Development of a Public Participation and Communication Protocol for Establishing Fish Consumption Advisories

Enabling people to make an informed choice on whether to change consumption behavior is ultimately the objective of any fish consumption advisory. This will occur only if people are aware of the advisory, know and understand the advisory information, and believe the information to be true. Interactive, meaningful communication and the opportunity to participate in the process to develop and review advisories are key to achieving these attributes. A case study was undertaken in a community in Alberta, Canada (where an existing advisory was under consideration for review) to determine public awareness, knowledge, compliance, communication effectiveness, information needs, and desire for involvement related to the advisory. The information obtained from this case study was used to develop 14 guiding principles as a foundation for the incorporation of public participation and risk communication into the process of developing and reviewing fish consumption advisories.     

Context,Cultural Bias,and Health Risk Perception: The “Everyday” Nature of Pesticide Policy Preferences in London,Calgary, and Halifax

Risk perception and the cultural theory of risk have often been contrasted in relation to risk‐related policy making; however, the local context in which risks are experienced, an important component of everyday decision making, remains understudied. What is unclear is the extent to which localized community beliefs and behaviors depend on larger belief systems about risk (i.e., worldviews). This article reports on a study designed to understand the relative importance of health risk perceptions (threat of harm); risk‐related worldviews (cultural biases); and the experiences of local context (situated risk) for predicting risk‐related policy preferences regarding cosmetic pesticides. Responses to a random telephone questionnaire are used to compare residents’ risk perceptions, cultural biases, and pesticide bylaw preferences in Calgary (Alberta), Halifax (Nova Scotia), and London (Ontario), Canada. Logistic regression shows that the most important determinants of pesticide bylaw preference are risk perception, lack of benefit, and pesticide “abstinence.” Though perception of health risk is the best single predictor of differences in bylaw preferences, social factors such as gender and situated risk factors like conflict over chemical pesticides, are also important. Though cultural biases are not important predictors of pesticide bylaw preference, as in other studies, they are significant predictors of health risk perception. Pesticide bylaw preference is therefore more than just a health risk perception or worldview issue; it is also about how health risk becomes situated—contextually—in the experiences of residents’ everyday lives.     

Place, Culture, and the Social Amplification of Risk

This article investigates the role of culture in the social production of risks and risk communication surrounding industrial development in a region located at a rural-urban interface. A case study examined a public consultation that was undertaken to inform local residents about an eco-industrial development proposal being planned near Edmonton, Alberta, Canada. The research employed the social amplification of risk framework (SARF) to examine the relationships among culture, place, and socially constructed risk. A total of 44 in-depth, semi-structured interviews were carried out with 33 landowners (farmers, acreage owners), public officials (municipal politicians, administrators), journalists, and industry representatives. Analysis revealed that risk communication occurred in relation to situated experiences of place that were based on conflicting cultural worldviews. The research shows that place is a useful component of the SARF, providing a spatial explanation for why some people amplify, and others attenuate, risks in locally contentious environmental debates.     

Approximations and Heuristics for the "Cause-Modified" Life Table

Life-table analysis can help to gauge the lifetime impacts that accrue from modifications to (age-specific) baseline mortality. Modifications of interest include those stemming from risk-factor-related exposures or from interventions. The specific algorithm used in these analyses can be called a cause-modified life table (a generalization of the cause-deleted life table). The author presents an approach for approximating that algorithm and uses it to obtain remarkably simplified expressions for approximating three indices of common interest: life-years lost (LYL), excess lifetime risk ratio (ELRR), and risk of exposure-induced death (REID). These efforts are restricted to the special case of multiplicative increases to baseline mortality (modeled as an excess rate ratio, ERR). The simplified expressions effectively "break open" what is often treated as a "black-box" calculation. Several insights result. For a practical range of risk factor impacts (ERRs), each index can be related to the ERR as a function of a baseline summary statistic and a "characteristic number" specific to the population and cause of interest. Conveniently, those numbers help form "rules of thumb" for translating among the three indices and suggest heuristics for extrapolating indices across populations and causes of death.     

Trust in Risk Regulation: Cause or Consequence of the Acceptability of GM Food?

Although there is ample empirical evidence that trust in risk regulation is strongly related to the perception and acceptability of risk, it is less clear what the direction of this relationship is. This article explores the nature of the relationship, using three separate data sets on perceptions of genetically modified (GM) food among the British public. The article has two discrete but closely interrelated objectives. First, it compares two models of trust. More specifically, it investigates whether trust is the cause (causal chain account) or the consequence (associationist view) of the acceptability of GM food. Second, this study explores whether the affect heuristic can be applied to a wider number of risk-relevant concepts than just perceived risk and benefit. The results suggest that, rather than a determinant, trust is an expression or indicator of the acceptability of GM food. In addition, and as predicted, "affect" accounts for a large portion of the variance between perceived risk, perceived benefit, trust in risk regulation, and acceptability. Overall, the results support the associationist view that specific risk judgments are driven by more general evaluative judgments The implications of these results for risk communication and policy are discussed.     

A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis

In risk analysis, the treatment of the epistemic uncertainty associated to the probability of occurrence of an event is fundamental. Traditionally, probabilistic distributions have been used to characterize the epistemic uncertainty due to imprecise knowledge of the parameters in risk models. On the other hand, it has been argued that in certain instances such uncertainty may be best accounted for by fuzzy or possibilistic distributions. This seems the case in particular for parameters for which the information available is scarce and of qualitative nature. In practice, it is to be expected that a risk model contains some parameters affected by uncertainties that may be best represented by probability distributions and some other parameters that may be more properly described in terms of fuzzy or possibilistic distributions. In this article, a hybrid method that jointly propagates probabilistic and possibilistic uncertainties is considered and compared with pure probabilistic and pure fuzzy methods for uncertainty propagation. The analyses are carried out on a case study concerning the uncertainties in the probabilities of occurrence of accident sequences in an event tree analysis of a nuclear power plant.     

Analysis of Affordance,Time, and Adaptation in the Assessment of Industrial Control System Cybersecurity Risk

Industrial control systems increasingly use standard communication protocols and are increasingly connected to public networks—creating substantial cybersecurity risks, especially when used in critical infrastructures such as electricity and water distribution systems. Methods of assessing risk in such systems have recognized for some time the way in which the strategies of potential adversaries and risk managers interact in defining the risk to which such systems are exposed. But it is also important to consider the adaptations of the systems’ operators and other legitimate users to risk controls, adaptations that often appear to undermine these controls, or shift the risk from one part of a system to another. Unlike the case with adversarial risk analysis, the adaptations of system users are typically orthogonal to the objective of minimizing or maximizing risk in the system. We argue that this need to analyze potential adaptations to risk controls is true for risk problems more generally, and we develop a framework for incorporating such adaptations into an assessment process. The method is based on the principle of affordances, and we show how this can be incorporated in an iterative procedure based on raising the minimum period of risk materialization above some threshold. We apply the method in a case study of a small European utility provider and discuss the observations arising from this.     

Artifactual Uncertainty in Risk Analysis

The field of comparative risk analysis of electrical energy alternatives has traditionally been plagued by highly uncertain estimates of risk rates, and consequently by conflicting judgements of relative risk. To the extent that this uncertainty arises from traditional sources–imperfect observations or actual variance in the data–it can be brought within a Bayesian statistical framework which allows policy conclusions to be formulted and tested at different levels of confidence. It is shown that there are important methodological or "artifactual" sources of uncertainty, however, that cannot be treated by statistical means; these require conceptual advances for their resolution. By identifying these sources of uncertainty in simple thought experiments and examples, it is shown in what ways the concept of attributable risk, which is the policy-maker's chief concern, must be sharpened and refined to have unambiguous meaning. The conventional "multilinear" formula for calculating risk indices is challenged as a measure of attributable risk, and directions for further research to improve the methodological foundations of comparative risk analysis are identified.     

Application of Correspondence Analysis in the Assessment of Mine Tailings Dam Breakage Risk in the Mediterranean Region

A new statistical approach for preliminary risk evaluation of breakage in tailings dam is presented and illustrated by a case study regarding the Mediterranean region. The objective of the proposed method is to establish an empirical scale of risk, from which guidelines for prioritizing the collection of further specific information can be derived. The method relies on a historical database containing, in essence, two sets of qualitative data: the first set concerns the variables that are observable before the disaster (e.g., type and size of the dam, its location, and state of activity), and the second refers to the consequences of the disaster (e.g., failure type, sludge characteristics, fatalities categorization, and downstream range of damage). Based on a modified form of correspondence analysis, where the second set of attributes are projected as "supplementary variables" onto the axes provided by the eigenvalue decomposition of the matrix referring to the first set, a "qualitative regression" is performed, relating the variables to be predicted (contained in the second set) with the "predictors" (the observable variables). On the grounds of the previously derived relationship, the risk of breakage in a new case can be evaluated, given observable variables. The method was applied in a case study regarding a set of 13 test sites where the ranking of risk obtained was validated by expert knowledge. Once validated, the procedure was included in the final output of the e-EcoRisk UE project (A Regional Enterprise Network Decision-Support System for Environmental Risk and Disaster Management of Large-Scale Industrial Spills), allowing for a dynamic historical database updating and providing a prompt rough risk evaluation for a new case. The aim of this section of the global project is to provide a quantified context where failure cases occurred in the past for supporting analogue reasoning in preventing similar situations.     

Managing Societal and Institutional Risk in Child Protection

Public sector services have been reshaped by two interacting factors: the growing dominance of risk management and the growing demands for transparency and accountability. For the caring professions, these have provoked radical reform. Using the child protection service as a case study, this article explores the impact of the changes on a service that deals with conflicting risks and has a poorly articulated knowledge base. Drawing on Rothstein et al. 's distinction between societal and institutional risks, it is argued that difficulties in managing societal risks are creating serious institutional risks. The latter are then being prioritized in the way the system operates. The preoccupation with such risks has been translated into concerted efforts to formalize the work of front line practitioners to make it transparent and auditable. Although done, in part, with the good intention of spreading good practice standards, this formalization has gone beyond the evidenced knowledge base to the extent that it is creating a new picture of "good practice" that omits significant dimensions of work and is distinct from measures of children's safety or welfare. Moreover, the process of formalization acts as an impediment to knowledge development in disciplines where such learning is urgently needed.     

Practical Methods for Meeting Remediation Goals at Hazardous Waste Sites

Risk-based cleanup goals or preliminary remediation goals (PRGs) are established at hazardous waste sites when contaminant concentrations in air, soil, surface water, or groundwater exceed specified acceptable risk levels. When derived in accordance with the Environmental Protection Agency's risk assessment guidance, the PRG is intended to represent the average contaminant concentration within an exposure unit area that is left on the site following remediation. The PRG, however, frequently has been used inconsistently at Superfund sites with a number of remediation decisions using the PRG as a not-to-exceed concentration (NTEC). Such misapplications could result in overly conservative and unnecessarily costly remedial actions. The PRG should be applied in remedial actions in the same manner in which it was generated. Statistical methods, such as Bower's Confidence Response Goal, and mathematical methods such as "iterative removal of hot spots," are available to assist in the development of NTECs that ensure the average postremediation contaminant concentration is at or below the PRG. These NTECs can provide the risk manager with a more practical cleanup goal. In addition, an acute PRG can be developed to ensure that contaminant concentrations left on-site following remediation are not so high as to pose an acute or short-term health risk if excessive exposure to small areas of the site should occur. A case study demonstrates cost savings of five to ten times associated with the more scientifically sound use of the PRG as a postremediation site average, and development of a separate NTEC and acute PRG based on the methods referenced in this article.     

How Do People Evaluate Risk Reduction When They Are Told Zero Risk Is Impossible?

Two studies examined how people evaluate risk reduction when they believe zero risk to be impossible. Measures collected were willingness to pay (WTP) for risk reduction, and degree of trust in the risk management agency. The findings from the combined studies are: (1) participants were more willing to pay a higher amount for the same reduction in risk in the "zero risk possible" than in the "zero risk impossible" condition; and (2) people's trust in the risk management agency did not differ between the "zero risk impossible" and "zero risk possible" conditions. These results suggest that it might be viable for agencies to accurately communicate the unattainability of zero risk without suffering a loss in public faith or trust, and thus that excessive expenditure for risk reduction might be prevented.     

Implications of Excessive Precision for Risk Comparisons: Lessons from the Past Four Decades

This article reviews five published "second-order" risk comparisons from the past four decades that implied precise understanding, and hence clear relationships or orderings, of the underlying risks. "Second order" here refers to efforts that extract information from original sources with the goal of relating diverse findings. All five of these publications have frequently been cited in the peer-reviewed literature and/or in risk regulatory debate in the United States. Each is associated with at least one contemporaneous critique that the findings were excessively precise. None of these critiques suggested that an alternative relationship or ordering of the risks evaluated was more appropriate. Instead, each critique concluded that alternative, contradictory relationships were at least as plausible given data and/or analytical limitations. In one case, the critique led to the withdrawal of the original publication. The original findings have been propagated or used uncritically in subsequent literature, including political support for cost-effectiveness analysis. In other cases, the critiques have been used to discredit quantitative risk analysis in general, especially in the cases of nuclear power and cost-benefit analysis. Both of these outcomes are undesirable. Future risk comparisons should avoid excessive precision, include explicit discussion of uncertainty, and differentiate between plausible estimates and expected values.     

Trust,Emotion, Sex,Politics, and Science: Surveying the Risk-Assessment Battlefield

Risk management has become increasingly politicized and contentious. Polarized views, controversy, and conflict have become pervasive. Research has begun to provide a new perspective on this problem by demonstrating the complexity of the concept risk and the inadequacies of the traditional view of risk assessment as a purely scientific enterprise. This paper argues that danger is real, but risk is socially constructed. Risk assessment is inherently subjective and represents a blending of science and judgment with important psychological, social, cultural, and political factors. In addition, our social and democratic institutions, remarkable as they are in many respects, breed distrust in the risk arena. Whoever controls the definition of risk controls the rational solution to the problem at hand. If risk is defined one way, then one option will rise to the top as the most cost-effective or the safest or the best. If it is defined another way, perhaps incorporating qualitative characteristics and other contextual factors, one will likely get a different ordering of action solutions. Defining risk is thus an exercise in power. Scientific literacy and public education are important, but they are not central to risk controversies. The public is not irrational. Their judgments about risk are influenced by emotion and affect in a way that is both simple and sophisticated. The same holds true for scientists. Public views are also influenced by worldviews, ideologies, and values; so are scientists' views, particularly when they are working at the limits of their expertise. The limitations of risk science, the importance and difficulty of maintaining trust, and the complex, sociopolitical nature of risk point to the need for a new approach—one that focuses upon introducing more public participation into both risk assessment and risk decision making in order to make the decision process more democratic, improve the relevance and quality of technical analysis, and increase the legitimacy and public acceptance of the resulting decisions.     

Statistical Issues in the Estimation of Assigned Shares for Carcinogenesis Liability

Congress is currently considering adopting a mathematical formula to assign shares in cancer causation to specific doses of radiation, for use in establishing liability and compensation awards. The proposed formula, if it were sound, would allow difficult problems in tort law and public policy to be resolved by reference to tabulated "probabilities of causation." This article examines the statistical and conceptual bases for the proposed methodology. We find that the proposed formula is incorrect as an expression for "probability and causation," that it implies hidden, debatable policy judgments in its treatment of factor interactions and uncertainties, and that it can not in general be quantified with sufficient precision to be useful. Three generic sources of statistical uncertainty are identified--sampling variability, population heterogeneity, and error propagation--that prevent accurate quantification of "assigned shares." These uncertainties arise whenever aggregate epidemiological or risk data are used to draw causal inferences about individual cases.     

Specifying Risk Goals: Inherent Problems with Democratic Institutions

Many risks have the property that large numbers of people are exposed and have little or no individual control over the risks they face. Dams, nuclear power plants, and recombinant DNA research have proven controversial not simply because there is vast uncertainty about the true level of risk associated with each, but because a fundamental issue is that in each case social risks can be lessened by spending more on safety: dams can be designed to withstand larger earthquakes, nuclear plants can have additional safety equipment, and DNA research could be done in yet more carefully isolated laboratories. Since each person will have preferences regarding the proper trade-off between increased cost and increased safety, there is little possibility of consensus. More importantly, we show that a voting process for expressing individual preferences can be manipulated and is seriously flawed in the sense that it does not lead to an "efficient" outcome. In addition, we show that virtually all people are unhappy with the safety decision, in the sense that each would prefer either a safer or a cheaper outcome. Thus, making safety decisions that affect a large group of people who will not be able to control the outcome is even more difficult than has been appreciated. We suggest some ways of handling some of the difficulties.     

A model for regulatory reform in Canada: From “command-and-control” to “assured compliance”

Due, in part, to severe budget constraints, the Alberta Energy and Utilities Board (EUB) moved from a traditional command-and-control regulatory system to a system that incorporates several characteristics of self-regulation. The EUB, the regulatory body for the energy industry in Alberta, Canada, refers to the new system as “compliance assured by audit and penalty”. This new system, which may have relevance to other nations struggling with similar developments, utilizes a classification scheme based on a company's past performance. This allows the EUB to target resources toward companies identified as having the greatest need for regulatory performance improvement. This case study discusses the drivers for change, the process of change and the lessons learned, and raises several questions relating to the business implications.     

Predicting Future Excess Events in Risk Assessment

Risk characterization in a study population relies on cases of disease or death that are causally related to the exposure under study. The number of such cases, so-called "excess" cases, is not just an indicator of the impact of the risk factor in the study population, but also an important determinant of statistical power for assessing aspects of risk such as age-time trends and susceptible subgroups. In determining how large a population to study and/or how long to follow a study population to accumulate sufficient excess cases, it is necessary to predict future risk. In this study, focusing on models involving excess risk with possible effect modification, we describe a method for predicting the expected magnitude of numbers of excess cases and assess the uncertainty in those predictions. We do this by extending Bayesian APC models for rate projection to include exposure-related excess risk with possible effect modification by, e.g., age at exposure and attained age. The method is illustrated using the follow-up study of Japanese Atomic-Bomb Survivors, one of the primary bases for determining long-term health effects of radiation exposure and assessment of risk for radiation protection purposes. Using models selected by a predictive-performance measure obtained on test data reserved for cross-validation, we project excess counts due to radiation exposure and lifetime risk measures (risk of exposure-induced deaths (REID) and loss of life expectancy (LLE)) associated with cancer and noncancer disease deaths in the A-Bomb survivor cohort.     

A Note on Compounded Conservatism

Compounded conservatism (or "creeping safety") describes the impact of using conservative, upper-bound estimates of the values of multiple input variates to obtain a conservative estimate of risk modeled as an increasing function of those variates. In a simple multiplicative model of risk, for example, if upper p -fractile (100 p th percentile) values are used for each of several statistically independent input variates, the resulting risk estimate will be the upper p' -fractile of risk predicted according to that multiplicative model, where p' > p . The amount of compounded conservativism reflected by the difference between p' and p may be substantial, depending on the number of inputs, their relative uncertainties, and the value of p selected. Particular numerical examples of compounded conservatism are often cited, but an analytic approach may better serve to conceptualize and communicate its potential quantitative impact. This note briefly outlines such an approach and illustrates its application to the case of risk modeled as a product of lognormally distributed inputs.