An international colloquium, "Strategies for Clean Air and Health," was organized by the Network of Environmental Risk Assessment and Management (NERAM) and the AIRNET European Network on Air Pollution and Health to identify directions for air quality policy development and research priorities to improve public health. A conference statement was prepared to provide guidance from the perspective of an international group of scientists, regulators, industries, and interest groups on a path forward to improve the interface between science and clean air policy strategies to protect public health. The statement represents the main findings of two breakout group discussion sessions, supported by perspectives of keynote speakers from North America and Europe on science-policy integration and views of the delegates expressed in plenary discussions. NERAM undertook a carefully considered process to try to ensure that the statement would accurately reflect the conference discussions, including documentation of supporting comments from the proceedings and inviting delegates' comments on two draft versions of the statement.

The advent of computerized record linkage methodology has facilitated the conduct of cohort mortality studies in which exposure data in one database are electronically linked with mortality data from another database. In this article, the impact of linkage errors on estimates of epidemiological indicators of risk such as standardized mortality ratios and relative risk regression model parameters is explored. It is shown that these indicators can be subject to bias and additional variability in the presence of linkage errors, with false links and nonlinks leading to positive and negative bias respectively in estimates of the standardized mortality ratio. Although linkage errors always increase the uncertainty in the estimates, bias can be effectively eliminated in the special case in which the false positive rate equals the false negative rate within homogeneous states defined by cross-classification of the covariates of interest.

Safety assessment for noncancer health effects generally has been based upon dividing a no observed adverse effect (NOAEL) by uncertainty (safety) factors to provide an acceptable daily intake (ADI) or reference dose (RfD). Since the NOAEL does not utilize all of the available dose–response data, allows higher ADI from poorer experiments, and may have an unknown, unacceptable level of risk, the benchmark dose (BD) with a specified, controlled low level of risk has become popular as an adjunct to the NOAEL or the low observed adverse effect level (LOAEL) in the safety assessment process. The purpose of this paper is to summarize statistical procedures available for calculating BDs and their confidence limits for noncancer endpoints. Procedures are presented and illustrated for quantal (binary), quasicontinuous (proportion), and continuous data. Quasicontinuous data arise in developmental studies where the measure of an effect for a fetus is quantal (normal or abnormal) but the experimental unit is the mother (litter) so that results can be expressed as the proportion of abnormal fetuses per litter. However, the correlation of effects among fetuses within a litter poses some additional statistical problems. Also, developmental studies usually include some continuous measures, such as fetal body weight or length. With continuous data there generally is not a clear demarcation between normal and adverse measurements. In such cases, extremely high and/or low measurements at some designated percentile(s) can be considered abnormal. Then the probability (risk) of abnormal individuals can be estimated as a function of dose. The procedure for estimating a BD with continuous data is illustrated using neurotoxicity data. When multiple measures of adverse effects are available, a BD can be estimated based on a selected endpoint or the appearance of any combination of endpoints. Multivariate procedures are illustrated using developmental and reproductive toxicity data.

' E u r o p e a n Inst i tu te of O n c o l o ~ ' , 20141 Milan , Italy; -~Environmental Hea l th Direc tora te , Hea l th Pro tec t ion Branch, Hea l th and Welfare Canada , Ot tawa, Ontar io , Canada K 1 A 0L2; ~Department of M a t h e m a t i c s & Statistics, Car l e ton Univers i ty , Ot tawa, Ontar io , Canada K1S 5B6; ~Bureau of Radia t ion and Medica l Devices , E n v i r o n m e n t a l Hea l th Di rec tora te , Hea l th Pro tec t ion Branch, Hea l th and Welfare Canada , Ot tawa, Ontar io , C a n a d a K 1 A 0L2; a n d ~Office of the Direc tor , In te rna t iona l Agency for Research on Cancer , Lyon, F rance

Tests for trend in tumour response rates with increasing dose in long-term laboratory studies of carcinogenicity that take into account historical control information are discussed. The theoretical basis for these tests is described, and their small-sample properties evaluated using computer simulation. The performance of these tests is also evaluated using data from carcinogenicity experiments conducted under the U.S. National Toxicology Program. Based on these results, recommendations are made as to the most appropriate tests in practice. When the assumptions underlying these tests are satisfied, the use of historical control information is shown to result in an increase in power relative to the classical Cochran-Armitage test that is widely used without historical controls.

Quantitative estimates of human health risk are often based on mathematical models fit to experimental or epidemiological data. Recent years have witnessed a trend towards the use of mechanistic models in risk assessment applications. Such models afford a more biologically based interpretation of the data and a firmer scientific basis for extrapolation beyond the conditions under which the original data were obtained. In this article, we review some recent advances in the development of biologically based models for mutagenesis, carcinogenesis and developmental toxicity. Pharmacokinetic and receptor-binding models and their roles in mechanistic risk assessment are also discussed. The future of mechanistic research in risk assessment is contemplated, including the need for more elaborate experiments to obtain the data necessary for mechanistic modeling.

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 uncorrelated 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.

Statistical methods are proposed to analyze parallel time series of hospital-based health data and measurements of ambient air pollution. Specifically, associations between the number of daily health events (hospital admissions or emergency-room visits for repsiratory illnesses) and daily levels of ambient air pollutants in the vicinity of several hospitals are examined. A relative-risk regression model is proposed in which the regression parameters are assumed to vary at random among hospitals. Adjustment for seasonal trends in admissions are also considered. Simple computational methods based on generalized estimating equations are explored as the basis for statistical inference. The proposed methods are illustrated on data obtained from 164 acute-care hospitals in Ontario over the May-to-August period for 1983 to 1988. These admission rates are related to ozone levels obtained from 22 monitoring stations maintained by the Ontario Ministry of the Environment.

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-Armitge 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.

Canadian Journal of StatisticsVolume 21, Issue 4 p. 448-459 Article Components of variation in mutagenic potency values based on the Ames almonella test B. G. Leroux, B. G. Leroux Health CanadaSearch for more papers by this authorD. Krewski, D. Krewski Health CanadaSearch for more papers by this author B. G. Leroux, B. G. Leroux Health CanadaSearch for more papers by this authorD. Krewski, D. Krewski Health CanadaSearch for more papers by this author First published: December 1993 https://doi.org/10.2307/3315709Citations: 3AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat References Bernstein, L., Kaldor, J., McCann, J., and Pike, M.C. (1982). An empirical approach to the statistical analysis of mutagenesis data from the Salmonella test. Mutation Res., 97, 267–281. 10.1016/0165-1161(82)90026-7 CASPubMedWeb of Science®Google Scholar Broekhoven, L.H., and Nestmann, E. (1991). Statistical analysis of the Ames test. Statistics in Toxicology ( D. Krewski and C.A. Franklin, eds.), Gordon & Breach, New York, 205–263. Google Scholar Claxton, L.D., Toney, S., Perry, E., and King, L. (1984). Assessing the effect of colony counting methods and genetic drift on Ames bioassay results. 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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.

Twenty laboratories worldwide participated in a collaborative trial sponsored by the International Programme on Chemical Safety on the mutagenicity of complex mixtures as expressed in the Salmonella/microsome assay. The U.S. National Institute of Standards and Technology provided homogeneous reference samples of urban air and diesel particles and a coal tar solution to each participating laboratory, along with samples of benzo[a]pyrene and 1-nitropyrene which served as positive controls. Mutagenic potency was characterized by the slope of the initial linear component of the dose-response curve. Analysis of variance revealed significant interlaboratory variation in mutagenic potency, which accounted for 57-96% of the total variance on a logarithmic scale, depending on the sample, strain and activation conditions. Variation among replicate extractions of organic material (required for the air and diesel particles) and among replicate bioassays within the same laboratory was also appreciable. The average potencies for air and diesel particles in laboratories using Soxhlet extracts were not significantly different from those in laboratories using sonication, although there was larger interlaboratory variation for the Soxhlet method.Repeatability (which approximates the coefficient of variation within laboratories) ranged from 18 to 40% for air and diesel particles extracted using sonication, depending on the strain and activation conditions. Repeatability of Soxhlet-extracted air and diesel particles, however, ranged from about 37 to 89% including outliers and from about 11 to 31% excluding outliers. Repeatability of the coal tar sample and the 2 positive controls was in the range 18-34%. Reproducibility (which approximates the coefficient of variation between laboratories) was generally at least twice repeatability, and exceeded 100% for Soxhlet-extracted air and diesel particles, as well as 1-nitropyrene. Reanalysis of the data omitting observations of more than 1500 revertants/plate generally had little effect on these results. Elimination of outlying observations had limited impact, with the exception of Soxhlet-extracted air and diesel particles. In this case, reproducibility of bioassay results was notably improved, due largely to the omission of results for replicate extractions which varied more than 5-fold within one laboratory. Normalization of the log potency slopes for the mixtures by the corresponding slopes for benzo[a]pyrene tended to reduce this variation, although variation was increased after normalization by 1-nitropyrene. Adjustment for the percentage of organic matter extracted from the air and diesel particulate samples had little effect on variation for sonication-extracted particles, whereas variation was reduced for diesel particles and increased for air particles for Soxhlet.

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 test 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 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.

Considerable information on the carcinogenic potential of chemical and radiological agents has accumulated from the epidemiological and toxicological studies conducted to date. In this article, we discuss dose-response relationships in carcinogenesis from both empirical and theoretical points of view. Emphasis is placed on the application of biologically based models to describe observed dose-response relationships for exposure to single and multiple agents known to increase cancer risk. The implications of these observations for inferences about possible mechanisms of carcinogenesis are explored.

The International Programme on Chemical Safety (IPCS) sponsored an international collaborative study to examine the variability associated with the extraction and bioassay of standard reference materials (SRMs) that are complex environmental mixtures provided by the U.S. National Institute of Standards and Technology (NIST). The study was also intended to evaluate the feasibility of establishing bioassay reference values and ranges for the SRMs. Twenty laboratories from North America, Europe, and Japan participated in the study. As part of the mandatory core protocol, each laboratory extracted the organic material from two particulate samples and bioassayed these extracts. A coal tar polycyclic aromatic hydrocarbon (PAH) solution and two mutagenic control compounds were also subjected to bioassay without prior extraction by the participating laboratories. The bioassay used was the Salmonella/microsomal plate incorporation assay. For the optional portion of the study, a laboratory was free to use the SRMs for any type of exploratory research.The primary purpose of the required portion of the study was to estimate the intra- and inter-laboratory variability in mutagenic potencies of the test materials and to determine whether or not the NIST mixtures could be used as reference materials by others performing the Salmonella assay. Repeatability (intra-laboratory variance) of the bioassay results ranged from 16% to 88% depending on the SRM and the bioassay conditions (tester strain and metabolic activation), whereas reproducibility (inter-laboratory variance) ranged from 33% to 152%. Between-laboratory variability was the main source of variation accounting for approximately 55-95% of the total variation for the three environmental samples. Variation in the mutagenic potency of the control compounds was comparable, with the exception of 1-nitropyrene for which the reproducibility ranged from 127% to 132%.In summary, NIST SRMs provided useful materials for an international inter-laboratory study of complex mixtures. By establishing both intra-and inter-laboratory variance for the mutagenicity results for these materials, the usefulness of these SRMs as reference materials for the Salmonella bioassay was established, critical procedures within the bioassay protocol were identified, and recommendations for future efforts were delineated.