Restaurant-associated foodborne illness outbreaks in the United States: an epidemiological assessment comparing outbreak occurrence and density before [2000–2019] and during [2020] the COVID-19 pandemic
Bibliographic record
Abstract
Background: Dining experiences and safety protocols at restaurants changed during the COVID-19 pandemic as efforts were made to decrease SARS-CoV-2 transmission. This retrospective observational study evaluated trends in foodborne illness outbreaks associated with restaurant dining prior to and during the COVID-19 pandemic in the United States (US) to assess whether COVID-19-specific risk mitigation strategies may have had a potential effect on foodborne enteric illness outbreaks. Using key concepts learned from the data, this study provides recommendations for hazard control and risk mitigation. Methods: Publicly available data from the Center for Disease Control and Prevention’s National Outbreak Reporting System database were collected on foodborne illness outbreaks associated with restaurant dining from 2000 through 2020. The number of outbreaks and number of cases per outbreak were summarized by year, month, etiology, and suspected exposure setting origin. A Wilcoxon Rank Sum test and a one sample t-test were used to assess for differences in the crude number of restaurant-associated foodborne illness cases and outbreaks (i.e., occurrence) and the number of restaurant-associated foodborne illness cases per outbreak (i.e., density) prior to and during the COVID-19 pandemic, respectively. Results: Approximately 45% of 4,637 foodborne outbreaks were associated with exposure at a restaurant between 2000 and 2020. Overall, there was a 49% decrease in the average number of outbreaks per year in 2020 compared to 2000 through 2019 and a statistically significant decrease in the occurrence of restaurant-associated outbreaks per year in 2020 compared to the 20 years prior was observed (P<0.001). However, there was no statistically significant difference in the density, defined as the number of illnesses per restaurant-associated foodborne illness outbreak, between 2000 and 2019 when compared to 2020 (P=0.439). Conclusions: The findings of this study suggest that increased infection prevention practices specific to COVID-19 may be potentially effective in minimizing the number of enteric illness outbreaks, but they may not be as effective at reducing the density of outbreaks. Establishing multilayered infection control plans that incorporate three well-established frameworks: Hazard Analysis and Critical Control Points (HACCP), the chain of infection, and the National Institute for Occupational Safety and Health hierarchy of controls, may help restaurants more holistically prepare for and respond to future outbreaks or pandemics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".