Risk Analysis最新文献

Imported agricultural pests can cause substantial damage to agriculture, food security, and ecosystems. In the United States, the Agricultural Quarantine Inspection Monitoring (AQIM) program conducts random sampling to estimate the probabilities that cargo and passengers arriving at ports of entry carry pests. Assessing these risks accurately is critical to enable effective policies and operational procedures. This study introduces a pathway-level analysis with an objective function aligned with AQIM's goal, offering a new perspective compared to the current container-by-container approach, which relies on heuristics to set inspection rates. We formulate an optimization model that minimizes the mean squared error of the probability estimates that AQIM obtains. The central decision-making tradeoff that the model explores is whether it is preferable to sample more arriving containers (and fewer boxes per container) or more boxes per container (and fewer containers), given limited resources. We first derive an analytical solution for the optimal sampling strategy by leveraging several approximations. Then, we apply our model to a numerical case study of maritime cargo sampling at the Port of Long Beach. Across a wide range of parameter settings, the optimal strategy samples more containers (but fewer boxes per container) than the current AQIM protocol. The difference between the two strategies and the accuracy improvement with the optimal approach are larger if the pest statuses of boxes in the same container are more strongly correlated. We recommend that AQIM record box-level (beyond only container-level) inspection data, which could be used to estimate this correlation and other model parameters.

The concept of resilience intrinsically links with both complexity and adaptive capacity. Scholars from different fields agree on this. Still, the detailed relations between resilience, complexity, and adaptive capacity need a more thorough theoretical analysis. This article analyses resilience with the help of assumptions from complex adaptive systems (CAS) theory to answer two questions in more detail: What is the relation between resilience and complexity? How can adaptive capacity contribute to resilience? By applying basic ideas from CAS theory to the resilience discourse, the article deduces that complexity of a system is a necessary condition for resilience because complex systems consist of agents that possess adaptive capacity, whereas simple systems consist of mere elements that cannot adapt to unexpected disruptions. The relation between complexity and resilience is multidimensional. Growing complexity leads to a growing need for resilience because the chances for severe, unexpected disruptions increase. The analysis of adaptive capacities revealed that systems and the agents they consist of can possess of specialized and general adaptive capacity. General adaptive capacity is the core feature of resilience because it enables systems to cope with unexpected disruptions. System design principles such as diversity within functional groups and redundancy help to increase general adaptive capacity. The same is true on the community level for social capital and on the individual level for disaster preparedness measures because they increase coping capacities independent of specific hazards.

Although persuasive messages are designed to motivate individuals to engage in intended behaviors, they do not always work. Often, people follow previously established values and ideologies and dismiss persuasive messages. We examine how participants react to a persuasive message related to plastic pollution and how these reactions shape their willingness to recycle and reuse. Results indicate that environmental values and political ideology are associated with message derogation in distinct ways, which, in turn, affect risk perception, self-efficacy, and intention to recycle and reuse. Further, past behavior moderates the relationship between message derogation and perceived risk, but not the relationship between message derogation and self-efficacy. These results suggest that pre-existing values and ideologies play an important role in message derogation, a hitherto under-researched phenomenon that has key implications for self-reported behavioral change. Moreover, past behavior could serve as a powerful lever in steering risk perception and behavioral intent.

Following a previous article that focused on integrating epidemiological data from prospective cohort studies into toxicological risk assessment, this paper shifts the focus to case-control studies. Specifically, it utilizes the odds ratio (OR) as the main epidemiological measure, aligning it with the benchmark dose (BMD) methodology as the standard dose-response modeling approach to determine chemical toxicity values for regulatory risk assessment. A standardized BMD analysis framework has been established for toxicological data, including input data requirements, dose-response models, definitions of benchmark response, and consideration of model uncertainty. This framework has been enhanced by recent methods capable of handling both cohort and case-control studies using summary data that have been adjusted for confounders. The present study aims to investigate and compare the "effective count" based BMD modeling approach, merged with an algorithm used for converting odds ratio to relative risk in cohort studies with partial data information (i.e., the Wang algorithm), with the adjusted OR-based BMD analysis approach. The goal is to develop an adequate BMD modeling framework that can be generalized for analyzing published case-control study data. As in the previous study, these methods were applied to a database examining the association between bladder and lung cancer and inorganic arsenic exposure. The results indicate that estimated BMDs and BMDLs are relatively consistent across both methods. However, modeling adjusted OR values as continuous data for BMD estimation aligns better with established practices in toxicological BMD analysis, making it a more generalizable approach.

Culture can have a major impact on how we perceive different hazards. In the Romantic period, nature was described and portrayed as mysterious and benevolent. A deep connection to nature was perceived as important. We proposed that this romantic view would be positively related to people's risk perceptions of man-made hazards and, more specifically, to concerns about climate change. Further, we hypothesized that the Romantic perception of nature leads to a biased perception of natural hazards and that the moral component of action is of particular importance above and beyond the mere efficacy of the action. We conducted an online survey in Germany (N = 531), a country where Romanticism had a very widespread influence. The study shows that individuals with a Romantic view of nature perceived greater risks associated with climate change than those without such a view. In addition, those with a Romantic view of nature were more likely to support measures to reduce the risks of climate change, even when it is said that such measures are not effective. Finally, the study found a significantly higher positive correlation between Romantic views of nature and risk perceptions of man-made versus natural hazards. The results suggest that ideas developed during the Romantic era continue to influence hazard perception in Germany.

Decisions in many disciplines are based on understanding and evidence. More evidence is better than less when it enhances the decision-maker's understanding. This is achieved by reducing uncertainty confronting the decision-maker and reducing the potential for misunderstanding and failure. However, some evidence may actually augment uncertainty by revealing prior error or ignorance. True evidence that augments uncertainty is important because it identifies inadequacies of current understanding and may suggest directions for rectifying this. True evidence that reduces uncertainty may simply reconfirm or strengthen prior understanding. Uncertainty-augmenting evidence, when it is true, can support the expansion of one's previously incomplete understanding. A dilemma arises because both reduction and enhancement of uncertainty can be beneficial, and both are not simultaneously possible on the same issue. That is, uncertainty can be either pernicious or propitious. Info-gap theory provides a response. The info-gap robustness function enables protection against pernicious uncertainty by inhibiting failure. The info-gap opportuneness function enables exploitation of propitious uncertainty by facilitating wonderful windfall outcomes. The dilemma of uncertainty-augmenting evidence is that robustness and opportuneness are in conflict; a decision that enhances one, worsens the other. This antagonism between robustness and opportuneness-between protecting against pernicious uncertainty and exploiting propitious uncertainty-is characterized in a generic proposition and corollary. These results are illustrated in an example of allocation of limited resources.

In recent years, longer and heavier trains have become more common, primarily driven by efficiency and cost-saving measures in the railroad industry. Regulation of train length is currently under consideration in the United States at both the federal and state levels, because of concerns that longer trains may have a higher risk of derailment, but the relationship between train length and risk of derailment is not yet well understood. In this study, we use data on freight train accidents during the 2013-2022 period from the Federal Railroad Administration (FRA) Rail Equipment Accident and Highway-Rail Grade Crossing Accident databases to estimate the relationship between freight train length and the risk of derailment. We determine that longer trains do have a greater risk of derailment. Based on our analysis, running 100-car trains is associated with 1.11 (95% confidence interval: 1.10-1.12) times the derailment odds of running 50-car trains (or a 11% increase), even accounting for the fact that only half as many 100-car trains would need to run. For 200-car trains, the odds increase by 24% (odds ratio 1.24, 95% confidence interval: 1.20-1.28), again accounting for the need for fewer trains. Understanding derailment risk is an important component for evaluating the overall safety of the rail system and for the future development and regulation of freight rail transportation. Given the limitations of the current data on freight train length, this study provides an important step toward such an understanding.

Qualitative frameworks are widely employed to tackle urgent animal or public health issues when data are scarce and/or urgent decisions need to be made. In qualitative models, the degree of belief regarding the probabilities of the events occurring along the risk pathway(s) and the outcomes is described in nonnumerical terms, typically using words such as Low, Medium, or High. The main methodological challenge, intrinsic in qualitative models, relates to performing mathematical operations and adherence to the rule of probabilities when probabilities are nonnumerical. Although methods to obtain the qualitative probability from the conditional realization of n events are well-established and consistent with the multiplication rule of probabilities, there is a lack of accepted methods for addressing situations where the probability of an event occurring can increase, and the rule of probability P(AUB) = P(A) + P(B) - P(A∩B) should apply. In this work, we propose a method based on the pairwise summation to fill this methodological gap. Our method was tested on two qualitative models and compared by means of scenario analysis to other approaches found in literature. The qualitative nature of the models prevented formal validation; however, when using the pairwise summation, results consistently appeared more coherent with probability rules. Even if the final qualitative estimate can only represent an approximation of the actual probability of the event occurring, qualitative models have proven to be effective in providing scientific-based evidence to support decision-making. The method proposed in this study contributes to reducing the subjectivity that characterizes qualitative models, improving transparency and reproducibility.

There is an unprecedented exposure of living organisms to mobile communications radiofrequency electromagnetic field (RF-EMF) emissions. Guidelines on exposure thresholds to limit thermal effects from these emissions are restricted to humans. However, tissue heating can occur in all living organisms that are exposed. In addition, exposure at millimetric frequencies used by 5G may impact surface tissues and organs of plants and small-size species. It is also expected that the addition of 5G to existing networks will intensify radiofrequency absorption by living organisms. A European Parliament report proposed policy options on the effects of RF-EMF exposure of plants, animals, and other living organisms in the context of 5G: funding more research, implementing monitoring networks, accessing more information from operators on antennas and EMF emissions, and developing compliance studies when antennas are installed. However, there is no evidence on the preferences of relevant stakeholders regarding these policy options. This paper reports the findings of a survey of key European stakeholders' policy option preferences based on the European Parliament's report. It reveals a broad consensus on funding more research on the effects of exposure of plants, animals, and other living organisms to EMFs. It also highlights the need for deliberation concerning the other policy options that could provide solutions for regulatory authorities, central administrations, the private sector, nongovernmental associations and advocates, and academics. Such deliberation would pave the way for effective solutions, focusing on long-term output from funding research, and enabling short-term socially and economically acceptable actions for all parties concerned.

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.