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Developmental anomalies induced by toxic chemicals may be identified using laboratory experiments with rats, mice or rabbits. Multinomial responses of fetuses from the same mother are often positively correlated, resulting in overdispersion relative to multinomial variation. In this article, a simple data transformation based on the concept of generalized design effects due to Rao‐Scott is proposed for dose‐response modeling of developmental toxicity. After scaling the original multinomial data using the average design effect, standard methods for analysis of uncorrected multinomial data can be applied. Benchmark doses derived using this approach are comparable to those obtained using generalized estimating equations with an extended Dirichlet‐trinomial covariance function to describe the dispersion of the original data. This empirical agreement, coupled with a large sample theoretical justification of the Rao‐Scott transformation, confirms the applicability of the statistical methods proposed in this article for developmental toxicity risk assessment.

Reproductive and developmental anomalies induced by toxic chemicals may be identified using laboratory experiments with small mammalian species such as rats, mice, and rabbits. In this paper, dose‐response models for correlated multinomial data arising in studies of developmental toxicity are discussed. These models provide a joint characterization of dose‐response relationships for both embryolethality and teratogenicity. Generalized estimating equations are used for model fitting, incorporating overdispersion relative to the multinomial variation due to correlation among littermates. The fitted dose‐response models are used to estimate benchmark doses in a series of experiments conducted by the U.S. National Toxicology Program. Joint analysis of prenatal death and fetal malformation using an extended Dirichlet‐trinomial covariance function to characterize overdispersion appears to have statistical and computational advantages over separate analysis of these two end points. Benchmark doses based on overall toxicity are below the minimum of those for prenatal death and fetal malformation and may, thus, be preferred for risk assessment purposes.

Currently, there is a trend away from the use of single (often conservative) estimates of risk to summarize the results of risk analyses in favor of stochastic methods which provide a more complete characterization of risk. The use of such stochastic methods leads to a distribution of possible values of risk, taking into account both uncertainty and variability in all of the factors affecting risk. In this article, we propose a general framework for the analysis of uncertainty and variability for use in the commonly encountered case of multiplicative risk models, in which risk may be expressed as a product of two or more risk factors. Our analytical methods facilitate the evaluation of overall uncertainty and variability in risk assessment, as well as the contributions of individual risk factors to both uncertainty and variability which is cumbersome using Monte Carlo methods. The use of these methods is illustrated in the analysis of potential cancer risks due to the ingestion of radon in drinking water.

Estimates were made of the numbers of liver carcinogens in 390 long‐term bioassays conducted by the National Toxicology Program (NTP). These estimates were obtained from examination of the global pattern of p‐values obtained from statistical tests applied to individual bioassays. Representative estimates of the number of liver carcinogens (90% confidence interval in parentheses) obtained in our analysis compared to NTP's determination are as follows: female rats—49 (23, 76), NTP = 30; male rats—88 (59, 116), NTP = 35; female mice—131 (105, 157), NTP = 81; male mice—100 (73, 126), NTP = 61; overall—166 (135, 197), NTP = 108. The estimator from which these estimates were obtained is biased low by an unknown amount. Consequently, this study provides persuasive evidence of the existence of more rodent liver carcinogens than were identified by the NTP.

Current practice in carcinogen bioassay calls for exposure of experimental animals at doses up to and including the maximum tolerated dose (MTD). Such studies have been used to compute measures of carcinogenic potency such as the TD50 as well as unit risk factors such as q1* for predicting low‐dose risks. Recent studies have indicated that these measures of carcinogenic potency are highly correlated with the MTD. Carcinogenic potency has also been shown to be correlated with indicators of mutagenicity and toxicity. Correlation of the MTDs for rats and mice implies a corresponding correlation in TD50 values for these two species. The implications of these results for cancer risk assessment are examined in light of the large variation in potency among chemicals known to induce tumors in rodents.

In this article, the operating characteristics of recently proposed tests for trend in correlated binary data arising in laboratory studies of developmental toxicity are examined using both computer‐generated and experimental data. Specifically, we consider adjusted Cochran‐Armitgc tests based on the Rao‐Scott transformation which are of the same general form as that for uncorrelated data. In addition, generalized score tests based on generalized estimating equations allowing for extra‐binomial variation in the data are discussed. Specific forms of these statistics demonstrating favorable type I and type II error rates are identified and recommended for use in practice. The application of these tests is illustrated using data from studies of developmental toxicity that have been reported in the literature.

OBJECTIVE: Quantitative estimates of air pollution health impacts have become an increasingly critical input to policy decisions. The WHO project "Health risks of air pollution in Europe--HRAPIE" was implemented to provide the evidence-based concentration-response functions for quantifying air pollution health impacts to support the 2013 revision of the air quality policy for the European Union (EU). METHODS: A group of experts convened by WHO Regional Office for Europe reviewed the accumulated primary research evidence together with some commissioned reviews and recommended concentration-response functions for air pollutant-health outcome pairs for which there was sufficient evidence for a causal association. RESULTS: The concentration-response functions link several indicators of mortality and morbidity with short- and long-term exposure to particulate matter, ozone and nitrogen dioxide. The project also provides guidance on the use of these functions and associated baseline health information in the cost-benefit analysis. CONCLUSIONS: The project results provide the scientific basis for formulating policy actions to improve air quality and thereby reduce the burden of disease associated with air pollution in Europe.

© 2015, The Author(s). Objective: Quantitative estimates of air pollution health impacts have become an increasingly critical input to policy decisions. The WHO project "Health risks of air pollution in Europe—HRAPIE" was implemented to provide the evidence-based concentration–response functions for quantifying air pollution health impacts to support the 2013 revision of the air quality policy for the European Union (EU). Methods: A group of experts convened by WHO Regional Office for Europe reviewed the accumulated primary research evidence together with some commissioned reviews and recommended concentration–response functions for air pollutant–health outcome pairs for which there was sufficient evidence for a causal association. Results: The concentration–response functions link several indicators of mortality and morbidity with short- and long-term exposure to particulate matter, ozone and nitrogen dioxide. The project also provides guidance on the use of these functions and associated baseline health information in the cost–benefit analysis. Conclusions: The project results provide the scientific basis for formulating policy actions to improve air quality and thereby reduce the burden of disease associated with air pollution in Europe.