Over 40% of all U.S. Salmonella illnesses are attributed to consumption of contaminated meat and poultry products each year. Determining which serotypes cause the most outbreak illnesses associated with specific meat and poultry types can inform prevention measures. We developed an approach to categorize serotypes using outbreak illness burden (high, moderate, low) and trajectory (increased, stable, decreased). We used data from 192 foodborne Salmonella outbreaks resulting in 7,077 illnesses, 1,330 hospitalizations, and 9 deaths associated with chicken, turkey, beef, or pork during 2012-2021. We linked each meat and poultry type to 1-3 serotypes that we categorized as high outbreak illness burden and increased trajectory during 2021. Calculation and public display of outbreak illness burden and trajectory annually could facilitate the prioritization of serotypes for prevention by federal and state health and regulatory agencies and by the meat and poultry industry.

UNASSIGNED: Foodborne diseases pose a significant public health concern globally. This study aims to analyze the correlation between disease prevalence and climatic conditions, forecast the pattern of foodborne disease outbreaks, and offer insights for effective prevention and control strategies and optimizing health resource allocation policies in Guizhou Province.
UNASSIGNED: This study utilized the χ2 test and four comprehensive prediction models to analyze foodborne disease outbreaks recorded in the Guizhou Foodborne Disease Outbreak system between 2012 and 2022. The best-performing model was chosen to forecast the trend of foodborne disease outbreaks in Guizhou Province, 2023-2025.
UNASSIGNED: Significant variations were observed in the incidence of foodborne disease outbreaks in Guizhou Province concerning various meteorological factors (all P≤0.05). Among all models, the SARIMA-ARIMAX combined model demonstrated the most accurate predictive performance (RMSE: Prophet model=67.645, SARIMA model=3.953, ARIMAX model=26.544, SARIMA-ARIMAX model=26.196; MAPE: Prophet model=42.357%, SARIMA model=37.740%, ARIMAX model=15.289%, SARIMA-ARIMAX model=13.961%).
UNASSIGNED: The analysis indicates that foodborne disease outbreaks in Guizhou Province demonstrate distinct seasonal patterns. It is recommended to concentrate prevention efforts during peak periods. The SARIMA-ARIMAX hybrid model enhances the precision of monthly forecasts for foodborne disease outbreaks, offering valuable insights for future prevention and control strategies.

Unsafe food causes 600 million cases of foodborne diseases and 420,000 deaths every year. Meanwhile, biological toxins such as poisonous mushrooms, saponins, and aflatoxin can cause significant damage to humans. Therefore, it is particularly important to study foodborne disease outbreaks caused by biotoxins (FDOB). We collected FDOB in Yantai City from 2013 to 2022 and further established a corresponding database. Statistical analysis was carried out according to time, place, pathogen, and contamination of pathogenic factors. There were 128 FDOB, resulting in 417 patients and 6 deaths. The third quarter was a high season for foodborne disease outbreaks, the number of events, patients and deaths accounted for 65.63% (84/128), 55.88% (233/417), and 100% (6/6) of the total number, respectively. The highest number of outbreaks per 10,000 persons was Qixia (0.41), followed by Zhifu (0.36) and Laiyang (0.33). The top three causes of outbreaks were poisonous mushroom toxin, saponins and hemagglutinin, and Lagenaria siceraria (Molina) Standl. Sixty-five (50.78%) outbreaks were attributed to poisonous mushroom toxin, 18 (14.06%) outbreaks to saponin and hemagglutinin, and 12 (9.38%) outbreaks to L. siceraria (Molina) Standl. The largest number of outbreaks, patients and deaths all occurred in families, accounting for 82.81% (106/128) outbreaks, 66.19% (276/417) patients, and 100% (6/6) deaths, respectively. Followed by catering service establishments, accounting for 14.84% (19/128), 30.22% (126/417), and 0% (0/6), respectively. The main poisoning link of outbreaks was ingestion and misuse, accounting for 72.66% (93/128), followed by improper processing, accounting for 20.31% (26/128). It is necessary to carry out targeted family publicity and education, strengthen the integration of medical and prevention, explore innovative monitoring and early warning mechanisms for foodborne diseases, and reduce the occurrence of underreporting of foodborne disease outbreaks.

Salmonella enterica serovar Enteritidis (SE) remains a frequent cause of foodborne illnesses associated with the consumption of contaminated hen eggs. Such a food-pathogen association has been demonstrated epidemiologically, but the molecular basis for this association has not been explored. Comparative genomic analysis was implemented to decipher the phylogenomic characteristics, antimicrobial resistance, and virulence potential of eggs-associated SE. Analyzing 1,002 genomes belonging to 841 sequence types of food-isolated SE strains suggests a high genomic similarity within the egg-related lineage, which is phylogenetically close to SE strains isolated from poultry but is different from those isolated from beef. Core genome- and single nucleotide polymorphism (SNP)-based phylogeny of 74 SE strains of egg origin showcased two distinct sublineages. Time-scaled phylogeny supported the possibility of a common ancestor of egg-related SE lineages. Additionally, genome mining revealed frequent antibiotic resistance due to the presence of aac(6\')-Iaa and mdsAB encoded on the genomes of egg-associated SE strains. For virulence gene profiling, 103-113 virulence determinants were identified in the egg-associated SE, which were comparable to 112 determinants found in human-associated SE, emphasizing the capacity of egg-associated strains to infect humans and cause diseases. The findings of this study proved the genomic similarity of egg-associated SE strains, and these were closely related to poultry strains. The egg-associated strains also harbor virulence genes equivalent to those found in human-associated SE strains. The analysis provided critical insights into the genetic structure, phylogenomics, dynamics of virulence, and antibiotic resistance of Salmonella Enteritidis, circulating in eggs and emphasizing the necessity of implementing anti-Salmonella intervention strategies, starting at the production stage of the poultry supply chain.

Since the late 1990s, food safety certification has emerged as a prominent and influential regulatory mechanism in both the private and public spheres of the contemporary agri-food system. Food safety standards protect consumers from foodborne illnesses and help producers avoid the massive economic losses associated with food safety breaches. We empirically examine the relationship between foodborne disease outbreaks and certification adoption by utilizing the data on food safety certification adoption in the United States and Europe from 2015 through 2020. In our regression models, food safety certification along with select economic variables such as gross domestic product are used to explain the number of illnesses caused by foodborne disease outbreaks. For the United States at the state level, we found that certifications to SQF, PrimusGFS, BRC, or FSSC 22000 are negatively associated with the number of foodborne illnesses. For the case of Europe at the country level, certifications to ISO 22000 or FSSC 22000 are negatively associated with the number of foodborne illnesses. We then proceed to use machine learning techniques to examine how well we can use food safety certification data to predict foodborne disease outbreaks. Applying several algorithms (ordinary least squares, multinomial, decision tree, and random forest) to the U.S. data, we found that our models with food safety certification adoption can predict the number of U.S. foodborne illnesses or deaths with a relatively high degree of precision (testing accuracy at around 70% or better). Feature importance analysis allows us to inspect the relative importance of each explanatory variable (or feature) for making accurate predictions of the illness or death numbers. Through ranking the importance of explanatory variables, our study reveals that certification information could be the second most important variable (after gross domestic product) contributing to explain foodborne disease outbreaks.

Shawarma, a popular meat-based fast food could be a source of foodborne outbreak due to non-typhoidal Salmonella (NTS). A clustering of acute gastrointestinal (GI) illness following intake of chicken shawarma occurred primarily among the staff and students of a tertiary care hospital in southern India.
A case-control study was conducted among 348 undergraduate medical students (33 cases, 315 controls).  Data was collected using direct interviews and a simple online questionnaire. Epidemiological associations of GI illness were evaluated at three levels of exposure namely-eating food from any restaurant, eating food from the implicated food outlet, eating chicken shawarma from the implicated outlet.
Of 33 cases, 26 had consumed food from a particular food outlet, 4 from other outlets, and 3 did not report eating out. Consumption of food from the suspected food outlet was significantly associated with GI illness (odds ratio 121.8 [95% CI 28.41 to 522.66]; P<0.001); all the 26 cases who had eaten from the particular outlet had eaten chicken shawarma. By comparison, only one of the 315 controls had eaten this dish. Of the 27 persons (cases as well as controls) who had consumed chicken shawarma from the outlet, 26 were ill. Culture of stool samples from 10 affected individuals and implicated food item yielded Salmonella Enteritidis.
Meat-based shawarma is a potential source of NTS infection. Food safety authorities should enforce guidelines for safe preparation and sale of shawarmas and similar products.

UNASSIGNED: Foodborne diseases are a growing public health problem and have caused a large burden of disease in China. This study analyzed epidemiological characteristics of foodborne diseases in China in 2020 to provide a scientific basis for prevention and control measures.
UNASSIGNED: Data were collected from 30 of 31 provincial-level administrative divisions (PLADs) in the mainland of China, excluding Xizang (Tibet) Autonomous Region, via the National Foodborne Disease Outbreaks Surveillance System. The number and proportion of outbreaks, illnesses, hospitalizations, deaths by setting, pathogen-food category pairs and etiology were calculated.
UNASSIGNED: In 2020, 7,073 foodborne disease outbreaks were reported, resulting in 37,454 illnesses and 143 deaths. Among the identified pathogens, microbial pathogens were the most common confirmed etiology, accounting for 41.7% of illnesses. Poisonous mushrooms caused the largest proportion of outbreaks (58.0%) and deaths (57.6%). For venues where foodborne disease outbreaks occur, household had the highest number of outbreaks (4,140) and deaths (128), and catering service locations caused the largest proportion of illnesses (59.9%). Outbreaks occurring between June and September accounted for 62.8% of total outbreaks.
UNASSIGNED: Foodborne disease outbreaks mainly occurred in households. Microbial pathogens remained the top cause of outbreak-associated illnesses. Poisonous mushrooms were ranked the top cause of deaths in private homes in China. The supervision and management of food safety and health education should be strengthened to reduce the burden of foodborne diseases. Publicity should be increased to reduce the incidence of mushroom poisonings in families, and supervision and management of food should be strengthened to reduce microbial contamination.

Efficient communication and coordination are needed between countries to prevent, detect and respond to international food safety events. While communication tools, networks and systems exist, current evidence suggests that they are only useful within particular contexts and several only target specific geographic areas. There is a need to unpack and explore the mechanisms of how and in what context such communication tools and their components are effective at facilitating international communication and coordination to keep food safe and mitigate the burden of foodborne disease around the world.A realist synthesis was undertaken to understand how and why certain processes and structures of communication tools, used during international food safety events, influence their utility and effectiveness according to different contextual factors. The focus of this review was explanatory and aimed to develop and refine theory regarding how contextual factors trigger specific processes and mechanisms to produce outcomes. Using the realist context-mechanism-outcome configuration of theory development, a range of sources was used to develop an initial programme theory, including the authors\' experience, a scoping review of published papers and grey literature and input from an expert reference committee. Literature was then systematically located and synthesised from several databases with input from the expert reference committee to refine the programme theory.The programme theory developed indicates that when a country has interests in food import or export, has the technical infrastructure to detect and respond to food safety events, and is governed in accordance with regional and/or global laws and regulations relating to food control and global health security, then specific mechanisms will facilitate various outcomes. Mechanisms include trust, experience, support, awareness, understanding, a sense of community, standardisation and intersectoral collaboration. The outcomes include using communication tools to relay information abroad and the prevention of foodborne diseases, among others.Components of such communication tools may be adapted according to different contextual factors to promote, support and improve their use. Improving international coordination and communication during international food safety events is in the interest of global health security and can mitigate the global burden of foodborne disease.

The China National Center for Food Safety Risk Assessment (CFSA) uses the Foodborne Disease Monitoring and Reporting System (FDMRS) to monitor outbreaks of foodborne diseases across the country. However, there are problems of underreporting or erroneous reporting in FDMRS, which significantly increase the cost of related epidemic investigations. To solve this problem, we designed a model to identify suspected outbreaks from the data generated by the FDMRS of CFSA. In this study, machine learning models were used to fit the data. The recall rate and F1-score were used as evaluation metrics to compare the classification performance of each model. Feature importance and pathogenic factors were identified and analyzed using tree-based and gradient boosting models. Three real foodborne disease outbreaks were then used to evaluate the best performing model. Furthermore, the SHapley Additive exPlanation value was used to identify the effect of features. Among all machine learning classification models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance, with the highest recall rate and F1-score of 0.9699 and 0.9582, respectively. In terms of model validation, the model provides a correct judgment of real outbreaks. In the feature importance analysis with the XGBoost model, the health status of the other people with the same exposure has the highest weight, reaching 0.65. The machine learning model built in this study exhibits high accuracy in recognizing foodborne disease outbreaks, thus reducing the manual burden for medical staff. The model helped us identify the confounding factors of foodborne disease outbreaks. Attention should be paid not only to the health status of those with the same exposure but also to the similarity of the cases in time and space.

UNASSIGNED: The Florida Complaint and Outbreak Reporting System (FL-CORS) database is used by the Florida Department of Health\'s Food and Waterborne Disease Program as one of the tools to detect foodborne disease outbreaks (FBOs). We present a descriptive and spatial network analysis of FL-CORS data collected during 2015 to 2018. We also quantified FBOs that were investigated and confirmed because of a filed complaint and the etiological agents involved in these outbreaks. An increasing number of unique complaints filed in FL-CORS was observed during 2015 to 2018, with a sharp increase during 2017 to 2018 and a different seasonal pattern in 2018. The preferred mechanism of reporting varied by age group, with younger people more frequently filing complaints online and older people preferring reporting in person or by phone. Spatial network analysis revealed that 87% of complaints had the same county of residence and county of presumed exposure. Frequency of complaints was negatively associated with linear distance between place of residence and place of exposure at the zip code level. Counties located in North and Central Florida, as well as some coastal areas in South Florida, had higher incidence rates of complaints. Those counties tend to have a large population density, and some are popular vacation destinations. On average, 96 FBOs were reported in Florida annually, of which 60% were confirmed with successful identification of the causative agent. The 56% of the confirmed FBOs were triggered by a complaint. Throughout the years, 2.4 to 2.8 FBOs and 1.4 confirmed FBOs were identified per 100 complaints. Ciguatera toxin was the cause of 40% of all FBOs in Florida, and only 28% of outbreaks were detected through complaints. In contrast, complaints were the main source of identifying outbreaks of norovirus, nontyphoidal Salmonella enterica, and scombroid food poisoning, as well as rare outbreaks of Clostridium perfringens, Cryptosporidium spp., Shigella spp., and Vibrio vulnificus.
CONCLUSIONS: