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prospective safety assessment tool increasingly applied in healthcare. a problem hampering stpa proliferation is the difficulty of modeling systems for analysis by creating control structures. in this work, a method is proposed to use existing process maps�commonly available in healthcare�when creating a control structure. the proposed method entails (1) extract information from the process map, (2) determine the modeling boundary of the control structure, (3) transfer the extracted information to the control structure, (4) add additional information to complete the control structure. two case studies were conducted: (1) ambulance patient offloading in the emergency department and (2) ischemic stroke care with intravenous thrombolysis. the amount of process map-derived information in the control structures was quantified. on average, 68% of the information in the final control structures was derived from the process map. additional control actions and feedback were added from nonprocess map sources for management and frontline controllers. despite the differences between process maps and control structures, much of the information in a process map can be used when creating a control structure. the method enables the creation of a control structure from a process map to be done in a structured fashion. 2. title: quantitative risk analysis of treatment plans for patients with tumor by mining historical similar patients from electronic health records using federated learning authors: yang liu, donghai bi abstract: the determination of a treatment plan for a target patient with tumor is a difficult problem due to the existence of heterogeneity in patients� responses, incomplete information about tumor states, and asymmetric knowledge between doctors and patients, and so on. in this paper, a method for quantitative risk analysis of treatment plans for patients with tumor is proposed. to reduce the impacts of the heterogeneity in patients� responses on analysis results, the method conducts risk analysis by mining historical similar patients from electronic health records (ehrs) in multiple hospitals using federated learning (fl). for this, the recursive feature elimination based on the support vector machine (svm) and deep learning important features (deeplift) are extended into the fl framework to select key features and determine key feature weights for identifying historical similar patients. then, in the database of each collaborative hospital, the similarities between the target patient and all historical patients are calculated, and the historical similar patients are determined. according to the statistics of tumor states and treatment outcomes of historical similar patients in all collaborative hospitals, the related data (including the probabilities of different tumor states and possible outcomes of different treatment plans) for risk analysis of the alternative treatment plans can be obtained, which can eliminate the asymmetric knowledge between doctors and patients. the related data are valuable for the doctor and patient to make their decisions. experimental studies have been conducted to verify the feasibility and effectiveness of the proposed method. 3. title: framework for cyber risk loss distribution of hospital infrastructure: bond percolation on mixed random graphs approach authors: stefano chiaradonna, petar jevti, nicolas lanchier abstract: networks like those of healthcare infrastructure have been a primary target of cyberattacks for over a decade. from just a single cyberattack, a healthcare facility would expect to see millions of dollars in losses from legal fines, business interruption, and loss of revenue. as more medical devices become interconnected, more cyber vulnerabilities emerge, resulting in more potential exploitation that may disrupt patient care and give rise to catastrophic financial losses. in this paper, we propose a structural model of an aggregate loss distribution across multiple cyberattacks on a prototypical hospital network. modeled as a mixed random graph, the hospital network consists of various patient-monitoring devices and medical imaging equipment as random nodes to account for the variable occupancy of patient rooms and availability of imaging equipment that are connected by bidirectional edges to fixed hospital and radiological information systems. our framework accounts for the documented cyber vulnerabilities of a hospital's trusted internal network of its major medical assets. to our knowledge, there exist no other models of an aggregate loss distribution for cyber risk in this setting. we contextualize the problem in the probabilistic graph-theoretical framework using a percolation model and combinatorial techniques to compute the mean and variance of the loss distribution for a mixed random network with associated random costs that can be useful for healthcare administrators and cybersecurity professionals to improve cybersecurity management strategies. by characterizing this distribution, we allow for the further utility of pricing cyber risk. 4. title: assumptions, uncertainty, and catastrophic/existential risk: national risk assessments need improved methods and stakeholder engagement authors: matt boyd, nick wilson abstract: two key shortcomings of national risk assessments (nras) are: (1) lack of justification and transparency around important foundational assumptions of the process, (2) omission of almost all the largest scale risks. using a demonstration set of risks, we illustrate how nra process assumptions around time horizon, discount rate, scenario choice, and decision rule impact on risk characterization and therefore any subsequent ranking. we then identify a neglected set of large-scale risks that are seldom included in nras, namely global catastrophic risks and existential threats to humanity. under a highly conservative approach that considers only simple probability and impact metrics, the use of significant discount rates, and harms only to those currently alive at the time, we find these risks have likely salience far greater than their omission from national risk registers might suggest. we highlight the substantial uncertainty inherent in nras and argue that this is reason for more engagement with stakeholders and experts. widespread engagement with an informed public and experts would legitimize key assumptions, encourage critique of knowledge, and ease shortcomings of nras. we advocate for a deliberative public tool that can support informed two-way communication between stakeholders and governments. we outline the first component of such a tool for communication and exploration of risks and assumptions. the most important factors for an �all hazards� approach to nra are ensuring license for key assumptions and that all the salient risks are included before proceeding to ranking of risks and considering resource allocation and value. 5. title: are the good spared? corporate social responsibility as insurance against cyber security incidents authors: vassiliki bamiatzi, michael dowling, fabian gogolin, fearghal kearney, samuel vigne abstract: despite the increasing consensus that socially responsible behavior can act as insurance against externally induced shocks, supporting evidence remains somewhat inconsistent. our study provides a clear demonstration of the insurance-like properties of corporate social responsibility (csr) in preserving corporate financial performance (cfp), in the event of a data (cyber) breach. exploring a sample of 230 breached firms, we find that data breaches lead to significantly negative cfp outcomes for low csr firms, with the dynamic being particularly pronounced in consumer-sensitive industries. further, we show that firms increase their csr activities in the aftermath of a breach to recover lost goodwill and regain stakeholder trust. overall, our results support the use of csr as a strategic risk-mitigation tool that can curtail the consequences of data breaches, particularly for firms operating in consumer-centric environments. 6. title: foodborne disease outbreaks in flour and flour-based food products from microbial pathogens in the united states, and their health economic burden authors: rubait rahman, robert l. scharff, felicia wu abstract: the most comprehensive and inclusive estimates for the economic burden of foodborne illness yield values as high as $97.4 billion usd annually. however, broad incidence and cost estimates have limited use if they cannot be attributed to specific foods, for the purposes of food safety control. in this study, we estimated the economic burden of foodborne illnesses resulting from flour and flour-based food products in the united states from the years 2001 to 2021. the outbreak, illness burden, and health economic data are combined to generate these estimates. our model combined outbreak data with published centers for disease control and prevention multipliers to estimate the annual number of illnesses associated with flour-borne pathogens. we then integrated illness severity data with an updated economic model that accounts for costs related to medical care, productivity loss, loss of life, along with the quality of life loss that entails pain and suffering. in total, 752 cases and 223 hospitalizations from flour-related illnesses were reported from 2001 to 2021, with an average of 37.6 cases of reported cases annually. however, the actual number of cases, accounting for underreporting and underdiagnosis, can be as high as 19,440 annually. pathogens involved in these outbreaks are salmonella, e. coli o157:h7, and e. coli o121. our estimates suggest average annual economic losses, including healthy years of life lost, of $108 and $258 million using two alternative models. 7. title: source attribution of campylobacteriosis in australia, 2017�2019 authors: angus mclure, james j. smith, simon matthew firestone, martyn d. kirk, nigel french, emily fearnley, rhiannon wallace, mary valcanis, dieter bulach, cameron r. m. moffatt, linda a. selvey, amy jennison, danielle m. cribb, kathryn glass abstract: campylobacter jejuni and campylobacter coli infections are the leading cause of foodborne gastroenteritis in high-income countries. campylobacter colonizes a variety of warm-blooded hosts that are reservoirs for human campylobacteriosis. the proportions of australian cases attributable to different animal reservoirs are unknown but can be estimated by comparing the frequency of different sequence types in cases and reservoirs. campylobacter isolates were obtained from notified human cases and raw meat and offal from the major livestock in australia between 2017 and 2019. isolates were typed using multi-locus sequence genotyping. we used bayesian source attribution models including the asymmetric island model, the modified hald model, and their generalizations. some models included an �unsampled� source to estimate the proportion of cases attributable to wild, feral, or domestic animal reservoirs not sampled in our study. model fits were compared using the watanabe�akaike information criterion. we included 612 food and 710 human case isolates. the best fitting models attributed >80% of campylobacter cases to chickens, with a greater proportion of c. coli (>84%) than c. jejuni (>77%). the best fitting model that included an unsampled source attributed 14% (95% credible interval [cri]: 0.3%�32%) to the unsampled source and only 2% to ruminants (95% cri: 0.3%�12%) and 2% to pigs (95% cri: 0.2%�11%) the best fitting model that did not include an unsampled source attributed 12% to ruminants (95% cri: 1.3%�33%) and 6% to pigs (95% cri: 1.1%�19%). chickens were the leading source of human campylobacter infections in australia in 2017�2019 and should remain the focus of interventions to reduce burden. 8. title: weighted bayesian network for the classification of unbalanced food safety data: case study of risk-based monitoring of heavy metals authors: xinxin wang, yamine bouzembrak, a. g. j. m. oude lansink, h. j. van der fels-klerx abstract: historical data on food safety monitoring often serve as an information source in designing monitoring plans. however, such data are often unbalanced: a small fraction of the dataset refers to food safety hazards that are present in high concentrations (representing commodity batches with a high risk of being contaminated, the positives) and a high fraction of the dataset refers to food safety hazards that are present in low concentrations (representing commodity batches with a low risk of being contaminated, the negatives). such unbalanced datasets complicate modeling to predict the probability of contamination of commodity batches. this study proposes a weighted bayesian network (wbn) classifier to improve the model prediction accuracy for the presence of food and feed safety hazards using unbalanced monitoring data, specifically for the presence of heavy metals in feed. applying different weight values resulted in different classification accuracies for each involved class; the optimal weight value was defined as the value that yielded the most effective monitoring plan, that is, identifying the highest percentage of contaminated feed batches. results showed that the bayesian network classifier resulted in a large difference between the classification accuracy of positive samples (20%) and negative samples (99%). with the wbn approach, the classification accuracy of positive samples and negative samples were both around 80%, and the monitoring effectiveness increased from 31% to 80% for pre-set sample size of 3000. results of this study can be used to improve the effectiveness of monitoring various food safety hazards in food and feed. 9. title: a place-based risk appraisal model for exploring residents� attitudes toward nature-based solutions to flood risks authors: sungju han, philip bubeck, annegret thieken, christian kuhlicke abstract: nature-based solutions (nbs) have gained popularity as a sustainable and effective way of dealing with increasing flood risks. one of the key factors that often hinders the successful implementation of nbs is residents� opposition to their implementation. in this study, we argue that the place where a hazard exists should be considered a critical contextual factor alongside flood risk appraisals and perceptions of nbs themselves. we have developed a theoretical framework�the �place-based risk appraisal model (pram)��that draws on constructs inspired by theories of place and risk perception. a citizen survey (n = 304) was conducted in five municipalities in saxony-anhalt, germany, where dike relocation and floodplain restoration projects have been conducted along the elbe river. structural equation modeling was adopted to test the pram. attitudes toward the projects were assessed in terms of �perceived risk-reduction effectiveness� and �supportive attitude.� with regard to risk-related constructs, well-communicated information and perceived co-benefits were consistently positive factors for both perceived risk-reduction effectiveness and supportive attitude. trust in local flood risk management was a positive and threat appraisal a negative predictor of perceived risk-reduction effectiveness affecting �supportive attitude� only through �perceived risk-reduction effectiveness.� regarding place attachment constructs, place identity was a negative predictor of a supportive attitude. the study emphasizes that risk appraisal, pluralities of place contexts to each individual, and their relations are key for determining attitudes toward nbs. understanding these influencing factors and their interrelationships enables us to provide theory- and evidence-based recommendations for the effective realization of nbs. 10. title: effects of communicating lifetime risks and screening rates of colorectal cancer and breast cancer authors: jiawei liu, jeff niederdeppe abstract: colorectal cancer and breast cancer are among the most common types of cancer in the united states, and cancer screening is an effective way to detect and treat these cancers early. health news stories, medical websites, and media campaigns regularly highlight the national lifetime risks of specific cancers and their screening rates, but recent research suggests that people tend to overestimate the prevalence of health problems but underestimate the prevalence of disease prevention behaviors in the absence of numerical information. this study featured two online experiments, one focused on breast cancer (n = 632) and one focused on colorectal cancer (n = 671), to examine the effects of communicating national cancer lifetime risks and screening rates among samples of screening-eligible adults in the united states. findings confirmed prior work in showing that people overestimated colorectal/breast cancer lifetime risks but underestimated colorectal/breast cancer screening rates. communicating the national lifetime risk of dying from colorectal/breast cancer lowered people's national risk estimates, which in turn was associated with lower perceived cancer risks for themselves. in contrast, communicating the national colorectal/breast cancer screening rate increased people's estimates of the prevalence of cancer screening, which in turn was associated with higher perceived self-efficacy to engage in cancer screening and greater screening intentions. we conclude that efforts to promote cancer screening may benefit from messages that include data on national cancer screening rates but may not benefit from including national rates of lifetime cancer risks. 11. title: the effects of death awareness and reactance on texting-and-driving prevention authors: elena bessarabova, zachary b. massey abstract: using a 2 (mortality: salient, control) � 2 (freedom-limiting language: freedom-limiting, autonomy-supportive) independent-group design, this study examined the relationship between mortality salience and psychological reactance in the context of texting-and-driving prevention messages. the terror management health model and the theory of psychological reactance guided study predictions. results showed mortality salience produced adaptive effects on attitudes toward texting-and-driving prevention and behavioral intentions to reduce unsafe driving practices. additionally, some evidence for the effectiveness of directive, albeit freedom-limiting communication, emerged. these and other results are discussed along with the implications, limitations, and future research directions. 12. title: linking cognitive and affective heuristic cues to interpersonal risk perceptions and behavior authors: erika a. waters, thorsten pachur, gabrielle pogge, jean hunleth, gregory d. webster, james a. shepperd abstract: people often use cognitive and affective heuristics when judging the likelihood of a health outcome and making health decisions. however, little research has examined how heuristics shape risk perceptions and behavior among people who make decisions on behalf of another person. we examined associations between heuristic cues and caregivers� perceptions of their child's asthma risk, the frequency of caregivers� asthma management behaviors, and child health outcomes. we used ipsos knowledgepanel to recruit 814 u.s. adult caregivers of children with asthma of the age <18 years. participants completed a survey at baseline (t1) and 3 months later (t2). caregivers who, at t1, reported greater negative affect about their child's asthma (affect heuristic cue), greater ease of imagining their child experiencing asthma symptoms (availability heuristic cue), and greater perceived similarity between their child and a child who has ever experienced asthma symptoms (representativeness heuristic cue) reported statistically significantly (p < 0.05) higher interpersonal perceived risk of their child having an exacerbation or uncontrolled asthma at t1. they also indicated at t2 that their child had poorer asthma control and more frequent exacerbations. greater t1 negative affect was associated with more frequent t2 actions to reduce inflammation, manage triggers, and manage symptoms, and with poorer t2 child health outcomes. heuristic cues are likely important for interpersonal�not just personal�risk perceptions. however, the interrelationship between caregivers� ratings of heuristic cues (in particular, negative affect) and risk judgments may signify a struggle with managing their child's asthma and need for extra support from health care providers or systems. 13. title: analyzing indirect economic impacts of wildfire damages on regional economies authors: euijune kim, younghyun john kwon abstract: this article estimates the economic impacts of wildfire damage on korea's regional economies, developing an integrated disaster-economic system for korea. the system is composed of four modules: an interregional computable general equilibrium (icge) model for the eastern mountain area (ema) and the rest of korea, a bayesian wildfire model, a transportation demand model, and a tourist expenditure model. the model has a hierarchical structure, with the icge model serving as a core module to link to three other modules. in the impact analysis of a wildfire, three external shocks are injected into the icge model: (1) the wildfire damaged area derived from the bayesian wildfire model, (2) changes in travel times among cities and counties derived from the transportation demand model, and (3) variations in visitors� expenditures derived from the tourist expenditure model. the simulation shows that the gross regional product (grp) of the ema would decrease by 0.25% to 0.55% without climate change and by 0.51%�1.23% with climate change. this article contributes to the development of quantitative linkages between macro and micro spatial models in a bottom-up system for the impact analysis of disasters, integrating a regional economic model with a place-based disaster model and the demands of tourism and transportation. 14. title: data-driven predictive modeling in risk assessment: challenges and directions for proper uncertainty representation authors: kaia st�dle, roger flage, seth d. guikema, terje aven abstract: data-driven predictive modeling is increasingly being used in risk assessments. while such modeling may provide improved consequence predictions and probability estimates, it also comes with challenges. one is that the modeling and its output does not measure and represent uncertainty due to lack of knowledge, that is, �epistemic uncertainty.� in this article, we demonstrate this point by conceptually linking the main elements and output of data-driven predictive models with the main elements of a general risk description, thereby placing data-driven predictive modeling on a risk science foundation. this allows for an evaluation of such modeling with reference to risk science recommendations for what constitutes a complete risk description. the evaluation leads us to conclude that, as a minimum, to cover all elements of a complete risk description a risk assessment using data-driven predictive modeling needs to be supported by assessments of the uncertainty and risk related to the assumptions underlying the modeling. in response to this need, we discuss an approach for assessing assumptions in data-driven predictive modeling. 15. title: who contributes to disaster preparedness? predicting decision making in social dilemmas pertaining to community resilience authors: paul m. johnson, hiba baroud, corey e. brady, mark abkowitz abstract: planning for community resilience through public infrastructure projects often engenders problems associated with social dilemmas, but little work has been done to understand how individuals respond when presented with opportunities to invest in such developments. using statistical learning techniques trained on the results of a web-based common pool resource game, we analyze participants' decisions to invest in hypothetical public infrastructure projects that bolster their community's resilience to disasters. given participants' dispositions and in-game circumstances, bayesian additive regression tree (bart) models are able to accurately predict deviations from players' decisions that would reasonably lead to pareto-efficient outcomes for their communities. participants tend to overcontribute relative to these pareto-efficient strategies, indicating general risk aversion that is analogous to individuals purchasing disaster insurance even though it exceeds expected actuarial costs. however, higher trait openness scores reflect an individual's tendency to follow a risk-neutral strategy, and fewer available resources predict lower perceived utilities derived from the infrastructure developments. in addition, several input variables have nonlinear effects  !# 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