Risk Analysis: An International Journal

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Table of Contents for Risk Analysis. List of articles from both the latest and EarlyView issues.
Updated: 1 hour 12 min ago

Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System

20 March 2019 - 4:12pm
Abstract

Multiple hazard resilience is of significant practical value because most regions of the world are subject to multiple natural and technological hazards. An analysis and assessment approach for multiple hazard spatiotemporal resilience of interdependent infrastructure systems is developed using network theory and a numerical analysis. First, we define multiple hazard resilience and present a quantitative probabilistic metric based on the expansion of a single hazard deterministic resilience model. Second, we define a multiple hazard relationship analysis model with a focus on the impact of hazards on an infrastructure. Subsequently, a relationship matrix is constructed with temporal and spatial dimensions. Further, a general method for the evaluation of direct impacts on an individual infrastructure under multiple hazards is proposed. Third, we present an analysis of indirect multiple hazard impacts on interdependent infrastructures and a joint restoration model of an infrastructure system. Finally, a simplified two‐layer interdependent infrastructure network is used as a case study for illustrating the proposed methodology. The results show that temporal and spatial relationships of multiple hazards significantly influence system resilience. Moreover, the interdependence among infrastructures further magnifies the impact on resilience value. The main contribution of the article is a new multiple hazard resilience evaluation approach that is capable of integrating the impacts of multiple hazard interactions, interdependence of network components (layers), and restoration strategy.

A Modular Bayesian Salmonella Source Attribution Model for Sparse Data

20 March 2019 - 4:09pm
Abstract

Several statistical models for salmonella source attribution have been presented in the literature. However, these models have often been found to be sensitive to the model parameterization, as well as the specifics of the data set used. The Bayesian salmonella source attribution model presented here was developed to be generally applicable with small and sparse annual data sets obtained over several years. The full Bayesian model was modularized into three parts (an exposure model, a subtype distribution model, and an epidemiological model) in order to separately estimate unknown parameters in each module. The proposed model takes advantage of the consumption and overall salmonella prevalence of the studied sources, as well as bacteria typing results from adjacent years. The latter were used for a smoothed estimation of the annual relative proportions of different salmonella subtypes in each of the sources. The source‐specific effects and the salmonella subtype‐specific effects were included in the epidemiological model to describe the differences between sources and between subtypes in their ability to infect humans. The estimation of these parameters was based on data from multiple years. Finally, the model combines the total evidence from different modules to proportion human salmonellosis cases according to their sources. The model was applied to allocate reported human salmonellosis cases from the years 2008 to 2015 to eight food sources.

Communicating with the Public About Marauding Terrorist Firearms Attacks: Results from a Survey Experiment on Factors Influencing Intention to “Run, Hide, Tell” in the United Kingdom and Denmark

20 March 2019 - 4:08pm
Abstract

Effective risk communication is an integral part of responding to terrorism, but until recently, there has been very little pre‐event communication in a European context to provide advice to the public on how to protect themselves during an attack. Following terrorist attacks involving mass shootings in Paris, France, in November 2015, the U.K. National Police Chiefs’ Council released a Stay Safe film and leaflet that advises the public to “run,” “hide,” and “tell” in the event of a firearms or weapons attack. However, other countries, including Denmark, do not provide preparedness information of this kind, in large part because of concern about scaring the public. In this survey experiment, 3,003 U.K. and Danish participants were randomly assigned to one of three conditions: no information, a leaflet intervention, and a film intervention to examine the impact of “Run, Hide, Tell” advice on perceptions about terrorism, the security services, and intended responses to a hypothetical terrorist firearms attack. Results demonstrate important benefits of pre‐event communication in relation to enhancing trust, encouraging protective health behaviors, and discouraging potentially dangerous actions. However, these findings also suggest that future communications should address perceived response costs and target specific problem behaviors. Cross‐national similarities in response suggest this advice is suitable for adaptation in other countries.

Toward Convergence Disaster Research: Building Integrative Theories Using Simulation

18 March 2019 - 7:00pm
Abstract

Scholars across disciplines use simulation methods as tools to build theories; however, the full potential of simulation methods has not been fully used for building theories in convergence disaster research. Simulation methods could provide four unique opportunities for building theories for convergence disaster research. First, simulation methods could help researchers model the underlying mechanisms of disaster phenomena by enabling integration of qualitative and quantitative data. Second, they could help researchers specify and characterize the mechanisms affecting specific disaster phenomena by facilitating integration of empirical information with existing theoretical elements from different disciplines. Third, simulation methods could enable multilevel understanding of relationships between factors influencing disaster phenomena and emergent behaviors across different levels of analysis (e.g., individual, household, neighborhood, and community levels). Fourth, simulation methods could help researchers integrate theoretical elements on disasters across different disciplines (e.g., engineering, social science, sociology, and epidemiology) for a more convergent understanding of complex relationships affecting resilience at different levels.

Risk Communication as Government Agency Organizational Practice

18 March 2019 - 10:05am
Abstract

The dynamics of organizational risk communication is an understudied topic in risk research. This article investigates how public officials at six government agencies in Sweden understand and relate to risk communication and its uses in the context of agency organizational work on policy and regulation. Qualitative interviews were used to explore the practitioners’ views on some key topics in the academic literature on risk communication. A main finding is that there is little consensus on what the goals of risk communication are; if, and how, uncertainty should be communicated; and what role is to be played by transparency in risk communication. However, the practitioners agree that dissemination (top down) to the public of robust scientific and expert knowledge is a crucial element. Dialogue and participation is used mainly with other agencies and elite stakeholders with whom agencies collaborate to implement policy goals. Dialogue with the public on issues of risk is very limited. Some implications of the findings for the practice of risk communication by government agencies are suggested.

Cigarette Smoking and Multiple Health Risk Behaviors: A Latent Class Regression Model to Identify a Profile of Young Adolescents

12 March 2019 - 7:58pm
Abstract

Cigarette smoking is often established during adolescence when other health‐related risk behaviors tend to occur. The aim of the study was to further investigate the hypothesis that risky health behaviors tend to cluster together and to identify distinctive profiles of young adolescents based on their smoking habits. To explore the idea that smoking behavior can predict membership in a specific risk profile of adolescents, with heavy smokers being more likely to exhibit other risk behaviors, we reanalyzed the data from the 2014 Health Behaviour in School‐Aged Children Italian survey of about 60,000 first‐ and third‐grade junior high school (JHS) and second‐grade high school (HS) students. A Bayesian approach was adopted for selecting the manifest variables associated with smoking; a latent class regression model was employed to identify smoking behaviors among adolescents. Finally, a health‐related risk pattern associated with different types of smoking behaviors was found. Heavy smokers engaged in higher alcohol use and abuse and experienced school failure more often than their peers. Frequent smokers reported below‐average academic achievement and self‐rated their health as fair/poor more frequently than nonsmokers. Lifetime cannabis use and early sexual intercourse were more frequent among heavy smokers. Our findings provide elements for constructing a profile of frequent adolescent smokers and for identifying behavioral risk patterns during the transition from JHS to HS. This may provide an additional opportunity to devise interventions that could be more effective to improve smoking cessation among occasional smokers and to adequately address other risk behaviors among frequent smokers.

Cigarette Smoking and Multiple Health Risk Behaviors: A Latent Class Regression Model to Identify a Profile of Young Adolescents

12 March 2019 - 7:58pm
Abstract

Cigarette smoking is often established during adolescence when other health‐related risk behaviors tend to occur. The aim of the study was to further investigate the hypothesis that risky health behaviors tend to cluster together and to identify distinctive profiles of young adolescents based on their smoking habits. To explore the idea that smoking behavior can predict membership in a specific risk profile of adolescents, with heavy smokers being more likely to exhibit other risk behaviors, we reanalyzed the data from the 2014 Health Behaviour in School‐Aged Children Italian survey of about 60,000 first‐ and third‐grade junior high school (JHS) and second‐grade high school (HS) students. A Bayesian approach was adopted for selecting the manifest variables associated with smoking; a latent class regression model was employed to identify smoking behaviors among adolescents. Finally, a health‐related risk pattern associated with different types of smoking behaviors was found. Heavy smokers engaged in higher alcohol use and abuse and experienced school failure more often than their peers. Frequent smokers reported below‐average academic achievement and self‐rated their health as fair/poor more frequently than nonsmokers. Lifetime cannabis use and early sexual intercourse were more frequent among heavy smokers. Our findings provide elements for constructing a profile of frequent adolescent smokers and for identifying behavioral risk patterns during the transition from JHS to HS. This may provide an additional opportunity to devise interventions that could be more effective to improve smoking cessation among occasional smokers and to adequately address other risk behaviors among frequent smokers.

The Use of Telematics Devices to Improve Automobile Insurance Rates

7 March 2019 - 1:13pm
Abstract

Most automobile insurance databases contain a large number of policyholders with zero claims. This high frequency of zeros may reflect the fact that some insureds make little use of their vehicle, or that they do not wish to make a claim for small accidents in order to avoid an increase in their premium, but it might also be because of good driving. We analyze information on exposure to risk and driving habits using telematics data from a pay‐as‐you‐drive sample of insureds. We include distance traveled per year as part of an offset in a zero‐inflated Poisson model to predict the excess of zeros. We show the existence of a learning effect for large values of distance traveled, so that longer driving should result in higher premiums, but there should be a discount for drivers who accumulate longer distances over time due to the increased proportion of zero claims. We confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. We discuss how telematics information can be used to design better insurance and to improve traffic safety.

The Impact of Portfolio Location Uncertainty on Probabilistic Seismic Risk Analysis

7 March 2019 - 1:13pm
Abstract

Probabilistic seismic risk analysis is a well‐established method in the insurance industry for modeling portfolio losses from earthquake events. In this context, precise exposure locations are often unknown. However, so far, location uncertainty has not been in the focus of a large amount of research. In this article, we propose a novel framework for treatment of location uncertainty. As a case study, a large number of synthetic portfolios resembling typical real‐world cases were created. We investigate the effect of portfolio characteristics such as value distribution, portfolio size, or proportion of risk items with unknown coordinates on the variability of loss frequency estimations. The results indicate that due to loss aggregation effects and spatial hazard variability, location uncertainty in isolation and in conjunction with ground motion uncertainty can induce significant variability to probabilistic loss results, especially for portfolios with a small number of risks. After quantifying its effect, we conclude that location uncertainty should not be neglected when assessing probabilistic seismic risk, but should be treated stochastically and the resulting variability should be visualized and interpreted carefully.

An Insurance Model for Risk Management of Process Facilities

7 March 2019 - 1:13pm
Abstract

Most existing risk management models for process industries do not consider the effect of insurance coverage, which results in an overestimation of overall risk. A model is presented in this article to study the effect of insurance coverage of health, safety, environmental, and business risks. The effect of insurance recovery is modeled through the application of adjustment factors by considering the stochastic factors affecting insurance recovery. The insurance contract's conditions, deductibles, and policy limits are considered in developing the insurance recovery adjustment factors. Copula functions and Monte Carlo simulations are used to develop the distribution of the aggregate loss by considering the dependence among loss classes. A case study is used to demonstrate both the practical application of the proposed insurance model to improve management decisions, and the mitigating effect of insurance to minimize the residual risk.

Cultural Values, Trust, and Benefit‐Risk Perceptions of Hydraulic Fracturing: A Comparative Analysis of Policy Elites and the General Public

7 March 2019 - 1:13pm
Abstract

Hydraulic fracturing (“fracking”) has recently become a very intensely debated process for extracting oil and gas. Supporters argue that fracking provides positive economic benefits and energy security and offers a decreased reliance on coal‐based electricity generation. Detractors claim that the fracking process may harm the environment as well as place a strain on local communities that experience new fracking operations. This study utilizes a recently conducted survey distributed to a sample of policy elites and the general public in Arkansas and Oregon to examine the role of cultural value predispositions and trust in shaping the perceptions of risks and benefits associated with fracking. Findings indicate that cultural values influence both trust and benefit‐risk perceptions of fracking for both policy elites and the general public. More specifically, we found that trust in information from various sources is derived from the intrinsic values held by an individual, which in turn impacts perceptions of related benefits and risks. We also found that while the overall pattern of relationships is similar, trust plays a larger role in the formulation of attitudes for policy elites than for the general public. We discuss the implications of the mediating role of trust in understanding value‐driven benefit‐risk perceptions, as well as the disparate role of trust between policy elites and the general public in the context of the policy‐making process for both theory and practice.

Feelings About Fracking: Using the Affect Heuristic to Understand Opposition to Coal Seam Gas Production

7 March 2019 - 1:13pm
Abstract

The rapid expansion of coal seam gas (CSG) extraction across Australia has polarized public opinion about the risks, benefits, and the future of the industry. We conducted a randomized controlled experiment to assess the impact of CSG messaging on opposition to the CSG industry. Residents of a major Australian city (N = 549), aged between 21 and 87 years, were randomly assigned to view one of three brief video messages (pro‐CSG, anti‐CSG, or a neutral control) sourced from the Internet. They then completed measures assessing CSG affective associations, perceived risks and benefits of CSG, and degree of opposition to the CSG industry. A subsample of 317 participants also completed the measures of affect, risks, benefits, and opposition two weeks following the initial message presentation. Message type significantly predicted message effects in a pattern consistent with the affect heuristic model, although overall, the message effects were modest in magnitude. Respondents who viewed the anti‐CSG video (relative to the control) reported more negative affective responses to CSG, perceived higher risks, fewer benefits, and greater opposition to the CSG industry. Those who viewed the pro‐CSG video (relative to the control) reported stronger positive affective responses to CSG, perceived more CSG benefits and fewer risks, and expressed less opposition to the industry. The effects persisted over a two‐week interval for the anti‐CSG message, but not for the pro‐CSG message. Our findings suggest that people's risk perceptions and views about the acceptability of CSG are malleable by messaging that targets affective pathways.

Modeling the Airborne Infection Risk of Tuberculosis for a Research Facility in eMalahleni, South Africa

7 March 2019 - 1:13pm
Abstract

A detailed mathematical modeling framework for the risk of airborne infectious disease transmission in indoor spaces was developed to enable mathematical analysis of experiments conducted at the Airborne Infections Research (AIR) facility, eMalahleni, South Africa. A model was built using this framework to explore possible causes of why an experiment at the AIR facility did not produce expected results. The experiment was conducted at the AIR facility from August 31, 2015 to December 4, 2015, in which the efficacy of upper room germicidal ultraviolet (GUV) irradiation as an environmental control was tested. However, the experiment did not produce the expected outcome of having fewer infections in the test animal room than the control room. The simulation results indicate that dynamic effects, caused by switching the GUV lights, power outages, or introduction of new patients, did not result in the unexpected outcomes. However, a sensitivity analysis highlights that significant uncertainty exists with risk of transmission predictions based on current measurement practices, due to the reliance on large viable literature ranges for parameters.

Modeling the Effectiveness of Respiratory Protective Devices in Reducing Influenza Outbreak

7 March 2019 - 1:13pm
Abstract

Outbreaks of influenza represent an important health concern worldwide. In many cases, vaccines are only partially successful in reducing the infection rate, and respiratory protective devices (RPDs) are used as a complementary countermeasure. In devising a protection strategy against influenza for a given population, estimates of the level of protection afforded by different RPDs is valuable. In this article, a risk assessment model previously developed in general form was used to estimate the effectiveness of different types of protective equipment in reducing the rate of infection in an influenza outbreak. It was found that a 50% compliance in donning the device resulted in a significant (at least 50% prevalence and 20% cumulative incidence) reduction in risk for fitted and unfitted N95 respirators, high‐filtration surgical masks, and both low‐filtration and high‐filtration pediatric masks. An 80% compliance rate essentially eliminated the influenza outbreak. The results of the present study, as well as the application of the model to related influenza scenarios, are potentially useful to public health officials in decisions involving resource allocation or education strategies.

From Ideal to Real Risk: Philosophy of Causation Meets Risk Analysis

7 March 2019 - 1:13pm
Abstract

A question has been raised in recent years as to whether the risk field, including analysis, assessment, and management, ought to be considered a discipline on its own. As suggested by Terje Aven, unification of the risk field would require a common understanding of basic concepts, such as risk and probability; hence, more discussion is needed of what he calls “foundational issues.” In this article, we show that causation is a foundational issue of risk, and that a proper understanding of it is crucial. We propose that some old ideas about the nature of causation must be abandoned in order to overcome certain persisting challenges facing risk experts over the last decade. In particular, we discuss the challenge of including causally relevant knowledge from the local context when studying risk. Although it is uncontroversial that the receptor plays an important role for risk evaluations, we show how the implementation of receptor‐based frameworks is hindered by methodological shortcomings that can be traced back to Humean orthodoxies about causation. We argue that the first step toward the development of frameworks better suited to make realistic risk predictions is to reconceptualize causation, by examining a philosophical alternative to the Humean understanding. In this article, we show how our preferred account, causal dispositionalism, offers a different perspective in how risk is evaluated and understood.

Terrorism Risk Assessment, Recollection Bias, and Public Support for Counterterrorism Policy and Spending

7 March 2019 - 1:13pm
Abstract

Recollection bias (RB) refers to the phenomenon whereby after an adverse event people report that their risk assessment about a similar future event is presently no higher than their recollection of their pre‐event risk assessment. While previous research has outlined this theoretical construct and generated important empirical findings, there were some limitations. We design and employ a new national representative survey to address these limitations in this study. We examine the existence and persistence of RB among the general public in the context of a number of domestic and international terrorist attacks. We further examine the socioeconomic and political base of RB and the influences of RB on a wide range of citizens’ counterterrorism policy preferences. Our data analyses reveal strong evidence showing the occurrence of RB and its persistence across various forms of terrorism risk. With regard to the socioeconomic and political base, we find that females, older people, political conservatives, and Republicans are less likely to be subject to RB. For the effects of RB on public counterterrorism policy preferences, our analyses demonstrate that this bias significantly dampens public support for a wide range of preventive policy measures and government anti‐terrorism spending. Overall, our study, based on a national representative sample and an extended survey design, provides robust evidence of RB in terrorism risk assessment, and adds further evidence to support the idea that RB is likely a generalizable phenomenon. Implications and suggestions for future research are discussed in the conclusion.

Media Disaster Reporting Effects on Public Risk Perception and Response to Escalating Tornado Warnings: A Natural Experiment

7 March 2019 - 1:13pm
Abstract

Previous research has evaluated public risk perception and response to a natural hazards in various settings; however, most of these studies were conducted either with a single scenario or after a natural disaster struck. To better understand the dynamic relationships among affect, risk perception, and behavioral intentions related to natural disasters, the current study implements a simulation scenario with escalating weather intensity, and includes a natural experiment allowing comparison of public response before and after a severe tornado event with extensive coverage by the national media. The current study also manipulated the display of warning information, and investigated whether the warning system display format influences public response. Results indicate that (1) affect, risk perception, and behavioral intention escalated as weather conditions deteriorated, (2) responses at previous stages predicted responses at subsequent stages of storm progression, and (3) negative affect predicted risk perception. Moreover, risk perception and behavioral intention were heightened after exposure to the media coverage of an actual tornado disaster. However, the display format manipulation did not influence behavioral responses. The current study provides insight regarding public perception of predisaster warnings and the influence of exposure to media coverage of an actual disaster event.

A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand

7 March 2019 - 1:13pm
Abstract

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate‐induced shifts in electricity demand and/or damaged physical assets due to hydro‐meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate‐induced demand shifts and propose a data‐driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate‐sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario‐based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.

Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis

7 March 2019 - 1:13pm
Abstract

Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose‐response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose‐response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose‐response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose‐response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database.

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