Scientific consensus links dietary choices to health outcomes, highlighting the urgency to prioritise healthy food production in food systems. At the EU level, however, defining healthy food and integrating it into food governance remains a challenge, particularly regarding the inclusion of health-related characteristics in food safety assessments. While the EU primarily relies on the risk analysis principle to address food safety concerns, the process still exhibits weaknesses that hinder its direct impact on fostering a healthier food production landscape. This review demonstrates how the risk analysis process, particularly scientific opinions in risk assessment and external factors (economic and political) in risk management, impact food governance and the healthiness of food systems. We find that while external factors play a crucial role in risk management decisions by incorporating non-food safety considerations, they often stem from an imbalance of power favouring major industry and political stakeholders. This imbalance disproportionately influences decision-making, often overshadowing nutritional and health aspects. To address these challenges, we recommend directing research towards filling knowledge gaps and exploring minority scientific findings, and separating external factors from risk management’s decision-making process, to ensure that food governance prioritises public health and healthy food production.

Self-sovereign identity (SSI) is an emerging and promising concept that enables users to control their identity while enhancing security and privacy compared to other identity management (IDM) approaches. Despite the recent advancements in SSI technologies, federated identity management (FIDM) systems continue to dominate the IDM market. Selecting an IDM to implement for a specific application is a complex task that requires a thorough understanding of the potential external cyber risks. However, existing research scarcely compares SSI and FIDM from the perspective of these external threats. In response to this gap, our article provides an attack surface analysis focused solely on external threats for both systems. This analysis can serve as a reference to compare the relevant security and privacy risks associated with these external threats. The threat landscapes of external attackers were systematically synthesized from the main components and functionalities of the common standards and designs. We further present a use case analysis that applies this attack surface analysis to compare the external cyber risks of the two systems in detail when managing cross-border identity between European countries. This work can be particularly useful for considering a more secure design for future IDM applications, taking into account the landscape of external threats.

Mass-casualty terrorism and terrorism involving unconventional weapons have received extensive academic and policy attention, yet few academics have considered the broader question of whether such behaviours could pose a plausible risk to humanity’s survival or continued flourishing. Despite several terrorist and other violent non-state actors having evinced an interest in causing existential harm to humanity, their ambition has historically vastly outweighed their capability. Nonetheless, three pathways to existential harm exist: existential attack, existential spoilers and systemic harm. Each pathway varies in its risk dynamics considerably. Although an existential attack is plausible, it would require extraordinary levels of terrorist capability. Conversely, modest terrorist capabilities might be sufficient to spoil risk mitigation measures or cause systemic harm, but such actions would only result in existential harm under highly contingent circumstances. Overall, we conclude that the likelihood of terrorism causing existential harm is extremely low, at least in the near to medium term, but it is theoretically possible for terrorists to intentionally destroy humanity.

The effect that breed standards and selective breeding practices have on the welfare of pedigree dogs has recently come under scrutiny from both the general public and scientific community. Recent research has suggested that breeding for particular aesthetic traits, such as tightly curled tails, highly domed skulls and short muzzles predisposes dogs with these traits to certain inherited defects, such as spina bifida, syringomyelia and brachycephalic airway obstruction syndrome, respectively. Further to this, there is a very large number of inherited diseases that are not related to breed standards, which are thought to be prevalent, partly as a consequence of inbreeding and restricted breeding pools. Inherited diseases, whether linked to conformation or not, have varying impact on the individuals affected by them, and affect varying proportions of the pedigree dog population. Some diseases affect few breeds but are highly prevalent in predisposed breeds. Other diseases affect many breeds, but have low prevalence within each breed. In this paper, we discuss the use of risk analysis and severity diagrams as means of mapping the overall problem of inherited disorders in pedigree dogs and, more specifically, the welfare impact of specific diseases in particular breeds.

Regulatory agencies aim to protect the public by moderating risks associated with innovation, but a good regulatory regime should also promote justified public trust. After introducing the USDA 2020 SECURE Rule for regulation of biotech innovation as a case study, this essay develops a theory of justified public trust in regulation. On the theory advanced here, to be trustworthy, a regulatory regime must (1) fairly and effectively manage risk, must be (2) “science based” in the relevant sense, and must in addition be (3) truthful, (4) transparent, and (5) responsive to public input. Evaluated with these norms, the USDA SECURE Rule is shown to be deeply flawed, since it fails appropriately to manage risk, and similarly fails to satisfy other normative requirements for justified trust. The argument identifies ways in which the SECURE Rule itself might be improved, but more broadly provides a normative framework for the evaluation of trustworthy regulatory policy-making.

Initial insurance losses are often reported with a textual description of the claim. The claims manager must determine the adequate case reserve for each known claim. In this paper, we present a framework for predicting the amount of loss given a textual description of the claim using a large number of words found in the descriptions. Prior work has focused on classifying insurance claims based on keywords selected by a human expert, whereas in this paper the focus is on loss amount prediction with automatic word selection. In order to transform words into numeric vectors, we use word cosine similarities and word embedding matrices. When we consider all unique words found in the training dataset and impose a generalised additive model to the resulting explanatory variables, the resulting design matrix is high dimensional. For this reason, we use a group lasso penalty to reduce the number of coefficients in the model. The scalable, analytical framework proposed provides for a parsimonious and interpretable model. Finally, we discuss the implications of the analysis, including how the framework may be used by an insurance company and how the interpretation of the covariates can lead to significant policy change. The code can be found in the TAGAM R package (github.com/scottmanski/TAGAM).

This article examines ‘essential use’ as a novel form of regulatory control. An essential use approach to the regulation of potentially hazardous chemicals has not been used extensively (if at all) in European Union (EU) regulatory law and warrants further consideration. Essential use, as initially proposed by scientists and later referred to in the EU 2020 Chemicals Strategy for Sustainability, is a radical departure from the current method of regulating hazardous substances. The purpose of this article is to contribute to legal scholarship on essential use by (i) scoping its origins in United States law and subsequently in international law; (ii) noting its limited incorporation into the EU legal order over the past 30 years; (iii) analyzing how it could be further incorporated into the EU legal order; and (iv) considering the impact of such a move on the future regulation of hazardous substances in the EU.

This article describes a methodology for a risk-informed benefit–cost analysis that includes (i) risk analysis to quantify risk reduction benefits and (ii) uncertainty analyses to quantify probability distributions over costs and benefits. It also summarizes the lessons from 25 applications of this methodology to evaluate R&D projects of the Science and Technology Directorate of the Department of Homeland Security. The article then illustrates the methodology with a specific application to evaluate the benefits and costs of the Advanced Personal Protection System (APPS), a new garment system developed to protect wildland firefighters. The goals of the APPS project were to reduce risk and to improve comfort. The cost analysis revealed that the APPS garments are more expensive by about $279 per garment system. Total costs were roughly $7.3 million, including the upfront project cost and the increased 5 year cost of purchasing the APPS. Benefits from reduced injuries and fatalities resulted in 5 year benefits of about $19.3 million, with an NPV of $13.6 million in 2019 dollars. In the base case, the benefit–cost ratio was 2.87 and the return on investment was 187 % over 5 years. Taking the perspective of a decision-maker when the project was first funded in 2011, NPVs are $11,993,728, $10,025,519, and $7,967,479 in 2011 dollars for discount rates of 0, 3, and 7 % respectively. An uncertainty analysis of the NPV showed a large variability, ranging from the 5th percentile of $6.4 million to a median of $19.3 million to the 95th percentile of $43.7 million in 2019 dollars. This large range was primarily due to the uncertainty about the reduction of fatality and injury risks and the market penetration rates of the new garments.

Landowners can engage in voluntary conservation with the help of incentive programs. Recommended conservation practices are selected based on management intentions as well as the contribution of those practices to the overall net returns. However, conservation motives are heterogeneous and based on individual risk behavior. Existing cost-share programs might either under-fund or over-fund conservation, which could lead to inefficient management of natural resources. The current analysis evaluates the economic feasibility of variable cover crop strategies, multiple seeding rates, within a soybean production system in silt loam and clay soils. The study utilizes stochastic efficiency with respect to a function, referred to as SERF, for determining the preferred strategies under various risk preferences. The SERF method accounts for the heterogeneity of individual decision-making with regards to conservation adoption. Results indicate that most risk-averse farmers chose tillage radish with medium seeding rate as their preferred strategy. However, as the risk-bearing capacity of an individual increases, the current level of incentives does not motivate to implement conservation. The most preferred plan for risk-neutral farmers is the fallow system in both silt loam and clay soils. The economic and risk assessment framework can improve understanding of the temporal dynamics of different practices and inform policy on conservation structure that promotes agricultural systems that are economically, environmentally, and socially sustainable.

Fasciolosis is a food-borne disease that causes great distress to a range of hosts, including humans. The objectives of this study were to (1) evaluate the liver damage and carcass weight of cattle naturally infected with Fasciola hepatica from the state of Rio Grande do Sul (RS), Brazil, and to (2) determine the distribution of adult flukes in 12,236 cattle liver from RS. The data from these experiments were used to calculate the overall economic loss due to F. hepatica infection. Eighteen adult Polled Hereford cows were divided into a triclabendazole (TbG) and a F. hepatica-positive group (FhG). For Experiment 1, a generalized linear mixed model revealed a statistical difference in carcass weight (49.8 kg) between TbG and FhG. The Monte Carlo analysis also revealed that the animals’ weight differences were due to the disease. For Experiment 2, the prevalence of infected livers was above 16% (1904/12,236), mostly (20.1%) from the south-west region of RS. The Susceptible-infected-recovered (SIR) epidemic model revealed the evolution of the infection using a high infectivity and low recovery rate. Other distinctive scenarios that occur in RS were also established with different rates of infectivity. The economic assessment showed a potential loss of US$45 million to the beef cattle industry of RS, with an overall State cost of US$90.3 million. These novel findings reveal the importance of fasciolosis infection, which can cause a significant health condition and poor animal welfare.

Global Health Security Index (GHSI) and Joint External Evaluation (JEE) are two well-known health security and related capability indices. We hypothesised that countries with higher GHSI or JEE scores would have detected their first COVID-19 case earlier, and would experience lower mortality outcome compared to countries with lower scores. We evaluated the effectiveness of GHSI and JEE in predicting countries' COVID-19 detection response times and mortality outcome (deaths/million). We used two different outcomes for the evaluation: (i) detection response time, the duration of time to the first confirmed case detection (from 31st December 2019 to 20th February 2020 when every country's first case was linked to travel from China) and (ii) mortality outcome (deaths/million) until 11th March and 1st July 2020, respectively. We interpreted the detection response time alongside previously published relative risk of the importation of COVID-19 cases from China. We performed multiple linear regression and negative binomial regression analysis to evaluate how these indices predicted the actual outcome. The two indices, GHSI and JEE were strongly correlated (r = 0.82), indicating a good agreement between them. However, both GHSI (r = 0.31) and JEE (r = 0.37) had a poor correlation with countries' COVID-19–related mortality outcome. Higher risk of importation of COVID-19 from China for a given country was negatively correlated with the time taken to detect the first case in that country (adjusted R2 = 0.63–0.66), while the GHSI and JEE had minimal predictive value. In the negative binomial regression model, countries' mortality outcome was strongly predicted by the percentage of the population aged 65 and above (incidence rate ratio (IRR): 1.10 (95% confidence interval (CI): 1.01–1.21) while overall GHSI score (IRR: 1.01 (95% CI: 0.98–1.01)) and JEE (IRR: 0.99 (95% CI: 0.96–1.02)) were not significant predictors. GHSI and JEE had lower predictive value for detection response time and mortality outcome due to COVID-19. We suggest introduction of a population healthiness parameter, to address demographic and comorbidity vulnerabilities, and reappraisal of the ranking system and methods used to obtain the index based on experience gained from this pandemic.

Evaluation of agricultural Research, Development, Extension and Management requires knowledge of farming systems economics and risk as well as broader adoption drivers. But until now, these factors have not been effectively combined when determining the success of agricultural research projects. To fill this gap, we developed Value-Ag, an integrated modelling platform using whole-farm economic analysis and prediction of the scaling potential in the context of production risk and household dynamics to provide an ex-ante estimate of the benefits of adopting an innovation. In this paper, we use a hypothetical case study to illustrate Value-Ag’s potential to evaluate agricultural innovations in a rigorous, systematic and participatory manner across a range of scenarios, thereby stimulating thinking and learning opportunities with the relevant stakeholders, and increasing the scrutiny of projects so that they deliver greater value for money while fostering a more results-focused culture in developing countries.

Short-term survival after paediatric cardiac surgery has improved significantly over the past 20 years and increasing attention is being given to measuring and reducing incidence of morbidities following surgery. How to best use routinely collected data to share morbidity information constitutes a challenge for clinical teams interested in analysing their outcomes for quality improvement. We aimed to develop a tool facilitating this process in the context of monitoring morbidities following paediatric cardiac surgery, as part of a prospective multi-centre research study in the United Kingdom.

We developed a prototype software tool to analyse and present data about morbidities associated with cardiac surgery in children. We used an iterative process, involving engagement with potential users, tool design and implementation, and feedback collection. Graphical data displays were based on the use of icons and graphs designed in collaboration with clinicians.

Our tool enables automatic creation of graphical summaries, displayed as a Microsoft PowerPoint presentation, from a spreadsheet containing patient-level data about specified cardiac surgery morbidities. Data summaries include numbers/percentages of cases with morbidities reported, co-occurrences of different morbidities, and time series of each complication over a time window.

Our work was characterised by a very high level of interaction with potential users of the tool, enabling us to promptly account for feedback and suggestions from clinicians and data managers. The United Kingdom centres involved in the project received the tool positively, and several expressed their interest in using it as part of their routine practice.

“Uncertain futures” refers to a set of policy problems that possess some combination of the following characteristics: (i) they potentially cause irreversible changes; (ii) they are widespread, so that policy responses may make sense only on a global scale; (iii) network effects are difficult to understand and may amplify (or moderate) consequences; (iv) time horizons are long; and (v) the likelihood of catastrophic outcomes is unknown or even unknowable. These characteristics tend to make uncertain futures intractable to market solutions because property rights are not clearly defined and essential information is unavailable. These same factors also pose challenges for benefit-cost analysis (BCA) and other traditional decision analysis tools. The diverse policy decisions confronting decision-makers today demand “dynamic BCA,” analytic frameworks that incorporate uncertainties and trade-offs across policy areas, recognizing that: perceptions of risks can be uninformed, misinformed, or inaccurate; risk characterization can suffer from ambiguity; and experts’ tendency to focus on one risk at a time may blind policymakers to important trade-offs. Dynamic BCA – which recognizes trade-offs, anticipates the need to learn from experience, and encourages learning – is essential for lowering the likelihoods and mitigating the consequences of uncertain futures while encouraging economic growth, reducing fragility, and increasing resilience.