A Review of Outbreaks of Foodborne Disease Associated with Passenger Ships: Evidence for Risk Management
Bibliographic record
Abstract
OBJECTIVE: Foodborne disease outbreaks on ships are of concern because of their potentially serious health consequences for passengers and crew and high costs to the industry. The authors conducted a review of outbreaks of foodborne diseases associated with passenger ships in the framework of a World Health Organization project on setting guidelines for ship sanitation. METHODS: The authors reviewed data on 50 outbreaks of foodborne disease associated with passenger ships. For each outbreak, data on pathogens/toxins, type of ship, factors contributing to outbreaks, mortality and morbidity, and food vehicles were collected. RESULTS: The findings of this review show that the majority of reported outbreaks were associated with cruise ships and that almost 10,000 people were affected. Salmonella spp were most frequently associated with outbreaks. Foodborne outbreaks due to enterotoxigenic E. coli spp, Shigella spp, noroviruses (formally called Norwalk-like viruses), Vibrio spp, Staphylococcus aureus, Clostridium perfringens, Cyclospora sp, and Trichinella sp also occurred on ships. Factors associated with the outbreaks reviewed include inadequate temperature control, infected food handlers, contaminated raw ingredients, cross-contamination, inadequate heat treatment, and onshore excursions. Seafood was the most common food vehicle implicated in outbreaks. CONCLUSIONS: Many ship-associated outbreaks could have been prevented if measures had been taken to ensure adequate temperature control, avoidance of cross-contamination, reliable food sources, adequate heat treatment, and exclusion of infected food handlers from work.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".