ALBERT

Notes: Trust in risk managers appears to be an important antecedent of public acceptance for many hazards. However, such trust may be fragile since research suggests that negative performance information has a greater impact than positive performance information (Slovic, 1993). Closer examination of these findings suggests two potential moderators of this valence-related asymmetry—information specificity and hazard risk potential. First, we predicted that the asymmetry would be less evident for low versus high specificity information (risk management policies vs. concrete events). Second, we predicted that it would also be less evident for a low- versus high-risk hazard (pharmaceutical vs. nuclear industry). Study 1 reanalyzed Slovic's original trust asymmetry data for the nuclear industry. In line with Prediction 1, trust asymmetry was less evident for policy than event-related information. Using a new set of items with more clearly defined levels of specificity, Study 2 replicated and extended these findings for the high-risk hazard (nuclear power). In line with Prediction 2, trust asymmetry was even less evident for the low-risk hazard (pharmaceuticals). Positive policies in this industry actually had a greater impact on trust than negative ones, in contrast to previous findings. Results support an information diagnosticity account of earlier findings and suggest that trust in risk managers may be more robust than previously believed.

Notes: Much attention has been addressed to the question of whether Europe or the United States adopts a more precautionary stance to the regulation of potential environmental, health, and safety risks. Some commentators suggest that Europe is more risk-averse and precautionary, whereas the United States is seen as more risk-taking and optimistic about the prospects for new technology. Others suggest that the United States is more precautionary because its regulatory process is more legalistic and adversarial, while Europe is more lax and corporatist in its regulations. The flip-flop hypothesis claims that the United States was more precautionary than Europe in the 1970s and early 1980s, and that Europe has become more precautionary since then. We examine the levels and trends in regulation of environmental, health, and safety risks since 1970. Unlike previous research, which has studied only a small set of prominent cases selected nonrandomly, we develop a comprehensive list of almost 3,000 risks and code the relative stringency of regulation in Europe and the United States for each of 100 risks randomly selected from that list for each year from 1970 through 2004. Our results suggest that: (a) averaging over risks, there is no significant difference in relative precaution over the period, (b) weakly consistent with the flip-flop hypothesis, there is some evidence of a modest shift toward greater relative precaution of European regulation since about 1990, although (c) there is a diversity of trends across risks, of which the most common is no change in relative precaution (including cases where Europe and the United States are equally precautionary and where Europe or the United States has been consistently more precautionary). The overall finding is of a mixed and diverse pattern of relative transatlantic precaution over the period.

Notes: This article tests the hypothesis that the exposure to the threat to societies posed by the introduction of new technologies is associated with a normalization of risk perception. Data collected in 2000 by the International Social Survey Programme (ISSP) on environmental issues were used to explore this hypothesis. Representative samples from 25 countries were employed to assess the national levels of perceived threat to the environment associated with a series of technologies and activities. These values were correlated with economic indicators (mainly from the World Bank) of the diffusion of each of the technologies or activities in each country. Results indicate a negative association of risk perception with the level of technological prevalence (societal normalization effect) and a positive association with the rate of growth of the technology (societal sensitivity effect). These results indicate that the most acute levels of perceived environmental risk are found in those countries where the level of technological prevalence is low but where there has recently been substantial technological development. Environmental awareness is a mediator of the relationship between risk perception and the indices of technological diffusion. This result means that: (1) societal normalization of risk is not a direct consequence of prevalence of the technology, but is driven by awareness of technological development and that (2) societal sensitivity to risk is associated with lower levels of environmental awareness.

Notes: This article investigates whether the choice experiment approach can be used to assess people's preferences and the determinants of these preferences in order to estimate the costs and benefits of different configurations of the transport of hazardous materials by rail. Changes in the exposure to hazardous materials that people are subjected to are used rather than changes in accident risk. To the best knowledge of the author, this has not been done before in a study of people's preferences toward hazardous materials. A mail survey, carried out in two cities in Sweden, is used to obtain tentative estimates of the willingness to pay for a reduction in exposure as well as the willingness to accept an increase in exposure. Special attention is given to viability, since the complexity of the activity studied, transport of hazardous materials, and the method used pose particular challenges. The response rate and tests of validity and consistency indicate that this method can be applied. Moreover, the results suggest that studies of this kind may provide guidance on changes in the transport of hazardous materials, especially because policymakers may influence the attributes presented here. Referring to the exposure of hazardous materials highlights the importance of providing the respondents with adequate information regarding hazardous transports. An important finding is that the amount of background information may have some effect on the stated preferences.

Notes: This article presents a Listeria monocytogenes growth model in milk at the farm bulk tank stage. The main objective was to judge the feasibility and value to risk assessors of introducing a complex model, including a complete thermal model, within a microbial quantitative risk assessment scheme. Predictive microbiology models are used under varying temperature conditions to predict bacterial growth. Input distributions are estimated based on data in the literature, when it is available. If not, reasonable assumptions are made for the considered context. Previously published results based on a Bayesian analysis of growth parameters are used. A Monte Carlo simulation that forecasts bacterial growth is the focus of this study. Three scenarios that take account of the variability and uncertainty of growth parameters are compared. The effect of a sophisticated thermal model taking account of continuous variations in milk temperature was tested by comparison with a simplified model where milk temperature was considered as constant. Limited multiplication of bacteria within the farm bulk tank was modeled. The two principal factors influencing bacterial growth were found to be tank thermostat regulation and bacterial population growth parameters. The dilution phenomenon due to the introduction of new milk was the main factor affecting the final bacterial concentration. The results show that a model that assumes constant environmental conditions at an average temperature should be acceptable for this process. This work may constitute a first step toward exposure assessment for L. monocytogenes in milk. In addition, this partly conceptual work provides guidelines for other risk assessments where continuous variation of a parameter needs to be taken into account.

Notes: Limited time and resources usually characterize environmental decision making at policy organizations such as the U.S. Environmental Protection Agency. In these climates, addressing uncertainty, usually considered a flaw in scientific analyses, is often avoided. However, ignoring uncertainties can result in unpleasant policy surprises. Furthermore, it is important for decisionmakers to know how defensible a chosen policy option is over other options when the uncertainties of the data are considered. The purpose of this article is to suggest an approach that is unique from other approaches in that it considers uncertainty in two specific ways—the uncertainty of stakeholder values within a particular decision context and data uncertainty in the light of the decision-contextual data–values relationship. It is the premise of this article that the interaction between data and stakeholder values is critical to how the decision options are viewed and determines the effect of data uncertainty on the relative acceptability of the decision options, making the understanding of this interaction important to decisionmakers and other stakeholders. This approach utilizes the recently developed decision analysis framework and process, multi-criteria integrated resource assessment (MIRA). This article will specifically address how MIRA can be used to help decisionmakers better understand the importance of uncertainty on the specific (i.e., decision contextual) environmental policy options that they are deliberating.

Notes: Perceptions of risks associated with mobile phones, base stations, and other sources of electromagnetic fields (EMF) were examined. Data from a telephone survey conducted in the German- and French-speaking parts of Switzerland are presented (N= 1,015). Participants assessed both risks and benefits associated with nine different sources of EMF. Trust in the authorities regulating these hazards was assessed as well. In addition, participants answered a set of questions related to attitudes toward EMF and toward mobile phone base stations. According to respondents' assessments, high-voltage transmission lines are the most risky source of EMF. Mobile phones and mobile phone base stations received lower risk ratings. Results showed that trust in authorities was positively associated with perceived benefits and negatively associated with perceived risks. People who use their mobile phones frequently perceived lower risks and higher benefits than people who use their mobile phones infrequently. People who believed they lived close to a base station did not significantly differ in their level of risks associated with mobile phone base stations from people who did not believe they lived close to a base station. Regarding risk regulation, a majority of participants were in favor of fixing limiting values based on the worst-case scenario. Correlations suggest that belief in paranormal phenomena is related to level of perceived risks associated with EMF. Furthermore, people who believed that most chemical substances cause cancer also worried more about EMF than people who did not believe that chemical substances are that harmful. Practical implications of the results are discussed.

Notes: Experts contend that weather and climate forecasts could have an important role in risk management strategies for community water systems. Yet, most water managers make minimal use of these forecasts. This research explores the determinants of the use of weather and climate forecasts by surveying managers of community water systems in two eastern American states (South Carolina and the Susquehanna River Basin of Pennsylvania). Assessments of the reliability of weather and climate forecasts are not driving their use as water managers who find forecasts reliable are no more likely to use them than are managers who find them unreliable. Although larger systems and those depending on surface water are more likely to use forecasts for some (but not all) purposes, the strongest determinant of forecast use is risk perceptions. Water managers who expect to face problems from weather events in the next decade are much more likely to use forecasts than are water managers who expect few problems. Their expectations of future problems are closely linked with past experience: water managers who have had problems with specific types of weather events (e.g., flood emergencies) in the last 5 years are likely to expect to experience problems in the next decade. Feeling at risk, regardless of the specific source of that weather-related risk, stimulates a decision to use weather and climate forecasts.

Notes: The growing international trade in live wildlife has the potential to result in continuing establishment of nonnative animal populations in the United States. Snakes may pose particularly high risks as potentially invasive species, as exemplified by the decimation of Guam's vertebrate fauna by the accidentally introduced brown tree snake. Herein, ecological and commercial predictors of the likelihood of establishment of invasive populations were used to model risk associated with legal commercial imports of 23 species of boas, pythons, and relatives into the United States during the period 1989–2000. Data on ecological variables were collected from multiple sources, while data on commercial variables were collated from import records maintained by the U.S. Fish and Wildlife Service. Results of the risk-assessment models indicate that species including boa constrictors (Boa constrictor), ball pythons (Python regius), and reticulated pythons (P. reticulatus) may pose particularly high risks as potentially invasive species. Recommendations for reducing risk of establishment of invasive populations of snakes and/or pathogens include temporary quarantine of imports to increase detection rates of nonnative pathogens, increasing research attention to reptile pathogens, reducing the risk that nonnative snakes will reach certain areas with high numbers of federally listed species (such as the Florida Keys), and attempting to better educate individuals purchasing reptiles.

Notes: This study examines the perceived risks to forest biodiversity and perceived effectiveness of biodiversity conservation strategies among the general public. It tests the hypotheses that perceived risk to forest biodiversity is influenced by cognitive factors (value orientation and knowledge) and social-cultural factors (such as gender and environmental membership) and that risk perceptions influence other cognitive constructs such as support for natural resource policy and management. Data were collected from a sample of the general public (n= 596) in British Columbia, Canada by mail survey in 2001. Results show that insects and disease were perceived as the greatest risk. Educating the public and industry about biodiversity issues was perceived as a more effective conservation strategy than restricting human uses of the forest. Value orientation was a better predictor of perceptions of risk and perceived effectiveness of conservation strategies than knowledge indicators or social-cultural variables. Examining the indirect effects of social-cultural variables, however, revealed that value orientation may amplify the effect of these variables and suggests that alternative paths of influence should be included. Perceived risk showed an inconsistent association with perceived effectiveness of conservation strategies.

Notes: A unique multidisciplinary perspective on the risk literature is used to establish a fresh and provocative argument regarding the epistemological understanding and definition of risk. Building on economic conceptualizations that distinguish risk from uncertainty and argue that risk is an ordered application of knowledge to the unknown, the survey identifies each of the disciplines as having a particular knowledge approach with which they confront the unknown so as to order its randomness and convert it into a risk proposition. This epistemological approach suggests the concept of risk can act as a mirror, reflecting the preoccupations, strengths, and weaknesses of each discipline as they grapple with uncertainty. The conclusion suggests that the different disciplines can, and desirably should, act in concert toward a cumulative appreciation of risk that progresses our understanding of the concept. One way in which the article challenges risk experts to join disciplinary forces in a collaborative effort is to holistically appreciate and articulate the concept of political risk calculation.

Notes: By displaying a risk reduction of 50% graphically rather than numerically, Stone, Yates, and Parker significantly increased professed risk-avoidant behavior. The current experiments replicated this effect at various risk ratios. Specifically, participants were willing to spend more money to reduce a risk when the risk information was displayed by asterisks rather than by numbers for risk-reduction ratios ranging from 3% to 97%. Transforming the amount participants were willing to spend to logarithms significantly improved a linear fit to the data, suggesting that participants convert this variable within the decision-making process. Moreover, a log-linear model affords an exceptional fit to both the graphical and numerical data, suggesting that a graphical presentation elicits the same decision-making mechanism as does the numerical display. In addition, the data also suggest that each person removed from harm is weighted more by some additional factor in the graphical compared to the numerical presentations.

Notes: While quantitative estimates of risk have been a standard practice in cancer risk assessment for many years, no similar practice is evident in noncancer risk assessment. We use two recent examples involving methylmercury and arsenic to illustrate the negative impact of this discrepancy on risk communication and cost-benefit analysis. We argue for a more balanced treatment of cancer and noncancer risks and suggest an approach for reaching this goal.

Notes: Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fitting the data and may result in wide ranging estimates of risk. Bayesian model averaging (BMA) offers a strategy for addressing uncertainty in the selection of statistical models when generating risk estimates. This strategy is illustrated with two examples: applying the multistage model to cancer responses and a second example where different quantal models are fit to kidney lesion data. BMA provides excess risk estimates or benchmark dose estimates that reflects model uncertainty.

Notes: Following a comprehensive evaluation of the health risks of radon, the U.S. National Research Council (US-NRC) concluded that the radon inside the homes of U.S. residents is an important cause of lung cancer. To assess lung cancer risks associated with radon exposure in Canadian homes, we apply the new (US-NRC) techniques, tailoring assumptions to the Canadian context. A two-dimensional uncertainty analysis is used to provide both population-based (population attributable risk, PAR; excess lifetime risk ratio, ELRR; and life-years lost, LYL) and individual-based (ELRR and LYL) estimates. Our primary results obtained for the Canadian population reveal mean estimates for ELRR, PAR, and LYL are 0.08, 8%, and 0.10 years, respectively. Results are also available and stratified by smoking status (ever versus never). Conveniently, the three indices (ELRR, PAR, and LYL) reveal similar output uncertainty (geometric standard deviation, GSD ≈ 1.3), and in the case of ELRR and LYL, comparable variability and uncertainty combined (GSD ≈ 4.2). Simplifying relationships are identified between ELRR, LYL, PAR, and the age-specific excess rate ratio (ERR), which suggest a way to scale results from one population to another. This insight is applied in scaling our baseline results to obtain gender-specific estimates, as well as in simplifying and illuminating sensitivity analysis.

Notes: Air pollution is a current and growing concern for Canadians, and there is evidence that ambient levels that meet current exposure standards may be associated with mortality and morbidity in Toronto, Canada. Evaluating exposure is an important step in understanding the relationship between particulate matter (PM) exposure and health outcomes. This report describes the PEARLS model (Particulate Exposure from Ambient to Regional Lung by Subgroup), which predicts exposure distributions for 11 age-gender population subgroups in Toronto to PM2.5 (PM with a median aerodynamic diameter of 2.5 μm or less) using Monte Carlo simulation techniques. The model uses physiological and activity pattern characteristics of each subgroup to determine region-specific lung exposure to PM2.5, which is defined as the mass of PM2.5 deposited per unit time to each of five lung regions (two extrathoracic, bronchial, bronchiolar, and alveolar). The modeling results predict that children, toddlers, and infants have the broadest distributions of exposure, and the greatest chance of experiencing extreme exposures in the alveolar region of the lung. Importance analysis indicates that the most influential model variables are air exchange rate into indoor environments, time spent outdoors, and time spent at high activity levels. Additionally, a “critical point” was defined and introduced to the PEARLS to investigate the effects of possible threshold-pathogenic phenomena on subgroup exposure patterns. The analysis indicates that the subgroups initially predicted to be most highly exposed were likely to have the highest proportion of their population exposed above the critical point. Substantial exposures above the critical point were predicted in all subgroups for ambient concentrations of PM2.5 commonly observed in Toronto after continuous exposure of 24 hours or more.

Notes: It has been shown that bathroom-type water uses dominate personal exposure to water-borne contaminants in the home. Therefore, in assessing exposure of specific population groups to the contaminants in the water, understanding population water-use behavior for bathroom activities as a function of demographic characteristics is vital to realistic exposure estimates. In this article, shower and bath frequencies and durations are analyzed, presented, and compared for various demographic groups derived from analyses of the National Human Activities Pattern Survey (NHAPS) database and the Residential End Uses of Water Study (REUWS) database as well as from a review of current literature. Analysis showed that age and level of education significantly influenced shower and bath frequency and duration. The frequency of showering and bathing reported in NHAPS agreed reasonably well with previous studies; however, durations of these events were found to be significantly longer. Showering frequency reported in REUWS was slightly less than that reported for NHAPS; however, durations of showers reported in REUWS are consistent with other studies. After considering the strengths and weaknesses of each data set and comparing their results to previous studies, it is concluded that NHAPS provides more reliable frequency data, while REUWS provides more reliable duration data. The shower- and bath-use behavior parameters recommended in this article can aid modelers in appropriately specifying water-use behavior as a function of demographic group in order to conduct reasonable assessments of exposure to contaminants that enter the home via the water supply.

Notes: Information about two uncertainties is superadditive in value if the value of information for resolving both uncertainties together exceeds the sum of the value of information for resolving each uncertainty alone. For the two-act linear loss decision problem with normal priors, conditions are derived for which the expected value of perfect information about two independent risks is superadditive. An approximate condition is also presented. The level of additivity of information value has implications for how risk-management resources should be allocated to acquisition of information. Several applications show how a variety of decision problems can reduce to the basic problem, and how the general results obtained here can be translated simply to prescriptions for specific situations.

Notes: Reference values, including an oral reference dose (RfD) and an inhalation reference concentration (RfC), were derived for propylene glycol methyl ether (PGME), and an oral RfD was derived for its acetate (PGMEA). These values were based on transient sedation observed in F344 rats and B6C3F1 mice during a two-year inhalation study. The dose-response relationship for sedation was characterized using internal dose measures as predicted by a physiologically-based pharmacokinetic (PBPK) model for PGME and its acetate. PBPK modeling was used to account for changes in rodent physiology and metabolism due to aging and adaptation, based on data collected during Weeks 1, 2, 26, 52, and 78 of a chronic inhalation study. The peak concentration of PGME in richly perfused tissues (i.e., brain) was selected as the most appropriate internal dose measure based on a consideration of the mode of action for sedation and similarities in tissue partitioning between brain and other richly perfused tissues. Internal doses (peak tissue concentrations of PGME) were designated as either no-observed-adverse-effect levels (NOAELs) or lowest-observed-adverse-effect levels (LOAELs) based on the presence or the absence of sedation at each time point, species, and sex in the two-year study. Distributions of the NOAEL and LOAEL values expressed in terms of internal dose were characterized using an arithmetic mean and standard deviation, with the mean internal NOAEL serving as the basis for the reference values, which was then divided by appropriate uncertainty factors. Where data were permitting, chemical-specific adjustment factors were derived to replace default uncertainty factor values of 10. Nonlinear kinetics, which was predicted by the model in all species at PGME concentrations exceeding 100 ppm, complicate interspecies, and low-dose extrapolations. To address this complication, reference values were derived using two approaches that differ with respect to the order in which these extrapolations were performed: (1) default approach of interspecies extrapolation to determine the human equivalent concentration (PBPK modeling) followed by uncertainty factor application, and (2) uncertainty factor application followed by interspecies extrapolation (PBPK modeling). The resulting reference values for these two approaches are substantially different, with values from the latter approach being seven-fold higher than those from the former approach. Such a striking difference between the two approaches reveals an underlying issue that has received little attention in the literature regarding the application of uncertainty factors and interspecies extrapolations to compounds where saturable kinetics occur in the range of the NOAEL. Until such discussions have taken place, reference values based on the former approach are recommended for risk assessments involving human exposures to PGME and PGMEA.

Notes: In the process of screening a soil against a certain contaminant, we define the health-risk-based preliminary remediation goal (PRG) as the contaminant concentration above which some remedial action may be required. Thus, PRG is the first standard (or guidance) for judging a site. An overestimated PRG (a too-large value) may cause us to miss some contaminated sites that can threaten human health and the environment. An underestimated PRG (a too-small value), on the other hand, may lead to unnecessary cleanup and waste tremendous resources. The PRGs for soils are often calculated on the assumption that the contaminant concentration in soil does not change with time. However, that concentration usually decreases with time as a result of different chemical and transport mechanisms. The static assumption thus exaggerates the long-term exposure dose and results in a too-small PRG. We present a box model that considers all important transport processes and obeys the law of mass conservation. We can use the model as a tool to estimate the transient contaminant concentrations in air, soil, and ground water. Using these concentrations in conjunction with appropriate health-risk parameters, we may estimate the PRGs for different contaminants. As an example, we calculated the tritium PRG for residential soils. The result is quite different from, but within the range of, the two versions of the corresponding PRG previously recommended by the U.S. EPA.

Notes: The extreme importance of critical infrastructures to modern society is widely recognized. These infrastructures are complex and interdependent. Protecting the critical infrastructures from terrorism presents an enormous challenge. Recognizing that society cannot afford the costs associated with absolute protection, it is necessary to identify and prioritize the vulnerabilities in these infrastructures. This article presents a methodology for the identification and prioritization of vulnerabilities in infrastructures. We model the infrastructures as interconnected digraphs and employ graph theory to identify the candidate vulnerable scenarios. These scenarios are screened for the susceptibility of their elements to a terrorist attack, and a prioritized list of vulnerabilities is produced. The prioritization methodology is based on multiattribute utility theory. The impact of losing infrastructure services is evaluated using a value tree that reflects the perceptions and values of the decisionmaker and the relevant stakeholders. These results, which are conditional on a specified threat, are provided to the decisionmaker for use in risk management. The methodology is illustrated through the presentation of a portion of the analysis conducted on the campus of the Massachusetts Institute of Technology.

Notes: This study assesses the fire safety risks associated with compressed natural gas (CNG) vehicle systems, comprising primarily a typical school bus and supporting fuel infrastructure. The study determines the sensitivity of the results to variations in component failure rates and consequences of fire events. The components and subsystems that contribute most to fire safety risk are determined. Finally, the results are compared to fire risks of the present generation of diesel-fueled school buses. Direct computation of the safety risks associated with diesel-powered vehicles is possible because these are mature technologies for which historical performance data are available. Because of limited experience, fatal accident data for CNG bus fleets are minimal. Therefore, this study uses the probabilistic risk assessment (PRA) approach to model and predict fire safety risk of CNG buses. Generic failure data, engineering judgments, and assumptions are used in this study. This study predicts the mean fire fatality risk for typical CNG buses as approximately 0.23 fatalities per 100-million miles for all people involved, including bus passengers. The study estimates mean values of 0.16 fatalities per 100-million miles for bus passengers only. Based on historical data, diesel school bus mean fire fatality risk is 0.091 and 0.0007 per 100-million miles for all people and bus passengers, respectively. One can therefore conclude that CNG buses are more prone to fire fatality risk by 2.5 times that of diesel buses, with the bus passengers being more at risk by over two orders of magnitude. The study estimates a mean fire risk frequency of 2.2 × 10−5 fatalities/bus per year. The 5% and 95% uncertainty bounds are 9.1 × 10−6 and 4.0 × 10−5, respectively. The risk result was found to be affected most by failure rates of pressure relief valves, CNG cylinders, and fuel piping.

Notes: Laypeople's perceptions of health and safety risks have been widely studied, but only a few studies have addressed perceptions of ecological hazards. We assembled a list of 39 attributes of ecological hazards from the literatures on comparative risk assessment, ecological health, environmental conservation and management, environmental psychology, and risk perception. In Study 1, 125 laypeople evaluated 83 hazards on subsets of this attribute set. Factor analysis of attribute ratings (averaged over participants) revealed six oblique factors: ecological impacts, human impacts, human benefits, aesthetic impacts, scientific understanding, and controllability. These factors predicted mean judgments of overall riskiness, ecological riskiness, acceptability, and regulatory strictness. In Study 2, 30 laypeople each evaluated 34 hazards on 17 attributes and 3 dependent variables. Aggregate-level factor analysis of these data replicated the appropriate portion of the factor solution and yielded similar regression results. Parallel analyses at the individual-participant level yielded factors that explained less variance in judgments of overall riskiness, ecological riskiness, and acceptability. However, the decrease in explanatory power was much less than is often reported for disaggregate-level analyses of psychometric data. This discrepancy illustrates the importance of distinguishing between the level of analysis (aggregate versus disaggregate) and the focus of analysis (distinctions among hazards versus distinctions among participants). In a hybrid analysis, aggregate-level factor scores predicted individual participants' riskiness judgments reasonably well. Psychometric studies such as these provide a sound empirical basis for selecting attributes of ecological hazards for use in comparative risk assessment.

Notes: Government and private sector organizations are increasingly turning to the use of maps and other visual models to provide a depiction of environmental hazards and the potential risks they represent to humans and ecosystems. Frequently, the graphic presentation is tailored to address a specific contaminant, its location and possible exposure pathways, and potential receptors. Its format is usually driven by the data available, choice of graphics technology, and the audience being served. A format that is effective for displaying one contaminant at one scale at one site, however, may be ineffective in accurately portraying the circumstances surrounding a different contaminant at the same site, or the same contaminant at a different site, because of limitations in available data or the graphics technology being used. This is the daunting challenge facing the U.S. Department of Energy (DOE), which is responsible for the nation's legacy wastes from nuclear weapons research, testing, and production at over 100 sites in the United States. In this article, we discuss the development and use of integrated geospatial mapping and conceptual site models to identify hazards and evaluate alternative long-term environmental clean-up strategies at DOE sites located across the United States. While the DOE probably has the greatest need for such information, the Department of Defense and other public and private responsible parties for many large and controversial National Priority List or Superfund sites would benefit from a similar approach.

Notes: The research team interviewed over 90 Finnish battalion members in Kosovo, visited 22 units or posts, registered its observations, and made any necessary measurements. Key persons were asked to list the most important risks for occupational safety and health in their area of responsibility. Altogether, 106 accidents and 40 cases of disease resulted in compensation claims in 2000. The risks to the peacekeeping force were about twice those of the permanent staff of military trainees in Finland. Altogether, 21 accidents or cases of disease resulted in sick leave for at least 3 months after service. One permanent injury resulted from an explosion. Biological, chemical, and physical factors caused 8 to 9 occupational illnesses each. Traffic accidents, operational factors, and munitions and mines were evaluated to be the three most important risk factors, followed by occupational hygiene, living conditions (mold, fungi, dust), and general hygiene. Possible fatal risks, such as traffic accidents and munitions and explosives, received a high ranking in both the subjective and the objective evaluations. One permanent injury resulted from an explosion, and two traffic accidents involved a fatality, although not of a peacekeeper. The reduction of sports and military training accidents, risk-control programs, and, for some tasks, better personal protection is considered a development challenge for the near future.

Notes: The aim of this study was to determine the impact on risk perceptions of disclosing genetic test results used to estimate the risk of Alzheimer's disease (AD). Adult children (n= 149) of people with AD were randomized to one of two groups—Intervention group: lifetime risk estimates of AD based on age, gender, family history, and Apolipoprotein E (APOE) genotype; Control group: lifetime risk estimates of AD based on the same risk factors excluding APOE genotype. Perceptions of personal risk (PPR) for AD were assessed six weeks after risk assessments. PPR were correlated with actual lifetime risk estimates (r= 0.501; p 〈 0.0001). After controlling for lifetime risks communicated to participants, age, and number of affected relatives, PPR scores among those with an ɛ4-positive test result (the test result associated with increased AD susceptibility) (adjusted mean: 3.4 (SD: 0.7)) were not different from the PPR scores in the Control group (adjusted mean: 3.4 (SD: 0.7) (F(1,91)= 1.98; p= 0.162). Again, controlling for lifetime risk estimates, age, and number of affected relatives, the PPR score of those receiving an ɛ4-negative test result was significantly lower (adjusted mean: 3.1 (SD: 0.8)) than those in the Control group (adjusted mean: 3.4 (SD: 0.7) (F(1,95)= 6.23; p= 0.014). Perceptions of risk of developing AD are influenced by genetic test disclosure in those receiving ɛ4-negative, but not those receiving ɛ4-positive test results. Despite the reduced perceptions of risk in the former group, there was no evidence of false reassurance (i.e., perceiving risks as equal to or lower than population risks of AD), although this possibility should be assessed in other testing contexts.

Notes: Society often sets social standards that define thresholds of damage to society or the environment above which compensation must be paid to the state or other parties. In this article, we analyze the interdependence between the use of social standards and investment evaluation under dynamic uncertainty where a negative externality above a threshold established by society requires an assessment and payment of damages. Under uncertainty, the party considering implementing a project or new technology must not only assess when the project is economically efficient to implement but when to abandon a project that could potentially exceed the social standard. Using real-option theory and simple models, we demonstrate how such a social standard can be integrated into cost-benefit analysis through the use of a development option and a liability option coupled with a damage function. Uncertainty, in fact, implies that both parties interpret the social standard as a target for safety rather than an inflexible barrier that cannot be overcome. The larger is the uncertainty, in fact, the greater will be the tolerance for damages in excess of the social standard from both parties.

Notes: There is increasing interest in being able to express the consequences of exposure to potentially toxic compounds in monetary terms in order to evaluate potential cost-benefit relationships of controlling exposure. Behavioral effects of acute toluene exposure could be subjected to cost-benefit analysis if the effects of toluene were quantitatively compared to those of ethanol ingestion, which has been monetized for applied contexts. Behavioral effects of toluene and ethanol were quantified by meta-analysis of studies from the peer-reviewed literature describing their effects on choice reaction time (reaction time in a test requiring a subject to choose among two or more alternatives before responding). The internal doses of these compounds were estimated by a general physiological and toxicokinetic (GPAT) simulation from exposure parameters provided in the reports. The reported effects were converted to a common metric (proportion of baseline) and related to the estimated internal doses of toluene and ethanol, from which dose-effect equations were fitted. The estimated effect of toluene was compared to the estimated effect of ethanol on the same dependent variable by deriving a dose-equivalence equation (DEE) to express the dose of toluene as an equivalent dose of ethanol on the basis of equal effect magnitude. A nomogram was constructed by GPAT simulation to relate the environmental exposure concentration of toluene to the equivalent effect magnitude of a range of ethanol internal doses. Behavioral effects and their evaluation are determined by internal doses, which in turn are determined by a variety of variables. In addition to concentration and duration of exposure, which determine internal dose by pharmacokinetic processes, the activity level of exposed persons is a major factor. This analysis provides a continuous function of the consequences of toluene exposure expressed as ethanol-equivalent doses within confidence limits. The resulting function has the potential to estimate the monetary values of behavioral deficits caused by a range of exposures to toluene from existing monetized information on ethanol.

Notes: Clostridium perfringens is a pathogen that mainly causes food poisoning outbreaks when large quantities of food are prepared. Therefore, a model was developed to predict the effect of different cooling procedures on the growth of this pathogen during cooling of food: Dutch pea soup. First, a growth rate model based on interpretable parameters was used to predict growth during linear cooling of pea soup. Second, a temperature model for cooling pea soup was constructed by fitting the model to experimental data published earlier. This cooling model was used to estimate the effect of various cooling environments on average cooling times, taking into account the effect of stirring and product volume. The growth model systematically overestimated growth of C. perfringens during cooling in air, but this effect was limited to less than 0.5 log N/ml and this was considered to be acceptable for practical purposes. It was demonstrated that the growth model for C. perfringens combined with the cooling model for pea soup could be used to sufficiently predict growth of C. perfringens in different volume sizes of pea soup during cooling in air as well as the effect of stirring, different cooling temperatures, and various cooling environments on the growth of C. perfringens in pea soup. Although fine-tuning may be needed to eliminate inaccuracies, it was concluded that the combined model could be a useful tool for designing good manufacturing practices (GMP) procedures.

Notes: The management of microbial risk in food products requires the ability to predict growth kinetics of pathogenic microorganisms in the event of contamination and growth initiation. Useful data for assessing these issues may be found in the literature or from experimental results. However, the large number and variety of data make further development difficult. Statistical techniques, such as meta-analysis, are then useful to realize synthesis of a set of distinct but similar experiences. Moreover, predictive modeling tools can be employed to complete the analysis and help the food safety manager to interpret the data. In this article, a protocol to perform a meta-analysis of the outcome of a relational database, associated with quantitative microbiology models, is presented. The methodology is illustrated with the effect of temperature on pathogenic Escherichia coli and Listeria monocytogenes, growing in culture medium, beef meat, and milk products. Using a database and predictive models, simulations of growth in a given product subjected to various temperature scenarios can be produced. It is then possible to compare food products for a given microorganism, according to its growth ability in these products, and to compare the behavior of bacteria in a given foodstuff. These results can assist decisions for a variety of questions on food safety.

Notes: The uncertainty associated with estimates should be taken into account in quantitative risk assessment. Each input's uncertainty can be characterized through a probabilistic distribution for use under Monte Carlo simulations. In this study, the sampling uncertainty associated with estimating a low proportion on the basis of a small sample size was considered. A common application in microbial risk assessment is the estimation of a prevalence, proportion of contaminated food products, on the basis of few tested units. Three Bayesian approaches (based on beta(0, 0), beta 〈inlineGraphic alt="inline image" href="urn:x-wiley:02724332:RISA565:RISA_565_mu1" location="equation/RISA_565_mu1.gif"/〉, and beta(l, 1)) and one frequentist approach (based on the frequentist confidence distribution) were compared and evaluated on the basis of simulations. For small samples, we demonstrated some differences between the four tested methods. We concluded that the better method depends on the true proportion of contaminated products, which is by definition unknown in common practice. When no prior information is available, we recommend the beta 〈inlineGraphic alt="inline image" href="urn:x-wiley:02724332:RISA565:RISA_565_mu2" location="equation/RISA_565_mu2.gif"/〉 prior or the confidence distribution. To illustrate the importance of these differences, the four methods were used in an applied example. We performed two-dimensional Monte Carlo simulations to estimate the proportion of cold smoked salmon packs contaminated by Listeria monocytogenes, one dimension representing within-factory uncertainty, modeled by each of the four studied methods, and the other dimension representing variability between companies.

Notes: A poultry-processing model for a quantitative microbiological risk assessment (QMRA) of campylobacter is presented, which can also be applied to other QMRAs involving poultry processing. The same basic model is applied in each consecutive stage of industrial processing. It describes the effects of inactivation and removal of the bacteria, and the dynamics of cross-contamination in terms of the transfer of campylobacter from the intestines to the carcass surface and the environment, from the carcasses to the environment, and from the environment to the carcasses. From the model it can be derived that, in general, the effect of inactivation and removal is dominant for those carcasses with high initial bacterial loads, and cross-contamination is dominant for those with low initial levels. In other QMRA poultry-processing models, the input-output relationship between the numbers of bacteria on the carcasses is usually assumed to be linear on a logarithmic scale. By including some basic mechanistics, it is shown that this may not be realistic. As nonlinear behavior may affect the predicted effects of risk mitigations; this finding is relevant for risk management. Good knowledge of the variability of bacterial loads on poultry entering the process is important. The common practice in microbiology to only present geometric mean of bacterial counts is insufficient: arithmetic mean are more suitable, in particular, to describe the effect of cross-contamination. The effects of logistic slaughter (scheduled processing) as a risk mitigation strategy are predicted to be small. Some additional complications in applying microbiological data obtained in processing plants are discussed.

Notes: A linear population risk model used by the U.S. Food and Drug Administration (FDA) Center for Veterinary Medicine (CVM) estimates the risk of human cases of campylobacteriosis caused by fluoroquinolone-resistant Campylobacter. Among the cases of campylobacteriosis attributed to domestically produced chicken, the fluoroquinolone resistance is assumed to result from the use of fluoroquinolones in poultry in the United States. Properties of the linear population risk model are contrasted with those of a farm-to-fork model commonly used for microbial risk assessments. The utility of the linear population model for the purpose for which it was used by CVM is discussed.

Notes: A mathematical model is presented, which addresses individual hygiene practices during food preparation and consumption patterns in private homes. Further, the model links food preparers and consumers based on their relationship to household types. For different age and gender groups, the model estimates (i) the probability of ingesting a meal where precautions have not been taken to avoid the transfer of microorganisms from raw food to final meal (a risk meal), exemplified by the event that the cutting board was not washed during food preparation, and (ii) the probability of ingesting a risk meal in a private home, where chicken was the prepared food item (a chicken risk meal). Chicken was included in the model, as chickens are believed to be the major source of human exposure to the foodborne pathogen Campylobacter. Monte Carlo simulations showed that the probability of ingesting a risk meal was highest for young males (aged 18–29 years) and lowest for the elderly above 60 years of age. Children aged 0–4 years had a higher probability of ingesting a risk meal than children aged 5–17 years. This difference between age and gender groups was ascribed to the variations in the hygiene levels of food preparers. By including the probability of ingesting a chicken meal at home, simulations revealed that all age groups, except the group above 60 years of age, had approximately the same probability of ingesting a chicken risk meal, the probability of females being slightly higher than that of males. The simulated results show that the probability of ingesting a chicken risk meal at home does not only depend on the hygiene practices of the persons preparing the food, but also on the consumption patterns of consumers, and the relationship between people preparing and ingesting food. This finding supports the need of including information on consumer behavior and preparation hygiene in the consumer phase of exposure assessments.

Notes: We discuss the issue of using benchmark doses for quantifying (excess) risk associated with exposure to environmental hazards. The paradigm of low-dose risk estimation in dose-response modeling is used as the primary application scenario. Emphasis is placed on making simultaneous inferences on benchmark doses when data are in the form of proportions, although the concepts translate easily to other forms of outcome data.

Notes: This report describes the use of Bayesian networks (BNs) to model statistical loss distributions in financial operational risk scenarios. Its focus is on modeling “long” tail, or unexpected, loss events using mixtures of appropriate loss frequency and severity distributions where these mixtures are conditioned on causal variables that model the capability or effectiveness of the underlying controls process. The use of causal modeling is discussed from the perspective of exploiting local expertise about process reliability and formally connecting this knowledge to actual or hypothetical statistical phenomena resulting from the process. This brings the benefit of supplementing sparse data with expert judgment and transforming qualitative knowledge about the process into quantitative predictions. We conclude that BNs can help combine qualitative data from experts and quantitative data from historical loss databases in a principled way and as such they go some way in meeting the requirements of the draft Basel II Accord (Basel, 2004) for an advanced measurement approach (AMA).

Notes: The number of collisions and fatalities at rail-highway intersections in the United States has declined significantly over the past 30 years, despite considerable increases in the volume of rail and highway traffic. This article disaggregates the improvement into its constituent causes. Negative binomial regressions are conducted on a pooled data set for 49 states from 1975 to 2001. The analysis concludes that about two-fifths of the decrease is due to factors such as reduced drunk driving and improved emergency medical response that have improved safety on all parts of the highway network. The installation of gates and/or flashing lights accounts for about a fifth of the reduction. The development in the 1970s and early 1980s of the Operation Lifesaver public education campaign, and the installation of additional lights on locomotives in the mid 1990s, each led to about a seventh of the reduction. Finally, about a tenth is due to closure of crossings resulting from line abandonments or consolidation of little-used crossings.

Notes: The value of a statistical life (VSL) is a key input for estimating the benefits of policies that save lives. Several recent studies have obtained estimates of the VSL from contingent valuation surveys, i.e., by asking people to say how much they would pay to reduce their risk of dying. This article examines statistical factors that may influence the estimates of the VSL obtained from such surveys. We examine the importance of distributional assumptions, the choice of the welfare statistics of interest, the procedure for computing them, outliers, undesirable response effects, and internal validity of the willingness-to-pay (WTP) responses. We illustrate the importance of these factors using dichotomous-choice and open-ended WTP data from four recent contingent valuation surveys.

Notes: Safety systems are important components of high-consequence systems that are intended to prevent the unintended operation of the system and thus the potentially significant negative consequences that could result from such an operation. This presentation investigates and illustrates formal procedures for assessing the uncertainty in the probability that a safety system will fail to operate as intended in an accident environment. Probability theory and evidence theory are introduced as possible mathematical structures for the representation of the epistemic uncertainty associated with the performance of safety systems, and a representation of this type is illustrated with a hypothetical safety system involving one weak link and one strong link that is exposed to a high temperature fire environment. Topics considered include (1) the nature of diffuse uncertainty information involving a system and its environment, (2) the conversion of diffuse uncertainty information into the mathematical structures associated with probability theory and evidence theory, and (3) the propagation of these uncertainty structures through a model for a safety system to obtain representations in the context of probability theory and evidence theory of the uncertainty in the probability that the safety system will fail to operate as intended. The results suggest that evidence theory provides a potentially valuable representational tool for the display of the implications of significant epistemic uncertainty in inputs to complex analyses.

Notes: Recent studies demonstrating a concentration dependence of elimination of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) suggest that previous estimates of exposure for occupationally exposed cohorts may have underestimated actual exposure, resulting in a potential overestimate of the carcinogenic potency of TCDD in humans based on the mortality data for these cohorts. Using a database on U.S. chemical manufacturing workers potentially exposed to TCDD compiled by the National Institute for Occupational Safety and Health (NIOSH), we evaluated the impact of using a concentration- and age-dependent elimination model (CADM) (Aylward et al., 2005) on estimates of serum lipid area under the curve (AUC) for the NIOSH cohort. These data were used previously by Steenland et al. (2001) in combination with a first-order elimination model with an 8.7-year half-life to estimate cumulative serum lipid concentration (equivalent to AUC) for these workers for use in cancer dose-response assessment. Serum lipid TCDD measurements taken in 1988 for a subset of the cohort were combined with the NIOSH job exposure matrix and work histories to estimate dose rates per unit of exposure score. We evaluated the effect of choices in regression model (regression on untransformed vs. ln-transformed data and inclusion of a nonzero regression intercept) as well as the impact of choices of elimination models and parameters on estimated AUCs for the cohort. Central estimates for dose rate parameters derived from the serum-sampled subcohort were applied with the elimination models to time-specific exposure scores for the entire cohort to generate AUC estimates for all cohort members. Use of the CADM resulted in improved model fits to the serum sampling data compared to the first-order models. Dose rates varied by a factor of 50 among different combinations of elimination model, parameter sets, and regression models. Use of a CADM results in increases of up to five-fold in AUC estimates for the more highly exposed members of the cohort compared to estimates obtained using the first-order model with 8.7-year half-life. This degree of variation in the AUC estimates for this cohort would affect substantially the cancer potency estimates derived from the mortality data from this cohort. Such variability and uncertainty in the reconstructed serum lipid AUC estimates for this cohort, depending on elimination model, parameter set, and regression model, have not been described previously and are critical components in evaluating the dose-response data from the occupationally exposed populations.

Notes: Approaches to risk assessment have been shown to vary among regulatory agencies and across jurisdictional boundaries according to the different assumptions and justifications used. Approaches to screening-level risk assessment from six international agencies were applied to an urban case study focusing on benzo[a]pyrene (B[a]P) exposure and compared in order to provide insight into the differences between agency methods, assumptions, and justifications. Exposure estimates ranged four-fold, with most of the dose stemming from exposure to animal products (8–73%) and plant products (24–88%). Total cancer risk across agencies varied by two orders of magnitude, with exposure to air and plant and animal products contributing most to total cancer risk, while the air contribution showed the greatest variability (1–99%). Variability in cancer risk of 100-fold was attributed to choices of toxicological reference values (TRVs), either based on a combination of epidemiological and animal data, or on animal data. The contribution and importance of the urban exposure pathway for cancer risk varied according to the TRV and, ultimately, according to differences in risk assessment assumptions and guidance. While all agency risk assessment methods are predicated on science, the study results suggest that the largest impact on the differential assessment of risk by international agencies comes from policy and judgment, rather than science.

Notes: In this article, we describe a straightforward method for solving the probability of at least one malignant cell by time t, and the associated hazard function, in the general (i.e., nonhomogeneous) two-stage Moolgavkar-Venzon-Knudson (MVK) model of cancer. The method consists of solving four coupled ordinary differential equations derived from the Kolmogorov backward equations for this process. The relationship of this method to previously proposed solutions is discussed.

Notes: To estimate the critical dose of lead inducing anemia in humans, the effects of lead on hemoglobin (Hb) and hematocrit (Hct) levels and red blood cell (RBC) count were examined in 388 male lead-exposed workers with blood lead (BPb) levels of 0.05–5.5 (mean 1.3) μmol/L by using the benchmark dose (BMD) approach. The BPb level was significantly related to Hb (regression coefficient β=–0.276), RBC (β=–11.35), and Hct (β=–0.563) among the workers (p 〈 0.001) when controlling for age and working status. The average BPb levels were significantly higher in the workers with anemia (1.85 μmol/L), based on the WHO criteria, than in those without anemia (1.26 μmol/L). The benchmark dose levels of BPb (i.e., lower 95% confidence limits of BMD), calculated from the K-power model set at an abnormal probability of 5% in unexposed workers and an excess risk of 5% in exposed workers were estimated to be 0.94 μmol/L (19.5 μg/dl) for Hb, 0.94 μmol/L (19.4 μg/dl) for RBC, and 1.43 μmol/L (29.6 μg/dl) for Hct. These findings suggest that reduction in hematopoietic indicators may be initiated at BPbs below the level currently considered without effect.

Notes: The U.S. Department of Agriculture (USDA) tests a subset of cattle slaughtered in the United States for bovine spongiform encephalitis (BSE). Knowing the origin of cattle (U.S. vs. Canadian) at testing could enable new testing or surveillance policies based on the origin of cattle testing positive. For example, if a Canadian cow tests positive for BSE, while no U.S. origin cattle do, the United States could subject Canadian cattle to more stringent testing. This article illustrates the application of a value-of-information (VOI) framework to quantify and compare potential economic costs to the United States of implementing tracking cattle origins to the costs of not doing so. The potential economic value of information from a tracking program is estimated to exceed its costs by more than five-fold if such information can reduce future losses in export and domestic markets and reduce future testing costs required to reassure or win back customers. Sensitivity analyses indicate that this conclusion is somewhat robust to many technical, scientific, and market uncertainties, including the current prevalence of BSE in the United States and/or Canada and the likely reactions of consumers to possible future discoveries of BSE in the United States and/or Canada. Indeed, the potential value of tracking information is great enough to justify locating and tracking Canadian cattle already in the United States when this can be done for a reasonable cost. If aggressive tracking and testing can win back lost exports, then the VOI of a tracking program may increase to over half a billion dollars per year.

Notes: In the Mexico City metropolitan area, poor air quality is a public health concern. Diesel vehicles contribute significantly to the emissions that are most harmful to health. Harmful diesel emissions can be reduced by retrofitting vehicles with one of several technologies, including diesel particulate filters. We quantified the social costs and benefits, including health benefits, of retrofitting diesel vehicles in Mexico City with catalyzed diesel particulate filters, actively regenerating diesel particulate filters, or diesel oxidation catalysts, either immediately or in 2010, when capital costs are expected to be lower. Retrofit with either type of diesel particulate filter or an oxidation catalyst is expected to provide net benefits to society beginning immediately and in 2010. At current prices, retrofit with an oxidation catalyst provides greatest net benefits. However, as capital costs decrease, retrofit with diesel particulate filters is expected to provide greater net benefits. In both scenarios, retrofit of older, dirtier vehicles that circulate only within the city provides greatest benefits, and retrofit with oxidation catalysts provides greater health benefits per dollar spent than retrofit with particulate filters. Uncertainty about the magnitude of net benefits of a retrofit program is significant. Results are most sensitive to values used to calculate benefits, such as the concentration-response coefficient, intake fraction (a measure of exposure), and the monetary value of health benefits.

Notes: In their regulations, the U.S. Environmental Protection Agency and the U.S. Nuclear Regulatory Commission permit the omission of features, events, or processes with probabilities of 〈10−4 in 104 yr (e.g., a constant frequency of 〈10−8 per yr) in assessments of the performance of radioactive waste disposal systems. Igneous intrusion (or “volcanism”) of a geologic repository at Yucca Mountain for radioactive waste is one disruptive event that has a probability with a range of uncertainty that straddles this regulatory criterion and is considered directly in performance assessment calculations. A self-sustained nuclear chain reaction (or “criticality”) is another potentially disruptive event to consider, although it was never found to be important when evaluating the efficacy of radioactive waste disposal since the early 1970s. The thesis of this article is that the consideration of the joint event—volcanism and criticality—occurring in any 10,000-year period following closure can be eliminated from performance calculations at Yucca Mountain. The probability of the joint event must be less than the fairly well-accepted but low probability of volcanism. Furthermore, volcanism does not “remove” or “fail” existing hydrologic or geochemical constraints at Yucca Mountain that tend to prevent concentration of fissile material. Prior to general corrosion failure of waste packages, the mean release of fissile mass caused by a low-probability, igneous intrusive event is so small that the probability of a critical event is remote, even for highly enriched spent nuclear fuel owned by the U.S. Department of Energy. After widespread failure of packages occurs, the probability of the joint event is less than the probability of criticality because of the very small influence of volcanism on the mean fissile mass release. Hence, volcanism plays an insignificant role in inducing criticality over any 104-yr period. We also argue that the Oklo reactors serve as a natural analogue and provide a rough bound on probability of criticality given favorable hydrologic or geochemical conditions on the scale of the repository that is less than 0.10. Because the product of this bound with the probability of volcanism represents the probability of the joint event and the product is less than 10−4 in 104 yr, consideration of the joint event can be eliminated from performance calculations.

Notes: Decision analysis is recognized as the right way to make risk management decisions, using probabilistic techniques to assess the accident risk. It is also accepted that the decisions that individuals in the organization make affect the likelihood of an accident and thus managerial and organizational factors should be included in the risk modeling process. However, decision analytic techniques have not been used to understand the decisions that are made by these individuals. The initial domain for this research is marine transportation. We use the framework of value-focused thinking in order to understand safety decisions made within our research partner organization, a major domestic oil tanker operator. We describe the results of interviews held with managers and employees from this organization. Through these interviews, we sought to understand the values these experts apply in their roles within the organization and the objectives they seek to achieve to contribute to its overall safety performance. The end result is a framework that not only portrays the fundamental objectives of safe operations for various roles in the organization, but also interconnects these different decision contexts. We believe that this approach is fundamentally different from those used in previous work and that this is an interesting application of value-focused thinking.

Notes: Forward Links to Citing Articles 
 
 Retraction. In the framework of the APHEIS program (Air Pollution and Health: A European Information System), a health impact assessment of air pollution in 26 European cities was performed for particles of an aerodynamic diameter less than or equal to 10 μm (PM10). For short-term effects, it was based on overall estimates from the APHEA-2 project (Air Pollution and Health: A European Approach). These city-specific risk assessments require city-specific concentration-response functions, raising the question of which concentration-response is most appropriate. Estimates from city-specific models are more specific, but have greater uncertainty than those provided from multicity analyses. We compared several estimates derived from the city-specific analyses in cities that were part of the APHEA-2 project, as well as in a city that was not included in APHEA-2 but was part of the APHEIS project. These estimates were: the estimates from a local regression model, the adjusted estimates based on two significant effect modifiers identified through meta-regression models, and the city-specific empirical Bayes (shrunken) estimates and their underlying distribution. The shrunken and adjusted estimates were used to improve the estimation of city-specific concentration-response function. From these different estimates, attributable numbers of deaths per year were calculated. The advantages and limits of the different approaches are discussed through real data and in a simulation study.

Notes: The weather forecast says that there is a “30% chance of rain,” and we think we understand what it means. This quantitative statement is assumed to be unambiguous and to convey more information than does a qualitative statement like “It might rain tomorrow.” Because the forecast is expressed as a single-event probability, however, it does not specify the class of events it refers to. Therefore, even numerical probabilities can be interpreted by members of the public in multiple, mutually contradictory ways. To find out whether the same statement about rain probability evokes various interpretations, we randomly surveyed pedestrians in five metropolises located in countries that have had different degrees of exposure to probabilistic forecasts––Amsterdam, Athens, Berlin, Milan, and New York. They were asked what a “30% chance of rain tomorrow” means both in a multiple-choice and a free-response format. Only in New York did a majority of them supply the standard meteorological interpretation, namely, that when the weather conditions are like today, in 3 out of 10 cases there will be (at least a trace of) rain the next day. In each of the European cities, this alternative was judged as the least appropriate. The preferred interpretation in Europe was that it will rain tomorrow “30% of the time,” followed by “in 30% of the area.” To improve risk communication with the public, experts need to specify the reference class, that is, the class of events to which a single-event probability refers.

Notes: A method for estimating long-term exposures from short-term measurements is validated using data from a recent EPA study of exposure to fine particles. The method was developed a decade ago but long-term exposure data to validate it did not exist until recently. In this article, exposure data from repeated visits to 37 persons over 1 year (up to 28 measurements per person) are used to test the model. Both fine particle mass and elemental concentrations measured indoors, outdoors, and on the person are examined. To provide the most stringent test of the method, only two single-day distributions are randomly selected for each element to predict the long-term distributions. The precision of the method in estimating the long-term geometric mean and geometric standard deviation appears to be of the order of 10%, with no apparent bias. The precision in estimating the 99th percentile ranges from 19% to 48%, again without obvious bias. The precision can be improved by selecting a number of pairs of single-day distributions instead of just one pair. Occasionally, the method fails to provide an estimate for the long-term distribution. In that case, a repeat of the random selection procedure can provide an estimate. Although the method assumes a log-normal distribution, most of the distributions tested failed the chi-square test for log-normality. Therefore, the method appears suitable for application to distributions that depart from log-normality.

Notes: Combining DNA variation data and risk assessment procedures offers important diagnostic and monitoring tools for evaluating the relative success of exotic species invasions. Risk assessment may allow us to understand how the numbers of founding individuals, genetic variants, population sources, and introduction events affect successful establishment and spread. This is particularly important in habitats that are “hotbeds” for invasive species—such as the North American Great Lakes. This study compares genetic variability and its application to risk assessment within and among three Eurasian groups and five species that successfully invaded the Great Lakes during the mid 1980s through early 1990s; including zebra and quagga mussels, round and tubenose gobies, and the ruffe. DNA sequences are compared from exotic and native populations in order to evaluate the role of genetic diversity in invasions. Close relatives are also examined, since they often invade in concert and several are saline tolerant and are likely to spread to North American estuaries. Results show that very high genetic diversity characterizes the invasions of all five species, indicating that they were founded by very large numbers of propagules and underwent no founder effects. Genetic evidence points to multiple invasion sources for both dreissenid and goby species, which appears related to especially rapid spread and widespread colonization success in a variety of habitats. In contrast, results show that the ruffe population in the Great Lakes originated from a single founding population source from the Elbe River drainage. Both the Great Lakes and the Elbe River populations of ruffe have similar genetic diversity levels—showing no founder effect, as in the other invasive species. In conclusion, high genetic variability, large numbers of founders, and multiple founding sources likely significantly contribute to the risk of an exotic species introduction's success and persistence.

Notes: Scholars have begun to explore the role of modes of information processing and related audience characteristics in reactions to risky situations and risk information.(11, 12, 14, 17, 18, 20)“Information processing” concerns how people attend to and consider available information: systematic processors analyze messages and situations carefully, while heuristic processors skim and use cues (e.g., opinions of trusted reference groups) for quick judgments. This article uses scenarios about a semi-hypothetical industrial facility, in particular risk comparisons being considered by its manager for inclusion in a talk to the community, to explore the impact of information processing. Information insufficiency, self-assessed capacity to understand information, and information-seeking propensities are tested for potential effects on information processing about industrial risks by people living near industry. As well as testing established models, this article explores the additional explanatory value of involvement, relevance, and ability (Earle et al., 1990) and objective knowledge. Both existing model variables and new ones have significant effects on information seeking and information processing in this case, and partly confirm earlier results. Trumbo(17,18) found that heuristic processors saw lower risk and systematic processors higher risk from suspected cancer clusters. In this study, reporting knowledge about local industrial risks as insufficient for one's purposes and self-reported avoidance of such information both raised ratings of the facility's risk and lowered ratings of its acceptability. Neither type of information processing significantly affected risk or acceptability judgments, but both increased risk ratings and heuristic processing had more effect than systematic processing. Positive ratings of risk comparisons' clarity and meaningfulness decreased risk and increased acceptability ratings, dominated other information variables in predictive power, and exceeded risk, benefit, and trust in contribution to acceptability judgments. Despite differences across studies in designs and variables, and the embryonic development of appropriate (self-reported) measures for use in field surveys, these results confirm the potential value of further research in how information seeking and processing affect risk beliefs and reactions to risk communications.

Notes: 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.

Notes: The past 15 years have seen an increasing governmental and corporate concern for the environment worldwide. For governments, information about the environmental performance of the industrial sector is required to inform macro-level decisions about environmental targets such as those required to meet UN directives. However, in many African, Asian, and Latin American countries, researching and reporting company environmental performance is limited. This article serves as a contribution to filling the gap by presenting evidence of physical and chemical risk in Nigerian factories. One hundred and three factories with a total of 5,021 workers were studied. One hundred and twenty physical and chemical hazards were identified and the result shows a high number of workers exposed to such hazards. The study also reveals that workers' awareness level of chemical hazards was high. Yet the danger was perceived in behavioral terms, especially by manufacturing firms, which tend to see environmental investment in an increasingly global economy as detrimental to profitability.

Notes: We address the problem of distributing safety-enhancing devices across a region, where each identical device provides for only local protection of the population. The devices protect nonidentical sectors of the population. The sectors of population are exposed to nonidentical intensities of hazard. A method for the screening and prioritizing of needs for the protective devices is described. An approach of risk-benefit-cost analysis under uncertainty is recommended as follows. Measures of hazard intensity and population exposure are identified. Exogenous parameters that influence assessments of risks, benefits, and costs are identified. Uncertainties of the exogenous parameters are propagated by interval analysis. Several tiers of the plausibility of need for protection are identified. The tiers are useful in setting priorities for the distribution of the safety devices. The method is demonstrated in an engineering application to roadway lighting, but has implications for disaster preparedness, anti-terrorism, transportation safety, and other arenas of public safety.

Notes: Qualitative systems for rating animal antimicrobial risks using ordered categorical labels such as “high,”“medium,” and “low” can potentially simplify risk assessment input requirements used to inform risk management decisions. But do they improve decisions? This article compares the results of qualitative and quantitative risk assessment systems and establishes some theoretical limitations on the extent to which they are compatible. In general, qualitative risk rating systems satisfying conditions found in real-world rating systems and guidance documents and proposed as reasonable make two types of errors: (1) Reversed rankings, i.e., assigning higher qualitative risk ratings to situations that have lower quantitative risks; and (2) Uninformative ratings, e.g., frequently assigning the most severe qualitative risk label (such as “high”) to situations with arbitrarily small quantitative risks and assigning the same ratings to risks that differ by many orders of magnitude. Therefore, despite their appealing consensus-building properties, flexibility, and appearance of thoughtful process in input requirements, qualitative rating systems as currently proposed often do not provide sufficient information to discriminate accurately between quantitatively small and quantitatively large risks. The value of information (VOI) that they provide for improving risk management decisions can be zero if most risks are small but a few are large, since qualitative ratings may then be unable to confidently distinguish the large risks from the small. These limitations suggest that it is important to continue to develop and apply practical quantitative risk assessment methods, since qualitative ones are often unreliable.

Notes: Great Britain has been rabies-free since 1922, which is often considered to be in part due to the strict laws requiring that imported cats and dogs be vaccinated and quarantined for 6 months immediately on entry into the country. Except for two isolated incidents, this quarantine policy has contributed to ensuring that Great Britain has remained free of rabies. In 2000, amendments to the UK quarantine laws were made and the Pet Travel Scheme (PETS) was launched for companion animals traveling from European Union countries and rabies-free islands. Since its introduction, it has been proposed that other countries including North America should be included within the UK scheme. A quantitative risk assessment was developed to assist in the policy decision to amend the long-standing quarantine laws for dogs and cats from North America. It was determined that the risk of rabies entry is very low and is dependent on the level of compliance (i.e., legally conforming to all of the required regulations) with PETS and the number of pets imported. Assuming 100% compliance with PETS and the current level of importation of cats and dogs from North America, the annual probability of importing rabies is lower for animals traveling via PETS (7.22 × 10−6, 95th percentile) than quarantine (1.01 × 10−5, 95th percentile). These results, and other scientific evidence, directly informed the decision to expand the PETS scheme to North America as of December 2002.

Notes: Recent work in the assessment of risk in maritime transportation systems has used simulation-based probabilistic risk assessment techniques. In the Prince William Sound and Washington State Ferries risk assessments, the studies’ recommendations were backed up by estimates of their impact made using such techniques and all recommendations were implemented. However, the level of uncertainty about these estimates was not available, leaving the decisionmakers unsure whether the evidence was sufficient to assess specific risks and benefits. The first step toward assessing the impact of uncertainty in maritime risk assessments is to model the uncertainty in the simulation models used. In this article, a study of the impact of proposed ferry service expansions in San Francisco Bay is used as a case study to demonstrate the use of Bayesian simulation techniques to propagate uncertainty throughout the analysis. The conclusions drawn in the original study are shown, in this case, to be robust to the inherent uncertainties. The main intellectual merit of this work is the development of Bayesian simulation technique to model uncertainty in the assessment of maritime risk. However, Bayesian simulations have been implemented only as theoretical demonstrations. Their use in a large, complex system may be considered state of the art in the field of computational sciences.

Notes: Microbial food safety has been the focus of research across various disciplines within the risk analysis community. Natural scientists involved in food microbiology and related disciplines work on the identification of health hazards, and the detection of pathogenic microorganisms. To perform risk assessment, research activities are increasingly focused on the quantification of microbial contamination of food products at various stages in the food chain, and modeling the impact of this contamination on human health. Social scientists conduct research into how consumers perceive food risks, and how best to develop effective risk communication with consumers in order to improve public health through improved food handling practices. The two approaches converge at the end of the food chain, where the activities regarding food preparation and food consumption are considered. Both natural and social sciences may benefit from input and expertise from the perspective of the alternative discipline, although, to date, the integration of social and natural sciences has been somewhat limited. This article therefore explores the potential of a transdisciplinary approach to food risk analysis in terms of delivering additional improvements to public health. Developing knowledge arising from research in both the natural and social sciences, we present a novel framework involving the integration of the two approaches that might provide the most effective way to improve the consumer health associated with food-borne illness.

Notes: Soil contaminated with heavy metals is a salient example of environmental risk. Consumption of vegetables cultivated in contaminated soil or direct ingestion of soil by small children can damage health. In contrast to other kinds of pollution or risks such as air pollution or exposure to ozone, the individual risk concerning soil contamination is highly dependent on the way one is exposed to the local source of risk. Thus, we wanted to know if risk perception varies according to the level of exposure. A quasi-experimental, questionnaire-based study was conducted in a community in northwest Switzerland, where the soil is widely contaminated. The level of contamination varies with the distance from the source of the contamination, a metal processing plant. We investigated the perception of risk of heavy-metal-contaminated soil by inhabitants with high-exposure levels (N= 27) and those with low-exposure levels (N= 30). Both groups judged the risk for oneself similarly whereas the low-exposure group, when compared to the high-exposure group, judged perceived risk for other affected people living in their community to be higher. Besides this exposure effect, risk perception was mainly determined by emotional concerns. Participants with higher scores in self-estimated knowledge tended to provide low-risk judgments, were less interested in further information, showed low emotional concern, and thus displayed high risk acceptance. In contrast, actual knowledge showed no correlation with any of theses variables. Judgments on the need for decontamination are determined by risk perception, less application of dissonance-reducing heuristics and commitment to sustainability. The desire for additional information is not affected by missing knowledge but is affected by emotional concerns.

Notes: In managing major accident hazards in industry risk, both the United Kingdom and the Netherlands use quantitative risk analysis and quantified risk criteria. These should be understood in the historical, legal, and political context in which they were drawn up. Even when criteria are numerically the same they do not necessarily mean the same thing. The legal and historical context differs widely between the two countries. Nevertheless, it is surprising that the final results in terms of land use and levels of risk are very similar.

Notes: The increase in the thyroid cancer incidence in France observed over the last 20 years has raised public concern about its association with the 1986 nuclear power plant accident at Chernobyl. At the request of French authorities, a first study sought to quantify the possible risk of thyroid cancer associated with the Chernobyl fallout in France. This study suffered from two limitations. The first involved the lack of knowledge of spontaneous thyroid cancer incidence rates (in the absence of exposure), which was especially necessary to take their trends into account for projections over time; the second was the failure to consider the uncertainties. The aim of this article is to enhance the initial thyroid cancer risk assessment for the period 1991–2007 in the area of France most exposed to the fallout (i.e., eastern France) and thereby mitigate these limitations. We consider the changes over time in the incidence of spontaneous thyroid cancer and conduct both uncertainty and sensitivity analyses. The number of spontaneous thyroid cancers was estimated from French cancer registries on the basis of two scenarios: one with a constant incidence, the other using the trend observed. Thyroid doses were estimated from all available data about contamination in France from Chernobyl fallout. Results from a 1995 pooled analysis published by Ron et al. were used to determine the dose-response relation. Depending on the scenario, the number of spontaneous thyroid cancer cases ranges from 894 (90% CI: 869–920) to 1,716 (90% CI: 1,691–1,741). The number of excess thyroid cancer cases predicted ranges from 5 (90% UI: 1–15) to 63 (90% UI: 12–180). All of the assumptions underlying the thyroid cancer risk assessment are discussed.

Notes: Nonparametric methods can be used to analyze failure times and estimate probability distributions for failures of systems due to successful attacks on confidentiality, integrity, and availability in information security. However, such methods do not take full advantage of supplemental information regarding the configurations of systems in an information infrastructure that is usually also available. One approach, which does take advantage of such information, views the risks of systems failing from various causes as competing risks and determines the correlation coefficients of different treatments to system longevity. Since the times and causes of failure in such studies are usually uncorrelated, the hazards associated with each risk are proportional. By correlating system survival times to the use of specific design enhancements and security countermeasures, as well as to system exposure based on choice of operational functionality, guidance can be obtained for making investments in information security.

Notes: After two decades of massive investigation, federal approval of a nuclear waste site is drawing to a close. Large-scale research to assure that major hazards such as this one are socially acceptable is often highly inefficient, as here. A regulatory remedy would be to require not only that risk currently assessed be acceptable, but also that risk would remain acceptable given any new information. Research to test compliance with these rules has to be cost effective. Research activities should be managed according to an explicit discipline that can be imposed on powerful conflicting interests. They might be required to (1) set targets for the first- and second-order risk assessments, (2) allocate research resources to close any gap between current and target assessments cost effectively, and (3) reallocate resources, as evolving findings dictate. The interests of license applicant (Department of Energy, DOE) and society are distinguished: the former would want the application approved; the latter would want to reject an unacceptable facility.

Notes: Fine particle (PM2.5) emissions from traffic have been associated with premature mortality. The current work compares PM2.5-induced mortality in alternative public bus transportation strategies as being considered by the Helsinki Metropolitan Area Council, Finland. The current bus fleet and transportation volume is compared to four alternative hypothetical bus fleet strategies for the year 2020: (1) the current bus fleet for 2020 traffic volume, (2) modern diesel buses without particle traps, (3) diesel buses with particle traps, and (4) buses using natural gas engines. The average population PM2.5 exposure level attributable to the bus emissions was determined for the 1996–1997 situation using PM2.5 exposure measurements including elemental composition from the EXPOLIS-Helsinki study and similar element-based source apportionment of ambient PM2.5 concentrations observed in the ULTRA study. Average population exposure to particles originating from the bus traffic in the year 2020 is assumed to be proportional to the bus emissions in each strategy. Associated mortality was calculated using dose-response relationships from two large cohort studies on PM2.5 mortality from the United States. Estimated number of deaths per year (90% confidence intervals in parenthesis) associated with primary PM2.5 emissions from buses in Helsinki Metropolitan Area in 2020 were 18 (0–55), 9 (0–27), 4 (0–14), and 3 (0–8) for the strategies 1–4, respectively. The relative differences in the associated mortalities for the alternative strategies are substantial, but the number of deaths in the lowest alternative, the gas buses, is only marginally lower than what would be achieved by diesel engines equipped with particle trap technology. The dose-response relationship and the emission factors were identified as the main sources of uncertainty in the model.

Notes: A simple procedure is proposed in order to quantify the tradeoff between a loss suffered from an illness due to exposure to a microbial pathogen and a loss due to a toxic effect, perhaps a different illness, induced by a disinfectant employed to reduce the microbial exposure. Estimates of these two types of risk as a function of disinfectant dose and their associated relative losses provide information for the estimation of the optimum dose of disinfectant that minimizes the total expected loss. The estimates of the optimum dose and expected relative total loss were similar regardless of whether the beta-Poisson, log-logistic, or extreme value function was used to model the risk of illness due to exposure to a microbial pathogen. This is because the optimum dose of the disinfectant and resultant expected minimum loss depend upon the estimated slope (first derivative) of the models at low levels of risk, which appear to be similar for these three models at low levels of risk. Similarly, the choice among these three models does not appear critical for estimating the slope at low levels of risk for the toxic effect induced by the use of a disinfectant. For the proposed procedure to estimate the optimum disinfectant dose, it is not necessary to have absolute values for the losses due to microbial-induced or disinfectant-induced illness, but only relative losses are required. All aspects of the problem are amenable to sensitivity analyses. The issue of risk/benefit tradeoffs, more appropriately called risk/risk tradeoffs, does not appear to be an insurmountable problem.

Notes: A structured expert judgment study was organized to obtain input data for a microbial risk-assessment model describing the transmission of campylobacter during broiler-chicken processing in the Netherlands. More specially, the expert study was aimed at quantifying the uncertainty on input parameters of this model and focused on the contamination of broiler-chicken carcasses with campylobacter during processing. Following the protocol for structured expert judgment studies, expert assessments were elicited individually through subjective probability distribution functions. The classical model was used to aggregate the individual experts' distributions in order to obtain a single combined distribution per variable. Three different weighting schemes were applied, including equal weighting and performance-based weighting with and without optimalization of the combined distributions. The individual experts' weights were based on their performance on the seed variables. Results of the various weighting schemes are presented in terms of performance, robustness, and combined distributions of the seed variables and some of the query variables. All three weighting schemes had adequate performance, with the optimized combined distributions significantly outperforming both the equal weight and the nonoptimized combined distributions. Hence, this weighting scheme, having adequate robustness, was chosen for further processing of the results.

Notes: Typical exposures to lead often involve a mix of long-term exposures to relatively constant exposure levels (e.g., residential yard soil and indoor dust) and highly intermittent exposures at other locations (e.g., seasonal recreational visits to a park). These types of exposures can be expected to result in blood lead concentrations that vary on a temporal scale with the intermittent exposure pattern. Prediction of short-term (or seasonal) blood lead concentrations arising from highly variable intermittent exposures requires a model that can reliably simulate lead exposures and biokinetics on a temporal scale that matches that of the exposure events of interest. If exposure model averaging times (EMATs) of the model exceed the shortest exposure duration that characterizes the intermittent exposure, uncertainties will be introduced into risk estimates because the exposure concentration used as input to the model must be time averaged to account for the intermittent nature of the exposure. We have used simulation as a means of determining the potential magnitude of these uncertainties. Simulations using models having various EMATs have allowed exploration of the strengths and weaknesses of various approaches to time averaging of exposures and impact on risk estimates associated with intermittent exposures to lead in soil. The International Commission of Radiological Protection (ICRP) model of lead pharmacokinetics in humans simulates lead intakes that can vary in intensity over time spans as small as one day, allowing for the simulation of intermittent exposures to lead as a series of discrete daily exposure events. The ICRP model was used to compare the outcomes (blood lead concentration) of various time-averaging adjustments for approximating the time-averaged intake of lead associated with various intermittent exposure patterns. Results of these analyses suggest that standard approaches to time averaging (e.g., U.S. EPA)(1) that estimate the long-term daily exposure concentration can, in some cases, result in substantial underprediction of short-term variations in blood lead concentrations when used in models that operate with EMATs exceeding the shortest exposure duration that characterizes the intermittent exposure. Alternative time-averaging approaches recommended for use in lead risk assessment(2) more reliably predict short-term periodic (e.g., seasonal) elevations in blood lead concentration that might result from intermittent exposures. In general, risk estimates will be improved by simulation on shorter time scales that more closely approximate the actual temporal dynamics of the exposure.

Notes: Widespread concerns about whether electric and magnetic fields (EMF) could adversely affect human health have been raised in epidemiologic studies reported since the 1980s. Possible EMF health effects have been widely publicized in the popular press since that time. We consider here three possible mechanisms of action of EMF on childhood leukemia. We identify the first as “magnetic fields”: this hypothesis relates the average level of magnetic field to the incidence of childhood leukemia. We identify a second, recently proposed, mechanism as “contact current”: this hypothesis relates the low voltage and consequent current that occurs on the domestic water pipe, due to U.S. grounding practices, as a source for exposure of children. The third hypothesis is that the relationship observed is spurious. Using a modified example taken from the work of Von Winterfeldt and Keeney, we use Decision Analysis to estimate the value of information for distinguishing between the three hypotheses. We believe that this improves on the usual process for deciding on research budgets. Depending on which hypothesis we favor a priori, the value of being informed ranges from $101 to $233 per “problem household.” Since there could be as many as 2 million such households, the value of information for resolving this issue could approach half a billion dollars! We find that there is no value of information for finding the odds ratio given the contact current hypothesis. In writing this article, we have consciously kept the computations as simple as possible so as to engage the reader's attention and interest. In a penultimate section, we suggest numerous possible extensions for a group interested in discussing and deciding on the value of research on the relationship between magnetic fields and cancer.

Notes: Stochastic effects and data uncertainties are present in any engineering calculation. Their impact may be particularly important if they concern the design of process equipment. A calculation model for the dynamic behavior of a heat exchanger and procedures to deal with the related uncertainties are presented. Their propagation through the calculation by means of a Monte Carlo approach is shown. The temperature at the heat exchanger outlet and the step response of a sudden variation in the heat exchanger inlet temperature are simulated and evaluated by way of example. It is demonstrated that the inclusion of stochastic effects and uncertainties provides a more reliable basis for design decisions and hence reduces the probability of errors.

Notes: The psychometric paradigm has been the most influential model in the field of risk analysis. The “cognitive maps” of hazards produced by the paradigm seem to explain how laypeople perceive the various risks they face. Because most of the studies used aggregated data, analyzed using principal component analysis, it is not known whether the model neglects individual differences in risk perception. There has been much criticism on the fact that few studies have examined individual differences in the cognitive representation of hazards. In order to detect and describe the internal structure of the three-way data, we conducted a three-way component analysis (3MPCA). Data for the present analysis were derived from a mail survey conducted in Switzerland. Participants were asked to judge 9 attributes for 26 hazards. Individual differences in the cognitive representation of hazards were correlated with external variables (e.g., general trust). The results suggest that methods permitting individual differences should be used more frequently and that utilizing different methods could provide greater insight into the cognitive representation of risks.

Notes: 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.

Notes: “Weight of evidence” (WOE) is a common term in the published scientific and policy-making literature, most often seen in the context of risk assessment (RA). Its definition, however, is unclear. A systematic review of the scientific literature was undertaken to characterize the concept. For the years 1994 through 2004, PubMed was searched for publications in which “weight of evidence” appeared in the abstract and/or title. Of the 276 papers that met these criteria, 92 were selected for review: 71 papers published in 2003 and 2004 (WOE appeared in abstract/title) and 21 from 1994 through 2002 (WOE appeared in title). WOE has three characteristic uses in this literature: (1) metaphorical, where WOE refers to a collection of studies or to an unspecified methodological approach; (2) methodological, where WOE points to established interpretative methodologies (e.g., systematic narrative review, meta-analysis, causal criteria, and/or quality criteria for toxicological studies) or where WOE means that “all” rather than some subset of the evidence is examined, or rarely, where WOE points to methods using quantitative weights for evidence; and (3) theoretical, where WOE serves as a label for a conceptual framework. Several problems are identified: the frequent lack of definition of the term “weight of evidence,” multiple uses of the term and a lack of consensus about its meaning, and the many different kinds of weights, both qualitative and quantitative, which can be used in RA. A practical recommendation emerges: the WOE concept and its associated methods should be fully described when used. A research agenda should examine the advantages of quantitative versus qualitative weighting schemes, how best to improve existing methods, and how best to combine those methods (e.g., epidemiology's causal criteria with toxicology's quality criteria).

Notes: The FDA Center for Veterinary Medicine (CVM) (Bartholomew et al., 2005) recently proposed an approach to risk management based on the linear modeling framework: Risk = K × Exposure. They suggest that, once K has been estimated from historical data, it can be used to predict how limiting future exposure will reduce future risk. They illustrate the approach for fluoroquinolone-resistant campylobacter in chicken. However, despite its appealing simplicity, the proposed approach confuses a possibly meaningless descriptive statistical ratio with a valid predictive causal relation. In general, the historical ratio K = (Risk/Exposure) may not predict how changing future exposures will affect future risks, and hence it does not necessarily provide an appropriate guide to current risk management actions. We identify several limitations of the proposed framework, including omission of frequency and severity of human health harm in quantifying “Risk” and omission of microbial load from “Exposure.” Finally, we show that an extended linear modeling approach that considers impacts of changing animal antibiotic use on susceptible as well as on resistant bacteria is consistent with the conclusion that reducing “Exposure” can greatly increase “Risk.”

Notes: In early 1979 Robert B. Cumming recognized the growing need for risk researchers and practitioners to publish their work in a dedicated professional journal. This led to the formation of an organization to support such a journal, with the Certificate of Incorporation for the Society for Risk Analysis (SRA) made official on August 28, 1980. The first issue of Risk Analysis: An International Journal appeared in March 1981. This article reviews the history of the SRA's first 25 years. It reviews the SRA's formation and growth, provides analyses of its major products (the journal, newsletter, conferences, and meetings), and discusses its impact. This article relies on published literature and a history of the SRA's first 20 years written by two of the authors. This history covers the SRA's many successes, which demonstrate the strength and vitality of the organization and provide optimism for its future. These successes include this journal, which published over 2,150 papers between March 1981 and this December 2005 issue. The successes also include its stable membership of approximately 2,000 members from 43 countries, well-attended annual meetings, and increasing support for true international growth as demonstrated by international risk forums like the World Congress on Risk held in Brussels in June 2003. Similarly, the history also covers the SRA's challenges and difficulties, with the recognition that these provide both an important context about the organization and the opportunity to learn from past experiences. These include the challenges associated with spin-off organizations that decreased the SRA membership in some disciplinary areas, notably in engineering and exposure assessment. This history also includes quantitative analyses of the contents of the first 25 years of Risk Analysis: An International Journal. The results show significant growth in the number of articles published each year, starting with approximately 30 articles published in the first few years to over 120 articles per year now. They also show a relatively even distribution of articles in the life, physical, and social sciences, which demonstrate the sustained commitment of the SRA and the journal to support interdisciplinary risk-related research. The SRA organizational structure currently includes two sections (SRA-Europe and SRA-Japan), 22 Chapters, and nine Specialty Groups, and the structure remains somewhat in flux. We present this history in five sections that cover major themes: (1) SRA formation, (2) membership and organization, (3) publications, (4) meetings, and (5) thematic issues. Like any organization of its size, the SRA boasts a long and diverse history, and no article can possibly capture it all. We hope that in documenting the first 25 years, we strengthen the SRA by providing some perspective on its roots and a rigorous quantitative analysis of some of its products.

Notes: Public risk perceptions can fundamentally compel or constrain political, economic, and social action to address particular risks. Public support or opposition to climate policies (e.g., treaties, regulations, taxes, subsidies) will be greatly influenced by public perceptions of the risks and dangers posed by global climate change. This article describes results from a national study (2003) that examined the risk perceptions and connotative meanings of global warming in the American mind and found that Americans perceived climate change as a moderate risk that will predominantly impact geographically and temporally distant people and places. This research also identified several distinct interpretive communities, including naysayers and alarmists, with widely divergent perceptions of climate change risks. Thus, “dangerous” climate change is a concept contested not only among scientists and policymakers, but among the American public as well.

Notes: The Homeland Security Act mandates the development of a national, risk-based system to support planning for, response to, and recovery from emergency situations involving large-scale toxic exposures. To prepare for and manage consequences effectively, planners and responders need not only to identify zones of potentially elevated individual risk but also to predict expected casualties. Emergency response support systems now define “consequences” by mapping areas in which toxic chemical concentrations do or may exceed Acute Exposure Guideline Levels (AEGLs) or similar guidelines. However, because AEGLs do not estimate expected risks, current unqualified claims that such maps support consequence management are misleading. Intentionally protective, AEGLs incorporate various safety/uncertainty factors depending on the scope and quality of chemical-specific toxicity data. Some of these factors are irrelevant, and others need to be modified, whenever resource constraints or exposure-scenario complexities require responders to make critical trade-off (triage) decisions in order to minimize expected casualties. AEGL-exceedance zones cannot consistently be aggregated, compared, or used to calculate expected casualties and so may seriously misguide emergency response triage decisions. Methods and tools well established and readily available to support environmental health protection are not yet developed for chemically-related environmental health triage. Effective triage decisions involving chemical risks require a new assessment approach that focuses on best estimates of likely casualties, rather than on upper plausible bounds of individual risk. If risk-based consequence management is to become a reality, federal agencies tasked with supporting emergency response must actively coordinate to foster new methods that can support effective environmental health triage.

Notes: A Monte Carlo method is presented to study the effect of systematic and random errors on computer models mainly dealing with experimental data. It is a common assumption in this type of models (linear and nonlinear regression, and nonregression computer models) involving experimental measurements that the error sources are mainly random and independent with no constant background errors (systematic errors). However, from comparisons of different experimental data sources evidence is often found of significant bias or calibration errors. The uncertainty analysis approach presented in this work is based on the analysis of cumulative probability distributions for output variables of the models involved taking into account the effect of both types of errors. The probability distributions are obtained by performing Monte Carlo simulation coupled with appropriate definitions for the random and systematic errors. The main objectives are to detect the error source with stochastic dominance on the uncertainty propagation and the combined effect on output variables of the models. The results from the case studies analyzed show that the approach is able to distinguish which error type has a more significant effect on the performance of the model. Also, it was found that systematic or calibration errors, if present, cannot be neglected in uncertainty analysis of models dependent on experimental measurements such as chemical and physical properties. The approach can be used to facilitate decision making in fields related to safety factors selection, modeling, experimental data measurement, and experimental design.

Notes: The purpose of this article is to quantify the public health risk associated with inhalation of indoor airborne infection based on a probabilistic transmission dynamic modeling approach. We used the Wells-Riley mathematical model to estimate (1) the CO2 exposure concentrations in indoor environments where cases of inhalation airborne infection occurred based on reported epidemiological data and epidemic curves for influenza and severe acute respiratory syndrome (SARS), (2) the basic reproductive number, R0 (i.e., expected number of secondary cases on the introduction of a single infected individual in a completely susceptible population) and its variability in a shared indoor airspace, and (3) the risk for infection in various scenarios of exposure in a susceptible population for a range of R0. We also employ a standard susceptible-infectious-recovered (SIR) structure to relate Wells-Riley model derived R0 to a transmission parameter to implicate the relationships between indoor carbon dioxide concentration and contact rate. We estimate that a single case of SARS will infect 2.6 secondary cases on average in a population from nosocomial transmission, whereas less than 1 secondary infection was generated per case among school children. We also obtained an estimate of the basic reproductive number for influenza in a commercial airliner: the median value is 10.4. We suggest that improving the building air cleaning rate to lower the critical rebreathed fraction of indoor air can decrease transmission rate. Here, we show that virulence of the organism factors, infectious quantum generation rates (quanta/s by an infected person), and host factors determine the risk for inhalation of indoor airborne infection.

Notes: Trust is important for the perception of many types of risk, including those relating to genetically modified (GM) food. Who the public trusts in any given circumstance, however, is not well understood. In this study of public trust regarding GM food, an exploratory factor analysis with Promax rotation reveals public classification of three common institutional types—evaluators, watchdogs, and merchants. The structure of relationships among these stakeholders can act to enable or constrain public support for this new technology. Evaluators—scientists, universities, and medical professionals—are the most trusted. Watchdogs—consumer advocacy organizations, environmental organizations, and media sources—are moderately trusted. Merchants—grocers and grocery stores, industry, and farmers—are least trusted. While the federal government is seen as closest to being an evaluator, it is not highly correlated with any of the factors. The lack of trust in the organizations with the greatest resources and responsibilities for ensuring the safety of GM food should be seen as an important obstacle to the adoption of the technology.

Notes: Uncertainty of environmental concentrations is calculated with the regional multimedia exposure model of EUSES 1.0 by considering probability input distributions for aqueous solubility, vapor pressure, and octanol-water partition coefficient, Kow. Only reliable experimentally determined data are selected from available literature for eight reference chemicals representing a wide substance property spectrum. Monte Carlo simulations are performed with uniform, triangular, and log-normal input distributions to assess the influence of the choice of input distribution type on the predicted concentration distributions. The impact of input distribution shapes on output variance exceeds the effect on the output mean by one order of magnitude. Both are affected by influence and uncertainty (i.e., variance) of the input variable as well. Distributional shape has no influence when the sensitivity function of the respective parameter is perfectly linear. For nonlinear relationships, overlap of probability mass of input distribution with influential ranges of the parameter space is important. Differences in computed output distribution are greatest when input distributions differ in the most influential parameter range.

Notes: Risk analysis is a widely used tool to understand problems in food safety policy, but it is seldom applied to nutrition policy. We propose that risk analysis be applied more often to inform debates on nutrition policy, and we conduct a risk assessment of the relationship of regular carbonated soft drink (RCSD) consumption in schools and body mass index (BMI) as a case study. Data for RCSD consumption in schools were drawn from three data sets: the Continuing Survey of Food Intake by Individuals 1994–1996, 1998 (CSFII), the National Health and Nutrition Examination Survey 1999–2000 (NHANES), and the National Family Opinion (NFO) WorldGroup Share of Intake Panel (SIP) study. We used the largest relationship between RCSD and BMI that was published by prospective observational studies to characterize the maximum plausible relationship in our study. Consumption of RCSD in schools was low in all three data sets, ranging from 15 g/day in NFO-SIP to 60 g/day in NHANES. There was no relationship between RCSD consumption from all sources and BMI in either the CSFII or the NHANES data. The risk assessment showed no impact on BMI by removing RCSD consumption in school. These findings suggest that focusing adolescent overweight prevention programs on RCSD in schools will not have a significant impact on BMI.

Notes: The mechanical risk index (MRI) is a numerical measure that quantifies the complexity of drilling a well. The purpose of this article is to examine the role of the component factors of the MRI and its structural and parametric assumptions. A meta-modeling methodology is applied to derive functional expressions of the MRI, and it is shown that the MRI can be approximated in terms of a linear functional. The variation between the MRI measure and its functional specification is determined empirically, and for a reasonable design space, the functional specification is shown to a good approximating representation. A drilling risk index is introduced to quantify the uncertainty in the time and cost associated with drilling a well. A general methodology is outlined to create an optimal MRI specification.

Notes: Since 1996, when bovine spongiform encephalopathy (BSE) was assessed as a possible human transmissible disease, a variant of Creutzfeldt-Jakob disease (vCJD), French people have entered into a long period of fear and avoidance of beef and bovine byproducts, which produced an unprecedented collapse in the beef market. This article deals with the perceived risk of the “mad cow disease” (MCD) in the French general population. Two surveys were conducted on a representative sample of the adult population, the first one in 2000 during the peak of the crisis and the second one 13 months later in a quieter period. The main assumption we made was that changes in beef consumption are strongly related to the perceived risk of MCD, which we defined as people's cognitive and affective responses to hazard. Our objective was to identify the determinants and consequences of this perceived risk and to compare them in different sociopolitical contexts. The results issued from a bivariate and multivariate analysis show that: (i) the distribution of most of the variables significantly related to the perceived risk identified in the first survey had changed in the second survey, in relation with the reduction of worry and the resumption of national beef consumption; (ii) the propensity for self-protection through avoiding or ceasing beef eating was more related to feelings of worry than to subjective vCJD risk assessments; and (iii) the main determinant of less avoidance to beef products was the preference for beef, a feeling identified prior to emergence of the risk of MCD, remaining unchanged in various contexts.

Notes: This study examines the effects of conversation mode and split-attention communication training on driving performance. The study is based on an experiment where drivers with and without communication training (pilots vs. nonpilots) completed a simulated driving course while involved in one of three conversation modes: no conversation, conversation with passenger, or conversation on a hands-free cellular telephone. Results indicate that cellular telephone conversations consume more attention and interfere more with driving than passenger conversations. Cell phone conversations lack the nonverbal cues available during close-contact conversations and conversation participants expend significant cognitive resources to compensate for the lack of such cues. The results also demonstrate that communication training may reduce the hazardous effects of cell phone conversations on driving performance.

Notes: Probabilistic risk assessment (PRA) is a methodology to assess the probability of failure or success of a system's operation. PRA has been proved to be a systematic, logical, and comprehensive technique for risk assessment. Software plays an increasing role in modern safety critical systems. A significant number of failures can be attributed to software failures. Unfortunately, current probabilistic risk assessment concentrates on representing the behavior of hardware systems, humans, and their contributions (to a limited extent) to risk but neglects the contributions of software due to a lack of understanding of software failure phenomena. It is thus imperative to consider and model the impact of software to reflect the risk in current and future systems. The objective of our research is to develop a methodology to account for the impact of software on system failure that can be used in the classical PRA analysis process. A test-based approach for integrating software into PRA is discussed in this article. This approach includes identification of software functions to be modeled in the PRA, modeling of the software contributions in the ESD, and fault tree. The approach also introduces the concepts of input tree and output tree and proposes a quantification strategy that uses a software safety testing technique. The method is applied to an example system, PACS.

Notes: There has been considerable scientific effort to understand the potential link between exposures to power-frequency electric and magnetic fields (EMF) and the occurrence of cancer and other diseases. The combination of widespread exposures, established biological effects from acute, high-level exposures, and the possibility of leukemia in children from low-level, chronic exposures has made it both necessary and difficult to develop consistent public health policies. In this article we review the basis of both numeric standards and precautionary-based approaches. While we believe that policies regarding EMF should indeed be precautionary, this does not require or imply adoption of numeric exposure standards. We argue that cutpoints from epidemiologic studies, which are arbitrarily chosen, should not be used as the basis for making exposure limits due to a number of uncertainties. Establishment of arbitrary numeric exposure limits undermines the value of both the science-based numeric EMF exposure standards for acute exposures and precautionary approaches. The World Health Organization's draft Precautionary Framework provides guidance for establishing appropriate public health policies for power-frequency EMF.