Foodborne pathogens represent a significant public health burden. Quantifying the relative importance of various potential sources of foodborne infection is challenging due to data scarcity and uncertainty in empirical studies. Structured expert judgment (SEJ) provides a valuable methodological alternative to gain insights into source attribution of foodborne pathogens. We conducted a SEJ study to attribute human cases of 26 foodborne pathogens in the Netherlands to seven major transmission pathways, 20 food groups, and two animal groups, in a typical post-COVID19 year, using Cooke's classical model. The elicitation process involved snowball recruitment, expertise self-assessment, and a workshop where experts answered calibration questions to capture their uncertainty as input for the model. Subsequently, experts completed the target questions to obtain attributable proportions at the ‘kitchen door’ level. Results indicated that transmission was predominantly (>50%) foodborne for Staphylococcus aureus , Listeria monocytogenes , Yersinia spp., Bacillus cereus , Clostridium perfringens , certain non-typhoidal Salmonella serotypes, Campylobacter spp., hepatitis E virus and Toxoplasma gondii , whereas person-to-person transmission was the primary pathway for astrovirus, rotavirus, norovirus, and sapovirus. Brucella spp. and typhoidal Salmonella were attributed primarily (>85%) to international travel. All other pathogens showed attributions of <50% to any individual pathway. Substantial differences were observed when dividing foodborne transmission into food groups. Key contributors included food handlers and vermin, various meats (e.g., pork, beef, chicken), and shellfish. These SEJ-derived estimates complement existing data by providing pathogen-specific insights in the Dutch context.

Background: We updated WHO estimates of the global, regional, and national foodborne disease burden caused by chemical hazards. We estimated incidence, mortality, and disability-adjusted life-years (DALYs) of aflatoxins B1 and M1, inorganic arsenic, lead, methylmercury, cadmium, dioxin, peanut allergy, and cassava cyanide for 2000–21. Methods: We used data from systematic reviews, established dose-response relationships, the Global Burden of Disease Study 2021, and, where applicable, a structured expert judgment study. We used disease-specific models and a hierarchical meta-regression model with geographical clustering and global linear time trend with uncertainty propagation. Findings: In 2021, the nine foodborne chemicals caused 6·26 million (95% uncertainty interval [UI] 3·36–10·30) cases, 1·12 million (0·40–2·10) deaths, and 29·8 million (12·9–53·1) DALYs globally. Inorganic arsenic and lead caused the highest burden. Cardiovascular diseases due to inorganic arsenic and lead caused 88·9% of foodborne chemical deaths and 76·5% of foodborne chemical DALYs. The greatest DALY rate was estimated for the South-East Asia region, with 789 DALYs (272–1660) per 100 000 people mostly due to inorganic arsenic and lead (94·2%). The region of the Americas carried the highest foodborne chemical DALY rate in children younger than 5 years, with 749 DALYs (435–1260) per 100 000 children, mainly due to the effect of methylmercury on intellectual disability (91·0%). DALY rates from dioxin showed the steepest decrease from 2000 to 2021. Interpretation: Dietary exposure to chemicals causes a substantial global disease burden. An integrated response with ongoing non-communicable disease prevention efforts is key. Granular assessment including subnational exposure contexts is essential to ensure equity. Funding: WHO.

Identifying the sources of foodborne diseases is crucial for guiding national food safety strategies and supporting policies that promote safe and sustainable food production. Here we present global estimates of the proportions of burden of disease attributable to foodborne transmission, other major transmission pathways, and specific food categories for 29 viral, bacterial, parasitic, and chemical hazards, based on a structured expert judgement (SEJ) study commissioned by the World Health Organization (WHO) and supervised by the WHO Foodborne Disease Burden Epidemiology Reference Group (FERG) for 2021–2025. One-hundred forty-six experts provided 1463 assessments across 17 subregions within six WHO regions. The assessments were analyzed using Cooke's Classical Model and reviewed by FERG. Results showed that 13 of the 29 hazards were mainly (>50%) attributable to foodborne transmission, with non-typhoidal Salmonella (59–74%) and Campylobacter (45–71%) estimated to be predominantly foodborne in nearly all subregions. While plant-based foods were important sources of several pathogens, such as hepatitis A virus (63–74%) and Cryptosporidium (60–95%), foods of animal origin, including poultry, beef, eggs, seafood, and dairy products, remain critical intervention targets for several others, e.g., Campylobacter (86–97%), Salmonella (77–92%), Shiga toxin-producing E. coli (67–96%), and Listeria monocytogenes (50–85%). Regional differences in attributions highlight the influence of local epidemiological patterns, food systems, sanitation, and cultural practices. These estimates provide a global, uncertainty-quantified knowledge base to guide context-specific food safety interventions and future empirical data collection. Given persistent data gaps, SEJ remains a feasible approach to generate evidence supporting efforts to reduce the burden of foodborne diseases.

Background: Over 10 years ago, WHO estimates of hazard-specific foodborne disease burdens showed that parasites exert considerable health burdens globally. We updated these estimates, focusing on 14 invasive parasitic diseases. Methods: Incidences, deaths, and disability-adjusted life-year (DALY) burdens were estimated for each parasitic disease from 2000 to 2021, using data from systematic reviews and the Global Burden of Diseases, Injuries, and Risk Factors Study. For some diseases, structured expert judgement was used to estimate proportions of foodborne infection. Data were pooled via hierarchical meta-regression models with uncertainty propagated through Monte Carlo simulations following disease-specific computational models defined by incidence rates and probability parameters. Findings: We estimated that 277 million illnesses were caused by potentially foodborne invasive parasites, with approximately 171 million attributable to foodborne transmission. Considerable heterogeneity by parasite, in magnitude and uncertainty, was observed. Of 4·89 million foodborne DALYs associated with these diseases, highest contributions were from Taenia solium (1·3 million) and Clonorchis sinensis (0·921 million), both also associated with most foodborne deaths. Burden was greatest in the region of the Americas, predominantly due to Chagas disease, followed by the African region, where neurocysticercosis-associated epilepsy caused most burden. Burdens decreased globally from 2000 to 2021, except in the Western Pacific region, where the burden, predominantly associated with clonorchiasis, is rising. Interpretation: Foodborne parasitoses cause considerable suffering, with some populations and regions particularly at risk. These data provide a baseline by which effects of interventions can be assessed and emphasis directed to those parasites exerting the greatest burden. Funding: WHO.

Background: Foodborne diseases cause significant illness and death globally. We updated WHO estimates of the burden caused by diarrhoeal hazards commonly transmitted by food: Campylobacter jejuni, Campylobacter coli, and other thermotolerant Campylobacter species; Cryptosporidium spp; Cyclospora cayetanensis; Entamoeba histolytica; enteroaggregative Escherichia coli; enteropathogenic E coli; enterotoxigenic E coli; Giardia duodenalis; norovirus; rotavirus; non-typhoidal Salmonella enterica; Shiga toxin-producing E coli; Shigella spp; and Vibrio cholerae. Methods: We estimated illnesses, deaths, and disability-adjusted life-years (DALYs) for 194 countries for the period 2000–21 using data from systematic reviews; the Global Burden of Diseases, Injuries, and Risk Factors Study 2021; a structured expert judgement study; and country consultations. We used disease-specific computational models, and a hierarchical meta-regression model with geographical clustering, a global linear time trend, and uncertainty propagation. Findings: In 2021, the 14 diarrhoeal hazards caused 666 million (95% UI 483–884) illnesses, 265 000 deaths (196 000–351 000), and 15·2 million (11·6–19·1) DALYs from foodborne transmission. Shigella spp, Campylobacter, and rotavirus caused the most DALYs from foodborne transmission. The greatest burden was in the African region (773·5 DALYs [95% UI 559·7–1033·3] per 100 000 population due to foodborne transmission). Mortality rates were 7·1 times higher and DALY rates 18·9 times higher in children younger than 5 years than in people aged 5 years or older. While the overall foodborne burden decreased between 2000 (692·3 DALYs [517·9–938·1] per 100 000 population) and 2021 (193·6 [147·2–243·0] per 100 000), this trend was not consistent for all hazards. Interpretation: Diarrhoeal hazards continue to cause a substantial foodborne disease burden, despite decreases over time. Children in low-income countries bear the greatest burden. Prevention requires concerted efforts, including expanding global diarrhoeal disease prevention efforts beyond water, sanitation, and hygiene and vaccination to include improvements in the safety of the food supply. Funding: WHO.

Background: Foodborne diseases cause substantial illness and death globally. We updated WHO estimates of the burden caused by non-diarrhoeal enteric disease hazards: Brucella spp; Clostridium botulinum; hepatitis A virus; Listeria monocytogenes; Mycobacterium bovis, Mycobacterium caprae, and Mycobacterium orygis; invasive non-typhoidal Salmonella enterica (iNTS); S enterica serotypes Paratyphi A, B, and C; and S enterica serotype Typhi (S Typhi). Methods: We estimated illnesses, deaths, and disability-adjusted life-years (DALYs) for 194 countries for 2000–21 using data from systematic reviews, the 2021 Global Burden of Diseases, Injuries, and Risk Factors Study 2021, a structured expert judgement study, and WHO country consultations. We used disease-specific computational models and hierarchical metaregression modelling with geographical clustering, a global linear time trend, and uncertainty propagation. Findings: In 2021, transmission of these eight hazards by food collectively caused 24·0 million illnesses (95% uncertainty interval 16·9–31·7), 106 000 deaths (63 900–169 000), and 7·26 million DALYs (4·15–12·0). S Typhi, iNTS, and hepatitis A virus caused most DALYs. The greatest burden was in the WHO African region, followed by the South-East Asia region. Mortality was 5·2 times higher and DALY rates were 8·3 times higher in children younger than 5 years compared with people aged 5 years and older. The burden for all hazards, except L monocytogenes, decreased from 2000 to 2021, with S Typhi replacing iNTS as the leading cause of foodborne DALYs. Interpretation: Non-diarrhoeal enteric diseases still cause considerable foodborne disease burden, despite decreases over time. Vulnerable populations, particularly children in low-income countries, bear the greatest burden. Integrated efforts including vaccination, food safety, clean water, sanitation, hygiene, and improved health-care access are required. Funding: WHO.

Background: Foodborne diseases are important causes of illness and death. The first estimates of their burden were published by WHO in 2015. We updated WHO estimates of the global, regional, subregional, and national foodborne disease burden caused by 42 infectious and chemical hazards in 2021, including time trends for 2000–21. Methods: We provide a high-level summary of foodborne disease burden, expressed as incidence, deaths, and disability-adjusted life-years (DALYs). Data for burden estimation were provided from a WHO-commissioned series of systematic reviews on the incidence, aetiology, sequelae, and case fatality or mortality of the hazards. Data were analysed using hierarchical meta-regression modelling with geographical clustering and a global linear time trend, disease-specific computational models, and uncertainty propagation through Monte Carlo simulations to calculate 95% uncertainty intervals. Attribution to foodborne transmission was principally based on a structured expert judgement process. Economic impact was measured as lost productivity. Findings: For 2021, foodborne transmission of the 42 hazards caused 866 million (95% uncertainty interval 680–1090) illnesses, 1·52 million (0·783–2·51) deaths, and 57·1 million (39·4–81·1) DALYs. Inorganic arsenic, lead, and non-typhoidal Salmonella enterica (diarrhoeal and invasive disease) resulted in the most DALYs. The greatest burden of foodborne disease was in the African and South-East Asia regions. The incidence in children younger than 5 years was 2·7 times higher than in people aged 5 years or older, resulting in 4·3 times the rate of DALYs. The total burden from all hazards decreased over time. In 2021, these 42 hazards resulted in productivity losses of US$310 billion in nominal terms, and US$647 billion after adjusting for purchasing power parity. Interpretation: Foodborne diseases causes a burden similar to that from tuberculosis, HIV and AIDS, or malaria. The high burden of both communicable and non-communicable foodborne diseases requires countries to prioritise developing strategies to improve the safety of the food supply. Funding: WHO.

Bayesian networks (BNs) are popular models that represent complex relationships among variables. In the discrete case, these relationships can be quantified by conditional probability tables (CPTs). CPTs can be derived from data, but if data are not sufficient, experts can be involved to assess the probabilities in the CPTs through Structured Expert Judgment (SEJ). This is often a burdensome task due to the large number of probabilities that need to be assessed and the structured protocols that need to be followed. To lighten the elicitation burden, several methods have previously been developed to construct CPTs using a limited number of input parameters, such as InterBeta, the Ranked Nodes Method (RNM), and Functional Interpolation. In this study, the burden/accuracy trade-off of InterBeta is researched by applying the method to reconstruct previously elicited CPTs and simulated CPTs, first by comparing these CPTs to ones constructed using RNM and Functional Interpolation. After that, InterBeta extensions are proposed and tested, including an extra mean function (shifted geometric mean), the elicitation of additional middle rows, and the newly proposed extension ExtraBeta. InterBeta with parent weights is found to be the best-performing method, and the ExtraBeta extension is found to be promising and is proposed for further exploration.

Wearable sensors enable remote, continuous patient monitoring at home, offering a promising approach for early detection of postoperative complications. However, analyzing continuous long-term physiological data remains challenging, particularly in the absence of precisely labeled deterioration events. Unsupervised change point detection methods can address this issue by identifying physiological deviations without requiring predefined event labels. This study investigates the feasibility of using a Long-Short-Term Memory (LSTM) autoencoder for detecting postoperative complications from continuous heart rate and respiration rate data using a wearable patch sensor while monitoring patients in their homes. The autoencoder was applied to identify physiological deviations that may indicate potential complications after major abdominal oncological surgeries in ten patients. The model was trained on data from seven patients to recognize deviations from normal physiological patterns and evaluated on three patients. The proposed model detected change points preceding the clinically documented complication time in two test patients, identifying these deteriorations an average of 3.25 hours earlier than the standard Remote Early Warning Score (REWS) alarm system. These findings suggest that LSTM autoencoder-based change point detection could be a valuable tool for identifying postoperative complications early in remote patient monitoring settings, to support timely intervention and potentially improving patient outcomes.

This study evaluates five scoring rules, or measures of statistical accuracy, for assessing uncertainty estimates from expert judgment studies and model forecasts. These rules — the Continuously Ranked Probability Score ((Formula presented.)), Kolmogorov-Smirnov ((Formula presented.)), Cramer-von Mises ((Formula presented.)), Anderson Darling ((Formula presented.)), and chi-square test — were applied to 6864 expert uncertainty estimates from 49 Classical Model (CM) studies. We compared their sensitivity to various biases and their ability to serve as performance-based weight for expert estimates. Additionally, the piecewise uniform and Metalog distribution were evaluated for their representation of expert estimates because four of the five rules require interpolating the experts' estimates. Simulating biased estimates reveals varying sensitivity of the considered test statistics to these biases. Expert weights derived using one measure of statistical accuracy were evaluated with other measures to assess their performance. The main conclusions are (1) (Formula presented.) overlooks important biases, while chi-square and (Formula presented.) behave similarly, as do (Formula presented.) and (Formula presented.). (2) All measures except (Formula presented.) agree that performance weighting is superior to equal weighting with respect to statistical accuracy. (3) Neither distributions can effectively predict the position of a removed quantile estimate. These insights show the behavior of different scoring rules for combining uncertainty estimates from expert or models, and extent the knowledge for best-practices.

This study focuses on measuring the influence of critical Human and Organizational Factors (HOFs) on human error occurrence in structural design and construction tasks within the context of the Dutch construction industry. The primary research question addressed in this paper concerns the extent of HOFs’ contribution to human error occurrence. To answer this question, the Classical Model for Structured Expert Judgement (SEJ) is employed, enabling experts to provide their judgments on task Human Error Probability (HEP) influenced by different HOFs, which are subsequently aggregated mathematically. SEJ is chosen as a suitable approach due to the limited availability of applicable data in the construction sector. As a result, the impacts of HOFs are quantified as multipliers, representing the ratio between the observed or evaluated task HEP and its baseline value. These multipliers are then compared with corresponding multipliers from existing Human Reliability Analysis methods and studies. The findings reveal that fitness-for-duty, organizational characteristics and fragmentation exhibit the most pronounced negative effects, whereas complexity, attitude and fitness-for-duty demonstrate the most significant positive impacts on task performance. These results offer valuable insights that can be applied to enhance structural safety assurance practices.

Evaluation systems have been long criticized for abusing and misusing bibliometric indicators. This has created a culture by which academics are constantly exposing their daily work to the standards they are expected to perform. In this study, we investigate whether researchers’ own values and expectations are in line with the expectations of the evaluation system. We conduct a multiple case study of five departments in two Dutch universities to examine how they balance between their own valuation regimes and the evaluation schemes. For this, we combine curriculum analysis with a series of semi-structured interviews. We propose a model to study the diversity of academic activities and apply it to the multiple case study to understand how such diversity is shaped by discipline and career stage. We conclude that the observed misalignment is not only resulting from an abuse of metrics but also by a lack of tools to evaluate performance in a contextualized and adaptable way.

BACKGROUND: The Girinka program in Rwanda has contributed to an increase in milk production, as well as to reduced malnutrition and increased incomes. But dairy products can be hazardous to health, potentially transmitting diseases such as bovine brucellosis, tuberculosis, and cause diarrhea. We analyzed the burden of foodborne disease due to consumption of raw milk and other dairy products in Rwanda to support the development of policy options for the improvement of the quality and safety of milk. METHODS: Disease burden data for five pathogens (Campylobacter spp., nontyphoidal Salmonella enterica, Cryptosporidium spp., Brucella spp., and Mycobacterium bovis) were extracted from the 2010 WHO Foodborne Disease Burden Epidemiology Reference Group (FERG) database and merged with data of the proportion of foodborne disease attributable to consuming dairy products from FERG and a separately published Structured Expert Elicitation study to generate estimates of the uncertainty distributions of the disease burden by Monte Carlo simulation. RESULTS: According to WHO, the foodborne disease burden (all foods) of these five pathogens in Rwanda in 2010 was like or lower than in the Africa E subregion as defined by FERG. There were 57,500 illnesses occurring in Rwanda owing to consumption of dairy products, 55 deaths and 3,870 Disability Adjusted Life Years (DALYs) causing a cost-of-illness of $3.2 million. 44% of the burden (in DALYs) was attributed to drinking raw milk and sizeable proportions were also attributed to traditionally (16-23%) or industrially (6-22%) fermented milk. More recent data are not available, but the burden (in DALYs) of tuberculosis and diarrheal disease by all causes in Rwanda has declined between 2010 and 2019 by 33% and 46%, respectively. CONCLUSION: This is the first study examining the WHO estimates of the burden of foodborne disease on a national level in Rwanda. Transitioning from consuming raw to processed milk (fermented, heat treated or otherwise) may prevent a considerable disease burden and cost-of-illness, but the full benefits will only be achieved if there is a simultaneous improvement of pathogen inactivation during processing, and prevention of recontamination of processed products.

We model the growth of scientific literature related to COVID-19 and forecast the expected growth from 1 June 2021. Considering the significant scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing models using the Dimensions database. This source has the particularity of including in the metadata information on the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by domain-specific repository (SSRN and MedRxiv) and by several research fields. We conclude by discussing our findings.

Background According to the World Health Organization, 600 million cases of foodborne disease occurred in 2010. To inform risk management strategies aimed at reducing this burden, attribution to specific foods is necessary. Objective We present attribution estimates for foodborne pathogens (Campylobacter spp., enterotoxigenic Escherichia coli (ETEC), Shiga-toxin producing E. coli, nontyphoidal Salmonella enterica, Crypto-sporidium spp., Brucella spp., and Mycobacterium bovis) in three African countries (Burkina Faso, Ethiopia, Rwanda) to support risk assessment and cost-benefit analysis in three projects aimed at increasing safety of beef, dairy, poultry meat and vegetables in these countries. Methods We used the same methodology as the World Health Organization, i.e., Structured Expert Judgment according to Cooke’s Classical Model, using three different panels for the three countries. Experts were interviewed remotely and completed calibration questions during the interview without access to any resources. They then completed target questions after the interview, using resources as considered necessary. Expert data were validated using two objective measures, calibration score or statistical accuracy, and information score. Per-formance-based weights were derived from the two measures to aggregate experts’ distributions into a so-called decision maker. The analysis was made using Excalibur software, and resulting distributions were normalized using Monte Carlo simulation. Results Individual experts’ uncertainty assessments resulted in modest statistical accuracy and high information scores, suggesting overconfident assessments. Nevertheless, the optimized item-weighted decision maker was statistically accurate and informative. While there is no evidence that animal pathogenic ETEC strains are infectious to humans, a sizeable propor-tion of ETEC illness was attributed to animal source foods as experts considered contamina-tion of food products by infected food handlers can occur at any step in the food chain. For all pathogens, a major share of the burden was attributed to food groups of interest. Within food groups, the highest attribution was to products consumed raw, but processed products were also considered important sources of infection. Conclusions Cooke’s Classical Model with performance-based weighting provided robust uncertainty estimates of the attribution of foodborne disease in three African countries. Attribution estimates will be combined with country-level estimates of the burden of foodborne disease to inform decision making by national authorities.

Foodborne disease is a significant global health problem, with low- and middle-income countries disproportionately affected. Given that most fresh animal and vegetable foods in LMICs are bought in informal food systems, much the burden of foodborne disease in LMIC is also linked to informal markets. Developing estimates of the national burden of foodborne disease and attribution to specific food products will inform decision-makers about the size of the problem and motivate action to mitigate risks and prevent illness. This study provides estimates for the burden of foodborne disease caused by selected hazards in two African countries (Burkina Faso and Ethiopia) and attribution to specific foods. Country-specific estimates of the burden of disease in 2010 for Campylobacter spp., enterotoxigenic Escherichia coli (ETEC), Shiga-toxin producing E. coli and non-typhoidal Salmonella enterica were obtained from WHO and updated to 2017 using data from the Global Burden of Disease study. Attribution data obtained from WHO were complemented with a dedicated Structured Expert Judgement study to estimate the burden attributable to specific foods. Monte Carlo simulation methods were used to propagate uncertainty. The burden of foodborne disease in the two countries in 2010 was largely similar to the burden in the region except for higher mortality and disability-adjusted life years (DALYs) due to Salmonella in Burkina Faso. In both countries, Campylobacter caused the largest number of cases, while Salmonella caused the largest number of deaths and DALYs. In Burkina Faso, the burden of Campylobacter and ETEC increased from 2010 to 2017, while the burden of Salmonella decreased. In Ethiopia, the burden of all hazards decreased. Mortality decreased relative to incidence in both countries. In both countries, the burden of poultry meat (in DALYs) was larger than the burden of vegetables. In Ethiopia, the burdens of beef and dairy were similar, and somewhat lower than the burden of vegetables. The burden of foodborne disease by the selected pathogens and foods in both countries was substantial. Uncertainty distributions around the estimates spanned several orders of magnitude. This reflects data limitations, as well as variability in the transmission and burden of foodborne disease associated with the pathogens considered.

Coronavirus disease 2019 (COVID-19) forecasts from over 100 models are readily available. However, little published information exists regarding the performance of their uncertainty estimates (i.e. probabilistic performance). To evaluate their probabilistic performance, we employ the classical model (CM), an established method typically used to validate expert opinion. In this analysis, we assess both the predictive and probabilistic performance of COVID-19 forecasting models during 2021. We also compare the performance of aggregated forecasts (i.e. ensembles) based on equal and CM performance-based weights to an established ensemble from the Centers for Disease Control and Prevention (CDC). Our analysis of forecasts of COVID-19 mortality from 22 individual models and three ensembles across 49 states indicates that - (i) good predictive performance does not imply good probabilistic performance, and vice versa; (ii) models often provide tight but inaccurate uncertainty estimates; (iii) most models perform worse than a naive baseline model; (iv) both the CDC and CM performance-weighted ensembles perform well; but (v) while the CDC ensemble was more informative, the CM ensemble was more statistically accurate across states. This study presents a worthwhile method for appropriately assessing the performance of probabilistic forecasts and can potentially improve both public health decision-making and COVID-19 modelling.