Statistical modeling suggests that antiandrogens in effluents from wastewater treatment works contribute to widespread sexual disruption in fish living in English rivers.

BACKGROUND: The widespread occurrence of feminized male fish downstream of some wastewater treatment works has led to substantial interest from ecologists and public health professionals. This concern stems from the view that the effects observed have a parallel in humans, and that both phenomena are caused by exposure to mixtures of contaminants that interfere with reproductive development. The evidence for a "wildlife-human connection" is, however, weak: Testicular dysgenesis syndrome, seen in human males, is most easily reproduced in rodent models by exposure to mixtures of antiandrogenic chemicals. In contrast, the accepted explanation for feminization of wild male fish is that it results mainly from exposure to steroidal estrogens originating primarily from human excretion. OBJECTIVES: We sought to further explore the hypothesis that endocrine disruption in fish is multi-causal, resulting from exposure to mixtures of chemicals with both estrogenic and antiandrogenic properties. METHODS: We used hierarchical generalized linear and generalized additive statistical modeling to explore the associations between modeled concentrations and activities of estrogenic and antiandrogenic chemicals in 30 U.K. rivers and feminized responses seen in wild fish living in these rivers. RESULTS: In addition to the estrogenic substances, antiandrogenic activity was prevalent in almost all treated sewage effluents tested. Further, the results of the modeling demonstrated that feminizing effects in wild fish could be best modeled as a function of their predicted exposure to both anti-androgens and estrogens or to antiandrogens alone. CONCLUSION: The results provide a strong argument for a multicausal etiology of widespread feminization of wild fish in U.K. rivers involving contributions from both steroidal estrogens and xeno-estrogens and from other (as yet unknown) contaminants with antiandrogenic properties. These results may add farther credence to the hypothesis that endocrine-disrupting effects seen in wild fish and in humans are caused by similar combinations of endocrine-disrupting chemical cocktails.

Concentration of aluminium in breast cyst fluids collected from women affected by gross cystic breast disease

Gross cystic breast disease (GCBD) is the most common benign breast disorder, but the molecular basis of cyst formation remains to be identified. If the use of aluminium-based antiperspirant salts is involved in the etiology of gross breast cyst formation, it might be expected that aluminium would be at elevated levels in human breast cyst fluid (BCF). Aluminium was measured by ICP-MS in 48 samples of BCF, 30 samples of human blood serum and 45 samples of human breast milk at different stages of lactation (colostrum, intermediate, mature). The median level of aluminium in apocrine type I BCF (n:= 27, 150 mu g I-1) was significantly higher than in transuclative type II BCF (n = 21, 32 mu g I-1; P < 0.0001). By comparison, aluminium measurements gave a median concentration of 6 mu g I-1 in human serum and 25 mu g I-1 in human breast milk, with no difference between colostrum, intermediate and mature milk. Levels of aluminium were significantly higher in both types of BCF than in human serum (P < 0.0001). However when compared with human breast milk, aluminium levels were only significantly higher in apocrine type I BCF (P < 0.0001) and not in transudative type II BCF (P = 0.152). It remains to be identified why such high levels of aluminium were found in the apocrine type I BCF and from where the aluminium originated. However, if aluminium-based antiperspirants are found to be the source and to play any causal role in development of breast cysts, then it might become possible to prevent this common breast disorder. Copyright (C) 2008 John Wiley & Sons, Ltd.

Extending Zelterman’s approach for robust estimation of population size to zero-truncated clustered data

Estimation of population size with missing zero-class is an important problem that is encountered in epidemiological assessment studies. Fitting a Poisson model to the observed data by the method of maximum likelihood and estimation of the population size based on this fit is an approach that has been widely used for this purpose. In practice, however, the Poisson assumption is seldom satisfied. Zelterman (1988) has proposed a robust estimator for unclustered data that works well in a wide class of distributions applicable for count data. In the work presented here, we extend this estimator to clustered data. The estimator requires fitting a zero-truncated homogeneous Poisson model by maximum likelihood and thereby using a Horvitz-Thompson estimator of population size. This was found to work well, when the data follow the hypothesized homogeneous Poisson model. However, when the true distribution deviates from the hypothesized model, the population size was found to be underestimated. In the search of a more robust estimator, we focused on three models that use all clusters with exactly one case, those clusters with exactly two cases and those with exactly three cases to estimate the probability of the zero-class and thereby use data collected on all the clusters in the Horvitz-Thompson estimator of population size. Loss in efficiency associated with gain in robustness was examined based on a simulation study. As a trade-off between gain in robustness and loss in efficiency, the model that uses data collected on clusters with at most three cases to estimate the probability of the zero-class was found to be preferred in general. In applications, we recommend obtaining estimates from all three models and making a choice considering the estimates from the three models, robustness and the loss in efficiency. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)