Review: Impact of food safety on global trade.

Food safety encompasses the supply and assurance of safe, high-quality food for consumers. It is a crucial aspect of food security, gaining greater global attention due to the increasing number of widespread foodborne incidents. International trade is expanding as countries increasingly rely on each other to secure a sufficient and diverse food supply. Beyond this, concerns about food safety have become more prevalent due to various factors. Therefore, this review aims to investigate the effects of food safety-associated risks on the international trade of food and related products. A total of 37 published studies retrieved using different search engines were included in this review. This review revealed that because of rapid population growth and rising food demand in developing nations, agricultural intensification is growing. It has been found that foodborne illnesses and associated discrepancies can impede the international trade of food commodities. Trade bans due to the fear of foodborne illnesses are growing. The consequences of foodborne diseases are multifaceted and include financial losses from trade restrictions, medical costs for prevention or control, resource depletion and a decline in food production. The overall effects are increased international trade tensions and livelihood vulnerability to poverty, notably for small-scale livestock producers. Potential food contaminants include microbes, pesticides, pharmaceutical residues, heavy metals and fraudulent such as improper food processing, mislabelling, poor packaging, adulteration and substitution. Hence, countries are encouraged to harmonize the rights and duties set by the World Trade Organization under sanitary and phytosanitarys to maximize their advantages in global markets. Based on this evidence, we recommend that each country develop and integrate regulations that would ensure the safety of both domestic and international food production systems. Furthermore, the global community should either revise the current functioning food regulatory and monitoring body or establish a more genuine collaborative network.

Comprehensive analysis of predominant pathogenic bacteria and viruses in seafood products.

Given the growing global demand for seafood, it is imperative to conduct a comprehensive study on the prevalence and persistence patterns of pathogenic bacteria and viruses associated with specific seafood varieties. This assessment thoroughly examines the safety of seafood products, considering the diverse processing methods employed in the industry. The importance of understanding the behavior of foodborne pathogens, such as Salmonella typhimurium, Vibrio parahaemolyticus, Clostridium botulinum, Listeria monocytogenes, human norovirus, and hepatitis A virus, is emphasized by recent cases of gastroenteritis outbreaks linked to contaminated seafood. This analysis examines outbreaks linked to seafood in the United States and globally, with a particular emphasis on the health concerns posed by pathogenic bacteria and viruses to consumers. Ensuring the safety of seafood is crucial since it directly relates to consumer preferences on sustainability, food safety, provenance, and availability. The review focuses on assessing the frequency, growth, and durability of infections that arise during the processing of seafood. It utilizes next-generation sequencing to identify the bacteria responsible for these illnesses. Additionally, it analyzes methods for preventing and intervening of infections while also considering the forthcoming challenges in ensuring the microbiological safety of seafood products. This evaluation emphasizes the significance of the seafood processing industry in promptly responding to evolving consumer preferences by offering current information on seafood hazards and future consumption patterns. To ensure the continuous safety and sustainable future of seafood products, it is crucial to identify and address possible threats.

Foodborne pathogenic bacteria in wild European hedgehogs (Erinaceus europaeus).

BACKGROUND: European hedgehogs (Erinaceus europaeus) are widely distributed across Europe. They may play an important role by spreading zoonotic bacteria in the environment and to humans and animals. The aim of our work was to study the prevalence and characteristics of the most important foodborne bacterial pathogens in wild hedgehogs. RESULTS: Faecal samples from 148 hospitalised wild hedgehogs originating from the Helsinki region in southern Finland were studied. Foodborne pathogens were detected in 60% of the hedgehogs by PCR. Listeria (26%) and STEC (26%) were the most common foodborne pathogens. Salmonella, Yersinia, and Campylobacter were detected in 18%, 16%, and 7% of hedgehogs, respectively. Salmonella and Yersinia were highly susceptible to the tested antimicrobials. Salmonella Enteritidis and Listeria monocytogenes 2a were the most common types found in hedgehogs. All S. Enteritidis belonged to one sequence type (ST11), forming four clusters of closely related isolates. L. monocytogenes was genetically more diverse than Salmonella, belonging to 11 STs. C. jejuni ST45 and ST677, Y. pseudotuberculosis O:1 of ST9 and ST42, and Y. enterocolitica O:9 of ST139 were also found. CONCLUSIONS: Our study shows that wild European hedgehogs should be considered an important source of foodborne pathogens, and appropriate hygiene measures after any contact with hedgehogs and strict biosecurity around farms are therefore important.

Reported Incidence of Infections Caused by Pathogens Transmitted Commonly Through Food: Impact of Increased Use of Culture-Independent Diagnostic Tests - Foodborne Diseases Active Surveillance Network, 1996-2023.

Reducing foodborne disease incidence is a public health priority. This report summarizes preliminary 2023 Foodborne Diseases Active Surveillance Network (FoodNet) data and highlights efforts to increase the representativeness of FoodNet. During 2023, incidences of domestically acquired campylobacteriosis, Shiga toxin-producing Escherichia coli infection, yersiniosis, vibriosis, and cyclosporiasis increased, whereas those of listeriosis, salmonellosis, and shigellosis remained stable compared with incidences during 2016-2018, the baseline used for tracking progress towards federal disease reduction goals. During 2023, the incidence and percentage of infections diagnosed by culture-independent diagnostic tests (CIDTs) reported to FoodNet continued to increase, and the percentage of cases that yielded an isolate decreased, affecting observed trends in incidence. Because CIDTs allow for diagnosis of infections that previously would have gone undetected, lack of progress toward disease reduction goals might reflect changing diagnostic practices rather than an actual increase in incidence. Continued surveillance is needed to monitor the impact of changing diagnostic practices on disease trends, and targeted prevention efforts are needed to meet disease reduction goals. During 2023, FoodNet expanded its catchment area for the first time since 2004. This expansion improved the representativeness of the FoodNet catchment area, the ability of FoodNet to monitor trends in disease incidence, and the generalizability of FoodNet data.

Prospective modeling and estimating the epidemiologically informative match rate within large foodborne pathogen genomic databases.

OBJECTIVES: Much has been written about the utility of genomic databases to public health. Within food safety these databases contain data from two types of isolates-those from patients (i.e., clinical) and those from non-clinical sources (e.g., a food manufacturing environment). A genetic match between isolates from these sources represents a signal of interest. We investigate the match rate within three large genomic databases (Listeria monocytogenes, Escherichia coli, and Salmonella) and the smaller Cronobacter database; the databases are part of the Pathogen Detection project at NCBI (National Center for Biotechnology Information). RESULTS: Currently, the match rate of clinical isolates to non-clinical isolates is 33% for L. monocytogenes, 46% for Salmonella, and 7% for E. coli. These match rates are associated with several database features including the diversity of the organism, the database size, and the proportion of non-clinical BioSamples. Modeling match rate via logistic regression showed relatively good performance. Our prediction model illustrates the importance of populating databases with non-clinical isolates to better identify a match for clinical samples. Such information should help public health officials prioritize surveillance strategies and show the critical need to populate fledgling databases (e.g., Cronobacter sakazakii).

DODGE: automated point source bacterial outbreak detection using cumulative long term genomic surveillance.

SUMMARY: The reliable and timely recognition of outbreaks is a key component of public health surveillance for foodborne diseases. Whole genome sequencing (WGS) offers high resolution typing of foodborne bacterial pathogens and facilitates the accurate detection of outbreaks. This detection relies on grouping WGS data into clusters at an appropriate genetic threshold. However, methods and tools for selecting and adjusting such thresholds according to the required resolution of surveillance and epidemiological context are lacking. Here we present DODGE (Dynamic Outbreak Detection for Genomic Epidemiology), an algorithm to dynamically select and compare these genetic thresholds. DODGE can analyse expanding datasets over time and clusters that are predicted to correspond to outbreaks (or "investigation clusters") can be named with established genomic nomenclature systems to facilitate integrated analysis across jurisdictions. DODGE was tested in two real-world Salmonella genomic surveillance datasets of different duration, 2 months from Australia and 9 years from the United Kingdom. In both cases only a minority of isolates were identified as investigation clusters. Two known outbreaks in the United Kingdom dataset were detected by DODGE and were recognized at an earlier timepoint than the outbreaks were reported. These findings demonstrated the potential of the DODGE approach to improve the effectiveness and timeliness of genomic surveillance for foodborne diseases and the effectiveness of the algorithm developed. AVAILABILITY AND IMPLEMENTATION: DODGE is freely available at https://github.com/LanLab/dodge and can easily be installed using Conda.

Inadequate food safety knowledge and hygiene practices among street food vendors in Dhaka, Bangladesh.

Food safety remains a critical issue with outbreaks of foodborne illness. The knowledge gap of food safety and improper hygienic practices of food handlers are the key factors for the transmission of foodborne diseases. This study was conducted to investigate the level of food safety knowledge and practices among street food vendors in Dhaka City, Bangladesh, and its implications on consumers' health. This cross-sectional study was conducted among 350 respondents in seven areas of Dhaka City. Trained data collectors gather data by interviewing street vendors using a pretested questionnaire. Most of the vendors (98%) were male, with 48% having secondary education and 85% having no food safety training. Although about 89% of the vendors were found to practice hand washing, only a small proportion of them practised using hand gloves while handling raw products (5.6%), cleaning tables (2.2%), preparing foods (1.3%) or handling garbage (0.9%). The education level of the vendors and their work experience were significantly correlated with their hand washing practice, wearing hand gloves and their knowledge about food-borne illnesses. The study demonstrated that formal education played a significant role in vendors' knowledge and practice of health safety measures for food handlers to prevent foodborne illness. Effective food safety training and monitoring are needed to increase vendors' knowledge and practices, and in reducing foodborne diseases.

Foodborne bacteria in milk and milk products along the water buffalo milk chain in Bangladesh.

Controlling foodborne pathogens in buffalo milk is crucial for ensuring food safety. This study estimated the prevalence of nine target genes representing seven critical foodborne bacteria in milk and milk products, and identified factors associated with their presence in buffalo milk chain nodes in Bangladesh. One hundred and forty-three milk samples from bulk tank milk (n = 34), middlemen (n = 37), milk collection centers (n = 37), and milk product shops (n = 35) were collected and analyzed using RT-PCR. Escherichia (E.) coli, represented through yccT genes, was the most prevalent throughout the milk chain (81-97%). Chi-squared tests were performed to identify the potential risk factors associated with the presence of foodborne bacteria encoded for different genes. At the middleman level, the prevalence of E. coli was associated with the Mymensingh, Noakhali, and Bhola districts (P = 0.01). The prevalence of Listeria monocytogenes, represented through inlA genes, and Yersinia (Y.) enterocolitica, represented through yst genes, were the highest at the farm level (65-79%). The prevalence of both bacteria in bulk milk was associated with the Noakhali and Bhola districts (P < 0.05). The prevalence of Y. enterocolitica in bulk milk was also associated with late autumn and spring (P = 0.01) and was higher in buffalo-cow mixed milk than in pure buffalo milk at the milk collection center level (P < 0.01). The gene stx2 encoding for Shiga toxin-producing (STEC) E. coli was detected in 74% of the milk products. At the middleman level, the prevalence of STEC E. coli was associated with the use of cloths or tissues when drying milk containers (P = 0.01). Salmonella enterica, represented through the presence of invA gene, was most commonly detected (14%) at the milk collection center. The use of plastic milk containers was associated with a higher prevalence of Staphylococcus aureus, represented through htrA genes, at milk product shops (P < 0.05). These results suggest that raw milk consumers in Bangladesh are at risk if they purchase and consume unpasteurized milk.

How heatwaves affect short-term emergency hospital admissions due to bacterial foodborne diseases.

The coming decades are likely to see of extreme weather events becoming more intense and frequent across Europe as a whole and around the Mediterranean in particular. The reproduction rate of some microorganisms, including the bacteria that cause foodborne diseases, will also be affected by these events. The aim of this study was thus to ascertain whether there might be a statistically significant relationship between emergency hospital admissions due to the principal bacterial foodborne diseases (BFDs) and the various meteorological variables, including heatwaves. We conducted a time-series study, with daily observations of both the dependent variable (emergency hospital admissions due to BFDs) and the independent variables (meteorological variables and control variables of chemical air pollution) across the period 2013-2018 in the Madrid Region (Spain), using Generalised Linear Models with Poisson regression, in which control and lag variables were included for the purpose of fitting the models. We calculated the threshold value of the maximum daily temperature above which such admissions increased statistically significantly, analysed data for the whole year and for the summer months alone, and estimated the relative and attributable risks. The estimated attributable risk was 3.6 % for every one-degree rise in the maximum daily temperature above 12 °C throughout the year, and 12.21 % for every one degree rise in temperature above the threshold heatwave definition temperature (34 °C) in summer. Furthermore, different meteorological variables displayed a statistically significant association. Whereas hours of sunlight and mean wind speed proved significant in the analyses of both the whole year and summer, the variables "rain" and "relative humidity", only showed a significant relationship in the analysis for the whole year. High ambient temperature is a risk factor that favours the increase in emergency hospitalisations attributable to the principal BFDs, with a greater impact being observed on days coinciding with heatwave periods. The results yielded by this study could serve as a basis for implementing BFD prevention strategies, especially on heatwave days.

Prevalence and characterization of foodborne pathogens isolated from fresh-cut fruits and vegetables in Beijing, China.

Pre-cut fresh fruits and vegetables are highly appealing to consumers for their convenience, however, as they are highly susceptible to microbial contamination in processing, the potential risks of foodborne illnesses to public health are not negligible. This study aimed to assess the prevalence, antibiotic susceptibility and molecular characteristics of major foodborne pathogens (Listeria monocytogenes, Escherichia coli, Staphylococcus aureus and Salmonella) isolated from fresh-cut fruits and vegetables in Beijing, China. 86 stains were isolated from 326 samples, with S. aureus being the highest prevalence (15.38 %), followed by E. coli (9.23 %) and L. monocytogenes (1.85 %), while no Salmonella was detected. The prevalence by type of food indicated that fruit trays and mixed vegetables were more susceptible to contamination by pathogens. 98 % of S. aureus were resistant to at least of one antibiotic, and showed a high resistance rate to benzylpenicillin (90 %) and oxacillin (48 %). Among 25 E. coli isolates, 57.67 % of which exhibited multi-drug resistance, with common resist to trimethoprim/sulfamethoxazole (66.67 %) and ampicillin (63.33 %). A total of 9 sequence types (STs) and 8 spa types were identified in 35 S. aureus isolates, with ST398-t34 being the predominant type (42.86 %). Additionally, analysis of 25 E. coli isolates demonstrated significant heterogeneity, characterized by 22 serotypes and 18 STs. Genomic analysis revealed that 5 and 44 distinct antibiotic resistance genes (ARGs) in S. aureus and E. coli, respectively. Seven quinolone resistance-determining regions (QRDRs) mutations were identified in E. coli isolates, of which GyrA (S83L) was the most frequently detected. All the S. aureus and E. coli isolates harbored virulence genes. ARGs in S. aureus and E. coli showed a significant positive correlation with plasmids. Furthermore, one L. monocytogenes isolate, which was ST101 and serogroupIIc from watermelon sample, harbored virulence genes (inlA and inlB) and LIPI-1 pathogenic islands (prfA, plcA, hly and actA), which posed potential risks for consumer's health. This study focused on the potential microbial risk of fresh-cut fruits and vegetables associated with foodborne diseases, improving the scientific understanding towards risk assessment related to ready-to-eat foods.

Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.

Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited data from food safety audits to predict spatiotemporal patterns of salmonellosis in northwestern Italy. Data on human cases confirmed in 2015-2018 (n = 1969) and food surveillance data collected in 2014-2018 were used to develop ML algorithms. We integrated the monthly municipal human incidence with 27 potential predictors, including the observed prevalence of Salmonella in food. We applied the tree regression, random forest and gradient boosting algorithms considering different scenarios and evaluated their predictivity in terms of the mean absolute percentage error (MAPE) and R2. Using a similar dataset from the year 2019, spatiotemporal predictions and their relative sensitivities and specificities were obtained. Random forest and gradient boosting (R2 = 0.55, MAPE = 7.5%) outperformed the tree regression algorithm (R2 = 0.42, MAPE = 8.8%). Salmonella prevalence in food; spatial features; and monitoring efforts in ready-to-eat milk, fruits and vegetables, and pig meat products contributed the most to the models' predictivity, reducing the variance by 90.5%. Conversely, the number of positive samples obtained for specific food matrices minimally influenced the predictions (2.9%). Spatiotemporal predictions for 2019 showed sensitivity and specificity levels of 46.5% (due to the lack of some infection hotspots) and 78.5%, respectively. This study demonstrates the added value of integrating data from human and veterinary health services to develop predictive models of human salmonellosis occurrence, providing early warnings useful for mitigating foodborne disease impacts on public health.

Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world.

Foodborne illnesses pose a substantial health and economic burden, presenting challenges in prevention due to the diverse microbial hazards that can enter and spread within food systems. Various factors, including natural, political and commercial drivers, influence food production and distribution. The risks of foodborne illness will continue to evolve in step with these drivers and with changes to food systems. For example, climate impacts on water availability for agriculture, changes in food sustainability targets and evolving customer preferences can all have an impact on the ecology of foodborne pathogens and the agrifood niches that can carry microorganisms. Whole-genome and metagenome sequencing, combined with microbial surveillance schemes and insights from the food system, can provide authorities and businesses with transformative information to address risks and implement new food safety interventions across the food chain. In this Review, we describe how genome-based approaches have advanced our understanding of the evolution and spread of enduring bacterial foodborne hazards as well as their role in identifying emerging foodborne hazards. Furthermore, foodborne hazards exist in complex microbial communities across the entire food chain, and consideration of these co-existing organisms is essential to understanding the entire ecology supporting pathogen persistence and transmission in an evolving food system.

Salmonella in Chicken and Pork Meat as a Likely Major Contributor to Foodborne Illness in Peru.

Nontyphoidal Salmonella is one of the major causes of self-limiting diarrheal disease and the most common foodborne pathogen worldwide. It is an important contributor to the burden of foodborne illness in South America, including Peru, where chicken and pork are important vehicles for Salmonella infection. Salmonella infections are underreported, particularly in low- and middle-income countries where concerted action tackling Salmonella along the chicken and pork chains, from primary production to retail, is urgently needed. To support and inform the implementation of new strategies to reduce Salmonella contamination of chicken and pork, this study describes the frequency and distribution of foodborne outbreaks attributed to Salmonella in Peru and evaluates the level of Salmonella in chicken and pork meat sold in markets of three regions of Peru. To that end, we analyzed historical reports of foodborne outbreaks, levels of Salmonella in chicken and pork sold in markets, and the number of mesophiles in the collected meat samples. As a result, the microbiological analysis reveals a widespread contamination of chicken (77.1%) and pork (26.8%) with Salmonella. It also pinpoints Salmonella as the causative agent in nearly half of the outbreaks (47.0%) where the potential origin is identified over a 11-year period with chicken, mayonnaise, and pork being the most likely food vehicles. These results suggest that Salmonella is a major contributor to foodborne illness in Peru and that the monitoring of mesophiles could be a good strategy for surveillance, generating data to support source attribution studies and ultimately evidence-informed policies.

Salmonella Typhimurium caused an unprecedentedly large foodborne outbreak in Finland in 2021.

AIMS: Salmonella infections are significant causes of foodborne outbreaks in the European Union. This study investigates a sudden increase in gastroenteritis patients in the hospital district of Central Finland in June 2021. The primary aim was to study the outbreak's magnitude and source of the outbreak. METHODS AND RESULTS: Epidemiological, microbiological, environmental and traceback investigations were conducted. Over 700 persons fell ill during the outbreak caused by Salmonella Typhimurium associated with a daycare lunch. Similar S. Typhimurium was found in the patients and a vegetable mix containing iceberg lettuce, cucumber and peas served during lunch. The traceback investigation revealed that the batch information of vegetables from the wholesaler was not complete. The wholesaler had received quality complaints about the iceberg lettuce from the central kitchen. The manufacturer did not test the suspected batch for Salmonella since the production plant had given a certificate declaring it Salmonella negative. CONCLUSIONS: The most suspect ingredient was one batch of iceberg lettuce due to quality complaints. The lettuce had not been served in two daycare centres without cases. We recommend that in order to enable thorough microbiological investigation, institutional kitchens store the food samples separately as part of the internal quality control and that food items should always be tested when Salmonella contamination in an outbreak is suspected.

Foodborne Disease Outbreaks Linked to Foods Eligible for Irradiation, United States, 2009-2020.

Food irradiation can reduce foodborne illnesses but is rarely used in the United States. We determined whether outbreaks related to Campylobacter, Salmonella, Escherichia coli, and Listeria monocytogenes were linked to irradiation-eligible foods. Of 482 outbreaks, 155 (32.2%) were linked to an irradiation-eligible food, none of which were known to be irradiated.

Missingness and algorithmic bias: an example from the United States National Outbreak Reporting System, 2009-2019.

Growing debates about algorithmic bias in public health surveillance lack specific examples. We tested a common assumption that exposure and illness periods coincide and demonstrated how algorithmic bias can arise due to missingness of critical information related to illness and exposure durations. We examined 9407 outbreaks recorded by the United States National Outbreak Reporting System (NORS) from January 1, 2009 through December 31, 2019 and detected algorithmic bias, a systematic over- or under-estimation of foodborne disease outbreak (FBDO) durations due to missing start and end dates. For 7037 (75%) FBDOs with complete date-time information, ~ 60% reported that the exposure period ended before the illness period started. For 2079 (87.7%) FBDOs with missing exposure dates, average illness durations were ~ 5.3 times longer (p < 0.001) than those with complete information, prompting the potential for algorithmic bias. Modern surveillance systems must be equipped with investigative capacities to examine and assess structural data missingness that can lead to bias.