A value of information framework for assessing the trade-offs associated with uncertainty, duration, and cost of chemical toxicity testing

A number of investigators have explored the use of value of information (VOI) analysis to evaluate alternative information collection procedures in diverse decision-making contexts. This paper presents an analytic framework for determining the value of toxicity information used in risk-based decision making. The framework is specifically designed to explore the trade-offs between cost, timeliness, and uncertainty reduction associated with different toxicity-testing methodologies. The use of the proposed framework is demonstrated by two illustrative applications which, although based on simplified assumptions, show the insights that can be obtained through the use of VOI analysis. Specifically, these results suggest that timeliness of information collection has a significant impact on estimates of the VOI of chemical toxicity tests, even in the presence of smaller reductions in uncertainty. The framework introduces the concept of the expected value of delayed sample information, as an extension to the usual expected value of sample information, to accommodate the reductions in value resulting from delayed decision making. Our analysis also suggests that lower cost and higher throughput testing also may be beneficial in terms of public health benefits by increasing the number of substances that can be evaluated within a given budget. When the relative value is expressed in terms of return-on-investment per testing strategy, the differences can be substantial.

Risk Assessment of Combined Exposure to Multiple Chemicals at the European Food Safety Authority: Principles, Guidance Documents, Applications and Future Challenges

Human health and animal health risk assessment of combined exposure to multiple chemicals use the same steps as single-substance risk assessment, namely problem formulation, exposure assessment, hazard assessment and risk characterisation. The main unique feature of combined RA is the assessment of combined exposure, toxicity and risk. Recently, the Scientific Committee of the European Food Safety Authority (EFSA) published two relevant guidance documents. The first one "Harmonised methodologies for the human health, animal health and ecological risk assessment of combined exposure to multiple chemicals" provides principles and explores methodologies for all steps of risk assessment together with a reporting table. This guidance supports also the default assumption that dose addition is applied for combined toxicity of the chemicals unless evidence for response addition or interactions (antagonism or synergism) is available. The second guidance document provides an account of the scientific criteria to group chemicals in assessment groups using hazard-driven criteria and prioritisation methods, i.e., exposure-driven and risk-based approaches. This manuscript describes such principles, provides a brief description of EFSA's guidance documents, examples of applications in the human health and animal health area and concludes with a discussion on future challenges in this field.

A Framework that Considers the Impacts of Time, Cost, and Uncertainty in the Determination of the Cost Effectiveness of Toxicity-Testing Methodologies

Regulatory agencies are required to evaluate the impacts of thousands of chemicals. Toxicological tests currently used in such evaluations are time-consuming and resource intensive; however, advances in toxicology and related fields are providing new testing methodologies that reduce the cost and time required for testing. The selection of a preferred methodology is challenging because the new methodologies vary in duration and cost, and the data they generate vary in the level of uncertainty. This article presents a framework for performing cost-effectiveness analyses (CEAs) of toxicity tests that account for cost, duration, and uncertainty. This is achieved by using an output metric-the cost per correct regulatory decision-that reflects the three elements. The framework is demonstrated in two example CEAs, one for a simple decision of risk acceptability and a second, more complex decision, involving the selection of regulatory actions. Each example CEA evaluates five hypothetical toxicity-testing methodologies which differ with respect to cost, time, and uncertainty. The results of the examples indicate that either a fivefold reduction in cost or duration can be a larger driver of the selection of an optimal toxicity-testing methodology than a fivefold reduction in uncertainty. Uncertainty becomes of similar importance to cost and duration when decisionmakers are required to make more complex decisions that require the determination of small differences in risk predictions. The framework presented in this article may provide a useful basis for the identification of cost-effective methods for toxicity testing of large numbers of chemicals.

Data Mining Approaches for Assessing Chemical Coexposures Using Consumer Product Purchase Data

The use of consumer products presents a potential for chemical exposures to humans. Toxicity testing and exposure models are routinely employed to estimate risks from their use; however, a key challenge is the sparseness of information concerning who uses products and which products are used contemporaneously. Our goal was to demonstrate a method to infer use patterns by way of purchase data. We examined purchase patterns for three types of personal care products (cosmetics, hair care, and skin care) and two household care products (household cleaners and laundry supplies) using data from 60,000 households collected over a one-year period in 2012. The market basket analysis methodology frequent itemset mining (FIM) was used to identify co-occurring sets of product purchases for all households and demographic groups based on income, education, race/ethnicity, and family composition. Our methodology captured robust co-occurrence patterns for personal and household products, globally and for different demographic groups. FIM identified cosmetic co-occurrence patterns captured in prior surveys of cosmetic use, as well as a trend of increased diversity of cosmetic purchases as children mature to teenage years. We propose that consumer product purchase data can be mined to inform person-oriented use patterns for high-throughput chemical screening applications, for aggregate and combined chemical risk evaluations.

Organizing mechanism-related information on chemical interactions using a framework based on the aggregate exposure and adverse outcome pathways

This paper presents a framework for organizing and accessing mechanistic data on chemical interactions. The framework is designed to support the assessment of risks from combined chemical exposures. The framework covers interactions between chemicals that occur over the entire source-to-outcome continuum including interactions that are studied in the fields of chemical transport, environmental fate, exposure assessment, dosimetry, and individual and population-based adverse outcomes. The framework proposes to organize data using a semantic triple of a chemical (subject), has impact (predicate), and a causal event on the source-to-outcome continuum of a second chemical (object). The location of the causal event on the source-to-outcome continuum and the nature of the impact are used as the basis for a taxonomy of interactions. The approach also builds on concepts from the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP). The framework proposes the linking of AEPs of multiple chemicals and the AOP networks relevant to those chemicals to form AEP-AOP networks that describe chemical interactions that cannot be characterized using AOP networks alone. Such AEP-AOP networks will aid the construction of workflows for both experimental design and the systematic review or evaluation performed in risk assessments. Finally, the framework is used to link the constructs of existing component-based approaches for mixture toxicology to specific categories in the interaction taxonomy.

Calibrating an agent-based model of longitudinal human activity patterns using the Consolidated Human Activity Database

Patterns of human behavior over extended periods of time are important for characterizing human exposure to hazardous chemicals. Because longitudinal behavior patterns for an individual are difficult to obtain, exposure-assessors have characterized such patterns by linking daily records from multiple individuals. In an earlier publication, we developed an alternative strategy that was based on agent-based simulation modeling. Specifically, we created a software program, Agent-Based Model of Human Activity Patterns (ABMHAP), that generates year-long longitudinal behavior patterns. In this paper, we both calibrate and evaluate ABMHAP using human behavior data from the U.S. Environmental Protection Agency's Consolidated Human Activity Database (CHAD). We use the longitudinal data (data on individuals' activities over multiple days) in CHAD to parameterize ABMHAP, and we use single-day behavior data from CHAD to evaluate ABMHAP predictions. We evaluate ABMHAP's ability to simulate sleeping, eating, commuting, and working (or attending school) for four populations: working adults, nonworking adults, school-age children, and preschool children. The results demonstrate that ABMHAP, when parameterized with empirical data, can capture both interindividual and intraindividual variation in behaviors in different types of individuals. We propose that simulating annual activity patterns via ABMHAP may allow exposure-assessors to characterize exposure-related behavior in ways not possible with traditional survey methods.

Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence

Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that simulates longitudinal patterns in human behavior. By basing the ABM upon an artificial intelligence (AI) system, we create agents that mimic human decisions on performing behaviors relevant for determining exposures to chemicals and other stressors. We implement the ABM in a computer program called the Agent-Based Model of Human Activity Patterns (ABMHAP) that predicts the longitudinal patterns for sleeping, eating, commuting, and working. We then show that ABMHAP is capable of simulating behavior over extended periods of time. We propose that this framework, and models based on it, can generate longitudinal human behavior data for use in exposure assessments.

Establishing a system of consumer product use categories to support rapid modeling of human exposure

Consumer product categorizations for use in predicting human chemical exposure provide a bridge between product composition data and consumer product use pattern information. Furthermore, the categories reflect other factors relevant to developing consumer product exposure scenarios, such as microenvironment of use (e.g., indoors or outdoors), method of application/form of release (e.g., spray versus liquid), release to various media, removal processes (e.g., rinse-off or wipe-off), and route-specific exposure factors (dermal surface areas of application, fraction of release in respirable form). While challenging, developing harmonized product categories can generalize the factors described above allowing for rapid parameterization of route-specific exposure scenario algorithms for new chemical/product applications and efficient utilization of new data on product use or composition. This can be accomplished via mapping product categories to likewise categorized release and use patterns or exposure factors. Here, hierarchical product use categories (PUCs) for consumer products that provide such mappings are presented and crosswalked with other internationally harmonized product categories for consumer exposure assessment. The PUCs were defined by applying use and exposure scenario information to the products in EPA's Chemical and Products Database (CPDat). This paper demonstrates how these PUCs are being used to rapidly parameterize algorithms for scenario-specific use, fate, and exposure in a probabilistic aggregate model of human exposure to chemicals used in consumer products. The PUCs provide a generic representation of consumer products for use in exposure assessment and provide an efficient framework for flexible and rapid data reporting and consumer exposure model parameterization.

Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

Prioritizing the potential risk posed to human health by chemicals requires tools that can estimate exposure from limited information. In this study, chemical structure and physicochemical properties were used to predict the probability that a chemical might be associated with any of four exposure pathways leading from sources-consumer (near-field), dietary, far-field industrial, and far-field pesticide-to the general population. The balanced accuracies of these source-based exposure pathway models range from 73 to 81%, with the error rate for identifying positive chemicals ranging from 17 to 36%. We then used exposure pathways to organize predictions from 13 different exposure models as well as other predictors of human intake rates. We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. The consensus model yields an R² of ∼0.8. We extrapolate to predict relevant pathway(s), median intake rate, and credible interval for 479 926 chemicals, mostly with minimal exposure information. This approach identifies 1880 chemicals for which the median population intake rates may exceed 0.1 mg/kg bodyweight/day, while there is 95% confidence that the median intake rate is below 1 μg/kg BW/day for 474572 compounds.

Temporal Trends in Exposures to Six Phthalates from Biomonitoring Data: Implications for Cumulative Risk

Phthalates are used in a wide range of consumer goods, resulting in exposures to specific phthalates that vary over time in accordance with changes in product use and how phthalates are utilized. We investigated trends in estimates of daily intake dose and several cumulative risk metrics, including the Hazard Quotient (HQ), Hazard Index (HI), and Maximum Cumulative Ratio (MCR) for six phthalates from 2005 to 2014 using metabolite biomonitoring data collected from spot urine samples under the National Health and Nutrition Examination Survey (NHANES). Over this period, there was a 2.2-fold decrease in the mean HI (0.34 to 0.15) and a 7.2-fold decrease in the percentage of participants with an HI > 1 (5.7% to 0.8%), indicating an overall decrease in combined exposure to these phthalates. Children (aged 6-11 years) had higher mean HI values than either adolescents (aged 12-19 years) or adults (aged 20+ years) during this period. MCR values were generally low and inversely correlated with HI. This indicated that a single phthalate usually drove the hazards for highly exposed individuals. However, the average value of MCR increased 1.2-fold (1.7-2.1) over this period indicating an increasing need to consider exposures to multiple phthalates in this group.

The Chemical and Products Database, a resource for exposure-relevant data on chemicals in consumer products

Quantitative data on product chemical composition is a necessary parameter for characterizing near-field exposure. This data set comprises reported and predicted information on more than 75,000 chemicals and more than 15,000 consumer products. The data's primary intended use is for exposure, risk, and safety assessments. The data set includes specific products with quantitative or qualitative ingredient information, which has been publicly disclosed through material safety data sheets (MSDS) and ingredient lists. A single product category from a refined and harmonized set of categories has been assigned to each product. The data set also contains information on the functional role of chemicals in products, which can inform predictions of the concentrations in which they occur. These data will be useful to exposure and risk assessors evaluating chemical and product safety.

Consumer product chemical weight fractions from ingredient lists

Assessing human exposures to chemicals in consumer products requires composition information. However, comprehensive composition data for products in commerce are not generally available. Many consumer products have reported ingredient lists that are constructed using specific guidelines. A probabilistic model was developed to estimate quantitative weight fraction (WF) values that are consistent with the rank of an ingredient in the list, the number of reported ingredients, and labeling rules. The model provides the mean, median, and 95% upper and lower confidence limit WFs for ingredients of any rank in lists of any length. WFs predicted by the model compared favorably with those reported on Material Safety Data Sheets. Predictions for chemicals known to provide specific functions in products were also found to reasonably agree with reported WFs. The model was applied to a selection of publicly available ingredient lists, thereby estimating WFs for 1293 unique ingredients in 1123 products in 81 product categories. Predicted WFs, although less precise than reported values, can be estimated for large numbers of product-chemical combinations and thus provide a useful source of data for high-throughput or screening-level exposure assessments.

An analysis of cumulative risks based on biomonitoring data for six phthalates using the Maximum Cumulative Ratio

The Maximum Cumulative Ratio (MCR) quantifies the degree to which a single chemical drives the cumulative risk of an individual exposed to multiple chemicals. Phthalates are a class of chemicals with ubiquitous exposures in the general population that have the potential to cause adverse health effects in humans. This work used the MCR to evaluate coexposures to six phthalates as measured in biomonitoring data from the most recent cycle (2013-2014) of the National Health and Nutrition Examination Survey (NHANES). The values of MCR, Hazard Index (HI), and phthalate-specific Hazard Quotients (HQs) were determined for 2663 NHANES participants aged six years and older by using reverse dosimetry techniques to calculate steady-state doses consistent with concentrations of metabolites of six phthalates in urine and using Tolerable Daily Intake values. There were 21 participants (0.8% of the NHANES sample) with HI>1. Of those, 43% (9/21) would have been missed by chemical-by-chemical assessments (i.e. all HQs were less than one). The mean MCR value in the 21 participants was 2.1. HI and MCR values were negatively correlated (p<0.001) indicating that most participants, especially those with elevated HI values, had their cumulative risks driven by relatively large doses of a single phthalate rather than doses of multiple phthalates. The dominate phthalate varied across participants. Children (aged 6-17years) had a higher HI values (p<0.01) than adults (18+ years). However, the probability of having HI>1 was not driven by age, gender, or ethnicity. The cumulative exposures of concern largely originated from a subset of three of the fifteen possible pairs of the six phthalates. These findings suggest that cumulative exposures were a potential concern for a small portion of the surveyed participants involving a subset of the phthalates explored. The largest risks tended to occur in individuals whose exposures were dominated by a single phthalate.

In vitro to in vivo extrapolation for high throughput prioritization and decision making

In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.

Use of a probabilistic PBPK/PD model to calculate Data Derived Extrapolation Factors for chlorpyrifos

A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model combined with Monte Carlo analysis of inter-individual variation was used to assess the effects of the insecticide, chlorpyrifos and its active metabolite, chlorpyrifos oxon in humans. The PBPK/PD model has previously been validated and used to describe physiological changes in typical individuals as they grow from birth to adulthood. This model was updated to include physiological and metabolic changes that occur with pregnancy. The model was then used to assess the impact of inter-individual variability in physiology and biochemistry on predictions of internal dose metrics and quantitatively assess the impact of major sources of parameter uncertainty and biological diversity on the pharmacodynamics of red blood cell acetylcholinesterase inhibition. These metrics were determined in potentially sensitive populations of infants, adult women, pregnant women, and a combined population of adult men and women. The parameters primarily responsible for inter-individual variation in RBC acetylcholinesterase inhibition were related to metabolic clearance of CPF and CPF-oxon. Data Derived Extrapolation Factors that address intra-species physiology and biochemistry to replace uncertainty factors with quantitative differences in metrics were developed in these same populations. The DDEFs were less than 4 for all populations. These data and modeling approach will be useful in ongoing and future human health risk assessments for CPF and could be used for other chemicals with potential human exposure.

Evaluation of potential human health effects associated with the agricultural uses of 1,3-D: Spatial and temporal stochastic risk analysis

Dow AgroSciences (DAS) markets and sells 1,3-Dichloropropene (1,3-D), the active ingredient in Telone®, which is used as a pre-plant soil fumigant nematicide in economically important crops in California. 1,3-D has been regulated as a "probable human carcinogen" and the California Department of Pesticide Regulation limits use of 1,3-D based on human health risk assessments for bystanders. This paper presents a risk characterization for bystanders based on advances in the assessment of both exposure and hazard. The revised bystander risk assessment incorporates significant advances: 1) new data on residency duration and mobility in communities where 1,3-D is in high demand; 2) new information on spatial and temporal concentrations of 1,3-D in air based on multi-year modeling using a validated model; and 3) a new stochastic spatial and temporal model of long-term exposures. Predicted distributions of long-term, chronic exposures indicate that current, and anticipated uses of 1,3-D would result in lifetime average daily doses lower than 0.002mg/kg/d, a dose associated with theoretical lifetime excess cancer risk of <10(-5) to >95% of the local population based on a non-threshold risk assessment approach. Additionally, examination of 1,3-D toxicity studies including new chronic toxicity data and mechanism of action supports the use of a non-linear, threshold based risk assessment approach. The estimated maximum annual average daily dose of <0.0016mg/kg/d derived from the updated exposure assessment was then compared with a threshold point of departure. The calculated margin of exposure is >1000-fold, a clear indication of acceptable risk for human health. In summary, the best available science supports 1,3-D's threshold nature of hazard and the revised exposure assessment supports that current agricultural uses of 1,3-D are associated with reasonable certainty of no harm, i.e., estimated long-term exposures pose insignificant health risks to bystanders even when the non-threshold approach is assumed.

Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals

Life Cycle Assessment (LCA) is a decision-making tool that accounts for multiple impacts across the life cycle of a product or service. This paper presents a conceptual framework to integrate human health impact assessment with risk screening approaches to extend LCA to include near-field chemical sources (e.g., those originating from consumer products and building materials) that have traditionally been excluded from LCA. A new generation of rapid human exposure modeling and high-throughput toxicity testing is transforming chemical risk prioritization and provides an opportunity for integration of screening-level risk assessment (RA) with LCA. The combined LCA and RA approach considers environmental impacts of products alongside risks to human health, which is consistent with regulatory frameworks addressing RA within a sustainability mindset. A case study is presented to juxtapose LCA and risk screening approaches for a chemical used in a consumer product. The case study demonstrates how these new risk screening tools can be used to inform toxicity impact estimates in LCA and highlights needs for future research. The framework provides a basis for developing tools and methods to support decision making on the use of chemicals in products.

Evaluation of OASIS QSAR Models Using ToxCast™ in Vitro Estrogen and Androgen Receptor Binding Data and Application in an Integrated Endocrine Screening Approach

BACKGROUND

Integrative testing strategies (ITSs) for potential endocrine activity can use tiered in silico and in vitro models. Each component of an ITS should be thoroughly assessed.

OBJECTIVES

We used the data from three in vitro ToxCast™ binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) platform covering both estrogen receptor (ER) and androgen receptor (AR) binding. For stronger binders (described here as AC50 < 1 μM), we also examined the relationship of QSAR predictions of ER or AR binding to the results from 18 ER and 10 AR transactivation assays, 72 ER-binding reference compounds, and the in vivo uterotrophic assay.

METHODS

NovaScreen binding assay data for ER (human, bovine, and mouse) and AR (human, chimpanzee, and rat) were used to assess the sensitivity, specificity, concordance, and applicability domain of two OASIS QSAR models. The binding strength relative to the QSAR-predicted binding strength was examined for the ER data. The relationship of QSAR predictions of binding to transactivation- and pathway-based assays, as well as to in vivo uterotrophic responses, was examined.

RESULTS

The QSAR models had both high sensitivity (> 75%) and specificity (> 86%) for ER as well as both high sensitivity (92-100%) and specificity (70-81%) for AR. For compounds within the domains of the ER and AR QSAR models that bound with AC50 < 1 μM, the QSAR models accurately predicted the binding for the parent compounds. The parent compounds were active in all transactivation assays where metabolism was incorporated and, except for those compounds known to require metabolism to manifest activity, all assay platforms where metabolism was not incorporated. Compounds in-domain and predicted to bind by the ER QSAR model that were positive in ToxCast™ ER binding at AC50 < 1 μM were active in the uterotrophic assay.

CONCLUSIONS

We used the extensive ToxCast™ HTS binding data set to show that OASIS ER and AR QSAR models had high sensitivity and specificity when compounds were in-domain of the models. Based on this research, we recommend a tiered screening approach wherein a) QSAR is used to identify compounds in-domain of the ER or AR binding models and predicted to bind; b) those compounds are screened in vitro to assess binding potency; and c) the stronger binders (AC50 < 1 μM) are screened in vivo. This scheme prioritizes compounds for integrative testing and risk assessment. Importantly, compounds that are not in-domain, that are predicted either not to bind or to bind weakly, that are not active in in vitro, that require metabolism to manifest activity, or for which in vivo AR testing is in order, need to be assessed differently.

CITATION

Bhhatarai B, Wilson DM, Price PS, Marty S, Parks AK, Carney E. 2016. Evaluation of OASIS QSAR models using ToxCast™ in vitro estrogen and androgen receptor binding data and application in an integrated endocrine screening approach. Environ Health Perspect 124:1453-1461; http://dx.doi.org/10.1289/EHP184.

Use of the Maximum Cumulative Ratio As an Approach for Prioritizing Aquatic Coexposure to Plant Protection Products: A Case Study of a Large Surface Water Monitoring Database

This paper uses the maximum cumulative ratio (MCR) as part of a tiered approach to evaluate and prioritize the risk of acute ecological effects from combined exposures to the plant protection products (PPPs) measured in 3 099 surface water samples taken from across the United States. Assessments of the reported mixtures performed on a substance-by-substance approach and using a Tier One cumulative assessment based on the lowest acute ecotoxicity benchmark gave the same findings for 92.3% of the mixtures. These mixtures either did not indicate a potential risk for acute effects or included one or more individual PPPs that had concentrations in excess of their benchmarks. A Tier Two assessment using a trophic level approach was applied to evaluate the remaining 7.7% of the mixtures. This assessment reduced the number of mixtures of concern by eliminating the combination of endpoint from multiple trophic levels, identified invertebrates and nonvascular plants as the most susceptible nontarget organisms, and indicated that a only a very limited number of PPPs drove the potential concerns. The combination of the measures of cumulative risk and the MCR enabled the identification of a small subset of mixtures where a potential risk would be missed in substance-by-substance assessments.

Acute Toxicity Prediction in Multiple Species by Leveraging Mechanistic ToxCast Mitochondrial Inhibition Data and Simulation of Oral Bioavailability

There is great interest in assessing the in vivo toxicity of chemicals using nonanimal alternatives. However, acute mammalian toxicity is not adequately predicted by current in silico or in vitro approaches. Mechanisms of acute toxicity are likely conserved across invertebrate, aquatic, and mammalian species, suggesting that dose-response concordance would be high and in vitro mechanistic data could predict responses in multiple species under conditions of similar bioavailability. We tested this hypothesis by comparing acute toxicity between rat, daphnia, and fish and by comparing their respective acute data to inhibition of mitochondria membrane potential (MMP) using U.S. Environmental Protection Agency ToxCast in vitro high-throughput screening data. Logarithmic scatter plots of acute toxicity data showed a clear relationship between fish, daphnia, and intravenous rat but not oral rat data. Similar plots versus MMP showed a well-delineated upper boundary for fish, daphnia, and intravenous data but were scattered without an upper boundary for rat oral data. Adjustments of acute oral rat toxicity values by simulating fractional absorption and CYP-based metabolism as well as removing compounds with hydrolyzable linkages or flagged as substrates for glucuronidation delineated an upper boundary for rat oral toxicity versus MMP. Mitochondrial inhibition at low concentrations predicted highly acutely toxic chemicals for fish and daphnia but not the rat where toxicity was often attenuated. This use of a single high-throughput screening assay to predict acute toxicity in multiple species represents a milestone and highlights the promise of such approaches but also the need for refined tools to address systemic bioavailability and the impact of limited absorption and first pass metabolism.

Applying the maximum cumulative ratio methodology to biomonitoring data on dioxin-like compounds in the general public and two occupationally exposed populations

Maximum cumulative ratio (MCR) is a person's cumulative exposure to multiple chemicals divided by the maximum chemical-specific exposure where exposure is expressed on a toxicologically equivalent basis. It is a tool for assessing the need for performing cumulative exposure assessments. In this paper, MCR values were calculated for the three groups of individuals with biomonitoring data of 26 dioxin-like compounds (DLCs) based on the World Health Organization toxic equivalent factors (TEFs). Although the two occupational groups have higher total toxicity equivalence (TEQ) levels than the NHANES group, average MCR values of the three groups are similar (3.5, 3.6, and 3.2). These MCR values are higher than those seen in our earlier studies, supporting the practice of performing cumulative assessments for DLCs. The MCR values also indicate that only 2-5 of the 26 chemicals make significant contributions to total TEQ values. Interestingly, MCR is negatively correlated with total TEQ (in all the three groups) and age (in the NHANES group). Additionally, MCR is lower in workers where occupational exposures are larger than background exposures. Although overall exposure is the first factor to consider in any mixtures assessment, this paper confirms the usefulness of MCR as a tool for analyzing the pattern of chemical-specific contributions to the total exposure levels of mixtures based on biomonitoring data when TEFs or similar approaches are available.

Incorporating nonchemical stressors into cumulative risk assessments

The role of nonchemical stressors in modulating the human health risk associated with chemical exposures is an area of increasing attention. On 9 March 2011, a workshop titled "Approaches for Incorporating Nonchemical Stressors into Cumulative Risk Assessment" took place during the 50th Anniversary Annual Society of Toxicology Meeting in Washington D.C. Objectives of the workshop included describing the current state of the science from various perspectives (i.e., regulatory, exposure, modeling, and risk assessment) and presenting expert opinions on currently available methods for incorporating nonchemical stressors into cumulative risk assessments. Herein, distinct frameworks for characterizing exposure to, joint effects of, and risk associated with chemical and nonchemical stressors are discussed.

Maximum cumulative ratio (MCR) as a tool for assessing the value of performing a cumulative risk assessment

Due to the vast number of possible combinations of chemicals to which individuals are exposed and the resource-intensive nature of cumulative risk assessments, there is a need to determine when cumulative assessments are most required. This paper proposes the use of the maximum cumulative ratio (MCR) as a tool for this evaluation. MCR is the ratio of the cumulative toxicity received by an individual from exposure to multiple chemical stressors to the largest toxicity from a single chemical stressor. The MCR is a quantitative measure of the difference in an individual’s toxicity estimated using a chemical-by-chemical approach and using an additive model of toxicity. As such, it provides a conservative estimate of the degree to which individuals’ toxicities could be underestimated by not performing a cumulative risk assessment. In an example application, MCR is shown to be applicable to the evaluation of cumulative exposures involving up to 81 compounds and to provide key insights into the cumulative effects posed by exposures to multiple chemicals. In this example, MCR values suggest that individuals exposed to combinations of chemicals with the largest Hazard Indices were dominated by the contributions of one or two compounds.

Linear low-dose extrapolation for noncancer heath effects is the exception, not the rule

The nature of the exposure-response relationship has a profound influence on risk analyses. Several arguments have been proffered as to why all exposure-response relationships for both cancer and noncarcinogenic endpoints should be assumed to be linear at low doses. We focused on three arguments that have been put forth for noncarcinogens. First, the general "additivity-to-background" argument proposes that if an agent enhances an already existing disease-causing process, then even small exposures increase disease incidence in a linear manner. This only holds if it is related to a specific mode of action that has nonuniversal properties-properties that would not be expected for most noncancer effects. Second, the "heterogeneity in the population" argument states that variations in sensitivity among members of the target population tend to "flatten out and linearize" the exposure-response curve, but this actually only tends to broaden, not linearize, the dose-response relationship. Third, it has been argued that a review of epidemiological evidence shows linear or no-threshold effects at low exposures in humans, despite nonlinear exposure-response in the experimental dose range in animal testing for similar endpoints. It is more likely that this is attributable to exposure measurement error rather than a true nonthreshold association. Assuming that every chemical is toxic at high exposures and linear at low exposures does not comport to modern-day scientific knowledge of biology. There is no compelling evidence-based justification for a general low-exposure linearity; rather, case-specific mechanistic arguments are needed.

Characterizing the noncancer toxicity of mixtures using concepts from the TTC and quantitative models of uncertainty in mixture toxicity

This article explores the use of an approach for setting default values for the noncancer toxicity, developed as part of the Threshold of Toxicological Concern (TTC), for the evaluation of the chronic noncarcinogenic effects of certain chemical mixtures. Individuals are exposed to many mixtures where there are little or no toxicological data on some or all of the mixture components. The approach developed in the TTC can provide a basis for conservative estimates of the toxicity of the mixture components when compound-specific data are not available. The application of this approach to multiple chemicals in a mixture, however, has implications for the statistical assumptions made in developing component-based estimates of mixtures. Specifically, conservative assumptions that are appropriate for one compound may become overly conservative when applied to all components of a mixture. This overestimation can be investigated by modeling the uncertainty in toxicity standards. In this article the approach is applied to both hypothetical and actual examples of chemical mixtures and the potential for overestimation is investigated. The results indicate that the use of the approach leads to conservative estimates of mixture toxicity and therefore its use is most appropriate for screening assessments of mixtures.

Using publicly available information to create exposure and risk-based ranking of chemicals used in the workplace and consumer products

Mandates that require the estimation of exposure and human health risk posed by large numbers of chemicals present regulatory managers with a significant challenge. Although these issues have been around for some time, the estimation of human exposure to chemicals from use of products in the workplace and by the consumer has been generally hindered by the lack of good tools. Logically and in the interest of cost-effective resource allocation and regulation one would typically and naturally first attempt to rank-order or prioritize the chemicals according to the human exposure potential that each might pose. We have developed an approach and systematic modeling construct that accomplishes this critical task by providing a quantitative estimate of human exposure for as many as several hundred chemicals initially; however, it could ultimately do this for any number of regulated chemicals starting only with the identity (Chemical Abstract Service number) for each chemical under consideration. These exposure estimates can then be readily linked to toxicological benchmarks for each item to estimate and rank the human health risk for the chemicals under consideration in a "worst things first" listing. This modeling construct, entitled Complex Exposure Tool (ComET) was developed by The LifeLine Group as a proof of concept under the sponsorship of Health Canada. ComET considers multiple routes of exposure, multiple subpopulations and different possible durations of exposure. A beta-version of ComET was issued and demonstrated in which users can change the assumptions in the model and see the impacts of these changes and the quality of information as they relate to the predicted exposure potential. We have advanced the operational elements of ComET into a tool entitled the Chemical Exposure Priority Setting Tool (CEPST) designed to provide quantitative estimation of the exposure potential of large groups of chemicals with little data and possibly multiple exposure scenarios. A basic feature of this tool is the utilization of an internally consistent approach and assumptions that are completely transparent. It uses publicly available information as critical input and is specifically designed to be continually reviewed, refined, expanded and updated using scientific peer review and stakeholder input.

Probabilistic exposure analysis for chemical risk characterization

This paper summarizes the state of the science of probabilistic exposure assessment (PEA) as applied to chemical risk characterization. Current probabilistic risk analysis methods applied to PEA are reviewed. PEA within the context of risk-based decision making is discussed, including probabilistic treatment of related uncertainty, interindividual heterogeneity, and other sources of variability. Key examples of recent experience gained in assessing human exposures to chemicals in the environment, and other applications to chemical risk characterization and assessment, are presented. It is concluded that, although improvements continue to be made, existing methods suffice for effective application of PEA to support quantitative analyses of the risk of chemically induced toxicity that play an increasing role in key decision-making objectives involving health protection, triage, civil justice, and criminal justice. Different types of information required to apply PEA to these different decision contexts are identified, and specific PEA methods are highlighted that are best suited to exposure assessment in these separate contexts.

Characterizing interspecies uncertainty using data from studies of anti-neoplastic agents in animals and humans

For most chemicals, the Reference Dose (RfD) is based on data from animal testing. The uncertainty introduced by the use of animal models has been termed interspecies uncertainty. The magnitude of the differences between the toxicity of a chemical in humans and test animals and its uncertainty can be investigated by evaluating the inter-chemical variation in the ratios of the doses associated with similar toxicological endpoints in test animals and humans. This study performs such an evaluation on a data set of 64 anti-neoplastic drugs. The data set provides matched responses in humans and four species of test animals: mice, rats, monkeys, and dogs. While the data have a number of limitations, the data show that when the drugs are evaluated on a body weight basis: 1) toxicity generally increases with a species' body weight; however, humans are not always more sensitive than test animals; 2) the animal to human dose ratios were less than 10 for most, but not all, drugs; 3) the current practice of using data from multiple species when setting RfDs lowers the probability of having a large value for the ratio. These findings provide insight into inter-chemical variation in animal to human extrapolations and suggest the need for additional collection and analysis of matched toxicity data in humans and test animals.

Exposure, epidemiology and human cancer incidence of naphthalene

This report provides a summary of deliberations conducted under the charge for members of Module B participating in the Naphthalene State-of-the-Science Symposium (NS(3)), Monterey, CA, October 9-12, 2006. The panel's charge was to derive consensus estimates of human exposure to naphthalene under various conditions, cancer incidence plausibly associated with these exposures, and identify quintessential research that could significantly reduce or eliminate material uncertainties to inform human cancer risk assessment. Relying in large part on a commissioned paper [Price, P.S., Jayjock, M.A., 2008. Available data on naphthalene exposures: strengths and limitations, in this issue], exposure levels were estimated for background (0.0001-0.003 microg/m(3)), ambient air (0.001-1.0 microg/m(3)), vehicles (0.003-3.0 microg/m(3)), residences (0.1-10 microg/m(3)), mothball use (on-label: 1-100 microg/m(3); off-label: 10-100 microg/m(3)), and occupational (low: 3-100 microg/m(3); high: 30-1,000 microg/m(3)). There have been few published reports of human cancer associated with naphthalene exposure. Several research projects are suggested that could reduce uncertainty in our understanding of human exposure. Using best scientific judgment, it is reasonably certain that the largest non-occupational exposures to naphthalene are indoor/residential exposures, particularly in households that use naphthalene-based products such as mothballs. However, even the highest of these exposures is likely to fall one or more orders of magnitude below moderate or high-level occupational exposure levels experienced by the few known cohorts exposed occupationally to naphthalene alone or as part of chemical mixtures such as jet fuel.

Modeling mixtures resulting from concurrent exposures to multiple sources

There is a growing recognition of the need to identify when exposures to specific combinations of chemicals result in toxicological effects of concern. In order to meet this need new tools are required to evaluate the doses of multiple chemicals that occur from the concurrent exposures to multiple sources of the chemicals. Limitations associated with the traditional approach for exposure modeling (source-to-dose models) have led to the development of a new approach that focuses on the person. These Person Oriented Models (POMs) use available data on personal characteristics that are statistically representative of the population receiving an exposure. Once the person's characteristics are defined, the information is used to model the probability of being exposed during a particular period of time. This process is repeated for different time periods and for hundreds or thousands of persons to produce a description of longitudinal exposures across a population. This approach allows the modeling of route-specific doses from multiple concurrent exposures; allows the modeling of doses from time varying exposures across, individuals, and provides a basis for modeling the person-related characteristics in subsequent steps in the process of assessing risks from mixtures.

A conceptual framework for modeling aggregate and cumulative exposures to chemicals

Computer simulation programs have been identified as useful tools for characterizing uncertainty and variability in longitudinal exposures to multiple sources by multiple routes of exposures. This paper provides a conceptual framework for such programs that separates and appropriately models the processes that determine uncertainty, inter- and intraindividual variability, as well as the processes that determine the relationships between the individuals and sources of exposure. The framework is based on a series of four nested loops. These are: the exposure event loop that models the route-specific doses to a person from one or more sources at one point in time; the time step loop that moves a person through time updating the sources and the person's characteristics, the interindividual variation loop that determines the initial characteristics of each person modeled, and finally the uncertainty loop that characterizes the uncertainty from model and parameter uncertainties. This framework provides a flexible and internally consistent approach for the design of simulation software.

Modeling interindividual variation in physiological factors used in PBPK models of humans

Modeling interindividual variation in internal doses in humans using PBPK models requires data on the variation in physiological parameters across the population of interest. These data should also reflect the correlations between the values of the various parameters in a person. In this project, we develop a source of data for human physiological parameters where (1) the parameter values for an individual are correlated with one another, and (2) values of parameters capture interindividual variation in populations of a specific gender, race, and age range. The parameters investigated in this project include: (1) volumes of selected organs and tissues; (2) blood flows for the organs and tissues; and (3) the total cardiac output under resting conditions and average daily inhalation rate. These parameters are expressed as records of correlated values for the approximately 30,000 individuals evaluated in the NHANES III survey. A computer program, Physiological Parameters for PBPK Modeling (P3M), is developed that allows records to be retrieved randomly from the database with specification of constraints on age, sex, and ethnicity. P3M is publicly available. The database and accompanying software provide a convenient tool for parameterizating models of interindividual variation in human pharmacokinetics.

An event-by-event probabilistic methodology for assessing the health risks of persistent chemicals in fish: a case study at the Palos Verdes Shelf

A human health risk assessment of recreational anglers who consume fish from the Palos Verdes Shelf was conducted. The uptake of DDT, DDE, and DDD (collectively total DDT or tDDT) and polychlorinated biphenyls (PCBs) due to fish ingestion was characterized using Monte Carlo techniques. This analysis relied upon 176 probability density functions developed from over 300,000 individual pieces of information to represent 17 different exposure factors that influence the human uptake of persistent organic chemicals in fish. The carcinogenic and noncarcinogenic risks were estimated using a microexposure event modeling approach that estimates exposure on an event-by-event basis. This evaluation relied upon several large studies that provided site-specific data on angler behavior and concentratioins of chemicals in 13 fish species. Our results indicate that the median theoretical increased lifetime cancer risk associated with estimated exposure to tDDT and PCBs was 5 x 10(-8) for anglers who fish on commercial passenger fishing vessels (CPFVs) and who catch and eat fish from the Palos Verdes Shelf. The mean risk for these anglers was 2 x 10(-7), and the 95th percentile risk was 8 x 10(-7). At the 9.5th percentile, the hazard quotients for anglers were less than 1, indicating that noncancer effects are unlikely. These results are in contrast with prior risk assessments of this site that suggested that consumption of white croaker alone posed a cancer risk of 2 x 10(-3) and a hazard quotient of 32. Our results were validated by their agreement with several independent local studies regarding fishing and consumption practices. This assessment indicates that the levels of tDDT and PCB in fish at the Palos Verdes Shelf do not pose a significant risk to human health among recreational anglers. Based on the size of the local angler population, no cases of cancer would be expected to result from eating Palos Verdes Shelf fish. The methodology used here should be applicable to characterizing the risks to those who ingest fish from the waterways of most industrialized nations.

Assessing aggregate and cumulative pesticide risks using a probabilistic model

Determining aggregate and cumulative risks from exposures to pesticides presents a number of challenges. The analysis must capture the correlations in residues that occur from both additive and exclusionary processes in the use of pesticides. The analysis also requires a quantitative mechanism for evaluating risks associated with exposures to mixtures of pesticides. This paper presents an analysis of aggregate exposures and risks associated with exposures to a pesticide, Alpha, and the cumulative exposure to and risk from three pesticides, Alpha, Beta, and Gamma. The cumulative risks are evaluated by determining the systemic (absorbed) doses that result from inhalation, dermal, and oral exposures to the pesticides. A 'relative toxicity' model is used to evaluate cumulative risks. The assessment of cumulative exposure was performed using the LifeLine Version 1.0. The model simulates pesticide exposure using an individual-based approach where daily exposures are evaluated for each person, season, and location.

The effect of cooking practices on the concentration of DDT and PCB compounds in the edible tissue of fish

Chemical contaminants in fish can be an important source of human exposure to chemicals. Assessments of the fish consumption pathway need to adjust the concentrations of the chemical to account for reductions in 1,1-bis(4-chlorophenyl)-2,2-dichloroethane (DDD), dichlorodiphenyldichloroethylene (DDE), and dichlorodiphenyltrichloroethane (DDT) (herein collectively referred to as total DDT or tDDT) and polychlorinated biphenyls (PCBs) that can occur during cooking. The results of this analysis indicate that baking, frying, broiling, boiling, smoking, and microwaving all effectively reduce the concentrations of tDDT and PCBs in fish tissue. Average reductions in tDDT ranged from 16 to 55% depending on the cooking method. Similar reductions in PCBs ranged from 26 to 68%. An evaluation of the factors influencing the degree of cooking loss indicated that neither initial chemical mass in the raw fillet, fillet lipid content, nor skin removal were significant predictors of the percent reduction in tDDT or PCB.

A probabilistic framework for the reference dose (probabilistic RfD)

Determining the probabilistic limits for the uncertainty factors used in the derivation of the Reference Dose (RfD) is an important step toward the goal of characterizing the risk of noncarcinogenic effects from exposure to environmental pollutants. If uncertainty factors are seen, individually, as "upper bounds" on the dose-scaling factor for sources of uncertainty, then determining comparable upper bounds for combinations of uncertainty factors can be accomplished by treating uncertainty factors as distributions, which can be combined by probabilistic techniques. This paper presents a conceptual approach to probabilistic uncertainty factors based on the definition and use of RfDs by the U.S. EPA. The approach does not attempt to distinguish one uncertainty factor from another based on empirical data or biological mechanisms but rather uses a simple displaced lognormal distribution as a generic representation of all uncertainty factors. Monte Carlo analyses show that the upper bounds for combinations of this distribution can vary by factors of two to four when compared to the fixed-value uncertainty factor approach. The probabilistic approach is demonstrated in the comparison of Hazard Quotients based on RfDs with differing number of uncertainty factors.

An approach for modeling noncancer dose responses with an emphasis on uncertainty

This paper presents an approach for characterizing the probability of adverse effects occurring in a population exposed to dose rates in excess of the Reference Dose (RfD). The approach uses a linear threshold (hockey stick) model of response and is based on the current system of uncertainty factors used in setting RfDs. The approach requires generally available toxicological estimates such as No-Observed-Adverse-Effect Levels (NOAELs) or Benchmark Doses and doses at which adverse effects are observed in 50% of the test animals (ED50s). In this approach, Monte Carlo analysis is used to characterize the uncertainty in the dose response slope based on the range and magnitude of the key sources of uncertainty in setting protective doses. The method does not require information on the shape of the dose response curve for specific chemicals, but is amenable to the inclusion of such data. The approach is applied to four compounds to produce estimates of response rates for dose rates greater than the RfD.

Uncertainty and variation in indirect exposure assessments: an analysis of exposure to tetrachlorodibenzo-p-dioxin from a beef consumption pathway

Indirect exposures to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and other toxic materials released in incinerator emissions have been identified as a significant concern for human health. As a result, regulatory agencies and researchers have developed specific approaches for evaluating exposures from indirect pathways. This paper presents a quantitative assessment of the effect of uncertainty and variation in exposure parameters on the resulting estimates of TCDD dose rates received by individuals indirectly exposed to incinerator emissions through the consumption of home-grown beef. The assessment uses a nested Monte Carlo model that separately characterizes uncertainty and variation in dose rate estimates. Uncertainty resulting from limited data on the fate and transport of TCDD are evaluated, and variations in estimated dose rates in the exposed population that result from location-specific parameters and individuals' behaviors are characterized. The analysis indicates that lifetime average daily dose rates for individuals living within 10 km of a hypothetical incinerator range over three orders of magnitude. In contrast, the uncertainty in the dose rate distribution appears to vary by less than one order of magnitude, based on the sources of uncertainty included in this analysis. Current guidance for predicting exposures from indirect exposure pathways was found to overestimate the intakes for typical and high-end individuals.

Exposure assessment: then, now, and quantum leaps in the future

Health risk assessments have become so widely accepted in the United States that their conclusions are a major factor in many environmental decisions. Although the risk assessment paradigm is 10 years old, the basic risk assessment process has been used by certain regulatory agencies for nearly 40 years. Each of the four components of the paradigm has undergone significant refinements, particularly during the last 5 years. A recent step in the development of the exposure assessment component can be found in the 1992 EPA Guidelines for Exposure Assessment. Rather than assuming worst-case or hypothetical maximum exposures, these guidelines are designed to lead to an accurate characterization, making use of a number of scientific advances. Many exposure parameters have become better defined, and more sensitive techniques now exist for measuring concentrations of contaminants in the environment. Statistical procedures for characterizing variability, using Monte Carlo or similar approaches, eliminate the need to select point estimates for all individual exposure parameters. These probabilistic models can more accurately characterize the full range of exposures that may potentially be encountered by a given population at a particular site, reducing the need to select highly conservative values to account for this form of uncertainty in the exposure estimate. Lastly, our awareness of the uncertainties in the exposure assessment as well as our knowledge as to how best to characterize them will almost certainly provide evaluations that will be more credible and, therein, more useful to risk managers.(ABSTRACT TRUNCATED AT 250 WORDS)

The effect of cooking processes on PCB levels in edible fish tissue

A significant factor in estimating human intake of polychlorinated biphenyls (PCBs) from fish consumption is the loss of PCBs during cooking. The total amount of PCBs actually consumed in the cooked fish may be significantly lower than the PCB level present before cooking because lipids and lipophilic compounds like PCBs tend to be removed from the fish during cooking. Several studies investigating the extent of loss of PCB compounds during the cooking process have been published in the peer-reviewed literature. However, because of what is perceived as inconsistent and inadequate data on the removal of these compounds, federal and state regulators typically do not assume that cooking reduces contaminant levels (EPA, 1990; 1991). In this paper, an attempt was made to reduce the uncertainty in the findings of these studies on PCB losses during the cooking process. This was accomplished by (1) eliminating studies that lacked statistical power to determine the degree of reduction, (2) reporting all of the results in a common format, and (3) characterizing studies by cooking method. In addition, the studies that reported increases in PCB concentration after cooking were carefully reviewed to provide a possible explanation of this occurrence. Based upon this analysis, it was concluded that cooking processes such as baking, broiling, microwave cooking, poaching, and roasting remove approximately 20 to 30% of the PCBs. Frying appears to remove more than 50%. PCB cooking losses also appears to be a function of the initial lipid concentration in the fish. Based upon this analysis, it is clear that the information from these studies do provide a reasonable basis for federal and state regulators to permit a quantitative adjust of PCB intakes.

Reevaluation of benzene exposure for the Pliofilm (rubberworker) cohort (1936-1976)

The Pliofilm cohort is the most intensely studied group of workers chronically exposed to benzene. Information on this cohort has been the basis for regulations and/or guidelines for occupational and environmental exposure to benzene. Rinsky et al. (1986, 1987) and Crump and Allen (1984) developed different approaches for reconstructing the exposure history of each member of the group. The predicted levels of exposure, combined with the data on the incidence of disease, have been used to estimate benzene's carcinogenic potency. In this paper, recent information from worker interviews and historical records from the National Archives and elsewhere were used to evaluate the accuracy of prior exposure estimates and to develop better ones for the cohort. The following factors were accounted for: (1) uptake of benzene due to short-term, high-level exposure to vapors, (2) uptake due to background concentrations in the manufacturing building, (3) uptake due to contact with the skin, (4) morbidity and mortality data on workers in the Pliofilm process, (5) the installation of industrial hygiene engineering controls, (6) extraordinarily long work weeks during the 1940s, (7) data indicating that airborne concentrations of benzene were underestimated due to inaccurate monitoring devices and the lack of adequate field calibration mated due to inaccurate monitoring devices and the lack of adequate field calibration of these devices, and (8) likely effectiveness of respirators and gloves. Our estimates suggest that Crump and Allen (1984) overestimated the exposure of workers in some job classifications and underestimated others, and that Rinsky et al. (1981, 1986) almost certainly underestimated the exposure of nearly all workers. Airborne concentrations of benzene at the St. Marys facility during the years of its operation were found (on average) to be about half those of the two Akron facilities. Our analysis indicates that short-term, high-level exposure to benzene vapors and dermal exposure significantly increased (by about 25-50%) the total absorbed dose of benzene for some workers. One of the key findings was that, unlike prior analyses, the three facilities probably had significantly different airborne concentrations of benzene, especially during the 1940s and 1950s.

Student nurses' assessment of children in pain

A self-administered questionnaire was developed to ascertain the criteria that student nurses used to assess children in pain. The sample consisted of 17 second-year registered general nurse students who had just completed their paediatric secondment. The questionnaire required the students to provide their own definition of pain, to rate the pain of four hypothetical children, giving reasons for their ratings and provide data about their assessment of a child they had cared for. Definitions of pain concentrated mainly on the physical effects of pain on patients. The students attributed a wide range of pain ratings to the hypothetical children, though the reasons for reaching these differing conclusions were often based on similar statements. There was limited reference to either personal episodes of pain or previous nursing experience. In their own assessment of children in pain the students appeared to use all the acknowledged criteria. The use of physiological signs was in some circumstances possibly inappropriate.