Risk Analysis: An International Journal
Whether on grounds of perceived safety, aesthetics, or overall quality of life, residents may wish to be aware of nearby energy sites such as nuclear reactors, refineries, and fracking wells. Yet people are not always accurate in their impressions of proximity. Indeed, our data show that only 54% of Americans living within 25 miles of a nuclear site say they do, and even fewer fracking‐proximal (30%) and refinery‐proximal (24%) residents respond accurately. In this article, we analyze factors that could either help people form more accurate perceptions or distort their impressions of proximity. We evaluate these hypotheses using a large national survey sample and corresponding geographic information system (GIS) data. Results show that among those living in close proximity to energy sites, those who perceive greater risk are less likely to report living nearby. Conversely, social contact with employees of these industries increases perceived proximity regardless of actual distance. These relationships are consistent across each site type we examine. Other potential factors—such as local news use—may play a role in proximity perception on a case‐by‐case basis. Our findings are an important step toward a more generalizable understanding of how the public forms perceptions of proximity to risk sites, showing multiple potential mechanisms of bias.
Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit
In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.
Work Conditions, Social Incorporations, and Foodborne Diseases Risk: Reflections About the (Non)Compliance of Food Safety Practices
The number of foodborne diseases has increased in all continents, and efforts must be made to control this urgent and expressive public health problem. This article aims to present and discuss situations related to the compliance and noncompliance of food safety practices (FSPs) in light of Bourdieu's social theory. This qualitative study was conducted in commercial restaurants in two cities in São Paulo, Brazil. Participant observation was used in the restaurants, and notes referring to the kitchen workers and their bosses’ work processes were registered in field journals. Thematic type content analysis was used to determine the meaning cores of field journals. It was found that aspects inherent to convenience and haste at work, deficient infrastructure, lack of employees, negative boss examples, exposure to noise, and body pain experienced by workers can contribute to noncompliance of FSPs and consolidate in the habitus and practical sense some dispositions that can increase the risk of foodborne diseases. This study highlights the necessity of creating environments that address food safety, which means being able to perform a service properly.
Safety Regulations and the Uncertainty of Work‐Related Road Accident Loss: The Triple Identity of Chinese Local Governments Under Principal–Agent Framework
This study examines how government safety regulations affect the uncertainty of work‐related road accident loss (UWRAL) by considering the multi‐identity of local governments in the relationship among the central government, the local governments, and enterprises. Fixed effects panel models and mediation analyses with bootstrapping were conducted to test the hypotheses using Chinese provincial panel data from 2008 to 2014. Given the complexity and nonlinear characteristics of road safety systems, a new approach based on self‐organized criticality theory is proposed to measure the uncertainty of road accident loss from a complex system perspective. We find that a regional government with detailed safety work planning (SWP), high safety supervision intensity (SSI), and safety information transparency (SIT) can decrease the UWRAL. Furthermore, our findings suggest that SSI and SIT partially mediate the relationship between the SWP of regional governments and the UWRAL, with 19.7% and 23.6% indirect effects, respectively. This study also provides the government with managerial implications by linking the results of risk assessment to decision making for risk management.
Uncertain Risk Assessment and Management: Case Studies of the Application of the Precautionary Principle in Portugal
This study intends to clarify how the precautionary principle (PP) has been interpreted and applied by the courts in Portugal in the analysis of conflicts associated with uncertain and serious potential risks to human health and the environment. It also aims to contribute to the debate of when and how to apply precautionary measures. To this end, recent court cases in the areas of waste incineration, high‐voltage power lines, as well as dam and wind farm construction were considered. The degree of consistency in the courts’ decisions and their reasons in the different judicial bodies was analyzed with the support of a theoretical framework based on three attributes: the level of seriousness of potential hazards, level of evidence required, and the severity of precautionary actions taken. Different positions among courts were observed, with contradictory arguments in the same case or in similar cases. A greater propensity for favorable decisions in the acceptance of restraining orders was verified in the courts of lower instances, where human health could be threatened. However, the decisions of the Supreme Administrative Court, which were always unfavorable to the restraining orders, seem to reflect the priority given to national economic and political interests over local or regional environmental interests. They may also reflect the Supreme Court's reluctancy to apply the PP in the absence of a firm legally binding PP in national legislation. To address this situation, more explicit legal requirements and criteria for the analysis of uncertain risks and the weighting of interests by area of activity are needed.
It is estimated that in the United States, people spend 90% of their time in buildings. In order to ensure quality of life for communities, we propose a human‐centric design approach to building “functionality.” “Functionality” is defined as the set of “essential services” to meet occupant needs for safety and well‐being. These services include lighting, heating and cooling, ventilation, water supply, and wastewater management. At present, a multidisciplinary top‐down approach exists where owners dictate the building operations to architects. Our central thesis is that a bottom‐up approach based on occupant safety and well‐being should drive the functionality design process. Research on occupant well‐being conducted by social scientists should be considered by architects in creating the building functionality layout. One of the results of this research should be a set of the type and level of services required for well‐being. Architects and engineers should work together to design physical systems to ensure that the derived acceptable levels of the services not be exceeded for various frequencies of occurrence tied to the weather conditions at the site. In order to make this approach viable, minimal amounts of continuous electric power must be made available such as through building integrated photovoltaic panels. The corresponding onsite power generation and storage needs are therefore a critical aspect of the proposed formulation. It is anticipated that significant interactions during the iterative building design process among the architects and social scientists with the engineering disciplines will change an existing multidisciplinary approach into an interdisciplinary one.
Previous studies of risk behavior observed weak or inconsistent relationships between risk perception and risk‐taking. One aspect that has often been neglected in such studies is the situational context in which risk behavior is embedded: Even though a person may perceive a behavior as risky, the social norms governing the situation may work as a counteracting force, overriding the influence of risk perception. Three food context studies are reported. In Study 1 (N = 200), we assess how norm strength varies across different social situations, relate the variation in norm strength to the social characteristics of the situation, and identify situations with consistently low and high levels of pressure to comply with the social norm. In Study 2 (N = 502), we investigate how willingness to accept 15 different foods that vary in terms of objective risk relates to perceived risk in situations with low and high pressure to comply with a social norm. In Study 3 (N = 1,200), we test how risk‐taking is jointly influenced by the perceived risk associated with the products and the social norms governing the situations in which the products are served. The results indicate that the effects of risk perception and social norm are additive, influencing risk‐taking simultaneously but as counteracting forces. Social norm had a slightly stronger absolute effect, leading to a net effect of increased risk‐taking. The relationships were stable over different social situations and food safety risks and did not disappear when detailed risk information was presented.
Evacuation is frequently used by emergency managers and other officials as part of an overall approach to reducing the morbidity and mortality associated with hurricane landfall. In this study, the evacuation shelter capacity of the Houston–Galveston Metropolitan Statistical Area (MSA) was spatially assessed and shelter deficits in the region were estimated. These data provide essential information needed to eliminate shelter deficits and ensure a successful evacuation from a future storm. Spatial statistical methods—Global Moran's I, Anselin Local Moran's I (Local Indicators of Spatial Association [LISA]), and Hot Spot Analysis (Getis‐Ord Gi*) were used to assess for regional spatial autocorrelation and clustering of evacuation shelters in the Houston–Galveston MSA. Shelter deficits were estimated in four ways—the aggregate deficit for the Houston–Galveston MSA, by evacuation Zip‐Zone, by county, and by distance or radii of evacuation Zip‐Zone. Evacuation shelters were disproportionately distributed in the region, with lower capacity shelters clustered closer to evacuation Zip‐Zones (50 miles from the Coastal Zip‐Zone), and higher capacity shelters clustered farther away from the zones (120 miles from the Coastal Zip‐Zone). The aggregate shelter deficit for the Houston–Galveston MSA was 353,713 persons. To reduce morbidity and mortality associated with future hurricanes in the Houston–Galveston MSA, authorities should consider the development and implementation of policies that would improve the evacuation shelter capacity of the region. Eliminating shelter deficits, which has been done successfully in the state of Florida, is an essential element of protecting the public from hurricane impacts.
Nearly 20 years after the year 2000 target for global wild poliovirus (WPV) eradication, live polioviruses continue to circulate with all three serotypes posing challenges for the polio endgame. We updated a global differential equation‐based poliovirus transmission and stochastic risk model to include programmatic and epidemiological experience through January 2020. We used the model to explore the likely dynamics of poliovirus transmission for 2019–2023, which coincides with a new Global Polio Eradication Initiative Strategic Plan. The model stratifies the global population into 72 blocks, each containing 10 subpopulations of approximately 10.7 million people. Exported viruses go into subpopulations within the same block and within groups of blocks that represent large preferentially mixing geographical areas (e.g., continents). We assign representative World Bank income levels to the blocks along with polio immunization and transmission assumptions, which capture some of the heterogeneity across countries while still focusing on global poliovirus transmission dynamics. We also updated estimates of reintroduction risks using available evidence. The updated model characterizes transmission dynamics and resulting polio cases consistent with the evidence through 2019. Based on recent epidemiological experience and prospective immunization assumptions for the 2019–2023 Strategic Plan, the updated model does not show successful eradication of serotype 1 WPV by 2023 or successful cessation of oral poliovirus vaccine serotype 2‐related viruses.
According to the class of de minimis decision principles, risks can be ignored (or at least treated very differently from other risks) if the risk is sufficiently small. In this article, we argue that a de minimis threshold has no place in a normative theory of decision making, because the application of the principle will either recommend ignoring risks that should not be ignored (e.g., the sure death of a person) or it cannot be used by ordinary bounded and information‐constrained agents.
Construal‐level theory suggests that high‐level abstract features weigh more in people's decision‐making at farther distance, while low‐level concrete features weigh more at closer distance. Based on this, we propose that psychological distance will influence the effect of risk versus efficacy framing on climate change engagement. In particular, risk perception related to the end‐state expectancy of climate change mitigation should influence people's climate change engagement at farther distance. In contrast, efficacy perception related to the perceived feasibility of attaining end‐state goals should influence engagement at closer distance. Results from an experimental survey based on a national sample that is both demographically and geographically representative (N = 1,282) supported our proposition. At closer spatial distance, perceived efficacy boosted by efficacy framing increased participants’ intention to perform climate mitigation behaviors. In contrast, at farther distance, risk framing increased behavioral intention through heightened risk perception. Based on these findings, we suggest that when communicating distant and abstract risks, highlighting their disastrous impacts may better motivate action. In contrast, when communicating impending and concrete risks, stressing the feasibility of action may have stronger motivational potential.
Human factors are widely regarded to be highly contributing factors to maritime accident prevention system failures. The conventional methods for human factor assessment, especially quantitative techniques, such as fault trees and bow‐ties, are static and cannot deal with models with uncertainty, which limits their application to human factors risk analysis. To alleviate these drawbacks, in the present study, a new human factor analysis framework called multidimensional analysis model of accident causes (MAMAC) is introduced. MAMAC combines the human factors analysis and classification system and business process management. In addition, intuitionistic fuzzy set theory and Bayesian Network are integrated into MAMAC to form a comprehensive dynamic human factors analysis model characterized by flexibility and uncertainty handling. The proposed model is tested on maritime accident scenarios from a sand carrier accident database in China to investigate the human factors involved, and the top 10 most highly contributing primary events associated with the human factors leading to sand carrier accidents are identified. According to the results of this study, direct human factors, classified as unsafe acts, are not a focus for maritime investigators and scholars. Meanwhile, unsafe preconditions and unsafe supervision are listed as the top two considerations for human factors analysis, especially for supervision failures of shipping companies and ship owners. Moreover, potential safety countermeasures for the most highly contributing human factors are proposed in this article. Finally, an application of the proposed model verifies its advantages in calculating the failure probability of accidents induced by human factors.
The Europa mission approved in 2019 is still in the development phase. It is designed to conduct a detailed reconnaissance of that moon of Jupiter as it could possibly support life as we know it. This article is based on a top‐down approach (mission → system → subsystems → components) to model the probability of mission failure. The focus here is on the case where the (uncertain) radiation load exceeds the (uncertain) capacity of critical subsystems of the spacecraft. The model is an illustrative quantification of the uncertainties about (1) the complex external radiation environment in repeated exposures, (2) the effectiveness of the shielding in different zones of the spacecraft, and (3) the components’ capacities, by modeling all three as dynamic random variables. A simulation including a sensitivity analysis is used to obtain the failure probability of the whole mission in forty‐five revolutions around Jupiter. This article illustrates how probabilistic risk analysis based on engineering models, test results and expert opinions can be used in the early stages of the design of space missions when uncertainties are large. It also describes the optimization of the spacecraft design, taking into account the decisionmakers’ risk attitude and the mission resource constraints.
Natural hazards pose an increasing challenge to public administrators, as the frequency, costs, and consequences of extreme events escalate in a complex, interdependent, world. This study examines organizational networks as instruments for mobilizing collective response to extreme events, but effective design has been elusive. Governments have focused on planned networks to anticipate risk before hazards occur; communities have formed emergent networks as voluntary efforts after the event. Using a framework of complex adaptive systems, we identify operational networks that adapt to their immediate context in real time, using technologies to support the search, exchange, and feedback of information to enable informed, collective action. Applying mixed research methods—documentary analysis of laws, policies, and procedures; content analysis of news articles; onsite observation; and semistructured interviews with experienced personnel—we document operational networks as a distinct form of multiorganizational response to urgent events that combines the structure of designated authority with the flexibility of information technologies. The integration of planned and emergent organizational forms into operational networks is measured through External/Internal (E/I) index analysis, based on empirical data collected on response systems that formed following the 2008 Wenchuan and 2013 Lushan earthquakes in the centralized administrative context of China. Findings show that planned networks provide the organizational structure and initial legitimacy essential for operational networks to form, but ready access to information technology—cell phones, short‐wave radio systems, internet access—enables rapid communication and exchange of information essential for flexible adaptation in real time to meet urgent needs.
Probability Size Matters: The Effect of Foreground‐Only versus Foreground+Background Graphs on Risk Aversion Diminishes with Larger Probabilities
Graphs are increasingly recommended for improving decision‐making and promoting risk‐avoidant behaviors. Graphs that depict only the number of people affected by a risk (“foreground‐only” displays) tend to increase perceived risk and risk aversion (e.g., willingness to get vaccinated), as compared to graphs that also depict the number of people at risk for harm (“foreground+background” displays). However, previous research examining these “foreground‐only effects” has focused on relatively low‐probability risks (<10%), limiting generalizability to communications about larger risks. In two experiments, we systematically investigated the moderating role of probability size on foreground‐only effects, using a wide range of probability sizes (from 0.1% to 40%). Additionally, we examined the moderating role of the size of the risk reduction, that is, the extent to which a protective behavior reduces the risk. Across both experiments, foreground‐only effects on perceived risk and risk aversion were weaker for larger probabilities. Experiment 2 also revealed that foreground‐only effects were weaker for smaller risk reductions, while foreground‐only displays decreased understanding of absolute risk magnitudes independently of probability size. These findings suggest that the greater effectiveness of foreground‐only versus foreground+background displays for increasing perceived risk and risk aversion diminishes with larger probability sizes and smaller risk reductions. Moreover, if the goal is to promote understanding of absolute risk magnitudes, foreground+background displays should be used rather than foreground‐only displays regardless of probability size. Our findings also help to refine and extend existing theoretical accounts of foreground‐only effects to situations involving a wide range of probability sizes.
Integrating sustainability into freight transportation systems (FTSs) is a complex and challenging task due to the sheer number of inherent sustainability risks. Sustainability risks disrupt the economic, social and environmental objectives of freight operations and act as impediments in the development of sustainable freight transportation systems. The area of sustainability risk management is still unexplored and immature in the operational research domain. This study addresses these research gaps and contributes in a threefold manner. First, a total of 36 potential sustainability risks related to FTSs are identified and uniquely classified into seven categories using a rigourous approach. Second, the research proposes two prominent perspectives, namely, ontological and epistemological perspectives to understand risks and develops a novel framework for managing sustainability risks in FTSs. Third, a novel approach by integrating fuzzy evidential reasoning algorithm (FERA) with expected utility theory is developed to quantitatively model and profile sustainability risk for different risk preferences, namely, risk‐averse, risk‐neutral, and risk‐taking scenarios. The proposed FERA is a flexible and robust approach, which transforms the experts’ inputs into belief structures and aggregates them using the evidence combination rule proposed in Dempster–Shafer theory to overcome the problem of imprecise results caused by average scoring in existing models. A sensitivity analysis is conducted to demonstrate the robustness of the proposed model. Unlike conventional perception, our study suggests that most of the high priority sustainability risks are behaviorally and socially induced rather than financially driven. The results provide significant managerial implications including a focus on skills development, and on social and behavioral dimensions while managing risks in FTSs.
The Potential Effects of Recall Bias and Selection Bias on the Epidemiological Evidence for the Carcinogenicity of Glyphosate
Glyphosate is a widely used herbicide worldwide. The International Agency for Research on Cancer in 2015 declared that glyphosate is probably carcinogenic to humans, noting a positive association for non‐Hodgkin lymphoma (NHL). The principal human data on glyphosate and NHL come from five case–control studies and two cohort studies. The case–control studies are at risk of recall bias resulting from information on exposure to pesticides being collected from cases and controls based on their memories. In addition, two of the case–control studies are additionally at risk of a form of selection bias that can exacerbate the effect of recall bias. Both biases are in the direction of making glyphosate appear carcinogenic. If odds ratios (ORs) are not biased and a pesticide plays no role in causing NHL, the probability that an OR for that pesticide is greater than 1.0 is approximately 0.5. The fractions of ORs for pesticides other than glyphosate that are greater than 1.0 in the case–control studies are 0.90 (n = 92), 0.90 (n = 152), 0.93 (n = 59), 0.76 (n = 140), and 0.53 (n = 54), the first two from studies that are at risk for both types of bias. In the two cohort studies, which are not subject to these biases, the comparable fractions for relative risks for all cancers are 0.51 (n = 70) and 0.48 (n = 158). These results comply closely with what would be expected if evidence for carcinogenicity of glyphosate in these studies results from statistical bias in the case–control studies.