The EFSA Scientific Committee has updated its 2010 Guidance on risk-benefit assessment (RBA) of foods. The update addresses methodological developments and regulatory needs. While it retains the stepwise RBA approach, it provides additional methods for complex assessments, such as multiple chemical hazards and all relevant health effects impacting different population subgroups. The updated guidance includes approaches for systematic identification, prioritisation and selection of hazardous and beneficial food components. It also offers updates relevant to characterising adverse and beneficial effects, such as measures of effect size and dose-response modelling. The guidance expands options for characterising risks and benefits, incorporating variability, uncertainty, severity categorisation and ranking of different (beneficial or adverse) effects. The impact of different types of health effects is assessed qualitatively or quantitatively, depending on the problem formulation, scope of the RBA question and data availability. The integration of risks and benefits often involves value-based judgements and should ideally be performed with the risk-benefit manager. Metrics such as Disability-Adjusted Life Years (DALYs) and Quality-Adjusted Life Years (QALYs) can be used. Additional approaches are presented, such as probability of all relevant effects and/or effects of given severities and their integration using severity weight functions. The update includes practical guidance on reporting results, interpreting outcomes and communicating the outcome of an RBA, considering consumer perspectives and responses to advice.

The presence of pesticide residues in Agrocybe aegerita has raised an extensive concern. In this paper, based on a 3-year monitoring survey, the dietary exposure risks through A. aegerita consumption for different population subgroups were assessed using both deterministic and semi-probabilistic approaches under the best-case and the worst-case scenarios. Among the 52 targeted pesticides, 28 different compounds were identified in the concentration range of 0.005-3.610 mg/kg, and 87.4 % of samples contained one or more pesticide residues. The most frequently detected pesticide was chlormequat, followed by chlorfenapyr and cyhalothrin. The overall risk assessment results indicated extremely low chronic, acute, and cumulative dietary exposure risks for consumers. Using the ranking matrix, intake risks of pesticides were ranked, revealing endsoluran, chlorpyrifos, and methamidophos to be in the high-risk group. Finally, considering various factors such as the toxicity and risk assessment outcomes of each positive pesticide, use suggestions were proposed for A. aegerita cultivation.

Consumers can be exposed to many foodborne biological hazards that cause diseases with varying outcomes and incidence and, therefore, represent different levels of public health burden. To help the French risk managers to rank these hazards and to prioritize food safety actions, we have developed a three-step approach. The first step was to develop a list of foodborne hazards of health concern in mainland France. From an initial list of 335 human pathogenic biological agents, the final list of \"retained hazards\" consists of 24 hazards, including 12 bacteria (including bacterial toxins and metabolites), 3 viruses and 9 parasites. The second step was to collect data to estimate the disease burden (incidence, Disability Adjusted Life Years) associated with these hazards through food during two time periods: 2008-2013 and 2014-2019. The ranks of the different hazards changed slightly according to the considered period. The third step was the ranking of hazards according to a multicriteria decision support model using the ELECTRE III method. Three ranking criteria were used, where two reflect the severity of the effects (Years of life lost and Years lost due to disability) and one reflects the likelihood (incidence) of the disease. The multicriteria decision analysis approach takes into account the preferences of the risk managers through different sets of weights and the uncertainties associated with the data. The method and the data collected allowed to estimate the health burden of foodborne biological hazards in mainland France and to define a prioritization list for the health authorities.

Bioaccumulation predictions can be substantially improved by combining in vitro metabolic rate measurements derived from rainbow trout hepatocytes and/or hepatic S9 fractions with quantitative structure-activity relationship (QSAR) modeling approaches. Compared with in vivo testing guidelines Organisation for Economic Co-operation and Development (OECD) 305 and Office of Chemical Safety and Pollution Prevention (OCSPP; an office of the US Environmental Protection Agency) 850.1730, the recently adopted OECD test guidelines 319A and 319B are in vitro approaches that have the potential to provide a time- and cost-efficient, humane solution, reducing animal use while addressing uncertainties in bioaccumulation across species. The present study compares the hepatic clearance of the S9 subcellular fraction of rainbow trout, bluegill, common carp, fathead minnow, and largemouth bass, discerning potential differences in metabolism between different warm- and cold-water species. With refinements to the in vitro metabolic S9 assay for high-throughput analysis, we measured in vitro clearance rates of seven chemicals crossing multiple classes of chemistry and modes of action. We confirmed that data from rainbow trout liver S9 fraction metabolic rates can be utilized to predict rainbow trout bioconcentration factors using an in vitro to in vivo extrapolation model, as intended in the OECD 319B applicability domain per the bioaccumulation prediction. Also, we determined that OECD 319B can be applied to other species, modified according to their habitat, adaptations to feeding behavior, and environmental conditions (e.g., temperature). Once toxicokinetics for each species is better understood and appropriate models are developed, this method can be an excellent tool to determine hepatic clearance and potential bioaccumulation across species. The present study could be leveraged prior to or in place of initiating in vivo bioconcentration studies, thus optimizing selection of appropriate fish species. Environ Toxicol Chem 2024;43:1390-1405. © 2024 SETAC.

Chemical hazards in foods, especially naturally occurring food contaminants like mycotoxins, are of serious public health concern. It is important to develop a practical framework to assess and rank health risks of chemical contaminants which can be further utilized by regulatory agencies to prioritize resources for risk assessment and management. In this study, a tiered hazard-prioritization and risk-ranking approach, which included two steps: exposure-based screening and margin of exposure (MOE)-based probabilistic risk ranking; was proposed to efficiently identify and rank chemicals of health concerns. Given the exposure-based hazard prioritization, chemicals with negligible or low health risks were first excluded. The remaining chemicals, imposing a higher health risk, were then ranked to facilitate risk-based decision making. The proposed approach was applied to identify and rank the mycotoxins with substantial health concerns in food commodities randomly sampled in China. A total of 19 mycotoxins were analyzed in 783 food commodities, including infant cookie, noodle, rice flour samples, wheat flour, millet, and rice. Results showed that the mycotoxins in infant foods with the highest health risk were Tenuazonic acid, Deoxynivalenol, and Enniatin B1, but as indicated by the probabilistic MOE estimation, the risks were still in the acceptable range and generally lower than the risks imposed by trace elements (e.g., Arsenic and Cadmium). The health risks of the other 16 mycotoxins were negligible mainly due to their low exposure levels. This study demonstrated that the proposed tiered approach was an efficient and effective tool to quantify and prioritize health risks in support of human health risk management.

This study presents a data-driven approach for classifying food safety alerts related to chemical and microbial contaminants in dairy products using the Rapid Alert System for Food and Feed (RASFF) and the World Health Organization (WHO)\'s Global Environmental Monitoring System (GEMS) food contaminants databases. This research aimed to prioritise microbial and chemical hazards based on their presence and severity through exploratory data analysis and to classify the severity of chemical hazards using machine learning (ML) approaches. It identified Listeria monocytogenes, Escherichia coli, Salmonella, Pseudomonas spp., Staphylococcus spp., Bacillus cereus, Clostridium spp., and Cronobacter sakazakii as the microbial hazards of priority in dairy products. The study also prioritised the top ten chemical hazards based on their presence and severity. These hazards include nitrate, nitrite, ergocornine, 3-MCPD ester, lead, arsenic, ochratoxin A, cadmium, mercury, and aflatoxin (G1, B1, G2, B2, G5 and M1). Using ML techniques, the accuracy rate of classifying food safety alerts as either \'serious\' or \'non-serious\' was up to 98 %. Additionally, the study identified Reference dose (RfD), substance amount, notification type, product, and substance as the most important features affecting the ML models\' performance. These ML models (decision trees, random forests, k-nearest neighbors, linear discriminant analysis, and support vector machines) were also validated on an external dataset of RASFF alerts related to chemical contaminants in dairy products. They achieved an accuracy of up to 95.1 %. The study\'s findings demonstrate the models\' robustness and ability to classify food safety alerts related to chemical contaminants in dairy products, even on new data. These results can enhance the development of more effective machine-learning models for classifying food safety alerts related to chemical contaminants in dairy products, highlighting the importance of developing accurate and efficient classification models for timely intervention.

Although the presence of antibiotic resistance genes (ARGs) in drinking water and their potential horizontal gene transfer to pathogenic microbes are known to pose a threat to human health, their pollution levels and potential anthropogenic sources are poorly understood. In this study, broad-spectrum ARG profiling combined with machine-learning-based source classification SourceTracker was performed to investigate the pollution sources of ARGs in household drinking water collected from 95 households in 47 cities of eight countries/regions. In total, 451 ARG subtypes belonging to 19 ARG types were detected with total abundance in individual samples ranging from 1.4 × 10-4 to 1.5 × 10° copies per cell. Source tracking analysis revealed that many ARGs were highly contributed by anthropogenic sources (37.1%), mainly wastewater treatment plants. The regions with the highest detected ARG contribution from wastewater (∼84.3%) used recycled water as drinking water, indicating the need for better ARG control strategies to ensure safe water quality in these regions. Among ARG types, sulfonamide, rifamycin and tetracycline resistance genes were mostly anthropogenic in origin. The contributions of anthropogenic sources to the 20 core ARGs detected in all of the studied countries/regions varied from 36.6% to 84.1%. Moreover, the anthropogenic contribution of 17 potential mobile ARGs identified in drinking water was significantly higher than other ARGs, and metagenomic assembly revealed that these mobile ARGs were carried by diverse potential pathogens. These results indicate that human activities have exacerbated the constant input and transmission of ARGs in drinking water. Our further risk classification framework revealed three ARGs (sul1, sul2 and aadA) that pose the highest risk to public health given their high prevalence, anthropogenic sources and mobility, facilitating accurate monitoring and control of anthropogenic pollution in drinking water.

Environmental risk assessment (ERA) of pharmaceuticals relies on available measured environmental concentrations, but often such data are sparse. Predicted environmental concentrations (PECs), calculated from sales weights, are an attractive alternative but often cover only prescription sales. We aimed to rank, by environmental risk in Norway, approximately 200 active pharmaceutical ingredients (APIs) over 2016-2019, based on sales PECs. To assess the added value of wholesale and veterinary data, we compared exposure and risk predictions with and without these additional sources. Finally, we aimed to characterize the persistence, mobility, and bioaccumulation of these APIs. We compared our PECs to available Norwegian measurements, then, using public predicted-no-effect concentrations, we calculated risk quotients (RQs) and appended experimental and predicted persistence and bioaccumulation. Our approach overestimated environmental concentrations compared with measurements for 18 of 20 APIs with comparable predictions and measurements. Seventeen APIs had mean RQs >1, indicating potential risk, while the mean RQ was 2.05 and the median 0.001, driven by sex hormones, antibiotics, the antineoplastic abiraterone, and common painkillers. Some high-risk APIs were also potentially persistent or bioaccumulative (e.g., levonorgestrel [RQ = 220] and ciprofloxacin [RQ = 56]), raising the possibility of impacts beyond their RQs. Exposure and risk were also calculated with and without over-the-counter sales, showing that prescriptions explained 70% of PEC magnitude. Likewise, human sales, compared with veterinary, explained 85%. Sales PECs provide an efficient option for ERA, designed to overestimate compared with analytical techniques and potentially held back by limited data availability and an inability to quantify uncertainty but, nevertheless, an ideal initial approach for identification and ranking of risks. Environ Toxicol Chem 2023;42:2253-2270. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Mathematical models to forecast the risk trend of the COVID-19 pandemic timely are of great significance to control the pandemic, but the requirement of manual operation and many parameters hinders their efficiency and value for application. This study aimed to establish a convenient and prompt one for monitoring emerging infectious diseases online and achieving risk assessment in real time.
The Optimized Moving Average Prediction Limit (Op-MAPL) algorithm model analysed real-time COVID-19 data online and was validated using the data of the Delta variant in India and the Omicron in the United States. Then, the model was utilized to determine the infection risk level of the Omicron in Shanghai and Beijing.
The Op-MAPL model can predict the epidemic peak accurately. The daily risk ranking was stable and predictive, with an average accuracy of 87.85% within next 7 days. Early warning signals were issued for Shanghai and Beijing on February 28 and April 23, 2022, respectively. The two cities were rated as medium-high risk or above from March 27 to April 20 and from April 24 to May 5, indicating that the pandemic had entered a period of rapid increase. After April 21 and May 26, the risk level was downgraded to medium and became stable by the algorithm, indicating that the pandemic had been controlled well and mitigated gradually.
The Op-MAPL relies on nothing but an indicator to assess the risk level of the COVID-19 pandemic with different data sources and granularities. This forward-looking method realizes real-time monitoring and early warning effectively to provide a valuable reference to prevent and control infectious diseases.

Microbiological (M), chemical (C), and nutritional (N) risks associated with food products are usually assessed and managed independently by experts in public services or food companies. This can render difficult the comparison of food products in term of overall risk for the consumer. The objective of this study was to suggest a relatively simple method to (i) classify food products based on their M, C and N risks, and (ii) aggregate these risks and rank the food products accordingly. The method was developed and applied to 17 ready-to-eat (RTE) dishes available on the French market. With regard to food safety, the individual M and C risks were characterized considering likelihood and severity as recommended by the Codex Alimentarius. With regard to nutrition/health, the N risk was estimated based on the tendency of the dish to contribute to nutrient adequacy and to a healthy eating pattern. Finally, the outranking method PROMETHEE was applied to aggregate the three M, C, N risks and rank the food dishes. Food products were ranked relatively to each other, not in absolute terms. When we attributed the same weight to M, C and N risks, the RTE dish \"Duck Parmentier\" had the highest risk score while \"Papillote of chicken, potatoes and small vegetables\" and \"Vegetarian plate vegetables and quinoa\" had the lowest. However, this overall ranking changed according to the weight assigned to individual M, C and N risks, at least for food products whose scores varied according to risk types, such as\"sushi discovery\" (high M and C risks, low N risk). Since the risk ranking method developed here was built with assumptions and hypotheses related to the specific case study, more applications are needed to assess whether it can be generic. Nevertheless, this method is well grounded, objective, transparent, relatively fast and easy to set up. It might lead to further development of decision tools, particularly for consumers. This study paves the way towards food product multi-risk ranking.