Characterizing Risk for Cumulative Risk Assessments

In assessing environmental health risks, the risk characterization step synthesizes information gathered in evaluating exposures to stressors together with dose–response relationships, characteristics of the exposed population, and external environmental conditions. This article summarizes key steps of a cumulative risk assessment (CRA) followed by a discussion of considerations for characterizing cumulative risks. Cumulative risk characterizations differ considerably from single chemical‐ or single source‐based risk characterization. CRAs typically focus on a specific population instead of a pollutant or pollutant source and should include an evaluation of all relevant sources contributing to the exposures in the population and other factors that influence dose–response relationships. Second, CRAs may include influential environmental and population‐specific conditions, involving multiple chemical and nonchemical stressors. Third, a CRA could examine multiple health effects, reflecting joint toxicity and the potential for toxicological interactions. Fourth, the complexities often necessitate simplifying methods, including judgment‐based and semi‐quantitative indices that collapse disparate data into numerical scores. Fifth, because of the higher dimensionality and potentially large number of interactions, information needed to quantify risk is typically incomplete, necessitating an uncertainty analysis. Three approaches that could be used for characterizing risks in a CRA are presented: the multiroute hazard index, stressor grouping by exposure and toxicity, and indices for screening multiple factors and conditions. Other key roles of the risk characterization in CRAs are also described, mainly the translational aspect of including a characterization summary for lay readers (in addition to the technical analysis), and placing the results in the context of the likely risk‐based decisions.     

The Development of Risk Analysis: A Personal Perspective

This article reflects on my experiences observing and participating in the development of risk analysis for environmental and health hazards since the 1970s with emphasis on its critical role in informing decisions with potentially high consequences, even for very low probability events once ignored or simply viewed as “acts of God.” I discuss how modern society wants to protect itself from hazards with limited or no immediate historical precedent such that prediction and protective actions must depend on models that offer varying degrees of reliability. I believe that we must invest in understanding risks and risk models to ensure health in the future and protect ourselves from large challenges, including climate change, whether anthropogenic or otherwise, terrorism, and perhaps even cosmic change.     

Life cycle impact assessment: a challenge for risk analysts.

Modern technology, together with an advanced economy, can provide a good or service in myriad ways, giving us choices on what to produce and how to produce it. To make those choices more intelligently, society needs to know not only the market price of each alternative, but the associated health and environmental consequences. A fair comparison requires evaluating the consequences across the whole "life cycle"--from the extraction of raw materials and processing to manufacture/construction, use, and end-of-life--of each alternative. Focusing on only one stage (e.g., manufacture) of the life cycle is often misleading. Unfortunately, analysts and researchers still have only rudimentary tools to quantify the materials and energy inputs and the resulting damage to health and the environment. Life cycle assessment (LCA) provides an overall framework for identifying and evaluating these implications. Since the 1960s, considerable progress has been made in developing methods for LCA, especially in characterizing, qualitatively and quantitatively, environmental discharges. However, few of these analyses have attempted to assess the quantitative impact on the environment and health of material inputs and environmental discharges Risk analysis and LCA are connected closely. While risk analysis has characterized and quantified the health risks of exposure to a toxicant, the policy implications have not been clear. Inferring that an occupational or public health exposure carries a nontrivial risk is only the first step in formulating a policy response. A broader framework, including LCA, is needed to see which response is likely to lower the risk without creating high risks elsewhere. Even more important, LCA has floundered at the stage of translating an inventory of environmental discharges into estimates of impact on health and the environment. Without the impact analysis, policymakers must revert to some simple rule, such as that all discharges, regardless of which chemical, which medium, and where they are discharged, are equally toxic. Thus, risk analysts should seek LCA guidance in translating a risk analysis into policy conclusions or even advice to those at risk. LCA needs the help of RA to go beyond simplistic assumptions about the implications of a discharge inventory. We demonstrate the need and rationale for LCA, present a brief history of LCA, present examples of the application of this tool, note the limitations of LCA models, and present several methods for incorporating risk assessment into LCA. However, we warn the reader not to expect too much. A comprehensive comparison of the health and environmental implications of alternatives is beyond the state of the art. LCA is currently not able to provide risk analysts with detailed information on the chemical form and location of the environmental discharges that would allow detailed estimation of the risks to individuals due to toxicants. For example, a challenge for risk analysts is to estimate health and other risks where the location and chemical speciation are not characterized precisely. Providing valuable information to decisionmakers requires advances in both LCA and risk analysis. These two disciplines should be closely linked, since each has much to contribute to the other.     

Experimental Evidence Against the Paradigm of Mortality Risk Aversion

This article deals with the question of how societal impacts of fatal accidents can be integrated into the management of natural or man‐made hazards. Today, many governmental agencies give additional weight to the number of potential fatalities in their risk assessments to reflect society's aversion to large accidents. Although mortality risk aversion has been proposed in numerous risk management guidelines, there has been no evidence that lay people want public decisionmakers to overweight infrequent accidents of large societal consequences against more frequent ones of smaller societal consequences. Furthermore, it is not known whether public decisionmakers actually do such overweighting when they decide upon the mitigation of natural or technical hazards. In this article, we report on two experimental tasks that required participants to evaluate negative prospects involving 1–100 potential fatalities. Our results show that neither lay people nor hazard experts exhibit risk‐averse behavior in decisions on mortality risks.     

Ecological Risk Ranking: Development and Evaluation of a Method for Improving Public Participation in Environmental Decision Making

This article reports an extension of the Carnegie Mellon risk-ranking method to incorporate ecological risks and their attributes. On the basis of earlier risk-perception studies, we identified a set of 20 relevant attributes for describing health, safety, and environmental hazards in standardized risk summary sheets. In a series of three ranking sessions, 23 laypeople ranked 10 such hazards in a fictional Midwestern U.S. county using both holistic and multiattribute ranking procedures. Results were consistent with those from previous studies involving only health and safety hazards, providing additional evidence for the validity of the method and the replicability of the resulting rankings. Holistic and multiattribute risk rankings were reasonably consistent both for individuals and for groups. Participants reported that they were satisfied with the procedures and results, and indicated their support for using the method to advise real-world risk-management decisions. Agreement among participants increased over the course of the exercise, perhaps because the materials and deliberations helped participants to correct their misconceptions and clarify their values. Overall, health and safety attributes were judged more important than environmental attributes. However, the overlap between the importance rankings of these two sets of attributes suggests that some information about environmental impacts is important to participants' judgments in comparative risk-assessment tasks.     

Consideration of Background Exposures in the Management of Hazardous Waste Sites: A New Approach to Risk Assessment

The current approach to health risk assessment of toxic waste sites in the U.S. may lead to considerable expenditure of resources without any meaningful reduction in population exposure. Risk assessment methods used generally ignore background exposures and consider only incremental risk estimates for maximally exposed individuals. Such risk estimates do not address true public health risks to which background exposures also contribute. The purpose of this paper is to recommend a new approach to risk assessment and risk management concerning toxic waste sites. Under this new approach, which we have called public health risk assessment, chemical substances would be classified into a level of concern based on the potential health risks associated with typical national and regional background exposures. Site assessment would then be based on the level of concern for the particular pollutants involved and the potential contribution of site contaminants to typical background human exposures. While various problems can be foreseen with this approach, the key advantage is that resources would be allocated to reduce the most important sources of human exposure, and site remediation decisions could be simplified by focussing on exposure assessment rather than questionable risk extrapolations.     

Game Theory and Risk Analysis

Risk analysts often analyze adversarial risks from terrorists or other intelligent attackers without mentioning game theory. Why? One reason is that many adversarial situations—those that can be represented as attacker‐defender games, in which the defender first chooses an allocation of defensive resources to protect potential targets, and the attacker, knowing what the defender has done, then decides which targets to attack—can be modeled and analyzed successfully without using most of the concepts and terminology of game theory. However, risk analysis and game theory are also deeply complementary. Game‐theoretic analyses of conflicts require modeling the probable consequences of each choice of strategies by the players and assessing the expected utilities of these probable consequences. Decision and risk analysis methods are well suited to accomplish these tasks. Conversely, game‐theoretic formulations of attack‐defense conflicts (and other adversarial risks) can greatly improve upon some current risk analyses that attempt to model attacker decisions as random variables or uncertain attributes of targets (“threats”) and that seek to elicit their values from the defender's own experts. Game theory models that clarify the nature of the interacting decisions made by attackers and defenders and that distinguish clearly between strategic choices (decision nodes in a game tree) and random variables (chance nodes, not controlled by either attacker or defender) can produce more sensible and effective risk management recommendations for allocating defensive resources than current risk scoring models. Thus, risk analysis and game theory are (or should be) mutually reinforcing.     

Variability of Site-Reactor Risk

Reactor accident consequence models have been developed (for example, the CRAC model of the Reactor Safety Study (RSS), WASH-1400) to predict the offsite health and economic consequences of severe accidents at a reactor site with generic demographic and meteorological characteristics. Application of a revised RSS accident consequence model, CRAC2, to 91 existing sites results in a band of risk curves around the earlier WASH-1400 average reactor/site predictions. This paper examines these calculations and important model assumptions such as population distribution, emergency response, and meteorological data with respect to their effects on site risk extremes—that is, the combination of high consequence/low probability events.     

震后多目标动态应急医疗设施选址-伤员转运问题研究

地震等灾害的突发性和破坏性常导致部分伤员无法得到快速有效的救治,同时会给伤员造成一定的负面心理,影响救援效率。本文综合考虑救护车辆(救护车、直升机)和医疗设施容量的动态变化、各类伤员生存概率随时间动态变化以及伤员心理状况变化,构建了最大化伤员生存数量和最小化心理成本的震后伤员二级后送模式的医疗设施选址-伤员转运双目标动态规划模型。运用epsilon约束法有效处理双目标模型,以玉树地震后伤员后送问题为例,采用CPLEX对模型进行求解,通过分析医疗资源数量对伤员转运数量的影响,表明在伤员后送过程中,增加临时医院数量或容量与救护车数量比增加后方医院数量或容量与直升机数量更有效;考虑伤员的心理成本,为了提高伤员存活率,在灾害前期,可以通过增加救护车数量,转运更多的重伤员,而中后期提高临时医院容量,尽量优先转运轻伤员。     

The problem of the last bed: Contextualization and a new simulation framework for analyzing physician decisions

Faced with a full Intensive Care Unit (ICU), physicians need to decide between turning away a new patient in need of critical care and creating a vacancy by prematurely discharging a current occupant. This dilemma is widely discussed in the medical literature, where the influencing factors are identified, the patient discharge process described and the patient health consequences analyzed. Nevertheless, the existing mathematical models of ICU management practices overlook many of the factors considered by physicians in real-world triage decisions.This paper offers a review of the medical and mathematical literature on patient discharge decisions, and a proposal for a new simulation framework to enable more realistic mathematical modeling of the real-world patient discharge process. Our model includes a) the times at which discharge decisions are made and setup times for patient transfer from the ICU to a general ward and preparation of an ICU bed for an incoming patient, in order to capture the impossibility of an immediate switch of patients; b) advance notice of the number of patients due to arrive from elective surgery requiring intensive postoperative care and potentially triggering the need for early discharges to avoid surgery cancelations; and c) patient health status (to reflect the dependency of physicians’ discharge decisions on health indicators) by modeling length of stay with a phase-type distribution in which a medical meaning is assigned to each state.A simulation-based optimization method is also proposed as a means to obtain optimal discharge decisions as a function of the health status of current patients, the bed occupancy level and the number of planned arrivals from elective surgery over the following days. Optimal decisions should strike a balance between patient rejection and LoS reduction.This new simulation framework generates an optimal discharge policy, which closely resembles real decision-making under a cautious discharge policy, where the frequency of early discharge increases with the ICU occupancy level. This is a contrast with previous simulation models, which consider only the triage of the last bed, disregarding the pressures on physicians faced with high bed occupancy levels.     

My Loss Is Your Loss … Sometimes: Loss Aversion and the Effect of Motivational Biases

Findings from previous studies of individual decision-making behavior predict that losses will loom larger than gains. It is less clear, however, if this loss aversion applies to the way in which individuals attribute value to the gains and losses of others, or if it is robust across a broad spectrum of policy and management decision contexts. Consistent with previous work, the results from a series of experiments reported here revealed that subjects exhibited loss aversion when evaluating their own financial gains and losses. The presence of loss aversion was also confirmed for the way in which individuals attribute value to the financial gains and losses of others. However, similar evaluations within social and environmental contexts did not exhibit loss aversion. In addition, research subjects expected that individuals who were unknown to them would significantly undervalue the subjects' own losses across all contexts. The implications of these findings for risk-based policy and management are many. Specifically, they warrant caution when relying upon loss aversion to explain or predict the reaction of affected individuals to risk-based decisions that involve moral or protected values. The findings also suggest that motivational biases may lead decisionmakers to assume that their attitudes and beliefs are common among those affected by a decision, while those affected may expect unfamiliar others to be unable to identify and act in accordance with shared values.     

Knowledge-Based Representations of Risk Beliefs

Beliefs about risks associated with two risk agents, AIDS and toxic waste, are modeled using knowledge-based methods and elicited from subjects via interactive computer technology. A concept net is developed to organize subject responses concerning the consequences of the risk agents. It is found that death and adverse personal emotional and sociological consequences are most associated with AIDS. Toxic waste is most associated with environmental problems. These consequence profiles are quite dissimilar, although past work in risk perception would have judged the risk agents as being quite similar. Subjects frequently used causal semantics to represent their beliefs and "% of time" instead of "probability" to represent likelihoods. The news media is the most prevalent source of risk information although experiences of acquaintances appear more credible. The results suggest that "broadly based risk" communication may be ineffective because people differ in their conceptual representation of risk beliefs. In general, the knowledge-based approach to risk perception representation has great potential to increase our understanding of important risk topics.     

Advances on a Decision Analytic Approach to Exposure-Based Chemical Prioritization

The volume and variety of manufactured chemicals is increasing, although little is known about the risks associated with the frequency and extent of human exposure to most chemicals. The EPA and the recent signing of the Lautenberg Act have both signaled the need for high-throughput methods to characterize and screen chemicals based on exposure potential, such that more comprehensive toxicity research can be informed. Prior work of Mitchell et al. using multicriteria decision analysis tools to prioritize chemicals for further research is enhanced here, resulting in a high-level chemical prioritization tool for risk-based screening. Reliable exposure information is a key gap in currently available engineering analytics to support predictive environmental and health risk assessments. An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors, and the values for each chemical associated with each metric were approximated with data from EPA's CP_CAT database. Three different versions of the model were evaluated using distinct weight profiles, resulting in three different ranked chemical prioritizations with only a small degree of variation across weight profiles. Future work will aim to include greater input from human factors experts and better define qualitative metrics.     

Asteroid Risk Assessment: A Probabilistic Approach

Following the 2013 Chelyabinsk event, the risks posed by asteroids attracted renewed interest, from both the scientific and policy‐making communities. It reminded the world that impacts from near‐Earth objects (NEOs), while rare, have the potential to cause great damage to cities and populations. Point estimates of the risk (such as mean numbers of casualties) have been proposed, but because of the low‐probability, high‐consequence nature of asteroid impacts, these averages provide limited actionable information. While more work is needed to further refine its input distributions (e.g., NEO diameters), the probabilistic model presented in this article allows a more complete evaluation of the risk of NEO impacts because the results are distributions that cover the range of potential casualties. This model is based on a modularized simulation that uses probabilistic inputs to estimate probabilistic risk metrics, including those of rare asteroid impacts. Illustrative results of this analysis are presented for a period of 100 years. As part of this demonstration, we assess the effectiveness of civil defense measures in mitigating the risk of human casualties. We find that they are likely to be beneficial but not a panacea. We also compute the probability—but not the consequences—of an impact with global effects (“cataclysm”). We conclude that there is a continued need for NEO observation, and for analyses of the feasibility and risk‐reduction effectiveness of space missions designed to deflect or destroy asteroids that threaten the Earth.     

Individual differences in numerical representations of risk in health decision making: A fuzzy-trace theory approach

Fuzzy-trace theory predicts that decisionmakers process numerical information about risk at multiple levels in parallel: the simplest level, nominal (categorical some-none) gist, and at more fine-grained levels, involving relative comparison (ordinal less-more gist) and exact quantities (verbatim representations). However, little is known about how individual differences in these numerical representations relate to judgments and decisions, especially involving health tradeoffs and relative risks. To investigate these differences, we administered measures of categorical and ordinal gist representations of number, objective numeracy, and intelligence in two studies (Ns = 978 and 956). In both studies, categorical and ordinal gist representations of number predicted risk judgments and decisions beyond objective numeracy and intelligence. Participants with higher scores in categorical gist were more likely to choose options to avoid cancer recurrence risks; those who were higher in ordinal gist of numbers were more likely to discriminate relative risk of skin cancer; and those with higher scores in objective numeracy were more likely to choose options that were numerically superior overall in terms of relative risk of skin cancer and of genetic risks of breast cancer (e.g., lower numerical probability of cancer). Results support parallel-processing models that assume multiple representations of numerical information about risk, which vary in precision, and illustrate how individual differences in numerical representations are relevant to tradeoffs and risk comparisons in health decisions. These representations cannot be reduced to one another and explain psychological variations in risk processing that go beyond low versus high levels of objective numeracy.     

An agent-based model for evaluating reforms of the National Flood Insurance Program: A benchmarked model applied to Jamaica Bay,NYC

Coastal flood risk is expected to increase as a result of climate change effects, such as sea level rise, and socioeconomic growth. To support policymakers in making adaptation decisions, accurate flood risk assessments that account for the influence of complex adaptation processes on the developments of risks are essential. In this study, we integrate the dynamic adaptive behavior of homeowners within a flood risk modeling framework. Focusing on building-level adaptation and flood insurance, the agent-based model (DYNAMO) is benchmarked with empirical data for New York City, USA. The model simulates the National Flood Insurance Program (NFIP) and frequently proposed reforms to evaluate their effectiveness. The model is applied to a case study of Jamaica Bay, NY. Our results indicate that risk-based premiums can improve insurance penetration rates and the affordability of insurance compared to the baseline NFIP market structure. While a premium discount for disaster risk reduction incentivizes more homeowners to invest in dry-floodproofing measures, it does not significantly improve affordability. A low interest rate loan for financing risk-mitigation investments improves the uptake and affordability of dry-floodproofing measures. The benchmark and sensitivity analyses demonstrate how the behavioral component of our model matches empirical data and provides insights into the underlying theories and choices that autonomous agents make.     

Uncertain Risk Assessment and Management: Case Studies of the Application of the Precautionary Principle in Portugal

This study intends to clarify how the precautionary principle (PP) has been interpreted and applied by the courts in Portugal in the analysis of conflicts associated with uncertain and serious potential risks to human health and the environment. It also aims to contribute to the debate of when and how to apply precautionary measures. To this end, recent court cases in the areas of waste incineration, high-voltage power lines, as well as dam and wind farm construction were considered. The degree of consistency in the courts’ decisions and their reasons in the different judicial bodies was analyzed with the support of a theoretical framework based on three attributes: the level of seriousness of potential hazards, level of evidence required, and the severity of precautionary actions taken. Different positions among courts were observed, with contradictory arguments in the same case or in similar cases. A greater propensity for favorable decisions in the acceptance of restraining orders was verified in the courts of lower instances, where human health could be threatened. However, the decisions of the Supreme Administrative Court, which were always unfavorable to the restraining orders, seem to reflect the priority given to national economic and political interests over local or regional environmental interests. They may also reflect the Supreme Court's reluctancy to apply the PP in the absence of a firm legally binding PP in national legislation. To address this situation, more explicit legal requirements and criteria for the analysis of uncertain risks and the weighting of interests by area of activity are needed.     

PER Pressure: New Jersey''s "Population Emissions Ratio" Environmental Equity Screening Model

In recent years, much attention has focused on how to incorporate environmental equity considerations into government permitting programs for environmentally regulated facilities. On February 4, 2002, the New Jersey Department of Environmental Protection (NJDEP) became the first state environmental agency to propose a broad legally binding rule intended to guard against environmental inequity in its permitting decisions. The proposed rule centered on an innovative computerized environmental equity (EE) screening model that used "Population Emissions Ratios" to identify small geographic areas in which environmental equity concerns might exist and to simulate the effect on statewide environmental equity of increasing environmental risks in small geographic areas. The NJDEP's model examined an extensive array of ethnic groups, included a variety of environmental risks, evaluated most of those risks in terms of human health, and used an innovative simulation process designed to identify permitting decisions that would worsen statewide environmental inequity. The results of the NJDEP's efforts, however, pose substantial concerns. For example, some key provisions of the NJDEP's model were inadequately explained and some were illogical and would bias its results. The model might be susceptible to generating implausible results due to small, meaningless, and/or essentially random fluctuations in its data inputs. The model used relatively large geographic areas as the units of analysis and interpolated results between them, rather than using smaller geographic areas and avoiding interpolation errors. Finally, the environmental risks evaluated by the model were both arguably over- and underinclusive. Thus, the NJDEP's efforts, although noteworthy, raised more issues than they settled.     

Surgical scheduling under uncertainty by approximate dynamic programming

Surgical scheduling consists of selecting surgeries to be performed within a day, while jointly assigning operating rooms, starting times and the required resources. Patients can be elective or emergency/urgent. The scheduling of surgeries in an operating theatre with common resources to emergency or urgent and elective cases is highly subject to uncertainties not only on the duration of an intervention but mainly on the arrival of emergency or urgent cases. At the beginning of the day we are given a candidate set of elective surgeries with and an expected duration and a time window the surgery must start, but the expected duration and the time window of an emergency or urgent case become known when the surgery arrives. The day is divided into decision stages. Due to the dynamic nature of the problem, at the beginning of each stage the planner can make decisions taking into account the new information available. Decisions can be to schedule arriving surgeries, and to reschedule or cancel surgeries not started yet. The objective is to minimize the total expected cost composed of terms related to refusing arriving surgeries, to canceling scheduled surgeries, and to starting surgeries out of their time window. We address the problem with an approximate dynamic programming approach embedding an integer programming formulation to support decision making. We propose a dynamic model and an approximate policy iteration algorithm making use of basis functions to capture the impact of decisions to the future stages. Computational experiments have shown with statistical significance that the proposed algorithm outperforms a lookahead reoptimization approach.     

Prioritizing Environmental Health Risks in the UAE

This article presents the results of a comparative environmental risk‐ranking exercise that was conducted in the United Arab Emirates (UAE) to inform a strategic planning process led by the Environment Agency‐Abu Dhabi (EAD). It represents the first national‐level application of a deliberative method for comparative risk ranking first published in this journal. The deliberative method involves a five‐stage process that includes quantitative risk assessment by experts and deliberations by groups of stakeholders. The project reported in this article considered 14 categories of environmental risks to health identified through discussions with EAD staff: ambient and indoor air pollution; drinking water contamination; coastal water pollution; soil and groundwater contamination; contamination of fruits, vegetables, and seafood; ambient noise; stratospheric ozone depletion; electromagnetic fields from power lines; health impacts from climate change; and exposure to hazardous substances in industrial, construction, and agricultural work environments. Results from workshops involving 73 stakeholders who met in five separate groups to rank these risks individually and collaboratively indicated strong consensus that outdoor and indoor air pollution are the highest priorities in the UAE. Each of the five groups rated these as being among the highest risks. All groups rated soil and groundwater contamination as being among the lowest risks. In surveys administered after the ranking exercises, participants indicated that the results of the process represented their concerns and approved of using the ranking results to inform policy decisions. The results ultimately shaped a strategic plan that is now being implemented.