Data-Derived Uncertainty Factors: Boric Acid (BA) as a Case Study

There is growing support for the use of data-derived uncertainty factors. In recent years, risk assessments of boric acid have been performed by several well-respected organizations, including IEHR, ECETOC, IPCS, and WHO. For each, the pivotal study was a developmental toxicity study in rats with a no-observed-adverse-effect level (NOAEL) of 55 mg BA/kg/day. These risk assessments employed reduced uncertainty factors in the range of 25 to 60 for boric acid, because available pharmacokinetic data for boric acid reduced uncertainty in evaluating the overall data base with this compound. However, a limitation of previous risk assessments was the absence of specific data on the renal clearance of boric acid in pregnant rats and pregnant women. New data has demonstrated that when renal clearance was normalized to body weight (ml/min/kg), pregnant rats cleared boric acid at a rate roughly three times greater than pregnant women. In addition, the boric acid specific allometric relationship was determined from the log-log plot of clearance vs. body weight. Based on the new renal clearance data, it was estimated that pregnant women and rats would have the same AUC when pregnant women are given 30% of the boric acid dose given to pregnant rats. In addition, the renal clearance of boric acid among pregnant women varied by a factor of about 2. Therefore, boric acid-specific data on renal clearance in pregnant women and rats supports reduced interspecies and intraspecies pharmacokinetic uncertainty factors of approximately 3 and 2, respectively. Further, growing evidence of the essentiality in animals, combined with consistency of effects among species in toxicity studies, suggests a reduced pharmacodynamic uncertainty factor is appropriate for boric acid. Total uncertainty factors in the range of 22 to 44 are scientifically justified for this compound. An acceptable daily intake of 1.25 to 2.5 mg BA/kg/day is estimated by applying an uncertainty factor of 22 to 44 to the NOAEL of 55 mg BA/kg/day. Data-derived uncertainty factors should be used whenever possible, and they should be determined and applied in a consistent manner. Ultimately, estimates based on target tissue dose-adjusted relationships should offer a better approach to risk assessment.     

Ten Years of the Relative Risk Model and Regional Scale Ecological Risk Assessment

It has been 10 years since the publication of the relative risk model (RRM) for regional scale ecological risk assessment. The approach has since been used successfully for a variety of freshwater, marine, and terrestrial environments in North America, South America, and Australia. During this period the types of stressors have been expanded to include more than contaminants. Invasive species, habitat loss, stream alteration and blockage, temperature, change in land use, and climate have been incorporated into the assessments. Major developments in the RRM have included the extensive use of geographical information systems, uncertainty analysis using Monte Carlo techniques, and its application to retrospective assessments to determine causation. The future uses of the RRM include assessments for forestry and conservation management, an increasing use in invasive species evaluation, and in sustainability. Developments in risk communication, the use of Bayesian approaches, and in uncertainty analyses are on the horizon.     

Treatments of Uncertainty and Variability in Ecological Risk Assessment of Single-Species Populations

The selection of the most appropriate model for an ecological risk assessment depends on the application, the data and resources available, the knowledge base of the assessor, the relevant endpoints, and the extent to which the model deals with uncertainty. Since ecological systems are highly variable and our knowledge of model input parameters is uncertain, it is important that models include treatments of uncertainty and variability, and that results are reported in this light. In this paper we discuss treatments of variation and uncertainty in a variety of population models. In ecological risk assessments, the risk relates to the probability of an adverse event in the context of environmental variation. Uncertainty relates to ignorance about parameter values, e.g., measurement error and systematic error. An assessment of the full distribution of risks, under variability and parameter uncertainty, will give the most comprehensive and flexible endpoint. In this paper we present the rationale behind probabilistic risk assessment, identify the sources of uncertainty relevant for risk assessment and provide an overview of a range of population models. While all of the models reviewed have some utility in ecology, some have more comprehensive treatments of uncertainty than others. We identify the models that allow probabilistic assessments and sensitivity analyses, and we offer recommendations for further developments that aim towards more comprehensive and reliable ecological risk assessments for populations.     

Comparison of Risk Assessments of Boron: Alternate Approaches to Chemical-Specific Adjustment Factors

Health risk assessments of boron (B) have been performed in recent years by seven well-respected regulatory and scientific organizations, including the Institute for Evaluating Health Risks, European Centre for Ecotoxicology and Toxicology of Chemicals, International Programme on Chemical Safety, World Health Organization, National Academy of Sciences Food and Nutrition Board, U.K. Expert Group of Vitamins and Minerals (draft) and U.S. Environmental Protection Agency (draft). Of interest, all of these risk assessments employed chemical-specific adjustment factors, resulting in total uncertainty factors in the range of 25–62 and estimates of tolerable intake levels ranging from 10 to 24 mg B/day. These risk assessments are particularly instructive because they all used the same critical developmental toxicity study and the same NOAEL (10 mg B/kg/day). Therefore, the primary differences among these risk assessments reside in the choice of chemical-specific adjustment factors. It is generally agreed that renal clearance is the primary determinant of B pharmacokinetic variability, both within and among species. However, the methods used to select chemical-specific adjustment factors for pharmacokinetics vary among B risk assessments. Several have estimated intraspecies pharmacokinetic variability based on glomerular filtration rates (GFR) in pregnant women. Based on the results of renal clearance studies of B, the use of GFR is scientifically appropriate to estimate the intraspecies pharmacokinetic variability in B renal clearance. B homeostasis in humans appears to be primarily regulated by the kidney, and at typical low doses in humans, there is evidence of tubular reabsorption of B in the kidneys. Human studies indicate that urinary B excretion is a sensitive indicator of recent dietary intake. The major source of B exposure in humans is consumption of fruits, vegetables, nuts and legumes, which are naturally rich in B. Human dietary consumption of B is below the estimated tolerable intake levels established in these recent risk assessments. Considering the growing evidence of the nutritional role of B, it is important to consider both the benefits and risks of B consumption. By legitimately reducing the uncertainty factor for toxicity, the margin of safety is effectively increased to protect against the possibility of insufficient dietary intake. Risk assessments of B provide valuable lessons regarding how chemical-specific adjustment factors may be selected.     

Analysis and Portrayal of Uncertainty in a Food-Web Exposure Model

The results of quantitative risk assessments are key factors in a risk manager's decision of the necessity to implement actions to reduce risk. The extent of the uncertainty in the assessment will play a large part in the degree of confidence a risk manager has in the reported significance and probability of a given risk. The two main sources of uncertainty in such risk assessments are variability and incertitude. In this paper we use two methods, a second-order two-dimensional Monte Carlo analysis and probability bounds analysis, to investigate the impact of both types of uncertainty on the results of a food-web exposure model. We demonstrate how the full extent of uncertainty in a risk estimate can be fully portrayed in a way that is useful to risk managers. We show that probability bounds analysis is a useful tool for identifying the parameters that contribute the most to uncertainty in a risk estimate and how it can be used to complement established practices in risk assessment. We conclude by promoting the use of probability analysis in conjunction with Monte Carlo analyses as a method for checking how plausible Monte Carlo results are in the full context of uncertainty.     

Identifying Uncertainty in Environmental Risk Assessments: The Development of a Novel Typology and Its Implications for Risk Characterization

Environmental risk analysts need to draw from a clear typology of uncertainties when qualifying risk estimates and/or significance statements about risk. However, categorizations of uncertainty within existing typologies are largely overlapping, contradictory, and subjective, and many typologies are not designed with environmental risk assessments (ERAs) in mind. In an attempt to rectify these issues, this research provides a new categorization of uncertainties based, for the first time, on the appraisal of a large subset of ERAs, namely 171 peer-reviewed environmental weight-of-evidence assessments. Using this dataset, a defensible typology consisting of seven types of uncertainty (data, language, system, extrapolation, variability, model, and decision) and 20 related sub-types is developed. Relationships between uncertainties and the techniques used to manage them are also identified and statistically evaluated. A highly preferred uncertainty management option is to take no action when faced with uncertainty, although where techniques are applied they are commensurate with the uncertainty in question. Key observations are applied in the form of guidance for dealing with uncertainty, demonstrated through ERAs of genetically modified higher plants in the European Union. The presented typology and accompanying guidance will have positive implications for the identification, prioritization, and management of uncertainty during risk characterization.     

Effects of Nutrient Level on Maternal Choice and Siring Success in <Emphasis Type="Italic">Cucumis sativus</Emphasis> (Cucurbitaceae)

Plants have evolved many mechanisms to increase the chance of gene dispersal mainly through pollen and environmental factors play an important role. Understanding the mechanism behind gene dispersal is therefore crucial in the correct evaluation of the use of genetically modified crops for cultivation. In this paper we address the question of weather nutrient availability for the female affects the outcome of pollen competition between two pollen donor cultivars of Cucumis sativus. We do this by carrying out controlled crosses of female plants grown at three different nutrient levels. We separated the effect of a specific donor from the effect of pollen tube growth rate by using reversed crosses of fast and slow pollen. Our results show that female effects on siring ability vary with nutrient level. Pollen with a high pollen tube growth rate was more successful when nutrient availability for the female was high. This could be the result of selection on the female to adjust preference according to environmental circumstances. Pollen tube growth rate was measured under nutrient rich circumstances, thus high performers possessed traits adapted to a nutrient rich situation. Due to trade-off effects, these traits might not be advantageous in poor environments. Instead, individuals adapted to low nutrient circumstances will have a higher pollen tube growth rate. If siring ability varies with the environment of the recipient plant, this means that assessments of gene flow must account for this variation and include both pollen donors and recipient plants subjected to a range of environmental circumstances. In risk assessments of transgenic plants, plants are often kept under experimental, homogenous conditions. If our results also apply to other species, estimates of gene flow under constant conditions may be misleading. Selection on siring ability and female preference have fundamental effects on gene flow and need to be considered in risk assessments of transgenic plants.Co-ordinating editor: I. Olivieri     

Chemical Mixtures: An Unsolvable Riddle?

It is difficult to overstate the complexity of assessing risks from chemical mixtures. For every valid reason to assess risks from mixtures, there appears an equally valid question as to whether it is possible to do so in a scientifically rigorous and relevant manner. Because so few data exist for mixtures, current mixture assessment methods must rely on untested assumptions and simplifications. That the accuracy of risk estimates improve with the number of chemicals assessed together as mixtures is a valid assumption only if assessment methods for mixtures are better than those based on individual chemicals. On the other hand, arbitrarily truncating a mixture assessment to make it manageable may lead to irrelevant risk estimates. Ideally, mixture assessments should be as broad as necessary to improve accuracy and reduce uncertainty over assessments that only use toxicity data for single chemicals. Further broadening the scope may be ill advised because of the tendency to increase rather than decrease uncertainty. Risk assessment methods that seek to be comprehensive at the expense of increased uncertainty can hardly be viewed as improvements. It would be prudent to verify that uncertainty can be reduced before burdening the risk assessment process with more complexity.     

Extrapolation in Risk Assessment: Improving the Quantification of Uncertainty,and Improving Information to Reduce the Uncertainty

Risk assessments inevitably extrapolate from the known to the unknown. The resulting calculation of risk involves two fundamental kinds of uncertainty: uncertainty owing to intrinsically unpredictable (random) components of the future events, and uncertainty owing to imperfect prediction formulas (parameter uncertainty and error in model structure) that are used to predict the component that we think is predictable. Both types of uncertainty weigh heavily both in health and ecological risk assessments. Our first responsibility in conducting risk assessments is to ensure that the reported risks correctly reflect our actual level of uncertainty (of both types). The statistical methods that lend themselves to correct quantification of the uncertainty are also effective for combining different sources of information. One way to reduce uncertainty is to use all the available data. To further sharpen future risk assessments, it is useful to partition the uncertainty between the random component and the component due to parameter uncertainty, so that we can quantify the expected reduction in uncertainty that can be achieved by investing in a given amount of future data. An example is developed to illustrate the potential for use of comparative data, from toxicity testing on other species or other chemicals, to improve the estimates of low-effect concentration in a particular case with sparse case-specific data.     

What Can Be Done to Ensure the Usefulness of Epidemiologic Data for Risk Assessment Purposes?

Epidemiologic studies can play a central role in risk assessments. They are used in all risk assessment phases: hazard identification, dose-response, and exposure assessment. Epidemiologic studies have often been the first to show that a particular environmental exposure is a hazard to health. They have numerous advantages with respect to other sources of data which are used in risk assessments, the most important being that they do not require the assumption that they are generalizable to humans. For this reason, fewer and lower uncertainty factors may be appropriate in risk characterization based on epidemiologic studies. Unfortunately, epidemiologic studies have numerous problems, the most important being that the exposures are often not precisely measured. This article presents in detail the advantages of and problems with epidemiologic studies. It discusses two approaches to ensure their usefulness, biomarkers and an ordinance which requires baseline and subsequent surveillance of possible exposures and health effects from newly sited potentially polluting facilities. Biomarkers are biochemical measures of exposure, susceptibility factors, or preclinical pathological changes. Biomarkers are a way of dealing with the problems of poor measures, differential susceptibility and lack of early measures of disease occurrence that inherent in many environmental epidemiologic studies. The advantages of biomarkers is they can provide objective information on exposure days, months or even years later and evidence of pathology perhaps years earlier. The ordinance makes possible the use of a powerful epidemiologic study design, the prospective cohort study, where confounder(s) are best measured, and exposures, pathological changes, and health effects can be detected as soon as possible.     

Public Reactions to Risk Messages Communicating Different Sources of Uncertainty: An Experimental Test

There is an abundant literature on the challenge of integrating uncertainties in experts’ risk assessments, but the evidence on the way they are understood by the public is scarce and mixed. This study aims to better understand the effect of communicating different sources of uncertainty in risk communication. A causal design was employed to test the effect of communicating risk messages varying in type of advisory warning (no risk and suggests no protective measure, or risk and recommends a protective measure) and sources of uncertainty (no uncertainty, divergence between experts, contradictory data, or lack of data) on public reactions. Participants from the general public (N = 434) were randomly assigned to read and react to variants of a fictitious government message discussing the presence of a new micro-organism found in tap water. Multiple analysis of variance showed that to report uncertainty from divergence between experts or from contradictory data reduced the adherence to the message, but not to mention the lack of data. Moreover, the communication of diverse sources of uncertainty did not affect trust in the government when the advisory warning stated there was a risk and recommended a protective measure. These findings have important implications for risk communication.     

Effects‐initiated assessments are not risk assessments

True risk assessments address the probability of a future risk occurring given a certain set of circumstances. However, “effects‐initiated assessments”; or “retrospective assessments”; often are improperly included under the broad appellation of “risk assessment”; and are conducted when an apparently adverse effect is seen in some environmental component and the question of cause (i.e., etiology) is raised. Base line risk assessments at Superfund sites or for Natural Resource Damage Assessments are examples of effects‐initiated assessments. We argue here that this type of study is not a risk assessment, either by strict definition of terminology or by logical approach taken in answering the posed question (s), and should more properly be called “diagnostic ecology.”; Diagnostic ecology starts from the premise that ecological effects have occurred and exposure to a Stressor has taken place. The problem then is to pose all possible etiologies and utilize deductive logic to systematically eliminate each agent except for one as the actual cause. A risk assessment, on the other hand, employs inductive reasoning. That is, hypotheses are generated about the possible sources of a stressor and the possible outcome if exposure occurs. Both exercises require an understanding of the ecological relationships of the various components in the ecosystem, both need an understanding of die cause‐and‐effect relationships of agents, and both require a proper framing of the questions being asked. However, risk assessors should not try to fit all environmental impact assessments into a single framework, but rather should recognize that biomedical techniques are better suited for solving diagnostic riddles than are prospective risk assessment approaches.     

Comparison and multi-model inference of excess risks models for radiation-related solid cancer

In assessments of detrimental health risks from exposures to ionising radiation, many forms of risk to dose–response models are available in the literature. The usual practice is to base risk assessment on one specific model and ignore model uncertainty. The analysis illustrated here considers model uncertainty for the outcome all solid cancer incidence, when modelled as a function of colon organ dose, using the most recent publicly available data from the Life Span Study on atomic bomb survivors of Japan. Seven recent publications reporting all solid cancer risk models currently deemed plausible by the scientific community have been included in a model averaging procedure so that the main conclusions do not depend on just one type of model. The models have been estimated with different baselines and presented for males and females at various attained ages and ages at exposure, to obtain specially computed model-averaged Excess Relative Risks (ERR) and Excess Absolute Risks (EAR). Monte Carlo simulated estimation of uncertainty on excess risks was accounted for by applying realisations including correlations in the risk model parameters. Three models were found to weight the model-averaged risks most strongly depending on the baseline and information criteria used for the weighting. Fitting all excess risk models with the same baseline, one model dominates for both information criteria considered in this study. Based on the analysis presented here, it is generally recommended to take model uncertainty into account in future risk analyses.

     

Scientific data and theories for salmonellosis dose–response assessment

An “expansive” risk assessment approach is illustrated, characterizing dose–response relationships for salmonellosis in light of the full body of evidence for human and murine superorganisms. Risk assessments often require analysis of costs and benefits for supporting public health decisions. Decision-makers and the public need to understand uncertainty in such analyses for two reasons. Uncertainty analyses provide a range of possibilities within a framework of present scientific knowledge, thus helping to avoid undesirable consequences associated with the selected policies. And, it encourages the risk assessors to scrutinize all available data and models, thus helping avoid subjective or systematic errors. Without the full analysis of uncertainty, decisions could be biased by judgments based solely on default assumptions, beliefs, and statistical analyses of selected correlative data. Alternative data and theories that incorporate variability and heterogeneity for the human and murine superorganisms, particularly colonization resistance, are emerging as major influences for microbial risk assessment. Salmonellosis risk assessments are often based on conservative default models derived from selected sets of outbreak data that overestimate illness. Consequently, the full extent of uncertainty of estimates of annual number of illnesses is not incorporated in risk assessments and the presently used models may be incorrect.     

Ethical Issues in the Use of Data from Testing of Human Subjects to Support Risk Assessment

The public understands and supports the ethical use of human subjects in medical research, recognizing the unique role for this type of study in the development of new drugs and therapeutic strategies for treatment of disease. The use of data from human subjects can also be of value in understanding the circumstances under which individuals exposed to chemicals in the food supply, in the workplace, or in the environment might experience toxicity, i.e., in support of risk assessment. However, questions have been raised as to whether this latter type of research is ethical, or can be performed in an ethical manner. Under what circumstances is it acceptable to intentionally expose human subjects to potentially toxic agents? This is an extremely important issue for the risk assessment community to address, because it affects in a fundamental way the types of information that will be available to conduct human health risk assessments. Four papers in this issue offer viewpoints on the value of human data, the circumstances under which human subjects might be exposed to toxic chemicals for research purposes, the ethical problems associated with this research, and the role of human vs. animal data in the development of toxicity values for human health risk assessment     

Economic framework for decision making in biological control

Economic analyses are a valuable input into the decision-making process for biological control programs. The challenge though is how to incorporate qualitative risk assessments of biological control programs, or the risk of nontargeted effects into mathematical economic models. A technique known as threshold cost/benefit analysis is presented and an example on how to apply this method is illustrated using the yellow starthistle biological control program. The results show that incorporating uncertainty into the analysis can have a significant impact on the decision to undertake a biological control program.     

Economic framework for decision making in biological control

Economic analyses are a valuable input into the decision-making process for biological control programs. The challenge though is how to incorporate qualitative risk assessments of biological control programs, or the risk of nontargeted effects into mathematical economic models. A technique known as threshold cost/benefit analysis is presented and an example on how to apply this method is illustrated using the yellow starthistle biological control program. The results show that incorporating uncertainty into the analysis can have a significant impact on the decision to undertake a biological control program.     

Criteria for selection of surrogates used to study the fate and control of pathogens in the environment

This article defines the term surrogate as an organism, particle, or substance used to study the fate of a pathogen in a specific environment. Pathogenic organisms, nonpathogenic organisms, and innocuous particles have been used as surrogates for a variety of purposes, including studies on survival and transport as well as for method development and as "indicators" of certain conditions. This article develops a qualitative surrogate attribute prioritization process and allows investigators to select a surrogate by systematically detailing the experimental process and prioritizing attributes. The results are described through the use of case studies of various laboratories that have used this process. This article also discusses the history of surrogate and microbial indicator use and outlines the method by which surrogates can be used when conducting a quantitative microbial risk assessment. The ultimate goal of selecting a sufficiently representative surrogate is to improve public health through a health-based risk assessment framework. Under- or overestimating the resistance, inactivation, or movement may negatively impact risk assessments that, in turn, will impact health assessments and estimated safety levels. Reducing uncertainty in a risk assessment is one of the objectives of using surrogates and the ultimate motive for any experiment investigating potential exposure of a pathogen.     

Exact propagation of uncertainties in multiplicative models

Propagation of uncertainties has been widely accepted as an integral component in health risk assessments. Historically, the approximations based on Taylor series expansions have been applied, but they are valid only when the model is linear or the coefficients of variation are small. Another approximation approach is Monte Carlo analysis. It is simpler and more practical for risk management but may become cumbersome if the model involves many variables containing large uncertainties. Recently, the exact log transform technique has been proposed for multiplicative models with lognormal uncertainties. The purpose of this paper is to present a novel exact analytical method as a complementary approach to simulation for general multiplicative models with independent variables, without a constraint on the types of uncertainty distributions. It also provides good estimates of the mean and variance of highly skewed distributions, determination of their existence, and verification of sufficient simulation sample size for simulation method. Two case examples are given to demonstrate the applications.     

Critical factors in assessing risk from exposure to nasal carcinogens

Anatomical, physiological, biochemical and molecular factors that contribute to chemical-induced nasal carcinogenesis are either largely divergent between test species and humans, or we know very little of them. These factors, let alone the uncertainty associated with our knowledge gap, present a risk assessor with the formidable task of making judgments about risks to human health from exposure to chemicals that have been identified in rodent studies to be nasal carcinogens. This paper summarizes some of the critical attributes of the hazard identification and dose–response aspects of risk assessments for nasal carcinogens that must be accounted for by risk assessors in order to make informed decisions. Data on two example compounds, dimethyl sulfate and hexamethylphosphoramide, are discussed to illustrate the diversity of information that can be used to develop informed hypotheses about mode of action and decisions on appropriate dosimeters for interspecies extrapolation. Default approaches to interspecies dosimetry extrapolation are described briefly and are followed by a discussion of a generalized physiologically based pharmacokinetic model that, unlike default approaches, is flexible and capable of incorporating many of the critical species-specific factors. Recent advancements in interspecies nasal dosimetry modeling are remarkable. However, it is concluded that without the development of research programs aimed at understanding carcinogenic susceptibility factors in human and rodent nasal tissues, development of plausible modes of action will lag behind the advancements made in dosimetry modeling.