Environmental toxicity has significant impact on agricultural system and ecology, and consequently, on human being. The population explosion, particularly, in the Indian sub-continent and China, and to boost production and combat wastage of agricultural products, fertilizers and pesticides have been used, more extensively in the past six decades. There is a countless number of environmental stressors, some man made and not all working reconcilably, whose composite impact on agricultural system, plants and all living creatures has been disasterous. A statistical appraisal of environmental toxicology in relation to human health and agricultural epidemiology is made. Routine data mining approaches may be quite misleading in this venture. Keywords: Animal husbandry, Arsenic contamination, Bacteria, Chemical dumping, Cold-storage contamination, Contamination of food and drinks, Data mining, De-forestation, E-coli, E-waste, Ecology, Epidemiology, Environmental health sciences, Farmentation, Genetics and genomics, Green house effects, Ground water, Horticulture, Human waste, Irrigation, Pesticides, Plant toxins, Pollution, Risk-analysis, Saline water effect, Sanitary landfill, Statistical assessment, Sub-soil contamination, Tsunami, Urbanization.

  In this paper we address the effect on the interaction from the joint misclassification of two exposure variables in three- way contingency tables. Two types of interaction, additive and multiplicative, are used to measure the effect of misclassification. Bias-adjusted cell proportions that account for the misclassification bias are presented. The data set of the lung cancer deaths from the mesothelioma tumors is used as an example to illustrate the effect on workers who are jointly exposed to two types of asbestos fibers, amphibole and chrysotile. Because no validation data are available, the theory of counterfactual is used to construct potential true [counterfactual] tables from the misclassified observed [factual] table. Because there are various possible true [counterfactual] tables, a study on how sensitive the effect on the interaction exerted by two misclassified exposure factors is then conducted. The result of the sensitivity analysis shows that the effect on the interaction by the joint misclassification of two exposure factors shouldn?t be ignored. In particular, the inference of no interaction could be dramatically changed under either the additive or multiplicative criterion if the data are misclassified. Keywords: Additive/ multiplicative interaction, Asbestos, Counterfactuals, Misclassification, Mesothelioma cancers.

  Humans are exposed to thousands of chemicals, some of which are potentially toxic and even carcinogenic. For example, farmers are exposed to pesticides, workers cleaning oil spills are exposed to complex mixtures of compounds, miners are exposed to various chemicals in the dust they inhale and so on. Identification of toxins and carcinogens among such exposures and determination of their effects on human health is a complex process. While epidemiological studies at the population level serve an important purpose to this end, laboratory based toxicological studies play an equally important role. Despite the fact that extrapolation from lower order animals and cell lines to humans is a challenge, a major advantage of laboratory based toxicological studies is that one can control for various confounders when evaluating a chemical. For this reason toxicological studies, e.g. the standard two-year rodent cancer bioassay, are widely used for evaluating toxicity and carcinogenicity of various chemicals. Although such assays are considered to be robust and informative, they tend to be slow and expensive. Since not every chemical humans are exposed to is a toxin or a carcinogen, performing a rodent cancer bioassay on every chemical may not be time or cost effective. Consequently, there is considerable interest in conducting high or medium throughput screening assays using cells or lower order animals such as nematodes (e.g. Caenorhabditiselegans). Such assays are designed to evaluate several thousands of chemicals in a single experimental run, resulting in substantial reduction in cost and time. There are, however, several statistical issues that need to be considered when designing and analyzing such studies. The focus of this paper is to survey some of the statistical methods used for analyzing data obtained from the rodent cancer bioassay and those obtained from quantitative high throughput screening (qHTS) assays. Some of the statistical challenges will also be described. Keywords: Carcinogenicity, Epidemiology, Nonlinear regression analysis, Optimal designs, Pesticides, Quantitative high through put screening assays, Toxicology.

  A repeated measures study was conducted in Pabna, Bangladesh to investigate factors that influence biomarkers of arsenic exposure. Drinking water arsenic concentrations were measured by inductively-coupled plasma mass spectrometry (ICP-MS) and urinary arsenic species [arsenite (As3), arsenate (As5), monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA)] were detected using High Performance Liquid Chromatography (HPLC) and Hydride Generated Atomic Absorption Spectrometry (HGAAS). Linear mixed effects models with random intercepts were used to evaluate the effects of arsenic contaminated drinking water, genetic polymorphisms in glutathione-S-transferase (GSTT1 and GSTM1) on total urinary arsenic, primary methylation index [MMA/(As3+As5)], secondary methylation index (DMA/MMA), and total methylation index [(MMA+DMA)/(As3+As5)]. Drinking water arsenic concentrations were positively associated with total urinary arsenic concentrations and total methylation index. A significant gene-environment interaction was observed between urinary arsenic exposure in drinking water GSTT1 but not GSTM1 where GSTT1 null individuals had a slightly higher excretion rate of arsenic compared to GSTT1 wildtypes after adjusting for other factors. Additionally, individuals with GSTT1 null genotypes had a higher primary methylation index and lower secondary methylation index compared to GSTT1 wildtype after adjusting for other factors. This data suggests that GSTT1 contributes to the observed variability in arsenic metabolism. Since individuals with a higher primary methylation index and lower secondary methylation index are more susceptible to arsenic related disease, these results suggest that GSTT1 null individuals may be more susceptible to arsenic-related toxicity. No significant associations were observed between GSTM1 and any of the arsenic methylation indices. Keywords: Arsenic, Methylation, Urinary arsenic, GSTT1, Gene environment interaction, Bangladesh, Environmental health.

  This article is based on a study by Carbonari et al. (1999) in which they used the transtheoretical stages of change model to study the smoking cessation process in smokers. They introduced Markov matrices which are constructed for each six month interval to study the movement from one stage of cessation to the next. In this paper the matrices derived by them are used to obtain the probability distribution of the amount of time an individual spends in the various stages during the 30 month period of the study. Keywords: Markov chains, Duration of stay.

  Robust statistical methods, such as M-estimators, are needed for nonlinear regression models because of the presence of outliers/influential observations and heteroscedasticity. Outliers and influential observations are commonly observed in many applications, especially in toxicology and agricultural experiments. For example, dose response studies, which are routinely conducted in toxicology and agriculture, sometimes result in potential outliers, especially in the high dose groups. This is because response to high doses often varies among experimental units (e.g., animals). Consequently, this may result in outliers (i.e., very low values) in that group. Unlike the linear models, in nonlinear models the outliers not only impact the point estimates of the model parameters but can also severely impact the estimate of the information matrix. Note that, the information matrix in a nonlinear model is a function of the model parameters. This is not the case in linear models. In addition to outliers, heteroscedasticity is a major concern when dealing with nonlinear models. Ignoring heteroscedasticity may lead to inaccurate coverage probabilities and Type I error rates. Robustness to outliers/influential observations and to heteroscedasticity is even more important when dealing with thousands of nonlinear regression models in quantitative high throughput screening assays. Recently, these issues have been studied very extensively in the literature (references are provided in this paper), where the proposed estimator is robust to outliers/influential observations as well as to heteroscedasticity. The focus of this paper is to provide the theoretical underpinnings of robust procedures developed recently. Keywords: Asymptotic linearity, Heteroscedasticity, M-estimation procedure, Nonlinear regression model.

  Millions of people are at risk due to high concentrations of arsenic in drinking water and thousands of them are suffering from arsenic related diseases (called Arsenicosis) in the state of West Bengal in India. The loss of human well being due to arsenicosis and health impact is multidimensional. Progress has been made over past two decades to understand the cause, magnitude and diversity of the arsenic problem and to try out some remedial measures. This article collects information from both official sources and first hand detailed field level personal interviews in 20 remote villages of Jalangi Block of Murshidabad district of West Bengal on arsenic remediation and social impact. Arsenicosis is found to have adverse impact on major constituents of human well being: labour productivity, income earning capacity, longevity, inter-generational poverty, and health status. Also, social exclusion enhances indirectly due to social attitude towards arsenic affected health disorders leading to social discontent. Any future arsenic remediation technology deployment needs very careful planning with a clear understanding of the complexity and the multiple challenges of the problem per se for social acceptance in an already intervened social context with living memories of non-functioning technologies in the field. Keywords: Arsenic poisoning, Remediation technology, Socio-economic fallout, Social exclusion.

  High inputs of agrochemicals and water, and widespread practice of monoculture are believed to be the major reasons for India?s success in modern agriculture practice. However, there are very few comprehensive analyses of potential adverse health outcomes that may be related to these changes. This study aims to compare high-input and low-input agricultural practices in the southern Indian state of Karnataka and the consequences for health of people in these communities. The study identified four major visible impacts: mosquito borne diseases, changing nutritional status, occupational hazards, and inequity in development. Ecological changes on account of widespread cultivation of rice have further augmented mosquito breeding, and thus there has been a surge in the incidence of Japanese encephalitis and malaria. The traditional coarse cereals have been replaced by mill-polished rice. The prevalence of overweight has emerged as a new public health challenge. In the high-input area, mechanization has resulted in more occurrences of serious accidents and injuries. Output-driven and market-oriented modern agricultural practices have changed the ecology and disease pattern in this area in India, and our survey indicated significant health effects associated with these changes. Keywords: Ecological changes, Food safety, Occupational hazards, Communicable diseases, Nutritional status.

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  During our last 25 years? field survey in India, we have registered groundwater arsenic contamination and its health effects from the states of West-Bengal, Jharkhand, Bihar, Uttar Pradesh in the flood plain of Ganga River; Assam and Manipur in the flood plain of Brahamaputra and Imphal Rivers. Groundwater of Rajnandgaon village in Chhattisgarh state is also arsenic contaminated and some people had arsenical skin lesions although the source of arsenic in Chhattisgarh is not from flood plains of Newer Alluvium (Holocene) as in Ganga, Brahmaputra, and Imphal rivers. The magnitude of arsenic contamination in Chhattisgarh state is negligible compared to flood plain contamination. The total area and population of these states are 529,674 km2& approx. 359 million respectively. So far we have analyzed 171,387 hand tube-well water samples from all these states and have found about 50% of hand tube-wells in affected districts of these states is arsenic affected above WHO guideline value of arsenic in drinking water (10 µg/L). In our preliminary survey screening 100,731 people from arsenic affected villages of West Bengal, Jharkhand, Bihar, Uttar Pradesh and Chhattisgarh, 10,113 patients were registered with different kinds of arsenical skin lesions. Arsenic neuropathy as well as adverse pregnancy outcomes such as spontaneous abortion, still-birth, preterm birth and low birth weight were recorded. We have found arsenic related cancer in many patients suffering from arsenic related skin lesions in the affected districts. Infants and children drinking arsenic contaminated water are at high risk. Analyses of 42,000 biological samples from arsenic affected areas showed elevated level of arsenic in both patients and non-patients indicating that many are sub-clinically affected. Groundwater arsenic contamination and its health effects from West-Bengal surfaced in 1982. Even, after thirty years, with our every new survey, we are finding new arsenic affected villages and people suffering from arsenic related diseases from West-Bengal. People in newly arsenic identified states are in more danger, as many are not aware of their arsenic contamination in hand tube-wells and unknowingly drinking arsenic contaminated groundwater. Even after spending millions dollars for arsenic testing in groundwater and for providing arsenic safe water to the villagers from contaminated hand tube-wells, the overall result has not been satisfactory. So far little attempts have been made for treating the patients suffering from arsenic toxicity and to reduce their arsenic related social and socio-economic problems. Proper watershed management and economical utilization of all available alternative safe sources of water has not been tried. From our last 25 years field experience we have realized that to combat the arsenic crises we need to educate and create awareness among the villagers about the dangers of arsenic toxicity and the importance of using arsenic safe water. This can only be achieved by active community participation and whole-hearted support from the government and the arsenic researchers. Keywords: Arsenic, Groundwater, Health effects, Arsenic in food chain, Social and socio-economic impact, Mitigation.