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Human populations are generally exposed simultaneously to a number of toxicants present in the environment, including complex mixtures of unknown and variable origin. While scientific methods for evaluating the potential carcinogenic risks of pure compounds are relatively well established, methods for assessing the risks of complex mixtures are somewhat less developed. This article provides a report of a recent workshop on carcinogenic mixtures sponsored by the Committee on Toxicology of the U.S. National Research Council, in which toxicological, epidemiological, and statistical approaches to carcinogenic risk assessment for mixtures were discussed. Complex mixtures, such as diesel emissions and tobacco smoke, have been shown to have carcinogenic potential. Bioassay‐directed fractionation based on short‐term screening tests for genotoxicity has also been used in identifying carcinogenic components of mixtures. Both toxicological and epidemiological studies have identified clear interactions between chemical carcinogens, including synergistic effects at moderate to high doses. To date, laboratory studies have demonstrated over 900 interactions involving nearly 200 chemical carcinogens. At lower doses, theoretical arguments suggest that risks may be near additive. Thus, additivity at low doses has been invoked as a working hypothesis by regulatory authorities in the absence of evidence to the contrary. Future studies of the joint effects of carcinogenic agents may serve to elucidate the mechanisms by which interactions occur at higher doses.

Previous applications of carcinogenic risk assessment using mathematical models of carcinogenesis have focused largely on the case where the level of exposure remains constant over time. In many situations, however, the dose of the carcinogen varies with time. In this paper, we discuss both the classical Armitage–Doll multistage model and the Moolgavkar–Venzon–Knudson two–stage birth–death–mutation model with time‐dependent dosing regimens. Bounds on the degree of underestimation of risk that can occur through the use of a simple time‐weighted average dose are derived by means of comparison with an equivalent constant dose corresponding to the actual risk under the time‐dependent dosing regimen.

Recent advances in risk assessment have led to the development of joint dose‐response models to describe prenatal death and fetal malformation rates in developmental toxicity experiments. These models can be used to estimate the effective dose corresponding to a 5% excess risk for both these toxicological endpoints, as well as for overall toxicity. In this article, we develop optimal experimental designs for the estimation of the effective dose for developmental toxicity using joint Weibull dose‐response models for prenatal death and fetal malformation. Based on an extended series of developmental studies, near‐optimal designs for prenatal death, malformation, and overall toxicity were found to involve three dose groups: an unexposed control group, a high dose equal to the maximum tolerated dose, and a low dose above or comparable to the effective dose. The effect on the optimal designs of changing the number of implants and the degree of intra‐litter correlation is also investigated. Although the optimal design has only three dose groups in most cases, practical considerations involving model lack of fit and estimation of the shape of the dose‐response curve suggest that, in practice, suboptimal designs with more than three doses will often be preferred.

The association between daily fluctuations in ambient particulate matter and daily variations in nonaccidental mortality have been extensively investigated. Although it is now widely recognized that such an association exists, the form of the concentration‐response model is still in question. Linear, no threshold and linear threshold models have been most commonly examined. In this paper we considered methods to detect and estimate threshold concentrations using time series data of daily mortality rates and air pollution concentrations. Because exposure is measured with error, we also considered the influence of measurement error in distinguishing between these two completing model specifications. The methods were illustrated on a 15‐year daily time series of nonaccidental mortality and particulate air pollution data in Toronto, Canada. Nonparametric smoothed representations of the association between mortality and air pollution were adequate to graphically distinguish between these two forms. Weighted nonlinear regression methods for relative risk models were adequate to give nearly unbiased estimates of threshold concentrations even under conditions of extreme exposure measurement error. The uncertainty in the threshold estimates increased with the degree of exposure error. Regression models incorporating threshold concentrations could be clearly distinguished from linear relative risk models in the presence of exposure measurement error. The assumption of a linear model given that a threshold model was the correct form usually resulted in overestimates in the number of averted premature deaths, except for low threshold concentrations and large measurement error.

Developmental anomalies resulting from prenatal toxicity can be manifested in terms of both malformations among surviving offspring and prenatal death. Although these two endpoints have traditionally been analyzed separately in the assessment of risk, multivariate methods of risk characterization have recently been proposed. We examined this and other issues in developmental toxicity risk assessment by evaluating the accuracy and precision of estimates of the effective dose (ED05) and the benchmark dose (BMD05) using computer simulation. Our results indicated that different variance structures (Dirichlet‐trinomial and generalized linear model) used to characterize overdispersion yielded comparable results when fitting joint dose response models based on generalized estimating equations. (The choice of variance structure in separate modeling was also not critical.) However, using the Rao‐Scott transformation to eliminate overdispersion tended to produce estimates of the ED05 with reduced bias and mean squared error. Because joint modeling ensures that the ED05 for overall toxicity (based on both malformations and prenatal death) is always less than the ED05 for either malformations or prenatal death, joint modeling is preferred to separate modeling for risk assessment purposes.

Applications of methods for carcinogenic risk assessment often focus on estimating lifetime cancer risk. With intermittent or time‐dependent exposures, lifetime risk is often approximated on the basis of a lifetime average daily dose (LADD). In this article, we show that there exists a lifetime equivalent constant dose (LECD)which leads to the same lifetime risk as the actual time‐dependent exposure pattern. The ratio C= LECD/LADD then provides a measure of accuracy of risk estimates based on the LADD, as well as a basis for correcting such estimates. Theoretical results derived under the classical multistage model and the two‐stage birth‐death‐mutation model suggest that the maximum value of C, which represents the factor by which the LADD may lead to underestimates of risk, will often lie in the range of 2‐ to 5‐fold. The practical application of these results is illustrated in the case of astronauts subjected to relatively short‐term exposure to volatile organics in a closed space station environment, and in the case of the ingestion of pesticide residues in food where consumption patterns vary with age.

AbstractResearch has documented that immigrants tend to experience more negative consequences from natural disasters compared to native‐born individuals, although research on how immigrants perceive and respond to natural disaster risks is sparse. We investigated how risk perception and disaster preparedness for natural disasters in immigrants compared to Canadian‐born individuals as justifications for culturally‐adapted risk communication and management. To this end, we analyzed the ratings on natural disaster risk perception beliefs and preparedness behaviors from a nationally representative survey (N = 1,089). Factor analyses revealed three underlying psychological dimensions of risk perception: external responsibility for disaster management, self‐preparedness responsibility, and illusiveness of preparedness. Although immigrants and Canadian‐born individuals shared the three‐factor structure, there were differences in the salience of five risk perception beliefs. Despite these differences, immigrants and Canadian‐born individuals were similar in the level of risk perception dimensions and disaster preparedness. Regression analyses revealed self‐preparedness responsibility and external responsibility for disaster management positively predicted disaster preparedness whereas illusiveness of preparedness negatively predicted disaster preparedness in both groups. Our results showed that immigrants' risk perception and disaster preparedness were comparable to their Canadian‐born counterparts. That is, immigrant status did not necessarily yield differences in risk perception and disaster preparedness. These social groups may benefit from a risk communication and management strategy that addresses these risk perception dimensions to increase disaster preparedness. Given the diversity of the immigrant population, the model remains to be tested by further population segmentation.

Following a comprehensive evaluation of the health risks of radon, the U.S. National Research Council (US‐NRC) concluded that the radon inside the homes of U.S. residents is an important cause of lung cancer. To assess lung cancer risks associated with radon exposure in Canadian homes, we apply the new (US‐NRC) techniques, tailoring assumptions to the Canadian context. A two‐dimensional uncertainty analysis is used to provide both population‐based (population attributable risk, PAR; excess lifetime risk ratio, ELRR; and life‐years lost, LYL) and individual‐based (ELRR and LYL) estimates. Our primary results obtained for the Canadian population reveal mean estimates for ELRR, PAR, and LYL are 0.08, 8%, and 0.10 years, respectively. Results are also available and stratified by smoking status (ever versus never). Conveniently, the three indices (ELRR, PAR, and LYL) reveal similar output uncertainty (geometric standard deviation, GSD ≈ 1.3), and in the case of ELRR and LYL, comparable variability and uncertainty combined (GSD ≈ 4.2). Simplifying relationships are identified between ELRR, LYL, PAR, and the age‐specific excess rate ratio (ERR), which suggest a way to scale results from one population to another. This insight is applied in scaling our baseline results to obtain gender‐specific estimates, as well as in simplifying and illuminating sensitivity analysis.

The effects of exposure to two carcinogens are explored within the context of the two‐stage clonal expansion model of carcinogenesis. This biologically based model provides a useful framework for the quantitative description of carcinogenesis, and for defining carcinogenic agents that act as initiators, promoters, and completers. This paper addresses the combined effects of simultaneous lifetime exposure to two carcinogens as well as nonoverlapping partial lifetime exposure to each agent. Whereas the age‐specific relative risk for exposure to two initiators or two completers is additive, a multiplicative relative risk model holds for exposure to an initiator and a completer, or to a promoter and a completer. Exposure to two promoters yields supra‐multiplicative relative risk. Exposure to an initiator and promoter leads to multiplicative and supra‐multiplicative relative risks for simultaneous lifetime and nonoverlapping partial lifetime exposures, respectively. Although departures from the additive relative risk model may thus occur at moderate to high doses, conditions are identified under which additivity will provide a good approximation to the joint risk at low doses. The methods of analysis used in this paper can also be used to determine the joint effects of exposure to two carcinogens which may affect more than one stage (initiation, promotion, completion) of the process of carcinogenesis. In general, the joint effects of exposure to such agents depends on the relative magnitude of the effects on individual stages.

Essential elements such as copper and manganese may demonstrate U‐shaped exposure‐response relationships due to toxic responses occurring as a result of both excess and deficiency. Previous work on a copper toxicity database employed CatReg, a software program for categorical regression developed by the U.S. Environmental Protection Agency, to model copper excess and deficiency exposure‐response relationships separately. This analysis involved the use of a severity scoring system to place diverse toxic responses on a common severity scale, thereby allowing their inclusion in the same CatReg model. In this article, we present methods for simultaneously fitting excess and deficiency data in the form of a single U‐shaped exposure‐response curve, the minimum of which occurs at the exposure level that minimizes the probability of an adverse outcome due to either excess or deficiency (or both). We also present a closed‐form expression for the point at which the exposure‐response curves for excess and deficiency cross, corresponding to the exposure level at which the risk of an adverse outcome due to excess is equal to that for deficiency. The application of these methods is illustrated using the same copper toxicity database noted above. The use of these methods permits the analysis of all available exposure‐response data from multiple studies expressing multiple endpoints due to both excess and deficiency. The exposure level corresponding to the minimum of this U‐shaped curve, and the confidence limits around this exposure level, may be useful in establishing an acceptable range of exposures that minimize the overall risk associated with the agent of interest.

At the request of the Environmental Protection Agency, the National Research Council (NRC) recently completed a major report entitled Toxicity Testing in the 21st Century: A Vision and a Strategy. The terms of reference for this report were to develop a long‐range vision and strategic plan to advance the practices of toxicity testing and human health assessment of environmental agents. The report describes how current and anticipated scientific advances can be expected to transform toxicity testing to permit broader coverage of the universe of potentially toxic chemicals to which humans may be exposed, using more timely and more cost‐effective methods for toxicity testing. The report envisages greatly expanded use of high‐ and medium‐throughput in vitro screening assays, computational toxicology, and systems biology, along with other emerging high‐content testing methodologies, such as functional genomics and transcriptomics. When fully implemented, the vision will transform the ways toxicity testing and chemical risk assessment are conducted, moving away from measuring apical health endpoints in experimental animals toward identification of significant perturbations of toxicity pathways using in vitro tests in human cells and cell lines. Population‐based studies incorporating relevant biomarkers will also be useful in identifying pathway perturbations directly in humans and in interpreting the results of in vitro tests in the context of human health risk assessment. The present article summarizes and extends the NRC report and examines its implications for risk assessment practice.