Exploring the gap between notified and diagnosed cases of Foodborne Diseases: evidence from a time-trend analysis in Italy.
Bibliographic record
Abstract
Background: Foodborne diseases are a major global public health concern, causing significant morbidity and mortality worldwide. The COVID-19 pandemic has had widespread effects on various aspects of life, including the food supply chain, potentially impacting the incidence of foodborne diseases. This study aims to analyze the differences between notified and diagnosed cases and investigate the potential impact of the COVID-19 pandemic on foodborne diseases in the metropolitan area of Bologna, Italy. Study Design: A retrospective time trend analysis from two databases was conducted. Methods: The Local Health Authority of Bologna collected data re/Emilia-Romagna Region on the infectious disease reporting system over a six-year period (2017-2022), which included three years of the COVID-19 pandemic. This data was compared with information collected during the same period at the microbiology laboratory serving the entire metropolitan area of Bologna. Statistical methods included percent change calculations, binomial tests, annual averages, gender and age stratification, and trend analysis with regressio. Results: An increase (+34.4%, P-value ≤ 0.01) in notified cases during the pandemic - compared to the pre-pandemic period - was found. However, no differences were observed in diagnosed cases when comparing the two periods. The year 2021 saw a significant increase in reported cases of foodborne diseases among schoolers (+300.0%) and workers (+133.3%) compared to 2020. On the other hand, diagnosed cases decreased significantly in 2020 (-19.1%, P<0.01) and increased in 2021 (+21.9%, P<0.01). In absolute terms, a stark difference was observed between notified and diagnosed cases across all the study years (2017-2022). Conclusions: This study highlights the discrepancy between notified and diagnosed cases of foodborne diseases and how the COVID-19 pandemic has increased reporting without affecting transmission. These findings contribute to the ongoing discussion on improving foodborne disease reporting systems.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".