Risk Analysis: An International Journal
Insights into the dynamics of human behavior in response to flooding are urgently needed for the development of effective integrated flood risk management strategies, and for integrating human behavior in flood risk modeling. However, our understanding of the dynamics of risk perceptions, attitudes, individual recovery processes, as well as adaptive (i.e., risk reducing) intention and behavior are currently limited because of the predominant use of cross‐sectional surveys in the flood risk domain. Here, we present the results from one of the first panel surveys in the flood risk domain covering a relatively long period of time (i.e., four years after a damaging event), three survey waves, and a wide range of topics relevant to the role of citizens in integrated flood risk management. The panel data, consisting of 227 individuals affected by the 2013 flood in Germany, were analyzed using repeated‐measures ANOVA and latent class growth analysis (LCGA) to utilize the unique temporal dimension of the data set. Results show that attitudes, such as the respondents’ perceived responsibility within flood risk management, remain fairly stable over time. Changes are observed partly for risk perceptions and mainly for individual recovery and intentions to undertake risk‐reducing measures. LCGA reveal heterogeneous recovery and adaptation trajectories that need to be taken into account in policies supporting individual recovery and stimulating societal preparedness. More panel studies in the flood risk domain are needed to gain better insights into the dynamics of individual recovery, risk‐reducing behavior, and associated risk and protective factors.
Individual Disaster Assistance For Socially Vulnerable People: Lessons Learned From the Pohang Earthquake in the Republic of Korea
The Republic of Korea has been considered to be relatively safe from earthquake hazards because of the geological location of the Korean Peninsula, which has a low level of intraplate seismic activity. However, an earthquake with a moment magnitude of 5.4 struck the city of Pohang on November 15, 2017, causing 90 casualties and 52 million USD in property losses. During the recovery process after the earthquake, the Korean government provided individual disaster assistance to victims who reported their damages. However, the government disaster assistance could have been unfairly distributed among the socially vulnerable victims who essentially relied on that assistance. This study identifies whether the government disaster assistance was fairly distributed to socially vulnerable victims using a statistical model based on the data from the Pohang earthquake that occurred in 2017 in Korea. A conceptual model was constructed using a structural equation model (SEM) of three factors—social vulnerability, physical vulnerability, and the amount paid out in individual disaster assistance. Furthermore, interviews with and a survey of the victims were conducted to verify the problems identified by the conceptual model. This study found that socially vulnerable victims were less likely to take advantage of the government disaster assistance program.
Estimating Listeria monocytogenes Growth in Ready‐to‐Eat Chicken Salad Using a Challenge Test for Quantitative Microbial Risk Assessment
Currently, there is a growing preference for convenience food products, such as ready‐to‐eat (RTE) foods, associated with long refrigerated shelf‐lives, not requiring a heat treatment prior to consumption. Because Listeria monocytogenes is able to grow at refrigeration temperatures, inconsistent temperatures during production, distribution, and at consumer's household may allow for the pathogen to thrive, reaching unsafe limits. L. monocytogenes is the causative agent of listeriosis, a rare but severe human illness, with high fatality rates, transmitted almost exclusively by food consumption. With the aim of assessing the quantitative microbial risk of L. monocytogenes in RTE chicken salads, a challenge test was performed. Salads were inoculated with a three‐strain mixture of cold‐adapted L. monocytogenes and stored at 4, 12, and 16 °C for eight days. Results revealed that the salad was able to support L. monocytogenes’ growth, even at refrigeration temperatures. The Baranyi primary model was fitted to microbiological data to estimate the pathogen's growth kinetic parameters. Temperature effect on the maximum specific growth rate (μ max) was modeled using a square‐root‐type model. Storage temperature significantly influenced μ max of L. monocytogenes (p < 0.05). These predicted growth models for L. monocytogenes were subsequently used to develop a quantitative microbial risk assessment, estimating a median number of 0.00008726 listeriosis cases per year linked to the consumption of these RTE salads. Sensitivity analysis considering different time–temperature scenarios indicated a very low median risk per portion (<−7 log), even if the assessed RTE chicken salad was kept in abuse storage conditions.
This article considers whether a nation that fares relatively well (or badly) on a particular dimension of mortality risk tends also to do so on others. Working with 2016 data from the Global Burden of Disease (GBD) Study, we focus on six causes of premature death: transport accidents, other accidents, homicide, early‐childhood diseases, and both communicable and noncommunicable diseases beyond early childhood. We consider data from all 26 nations that had populations of at least 50 million in 2016, as well as 15 clusters of smaller nations that are similar in longevity (e.g., Scandinavia). We use an analytic method that facilitates useful comparisons across nations, for it recognizes that some potential death risks can be underestimated because citizens die sooner from other causes. We estimate reductions in lifespan from each of the six causes relative to natural lifespan as defined by GBD. It emerges that, for all 15 pairings among the six causes, these reductions are positively correlated. We introduce metrics to summarize a nation's overall “safety status,” and find that losses of longevity because of premature deaths are nearly three decades fewer in the safest countries than in the least safe ones. Turning to possible explanations for the cross‐national differences, we find a strong association between a nation's safety status and both its economic wherewithal as indicated by the 2016 GDP per capita (adjusted for purchasing power parity) and its income inequality as reflected by its Gini coefficient.
Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose–Response Uncertainty
Model averaging for dichotomous dose–response estimation is preferred to estimate the benchmark dose (BMD) from a single model, but challenges remain regarding implementing these methods for general analyses before model averaging is feasible to use in many risk assessment applications, and there is little work on Bayesian methods that include informative prior information for both the models and the parameters of the constituent models. This article introduces a novel approach that addresses many of the challenges seen while providing a fully Bayesian framework. Furthermore, in contrast to methods that use Monte Carlo Markov Chain, we approximate the posterior density using maximum a posteriori estimation. The approximation allows for an accurate and reproducible estimate while maintaining the speed of maximum likelihood, which is crucial in many applications such as processing massive high throughput data sets. We assess this method by applying it to empirical laboratory dose–response data and measuring the coverage of confidence limits for the BMD. We compare the coverage of this method to that of other approaches using the same set of models. Through the simulation study, the method is shown to be markedly superior to the traditional approach of selecting a single preferred model (e.g., from the U.S. EPA BMD software) for the analysis of dichotomous data and is comparable or superior to the other approaches.
The increasing need to manage biosecurity threats, such as diseases, zoonoses, and biological weapons, poses serious challenges for risk analysts and policymakers. These threats are large in number, can occur concurrently, and may cause multiple tangible and intangible impacts. They often have an emerging nature, exacerbated by incomplete evidence about their probability of occurrence and potential impacts. There is also a limited amount of time and resources available to evaluate the risks posed by each threat, and it is difficult to learn from past projects. On the other hand, there is also a need to provide policymakers with transparent and consistent threat prioritizations, together with evidence‐based recommendations. In response to these challenges, we propose a risk analysis framework for the prioritization and management of biosecurity threats. The framework encompasses key design choices that analysts may use in risk analysis projects along three dimensions: risk support , risk group , and risk organization . The framework has prescriptive value, as a design tool to inform risk analysis projects in this context, along with descriptive value, as a learning tool to understand past projects. We applied the framework prescriptively in two biosecurity threat prioritization projects for the UK Department for Environment, Food and Rural Affairs, and illustrate its descriptive value by reporting our experience of these projects as in‐depth case studies. Overall, the proposed framework provides important insights into the impact of different design choices on the success of risk analysis projects for biosecurity threat prioritizations.
As part of the celebration of the 40th anniversary of the Society for Risk Analysis and Risk Analysis: An International Journal , this essay reviews the 10 most important accomplishments of risk analysis from 1980 to 2010, outlines major accomplishments in three major categories from 2011 to 2019, discusses how editors circulate authors’ accomplishments, and proposes 10 major risk‐related challenges for 2020–2030. Authors conclude that the next decade will severely test the field of risk analysis.
There are many reasons that people, when warned of an impending extreme event, do not take proactive, self‐defensive action. We focus on one possible reason, which is that, sometimes, people lack a sense of agency or even experience disempowerment, which can lead to passivity. This article takes up one situation where the possibility of disempowerment is salient, that of Rohingya refugees who were evicted from their homes in Myanmar and forced to cross the border into neighboring Bangladesh. In their plight, we see the twin elements of marginalization and displacement acting jointly to produce heightened vulnerability to the risks from extreme weather. Building on a relational model of risk communication, a consortium of researchers and practitioners designed a risk communication training workshop that featured elements of empowerment‐based practice. The program was implemented in two refugee camps. Evaluation suggests that the workshop may have had an appreciable effect in increasing participants' sense of agency and hope, while decreasing their level of fatalism. The outcomes were considerably more positive for female than male participants, which has important implications. This work underscores the potential for participatory modes of risk communication to empower the more marginalized, and thus more vulnerable, members of society.
A Factor Analysis Approach Toward Reconciling Community Vulnerability and Resilience Indices for Natural Hazards
The concepts of vulnerability and resilience help explain why natural hazards of similar type and magnitude can have disparate impacts on varying communities. Numerous frameworks have been developed to measure these concepts, but a clear and consistent method of comparing them is lacking. Here, we develop a data‐driven approach for reconciling a popular class of frameworks known as vulnerability and resilience indices. In particular, we conduct an exploratory factor analysis on a comprehensive set of variables from established indices measuring community vulnerability and resilience at the U.S. county level. The resulting factor model suggests that 50 of the 130 analyzed variables effectively load onto five dimensions: wealth, poverty, agencies per capita, elderly populations, and non–English‐speaking populations. Additionally, the factor structure establishes an objective and intuitive schema for relating the constituent elements of vulnerability and resilience indices, in turn affording researchers a flexible yet robust baseline for validating and expanding upon current approaches.
The Case against Commercial Antivirus Software: Risk Homeostasis and Information Problems in Cybersecurity
New cybersecurity technologies, such as commercial antivirus software (AV), sometimes fail to deliver on their promised benefits. This article develops and tests a revised version of risk homeostasis theory, which suggests that new cybersecurity technologies can sometimes have ill effects on security outcomes in the short run and little‐to‐no effect over the long run. It tests the preliminary plausibility of four predictions from the revised risk homeostasis theory using new survey data from 1,072 respondents. The estimations suggest the plausible operation of a number of risk homeostasis dynamics: (1) commercial AV users are significantly more likely to self‐report a cybersecurity event in the past year than nonusers, even after correcting for potential reverse causality and informational mechanisms; (2) nonusers become somewhat less likely to self‐report a cybersecurity event as the perceived riskiness of various e‐mail‐based behaviors increases, while commercial AV users do not; (3) the negative short‐run effect of commercial AV use on cybersecurity outcomes fade over time at a predicted rate of about 7.03 percentage points per year of use; and (4) after five years of use, commercial AV users are statistically indistinguishable from nonusers in terms of their probability of self‐reporting a cybersecurity event as perceptions of risky e‐mail‐based behaviors increase.
A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review
This article presents a discourse on the incorporation of organizational factors into probabilistic risk assessment (PRA)/probabilistic safety assessment (PSA), a topic of debate since the 1980s that has spurred discussions among industry, regulatory agencies, and the research community. The main contributions of this article include (1) identifying the four key open questions associated with this topic; (2) framing ongoing debates by considering differing perspectives around each question; (3) offering a categorical review of existing studies on this topic to justify the selection of each question and to analyze the challenges related to each perspective; and (4) highlighting the directions of research required to reach a final resolution for each question. The four key questions are: (I) How significant is the contribution of organizational factors to accidents and incidents? (II) How critical, with respect to improving risk assessment, is the explicit incorporation of organizational factors into PRA? (III) What theoretical bases are needed for explicit incorporation of organizational factors into PRA? (IV) What methodological bases are needed for the explicit incorporation of organizational factors into PRA? Questions I and II mainly analyze PRA literature from the nuclear domain. For Questions III and IV, a broader review and categorization is conducted of those existing cross‐disciplinary studies that have evaluated the effects of organizational factors on safety (not solely PRA‐based) to shed more light on future research needs.
The use of autonomous underwater vehicles (AUVs) for various applications have grown with maturing technology and improved accessibility. The deployment of AUVs for under‐ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extremities in the Antarctic. A thorough analysis of risks is therefore crucial for formulating effective risk control policies and achieving a lower risk of loss. Existing risk analysis approaches focused predominantly on the technical aspects, as well as identifying static cause and effect relationships in the chain of events leading to AUV loss. Comparatively, the complex interrelationships between risk variables and other aspects of risk such as human errors have received much lesser attention. In this article, a systems‐based risk analysis framework facilitated by system dynamics methodology is proposed to overcome existing shortfalls. To demonstrate usefulness of the framework, it is applied on an actual AUV program to examine the occurrence of human error during Antarctic deployment. Simulation of the resultant risk model showed an overall decline in human error incident rate with the increase in experience of the AUV team. Scenario analysis based on the example provided policy recommendations in areas of training, practice runs, recruitment policy, and setting of risk tolerance level. The proposed risk analysis framework is pragmatically useful for risk analysis of future AUV programs to ensure the sustainability of operations, facilitating both better control and monitoring of risk.
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.
Hierarchical Bayesian Modeling of Post‐Earthquake Ignition Probabilities Considering Inter‐Earthquake Heterogeneity
Post‐earthquake fires are high‐consequence events with extensive damage potential. They are also low‐frequency events, so their nature remains underinvestigated. One difficulty in modeling post‐earthquake ignition probabilities is reducing the model uncertainty attributed to the scarce source data. The data scarcity problem has been resolved by pooling the data indiscriminately collected from multiple earthquakes. However, this approach neglects the inter‐earthquake heterogeneity in the regional and seasonal characteristics, which is indispensable for risk assessment of future post‐earthquake fires. Thus, the present study analyzes the post‐earthquake ignition probabilities of five major earthquakes in Japan from 1995 to 2016 (1995 Kobe, 2003 Tokachi‐oki, 2004 Niigata–Chuetsu, 2011 Tohoku, and 2016 Kumamoto earthquakes) by a hierarchical Bayesian approach. As the ignition causes of earthquakes share a certain commonality, common prior distributions were assigned to the parameters, and samples were drawn from the target posterior distribution of the parameters by a Markov chain Monte Carlo simulation. The results of the hierarchical model were comparatively analyzed with those of pooled and independent models. Although the pooled and hierarchical models were both robust in comparison with the independent model, the pooled model underestimated the ignition probabilities of earthquakes with few data samples. Among the tested models, the hierarchical model was least affected by the source‐to‐source variability in the data. The heterogeneity of post‐earthquake ignitions with different regional and seasonal characteristics has long been desired in the modeling of post‐earthquake ignition probabilities but has not been properly considered in the existing approaches. The presented hierarchical Bayesian approach provides a systematic and rational framework to effectively cope with this problem, which consequently enhances the statistical reliability and stability of estimating post‐earthquake ignition probabilities.
Understanding Risk Information Seeking and Processing during an Infectious Disease Outbreak: The Case of Zika Virus
This study draws on the Planned Risk Information Seeking Model (PRISM) to assess Zika virus information seeking and systematic processing, paying particular attention to the relationship between perceived knowledge and knowledge insufficiency. Novel risks, such as Zika, provide an interesting context for examining whether information‐seeking models, such as PRISM, are able to predict information seeking when available information is limited or scarce. A cross‐sectional, online study of men and women of childbearing age (N = 494) residing in the state of Florida was conducted. Our results provide some support for the PRISM for predicting Zika information seeking intention, as well as systematic processing of information. We also found that individuals with high levels of perceived knowledge were more likely to report high level of knowledge insufficiency, illustrating that contextual factors may impact the fit of risk information seeking models.
The affordability of property‐level adaptation measures against flooding is crucial due to the movement toward integrated flood risk management, which requires the individuals threatened by flooding to actively manage flooding. It is surprising to find that affordability is not often discussed, given the important roles that affordability and social justice play regarding flood risk management. This article provides a starting point for investigating the potential rate of unaffordability of flood risk property‐level adaptation measures across Europe using two definitions of affordability, which are combined with two different affordability thresholds from within flood risk research. It uses concepts of investment and payment affordability, with affordability thresholds based on residual income and expenditure definitions of unaffordability. These concepts, in turn, are linked with social justice through fairness concerns, in that, all should have equal capability to act, of which affordability is one avenue. In doing so, it was found that, for a large proportion of Europe, property owners generally cannot afford to make one‐time payment of the cost of protective measures. These can be made affordable with installment payment mechanisms or similar mechanisms that spread costs over time. Therefore, the movement toward greater obligations for flood‐prone residents to actively adapt to flooding should be accompanied by socially accessible financing mechanisms.
Social Influence, Risk and Benefit Perceptions, and the Acceptability of Risky Energy Technologies: An Explanatory Model of Nuclear Power Versus Shale Gas
Risky energy technologies are often controversial and debates around them are polarized; in such debates public acceptability is key. Research on public acceptability has emphasized the importance of intrapersonal factors but has largely neglected the influence of interpersonal factors. In an online survey (N = 948) with a representative sample of the United Kingdom, we therefore integrate interpersonal factors (i.e., social influence as measured by social networks) with two risky energy technologies that differ in familiarity (nuclear power vs. shale gas) to examine how these factors explain risk and benefit perceptions and public acceptability. Findings show that benefit perceptions are key in explaining acceptability judgments. However, risk perceptions are more important when people are less familiar with the energy technology. Social network factors affect perceived risks and benefits associated with risky energy technology, hereby indirectly helping to form one's acceptability judgment toward the technology. This effect seems to be present regardless of the perceived familiarity with the energy technology. By integrating interpersonal with intrapersonal factors in an explanatory model, we show how the current “risk–benefit acceptability” model used in risk research can be further developed to advance the current understanding of acceptability formation.