Abstract:Various methods can be applied to build predictive models for the clinical data with binary outcome variables.This research aims to compare and explore the process of constructing common predictive models. Models based on an artificial neural network (the multilayer perceptron) and binary logistic regression were applied and compared in their ability to classifying disease-free subjects and those with diabetes mellitus(DM) diagnosed by glucose level. Demographic, enthropometric and clinical data were collected based on a total of 460 participants aged over 30 years from six villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic. Out of 460 participants 133 (28.91%) suffered from DM, 116 (25.27%) had impaired glucose tolerance (IGT) and the remainder 211 (45.86%) were disease free. Among other factors, family history of diabetes and arsenic exposure were found as significant risk factors for developing diabetes mellitus (DM), with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 73.79% of cases with IGT or DM in the training datasets, 70.96% in testing datasets and 70.4% of all subjects. On the other hand, the sensitivities of artificial neural network architecture for training and testing datasets and for all subjects were 83.4%, 82.25% and 84.33% respectively, indicate better performance than binary logistic regression model.

Objectives: This study was aimed to determine the names and alcohol content or strength of different alcoholic beverages used in different parts of Bangladesh and also to determine contamination with heavy metals and bacteria in some samples. Methods: Eight different types of alcoholic beverages consumed in different parts of Bangladesh were collected and studied in the laboratory of Bangladesh Council of Scientific and Industrial Research (BCSIR). Before sending to the laboratory, samples were stored in a refrigerator at temperature 4-8 degree Celsius. In all samples, strength of ethanol content was studied. Among the samples, Dochuani and Tari was tested for heavy metal, Chubichi and Pochani studied for total viable micro-bacterial contamination. Results: In this study one sample was from Khagrachari (Hilly area) not been reported as manufacture site by the Department of narcotics control of Bangladesh before. Out of eight samples, one was of a Brand company (Keru & Co) and others homemade. Highest concentration, 81.56% was observed in Spirit followed by 37.7% in Dochuani and lowest 2.2% in Tari. Insignificant amount of heavy metal detected in Dochuani and Tari. There was no viable micro-bacterial contamination in samples tested. Conclusions: Without knowing the strength, people are using different types of homemade alcoholic beverages as such in a risk of health hazards as well as death. A national survey need to be conducted to obtain how many types of alcoholic beverages being manufactured, their strength and true picture of alcohol use so that strategy plan can be developed of its healthy use if needed at all.

The well-documented fact that chronic arsenic exposure can lead to skin lesions, atherosclerotic diseases and cancers. The findings of association between arsenic exposure and diabetes mellitus indicate additional risk to human health. The aim of this study was to observe the association of chronic arsenic exposure from drinking water and risk of development of type 2 diabetes mellitus. To this end, a cross-sectional study was conducted in Comilla district of Bangladesh where ground water is heavily contaminated with arsenic. The individuals unexposed to arsenic were recruited from the Jhenaidah district. People with arsenic-related skin lesions were defined as subjects exposed to arsenic. Diabetes was defined if fasting blood glucose (FBG)>6.1 mmol/L following World Health Organization (WHO) guidelines. The common odds ratio for diabetes mellitus among subjects exposed to arsenic was 3.5 (95% confidence interval 1.1-10.9). After adjustment for age, sex and BMI, the Mantel-Haenszel weighted prevalence ratio was 3.5 (95% CI: 1.1-11.1); 3.7 (95% CI: 1.1-11.8) and 4.4 (95% CI: 1.1-17.2) respectively. The indicated relationships were significant (P<0.05). The observations suggested, chronic arsenic exposure through drinking water may be a risk factor of type 2 diabetes mellitus. J Bangladesh Coll Phys Surg 2019; 37(1): 5-12