Is Epidemiology the Key to Cumulative Risk Assessment?

Although cumulative risk assessment by definition evaluates the joint effects of chemical and nonchemical stressors, studies to date have not considered both dimensions, in part because toxicological studies cannot capture many stressors of interest. Epidemiology can potentially include all relevant stressors, but developing and extracting the necessary information is challenging given some of the inherent limitations of epidemiology. In this article, I propose a conceptual framework within which epidemiological studies could be evaluated for their inclusion into cumulative risk assessment, including a problem formulation/planning and scoping step that focuses on stressors meaningful for risk management decisions, extension of the chemical mixtures framework to include nonchemical stressors, and formal consideration of vulnerability characteristics of the population. In the long term, broadening the applicability and informativeness of cumulative risk assessment will require enhanced communication and collaboration between epidemiologists and risk assessors, in which the structure of social and environmental epidemiological analyses may be informed in part by the needs of cumulative risk assessment.     

Vehicle Emission Unit Risk Factors for Transportation Risk Assessments

When the transportation risk posed by shipments of hazardous chemical and radioactive materials is being assessed, it is necessary to evaluate the risks associated with both vehicle emissions and cargo-related risks. Diesel exhaust and fugitive dust emissions from vehicles transporting hazardous shipments lead to increased air pollution, which increases the risk of latent fatalities in the affected population along the transport route. The estimated risk from these vehicle-related sources can often be as large or larger than the estimated risk associated with the material being transported. In this paper, data from the U.S. Environmental Protection Agency's Motor Vehicle-Related Air Toxics Study are first used to develop latent cancer fatality estimates per kilometer of travel in rural and urban areas for all diesel truck classes. These unit risk factors are based on studies investigating the carcinogenic nature of diesel exhaust. With the same methodology, the current per-kilometer latent fatality risk factor used in transportation risk assessments for heavy diesel trucks in urban areas is revised and the analysis expanded to provide risk factors for rural areas and all diesel truck classes. These latter fatality estimates may include, but are not limited to, cancer fatalities and are based primarily on the most recent epidemiological data available on mortality rates associated with ambient air PM-10 concentrations.     

The Social Benefits of Expedited Risk Assessments

The present regulation of carcinogens is quite slow; hundreds of substances that have tested positive for carcinogenicity in animal bioassays have not been addressed by the U.S. regulatory system. This carries with it unappreciated social, economic, and public health costs. However, there are readily available expedited approximation procedures for assessing the potency of carcinogens whose use has substantial benefits that outweigh any costs from less science-intensive and less extensively documented assessments. These benefits can be seen by using a model to suggest the magnitude of social costs in regulating carcinogens by current conventional methods compared with expedited procedures for assessing the potency of known carcinogens. Two scenarios, one in accordance with current agency presumptions and one which assumes extreme unreliability in animal data and in the accuracy of potency assessments, compare conventional science-intensive and expedited procedures. On both, the total social costs of expedited procedures are lower than conventional procedures across a wide range of values assigned for individual mistakes of under regulation and over regulation. It appears better to evaluate a larger universe of known carcinogens somewhat less intensively for each substance than to evaluate a small proportion of that same universe very carefully and delay considering the rest.     

Assessments of Direct Human Exposure—The Approach of EU Risk Assessments Compared to Scenario-Based Risk Assessment

The awareness of potential risks emerging from the use of chemicals in all parts of daily life has increased the need for risk assessments that are able to cover a high number of exposure situations and thereby ensure the safety of workers and consumers. In the European Union (EU), the practice of risk assessments for chemicals is laid down in a Technical Guidance Document; it is designed to consider environmental and human occupational and residential exposure. Almost 70 EU risk assessment reports (RARs) have been finalized for high-production-volume chemicals during the last decade. In the present study, we analyze the assessment of occupational and consumer exposure to trichloroethylene and phthalates presented in six EU RARs. Exposure scenarios in these six RARs were compared to scenarios used in applications of the scenario-based risk assessment approach to the same set of chemicals. We find that scenarios used in the selected EU RARs to represent typical exposure situations in occupational or private use of chemicals and products do not necessarily represent worst-case conditions. This can be due to the use of outdated information on technical equipment and conditions in workplaces or omission of pathways that can cause consumer exposure. Considering the need for exposure and risk assessments under the new chemicals legislation of the EU, we suggest that a transparent process of collecting data on exposure situations and of generating representative exposure scenarios is implemented to improve the accuracy of risk assessments. Also, the data sets used to assess human exposure should be harmonized, summarized in a transparent fashion, and made accessible for all risk assessors and the public.     

Risk Characterization for Nanotechnology

Nanotechnology is a broad term that encompasses materials, structures, or processes that utilize engineered nanomaterials, which can be defined as materials intentionally designed to have one or more dimensions between 1 and 100 nm. Historically, risk characterization has been viewed as the final phase of a risk assessment process that integrates hazard identification, dose‐response assessment, and exposure assessment. The novelty and diversity of materials, structures, and tools that are covered by above‐defined “nanotechnology” raise substantial methodological issues and pose significant challenges for each of these phases of risk assessment. These issues and challenges culminate in the risk characterization phase of the risk assessment process, and this article discusses several of these key issues and approaches to developing risk characterization results and their implications for risk management decision making that are specific to nanotechnology.     

The Role of Quantitative Risk Assessments for Characterizing Risk and Uncertainty and Delineating Appropriate Risk Management Options, with Special Emphasis on Terrorism Risk

The purpose of this article is to discuss the role of quantitative risk assessments for characterizing risk and uncertainty and delineating appropriate risk management options. Our main concern is situations (risk problems) with large potential consequences, large uncertainties, and/or ambiguities (related to the relevance, meaning, and implications of the decision basis; or related to the values to be protected and the priorities to be made), in particular terrorism risk. We look into the scientific basis of the quantitative risk assessments and the boundaries of the assessments in such a context. Based on a risk perspective that defines risk as uncertainty about and severity of the consequences (or outcomes) of an activity with respect to something that humans value we advocate a broad risk assessment approach characterizing uncertainties beyond probabilities and expected values. Key features of this approach are qualitative uncertainty assessment and scenario building instruments.     

On the Need for Restricting the Probabilistic Analysis in Risk Assessments to Variability

It is common perspective in risk analysis that there are two kinds of uncertainties: i) variability as resulting from heterogeneity and stochasticity (aleatory uncertainty) and ii) partial ignorance or epistemic uncertainties resulting from systematic measurement error and lack of knowledge. Probability theory is recognized as the proper tool for treating the aleatory uncertainties, but there are different views on what is the best approach for describing partial ignorance and epistemic uncertainties. Subjective probabilities are often used for representing this type of ignorance and uncertainties, but several alternative approaches have been suggested, including interval analysis, probability bound analysis, and bounds based on evidence theory. It is argued that probability theory generates too precise results when the background knowledge of the probabilities is poor. In this article, we look more closely into this issue. We argue that this critique of probability theory is based on a conception of risk assessment being a tool to objectively report on the true risk and variabilities. If risk assessment is seen instead as a method for describing the analysts’ (and possibly other stakeholders’) uncertainties about unknown quantities, the alternative approaches (such as the interval analysis) often fail in providing the necessary decision support.     

Rapid Benefit-Risk Assessments: No Escape from Expert Judgments in Risk Management

The "human health impacts assessment" described by Cox and Popken (this issue) is intended to be a benefit-risk tool that avoids pitfalls of using expert judgments for policy analysis or during strict application of the precautionary principle in risk management. The proposed benefit-risk calculation uses numerous assumptions and suppositions to calculate a ratio of quality-adjusted life years (QALYs) lost for the number of human illness days prevented by the use of a food-animal antimicrobial drug, to the number of human illness days caused by the use of the antimicrobial drug. Assumptions about data--e.g., expert judgments on the representativeness of parameter estimates--are commonly used in risk assessment and risk management, including Cox and Popken's method. Cox and Popken apply the technique to specific examples of enrofloxacin and macrolides antimicrobial drugs, sometimes used in broiler chickens for human food. Although enthusiastically portrayed as a straightforward calculation of QALYs lost for two decision alternatives, Cox and Popken were silent on the pivotal expert judgment subsumed in their method: quality weights for illnesses caused by antimicrobial-resistant and antimicrobial-sensitive microbes are tacitly assumed to be equal. Yet, the costs in terms of prolonged illness, additional medications, repeat medical visits, and dread of more serious sequelae are expected to differ substantially for antimicrobial-resistant versus antimicrobial-sensitive illnesses. Despite their enthusiasm to the contrary, the "human health impacts assessment" by Cox and Popken is not immune from expert judgments in risk management.     

A Distributional Approach to Characterizing Low-Dose Cancer Risk

Since cancer risk at very low doses cannot be directly measured in humans or animals, mathematical extrapolation models and scientific judgment are required. This article demonstrates a probabilistic approach to carcinogen risk assessment that employs probability trees, subjective probabilities, and standard bootstrapping procedures. The probabilistic approach is applied to the carcinogenic risk of formaldehyde in environmental and occupational settings. Sensitivity analyses illustrate conditional estimates of risk for each path in the probability tree. Fundamental mechanistic uncertainties are characterized. A strength of the analysis is the explicit treatment of alternative beliefs about pharmacokinetics and pharmacodynamics. The resulting probability distributions on cancer risk are compared with the point estimates reported by federal agencies. Limitations of the approach are discussed as well as future research directions.     

Modeling Microbial Growth Within Food Safety Risk Assessments

Risk estimates for food-borne infection will usually depend heavily on numbers of microorganisms present on the food at the time of consumption. As these data are seldom available directly, attention has turned to predictive microbiology as a means of inferring exposure at consumption. Codex guidelines recommend that microbiological risk assessment should explicitly consider the dynamics of microbiological growth, survival, and death in foods. This article describes predictive models and resources for modeling microbial growth in foods, and their utility and limitations in food safety risk assessment. We also aim to identify tools, data, and knowledge sources, and to provide an understanding of the microbial ecology of foods so that users can recognize model limits, avoid modeling unrealistic scenarios, and thus be able to appreciate the levels of confidence they can have in the outputs of predictive microbiology models. The microbial ecology of foods is complex. Developing reliable risk assessments involving microbial growth in foods will require the skills of both microbial ecologists and mathematical modelers. Simplifying assumptions will need to be made, but because of the potential for apparently small errors in growth rate to translate into very large errors in the estimate of risk, the validity of those assumptions should be carefully assessed. Quantitative estimates of absolute microbial risk within narrow confidence intervals do not yet appear to be possible. Nevertheless, the expression of microbial ecology knowledge in "predictive microbiology" models does allow decision support using the tools of risk assessment.     

Characterizing the Risk of Infection from Mycobacterium tuberculosis in Commercial Passenger Aircraft Using Quantitative Microbial Risk Assessment

Quantitative microbial risk assessment was used to predict the likelihood and spatial organization of Mycobacterium tuberculosis ( Mtb ) transmission in a commercial aircraft. Passenger exposure was predicted via a multizone Markov model in four scenarios: seated or moving infectious passengers and with or without filtration of recirculated cabin air. The traditional exponential ( k  = 1) and a new exponential ( k  = 0.0218) dose-response function were used to compute infection risk. Emission variability was included by Monte Carlo simulation. Infection risks were higher nearer and aft of the source; steady state airborne concentration levels were not attained. Expected incidence was low to moderate, with the central 95% ranging from 10⁻⁶ to 10⁻¹ per 169 passengers in the four scenarios. Emission rates used were low compared to measurements from active TB patients in wards, thus a "superspreader" emitting 44 quanta/h could produce 6.2 cases or more under these scenarios. Use of respiratory protection by the infectious source and/or susceptible passengers reduced infection incidence up to one order of magnitude.     

Principles of Good Practice for the Use of Monte Carlo Techniques in Human Health and Ecological Risk Assessments

We propose 14 principles of good practice to assist people in performing and reviewing probabilistic or Monte Carlo risk assessments, especially in the context of the federal and state statutes concerning chemicals in the environment. Monte Carlo risk assessments for hazardous waste sites that follow these principles will be easier to understand, will explicitly distinguish assumptions from data, and will consider and quantify effects that could otherwise lead to misinterpretation of the results. The proposed principles are neither mutually exclusive nor collectively exhaustive. We think and hope that these principles will evolve as new ideas arise and come into practice.     

Quantitative Risk Assessments as Evidence in Civil Litigation

Those who prepare quantitative risk assessments do not always appreciate that those assessments might be used as evidence in civil litigation. This paper suggests that litigation attorneys, judges, and juries be regarded as audiences to whom the information in the risk assessment must be communicated. The way that a risk assessment is prepared can affect significantly whether litigation is brought at all, the resolution of evidentiary motions involving the risk assessment, as well as the ultimate outcome of the litigation. This paper discusses certain procedural and evidentiary aspects of the civil litigation process in the hope that a better understanding of that process might lead to the preparation of risk assessments that are more adequately understood by juries, judges, and litigants.     

Engaging Expert Peers in the Development of Risk Assessments

The participation of external technical experts in the development of risk assessment documents and methodologies has expanded and evolved in recent years. Many government agencies and authoritative organizations have experts peer review important works to evaluate the scientific and technical defensibility and judge the strength of the assumptions and conclusions (OMB, 2004; IPCS, 2005; IARC, 2006; Health Canada, 2007; U.S. EPA, 2006). Expert advice has been solicited in other forms of peer involvement, including peer consultation in, for example, the U.S. EPA's Voluntary Children's Chemical Evaluation Program (VCCEP). This article discusses how the principles and practices of peer review can be extended to other types of peer involvement activities (i.e., peer input and peer consultation) to develop high-quality risk assessment work products. A comprehensive process for incorporating peer input, peer consultation, and peer review into risk assessment science is outlined. Four key principles for peer involvement-independence, inclusion of appropriate experts, transparency, and a robust scientific process-are discussed. Recent examples of peer involvement in the development of Health Canada's Priority Substances and Domestic Substance List (DSL) programs under the Canadian Environmental Protection Act (CEPA) serve to highlight the concepts.     

Characterizing Environmental Harm: Developments in an Approach to Strategic Risk Assessment and Risk Management

Environmental policymakers and regulators are often in the position of having to prioritize their actions across a diverse range of environmental pressures to secure environmental protection and improvements. Information on environmental issues to inform this type of strategic analysis can be disparate; it may be too voluminous or even absent. Data on a range of issues are rarely presented in a common format that allows easy analysis and comparison. Nevertheless, judgments are required on the significance of various environmental pressures and on the inherent uncertainties to inform strategic assessments such as “state of the environment” reports. How can decisionmakers go about this type of strategic and comparative risk analysis? In an attempt to provide practical tools for the analysis of environmental risks at a strategic level, the Environment Agency of England and Wales has conducted a program of developmental research on strategic risk assessment since 1996. The tools developed under this program use the concept of “environmental harm” as a common metric, viewed from technical, social, and economic perspectives, to analyze impacts from a range of environmental pressures. Critical to an informed debate on the relative importance of these perspectives is an understanding and analysis of the various characteristics of harm (spatial and temporal extent, reversibility, latency, etc.) and of the social response to actual or potential environmental harm from a range of hazards. Recent developments in our approach, described herein, allow a presentation of the analysis in a structured fashion so as to better inform risk‐management decisions.     

Integrated Analysis: Combining Risk and Economic Assessments While Preserving the Separation of Powers

This article presents a process for an integrated policy analysis that combines risk assessment and benefit-cost analysis. This concept, which explicitly combines the two types of related analyses, seems to contradict the long-accepted risk analysis paradigm of separating risk assessment and risk management since benefit-cost analysis is generally considered to be a part of risk management. Yet that separation has become a problem because benefit-cost analysis uses risk assessment results as a starting point and considerable debate over the last several years focused on the incompatibility of the use of upper bounds or "safe" point estimates in many risk assessments with benefit-cost analysis. The problem with these risk assessments is that they ignore probabilistic information. As advanced probabilistic techniques for risk assessment emerge and economic analysts receive distributions of risks instead of point estimates, the artificial separation between risk analysts and the economic/decision analysts complicates the overall analysis. In addition, recent developments in countervailing risk theory suggest that combining the risk and benefit-cost analyses is required to fully understand the complexity of choices and tradeoffs faced by the decisionmaker. This article also argues that the separation of analysis and management is important, but that benefit-cost analysis has been wrongly classified into the risk management category and that the analytical effort associated with understanding the economic impacts of risk reduction actions need to be part of a broader risk assessment process.     

A Methodology for Grouping Source Terms for Consequence Calculations in Probabilistic Risk Assessments

The individual plant analyses in the U.S. Nuclear Regulatory Commission's reassessment of the risk from commercial nuclear power plants (NUREG-1150) consist of four parts: systems analysis, accident-progression analysis, source-term analysis, and consequence analysis. Careful definition of the interfaces between these parts is necessary for both information flow and computational efficiency. This paper describes the procedure used to define the interface between the source-term analysis and the consequence analysis. This interface is accomplished by forming groups of source terms with similar properties and then performing one set of MACCS calculations for each group.     

Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments

Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations.     

Characterizing Perception of Ecological Risk

Relatively little attention has been paid to the role of human perception and judgment in ecological risk management. This paper attempts to characterize perceived ecological risk, using the psychometric paradigm developed in the domain of human health risk perception. The research began by eliciting a set of scale characteristics and risk items (e.g., technologies, actions, events, beliefs) from focus group participants. Participants in the main study were 68 university students who completed a survey instrument that elicited ratings for each of 65 items on 30 characteristic scales and one scale regarding general risk to natural environments. The results are presented in terms of mean responses over individuals for each scale and item combination. Factor analyses show that five factors characterize the judgment data. These have been termed: impact on species, human benefits, impact on humans, avoidability, and knowledge of impacts. The factor results correspond with initial expectations and provide a plausible characterization of judgments regarding ecological risk. Some comparisons of mean responses for selected individual items are also presented.