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Background Bangladesh has seen a significant decline in child mortality in recent decades, but morbidity among children <5 y of age remains high. The aim of this analysis was to examine trends and identify risk factors related to child morbidity in Bangladesh. Methods This analysis is based on data from four successive cross-sectional Bangladesh Demographic and Health Surveys for the years 2007, 2011, 2014 and 2017–18. Several count regression models were fitted and the best model was used to identify risk factors associated with morbidity in children <5 y of age. Results According to the results of the trend analysis, the prevalence of non-symptomatic children increased and the prevalence of fever, diarrhoea and acute respiratory infections (ARIs) decreased over the years. The Vuong's non-nested test indicated that Poisson regression could be used as the best model. From the results of the Poisson regression model, child age, sex, underweight, wasted, stunting, maternal education, wealth status, religion and region were the important determinants associated with the risk of child morbidity. The risk was considerably higher among women with a primary education compared with women with a secondary or greater education in Bangladesh. Conclusions This analysis concluded that child morbidity is still a major public health problem for Bangladesh. Thus it is important to take the necessary measures to reduce child morbidity (particularly fever, diarrhoea and ARI) by improving significant influencing factors. [ABSTRACT FROM AUTHOR]

Background: This research aimed to examine the factors at both the individual and community levels that are linked to discrimination experienced by women aged 15–49 in Bangladesh. Methods: The relevant data was taken from the 2019 Multiple Indicator Cluster Survey in Bangladesh. The risk factors for discrimination against women in Bangladesh were determined using multilevel logistic regression models. Results: The overall prevalence of discrimination against women was found to be 10.4% (95% CI: 10.1–10.6). Based on the final model (Model 1V), at the individual level higher odds of discrimination were observed among women from poor (AOR:1.21,95%CI: 1.12–1.32) and middle income households (AOR:1.12, 95%CI:1.02–1.22) compared to those from rich households etc. Women who have never used ICT were 1.27 times (AOR = 1.27, 95% CI = 1.07–1.51) higher odds of discrimination when compared with women who were ICT exposed. Respondents who married before 18 years 10% more likely to (AOR = 1.10, 95% CI:1.02–1.19) discriminated than women married aged 18 years old or above. Women from urban communities were 15% less likely to experience discrimination than their rural counterparts. In comparison to the Sylhet Division, women in the Barisal, Chattogram, Dhaka, Khulna Mymensingh, Rajshahi, and Rangpur Divisions were respectively 3.02, 1.84, 1.68, 2.06, 4.97, 4.06, and 1.74 times more likely to experience discrimination. Conclusion: Findings revealed that various individual-level factors such as wealth index, CEB, ICT exposure, marital status, functional difficulty, age, women's happiness, magazine and radio exposure, age at marriage, current contraceptive use, polygamy, husband beating, place of attack, and household head age were found to have a significant association with women discrimination. Community-level factors such as residence and division were also found to have a notable impact on discrimination. Policymakers should incorporate substantial components targeting both individual and community levels into intervention programs with the goal of raising awareness about women's discrimination. [ABSTRACT FROM AUTHOR]

This study was designed to highlight the culture techniques and socio-economic conditions of cuchia farmers. The study used primary data collected from Adamdighi upazila in Bogura district of Bangladesh for a period of 120 days from 05 March to 26 June 2016. Three different experimental treatments were used. For sociodemographic factors, two-stage stratified sampling design was used in this study. The target population was Cuchia farmers. So, a total of 90 Cuchia farmers were finally selected for analysis. One way ANOVA with multiple comparison test was applied to identify significant treatments. Cobb-Douglass production function was used to determine the contribution of the most important variables in the production process of Cuchia farming. Analysis showed that, more than 80 percent of Cuchia fish survived with fish fry and snail meat feed. All the mean differences were statistically significant to compare Cuchia meals between two periods of time. For both cases weight and height, the result showed that vermi meals were significantly different from dried fish, while fish fry and dried fish were also significantly different. The estimated result showed that a significant negative association was found between cost of fish fry feed and gross income of Cuchia farming. The cost of seed also showed a negative association. In the contrary, this study found positive and significant association in cost of Dried fish, cost of human labor. It is well known fact that Cuchia contain higher amount of nutritious value which is important for human health. Therefore, policymakers should pay special attention to this sector. [ABSTRACT FROM AUTHOR]

Background: Natural restoratives from traditional medicinal plants are considered to be a convenient, potent, and risk-free substitute treatment for hyperglycaemia. Our objective was to explore the activity of the crude extract of Heritiera fomes on postprandial hyperglycaemia by assessing relative measurements in a laboratory animal model. Methods: The Streptozotocin induced diabetic rat (n = 88, twenty-two per group) was used for the glucose tolerance test as an initial support for the study. BaSO4 was administered orally as a marker to measure gut motility after one hour of methanolic extract (500 mg/kg body weight) administration where, only purified water (10 ml/kg) was used to treat the control group (n = 12) and a dose (500 mg/kg) of H. fomes extract was used for the test group (n = 12 in each group). After 60 min of incubation of the mixture of extract and glucose with 10% (v/v) yeast cell suspension, the absorbance was measured to determine the capacity of glucose absorption by yeast cells. Sixty Long Evans rats (n = 12 in each group) were used to assess disaccharidase enzyme activity as µmol/mg protein per hour by Lowry's protein estimation method. The carbohydrate absorption investigation was executed to evaluate the leftover sucrose content in the gastrointestinal system (n = 64). Results: After oral administration of MHFL (71.84%), MHFB (71.41%), and MHFR (72.55%), GI motility (%) increased significantly (p < 0.001) compared to the control group (59.06%). A significant increase in glucose uptake and adsorption capacity measured by different concentrations of glucose ensures the decrease of glucose bound rate and a significant drop in blood glucose concentration. The significant (p < 0.001) decrease in intestinal disaccharidase activity of MHFL (1.40), MHFB (1.36), and MHFR (1.20) in comparison to the control group (1.50) indicates that the presence of H. fomes may reduce glucose absorption in the small bowel. Significant (p < 0.001 & p < 0.05) accumulation of sucrose content in the six different parts of the GI tract suggests the absorption of sucrose was decreased. Conclusions: The findings of this study provide evidence on probable mechanisms for the anti-diabetic characteristics of H. fomes, and it is predicted that this plant will be studied further for the development of strong anti-hyperglycemic medicines. [ABSTRACT FROM AUTHOR]

Introduction: Handwashing practice is an effective way to minimize severe infectious diseases such as COVID-19, diarrhea, and pneumonia. The study aimed to explore the prevalence of handwashing behavior and associated determinants in rural and urban areas of Bangladesh. Methods: The research was performed using cross-sectional survey data from the 2019 Bangladesh Multiple Indicator Cluster Survey, and 61,242 household members were the sample for this study. The Chi-square test was applied for the bivariate analysis. A generalized linear mixed-effects model was used to identify the risk factors of practicing handwashing in both urban and rural areas of Bangladesh. Results: Only 65% of the country's households had access to handwashing facilities (place, water, and materials). While urban dwellers were more likely to wash their hands, rural dwellers were only 63% likely to do so. The level of education of household heads, wealth status, division, number of family members, sanitation facilities, and water source were the key factors associated with handwashing behavior. This study revealed that the odds were significantly lower among illiterate respondents compared to those with secondary and above-secondary education in both areas of Bangladesh, and a positive association was found between wealth status and handwashing behavior practiced in both urban and rural areas. In this study, the size of the family was statistically significant for both rural and urban areas of Bangladesh. Conclusion: Handwashing is the most prominent consideration for controlling COVID-19. Policymakers are striving to improve handwashing facilities by increasing awareness-raising programs, especially among rural residents of Bangladesh. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of this research was to predict mental illness among university students using various machine learning (ML) algorithms. Methods: A structured questionnaire-based online survey was conducted on 2121 university students (private and public) living in Bangladesh. After obtaining informed consent, the participants completed a web-based survey examining sociodemographic variables and behavioral tests (including the Patient Health Questionnaire (PHQ-9) scale and the Generalized Anxiety Disorder Assessment-7 scale). This study applied six well-known ML algorithms, namely logistic regression, random forest (RF), support vector machine (SVM), linear discriminate analysis, K-nearest neighbors, Naïve Bayes, and which were used to predict mental illness among university students from Dhaka city in Bangladesh. Results: Of the 2121 eligible respondents, 45% were male and 55% were female, and approximately 76.9% were 21-25 years old. The prevalence of severe depression and severe anxiety was higher for women than for men. Based on various performance parameters, the results of the accuracy assessment showed that RF outperformed other models for the prediction of depression (89% accuracy), while SVM provided the best result than other models for the prediction of anxiety (91.49% accuracy). Conclusion: Based on these findings, we recommend that the RF algorithm and the SVM algorithm were more moderate than any other ML algorithm used in this study to predict the mental health status of university students in Bangladesh (depression and anxiety, respectively). Finally, this study proposes to apply RF and SVM classification when the prediction of mental illness status is the core interest. [ABSTRACT FROM AUTHOR]