Valuation of childhood risk reduction: the importance of age, risk preferences, and perspective.

This article explores two problems analysts face in determining how to estimate values for children's health and safety risk reductions. The first addresses the question: Do willingness‐to‐pay estimates for health risk changes differ across children and adults and, if so, how? To answer this question, the article first examines the potential effects of age and risk preferences on willingness to pay. A summary of the literature reporting empirical evidence of differences between willingness to pay for adult health and safety risk reductions and willingness to pay for health and safety risk reductions in children is also provided. The second dimension of the problem is a more fundamental issue: Whose perspective is relevant when valuing children's health effects—society's, children's, adults‐as‐children, or parents'? Each perspective is considered, followed ultimately by the conclusion that adopting a parental perspective through an intrahousehold allocation model seems closest to meeting the needs of the estimation problem at hand. A policy example in which the choice of perspective affects the outcome of a regulatory benefit‐cost analysis rounds out the article and emphasizes the importance of perspective.     

Benchmark Analysis: Shopping with Proper Confidence

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.     

The Role of Cancer Risk in the Regulation of Industrial Pollution

The extent of carcinogen regulation under existing U.S. environmental statutes is assessed by developing measures of the scope and stringency of regulation. While concern about cancer risk has played an important political role in obtaining support for pollution control programs, it has not provided the predominant rationale for most regulatory actions taken to date. Less than 20% of all standards established to limit concentrations of chemicals in various media address carcinogens. Restrictions on chemical use are more frequently based on concerns about noncancer human health or ecological effects. Of the chemicals in commercial use which have been identified as potential human carcinogens on the basis of rodent bioassays, only a small proportion are regulated. There is an inverse relationship between the scope of regulatory coverage and the stringency of regulatory requirements: the largest percentages of identified carcinogens are affected by the least stringent requirements, such as information disclosure. Standards based on de minimis cancer risk levels have been established for only 10% of identified carcinogens and are restricted to one medium: water. Complete bans on use have affected very few chemicals. The general role that carcinogenicity now plays in the regulatory process is not dramatically different from that of other adverse human health effects: if a substance is identified as a hazard, it may eventually be subject to economically achievable and technically feasible restrictions.     

Physiologically Based Liver Modeling and Risk Assessment

Because of the inherent complexity of biological systems, there is often a choice between a number of apparently equally applicable physiologically based models to describe uptake and metabolism processes in toxicology or risk assessment. These models may fit the particular data sets of interest equally well, but may give quite different parameter estimates or predictions under different (extrapolated) conditions. Such competing models can be discriminated by a number of methods, including potential refutation by means of strategic experiments, and their ability to suitably incorporate all relevant physiological processes. For illustration, three currently used models for steady-state hepatic elimination--the venous equilibration model, the parallel tube model, and the distributed sinusoidal perfusion model--are reviewed and compared with particular reference to their application in the area of risk assessment. The ability of each of the models to describe and incorporate such physiological processes as protein binding, precursor-metabolite relations and hepatic zones of elimination, capillary recruitment, capillary heterogeneity, and intrahepatic shunting is discussed. Differences between the models in hepatic parameter estimation, extrapolation to different conditions, and interspecies scaling are discussed, and criteria for choosing one model over the others are presented. In this case, the distributed model provides the most general framework for describing physiological processes taking place in the liver, and has so far not been experimentally refuted, as have the other two models. These simpler models may, however, provide useful bounds on parameter estimates and on extrapolations and risk assessments.     

Government Risk Management Priorities: A Comparison of the Preferences of Asian Indian Americans and Other Americans

A survey was conducted of approximately 200 Asian Indian Americans and 200 other residents of New Jersey in order to understand the risk management priorities that they want government to have. We found that Asian Indian Americans, especially younger women, focused on personal/family risks, such as alcohol and drug abuse, sexual abuse, and domestic violence. The New Jersey comparison group, in contrast, placed war/terrorism and loss of health care services and insurance at the top of their priorities for government. These results suggest stressful acculturation-related issues within the Asian Indian community. Both populations want more risk management from government than they believe government is currently providing. Respondents who wanted more from government tended to dread the risk, be fearful of the consequences, trust government, and have a feeling of personal efficacy. Within the Asian Indian American sample, wide variations were observed by language spoken at home and religious affiliation. Notably, Muslims and Hindi language speakers tended not to trust government and hence wanted less government involvement. This study supports our call for studies of recent migrant populations and Johnson's for testing ethnic identity and acculturation as factors in risk judgments.     

Ranking Nuclear and Radiological Terrorism Scenarios: The Italian Case

A quantitative criterion for ranking the different scenarios of nuclear and radiological terrorism has been developed. The aim of the model is not to predict terroristic events but only to indicate which scenario has the higher utility from the point of view of a terroristic organization in terms of balance between factors favoring and discouraging the attack, respectively. All these factors were quantified according to a scoring system that takes into account the logarithmic relationship between perceptions and stimuli. The criterion was applied to several scenarios, each of which was modeled in a simple but not trivial way in order to estimate the expected damage in terms of probable life losses from both radiative and nonradiative effects. The outcome from the ranking method indicates that the attractive scenario appears to be the detonation of a low yield improvised nuclear device in the metropolitan area of a major city.     

Loss functions for estimation of extrema with an application to disease mapping

It is often of interest to find the maximum or near maxima among a set of vector‐valued parameters in a statistical model; in the case of disease mapping, for example, these correspond to relative‐risk “hotspots” where public‐health intervention may be needed. The general problem is one of estimating nonlinear functions of the ensemble of relative risks, but biased estimates result if posterior means are simply substituted into these nonlinear functions. The authors obtain better estimates of extrema from a new, weighted ranks squared error loss function. The derivation of these Bayes estimators assumes a hidden‐Markov random‐field model for relative risks, and their behaviour is illustrated with real and simulated data.     

Modeling External Risks in Project Management

To ascertain the viability of a project, undertake resource allocation, take part in bidding processes, and other related decisions, modern project management requires forecasting techniques for cost, duration, and performance of a project, not only under normal circumstances, but also under external events that might abruptly change the status quo. We provide a Bayesian framework that provides a global forecast of a project's performance. We aim at predicting the probabilities and impacts of a set of potential scenarios caused by combinations of disruptive events, and using this information to deal with project management issues. To introduce the methodology, we focus on a project's cost, but the ideas equally apply to project duration or performance forecasting. We illustrate our approach with an example based on a real case study involving estimation of the uncertainty in project cost while bidding for a contract.     

Applicability of a Neuroprobabilistic Integral Risk Index for the Environmental Management of Polluted Areas: A Case Study

Recently, we developed a GIS-Integrated Integral Risk Index (IRI) to assess human health risks in areas with presence of environmental pollutants. Contaminants were previously ranked by applying a self-organizing map (SOM) to their characteristics of persistence, bioaccumulation, and toxicity in order to obtain the Hazard Index (HI). In the present study, the original IRI was substantially improved by allowing the entrance of probabilistic data. A neuroprobabilistic HI was developed by combining SOM and Monte Carlo analysis. In general terms, the deterministic and probabilistic HIs followed a similar pattern: polychlorinated biphenyls (PCBs) and light polycyclic aromatic hydrocarbons (PAHs) were the pollutants showing the highest and lowest values of HI, respectively. However, the bioaccumulation value of heavy metals notably increased after considering a probability density function to explain the bioaccumulation factor. To check its applicability, a case study was investigated. The probabilistic integral risk was calculated in the chemical/petrochemical industrial area of Tarragona (Catalonia, Spain), where an environmental program has been carried out since 2002. The risk change between 2002 and 2005 was evaluated on the basis of probabilistic data of the levels of various pollutants in soils. The results indicated that the risk of the chemicals under study did not follow a homogeneous tendency. However, the current levels of pollution do not mean a relevant source of health risks for the local population. Moreover, the neuroprobabilistic HI seems to be an adequate tool to be taken into account in risk assessment processes.     

Using Probabilistic Terrorism Risk Modeling for Regulatory Benefit-Cost Analysis: Application to the Western Hemisphere Travel Initiative in the Land Environment

This article presents a framework for using probabilistic terrorism risk modeling in regulatory analysis. We demonstrate the framework with an example application involving a regulation under consideration, the Western Hemisphere Travel Initiative for the Land Environment, (WHTI‐L). First, we estimate annualized loss from terrorist attacks with the Risk Management Solutions (RMS) Probabilistic Terrorism Model. We then estimate the critical risk reduction, which is the risk‐reducing effectiveness of WHTI‐L needed for its benefit, in terms of reduced terrorism loss in the United States, to exceed its cost. Our analysis indicates that the critical risk reduction depends strongly not only on uncertainties in the terrorism risk level, but also on uncertainty in the cost of regulation and how casualties are monetized. For a terrorism risk level based on the RMS standard risk estimate, the baseline regulatory cost estimate for WHTI‐L, and a range of casualty cost estimates based on the willingness‐to‐pay approach, our estimate for the expected annualized loss from terrorism ranges from $2.7 billion to $5.2 billion. For this range in annualized loss, the critical risk reduction for WHTI‐L ranges from 7% to 13%. Basing results on a lower risk level that results in halving the annualized terrorism loss would double the critical risk reduction (14–26%), and basing the results on a higher risk level that results in a doubling of the annualized terrorism loss would cut the critical risk reduction in half (3.5–6.6%). Ideally, decisions about terrorism security regulations and policies would be informed by true benefit‐cost analyses in which the estimated benefits are compared to costs. Such analyses for terrorism security efforts face substantial impediments stemming from the great uncertainty in the terrorist threat and the very low recurrence interval for large attacks. Several approaches can be used to estimate how a terrorism security program or regulation reduces the distribution of risks it is intended to manage. But, continued research to develop additional tools and data is necessary to support application of these approaches. These include refinement of models and simulations, engagement of subject matter experts, implementation of program evaluation, and estimating the costs of casualties from terrorism events.