risk to vulnerable populations. Recommendations from this outbreak include more frequent testing at the processor, a review of the chlorination and mincing process and a review of hospital food services practices to mitigate temperature abuse.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".