In many underdeveloped and developing countries, epidemiological and nutritional transitions are leading to an increase in malnutrition, resulting in pediatric diseases and eventually deaths. Therefore, this study intents to determine the important factors of the presence of coexisting forms of malnutrition (CFM), i.e., pediatric undernutrition. This study used the latest Bangladesh Demographic and Health Survey (BDHS)-2017/18 dataset consisting of 7127 under-five children. The logistic regression model has been utilized to gain explicit and in-depth knowledge of the relationship between the presence of pediatric undernutrition with socioeconomic and demographic factors. Findings revealed that about 31%, 22%, and 8% suffered from stunted, underweight, and wasted, respectively. The prevalence of stunted, underweighted, wasted, and CFM among children in the Sylhet division is higher than in any other region. A child of a secondary-level completed mother is 27.6% (OR: 0.724, 95% CI: 0.58-0.90) less likely to suffer from undernutrition than a child of an uneducated mother. The rate of undernutrition of children was less among children of highly educated parents. Age, birth order of the child, twin status, mother's age, body mass index (BMI), working status, parental educational qualification, cooking fuel, toilet facility, region, residence, and wealth index are important for determining the nutritional status of a child. The authors believe that the study findings will be helpful to the policymakers to take proper actions for achieving the sustainable development goal (SDGs) by reducing pediatric undernutrition in Bangladesh by 2030.
BACKGROUND: The reserve of a country is a reflection of the strength of fulfilling its financial liabilities. However, during the past several years, a regular variation of the total reserve has been observed on a global scale. The reserve of Bangladesh is also influenced by several economic and financial indicators such as total debt, net foreign assets, net domestic credit, inflation GDP deflator, net exports (% of GDP), and imports of goods and services (% of GDP), as well as foreign direct investment, GNI growth, official exchange rate, personal remittances, and so on. Therefore, the authors aimed to identify the nature of the relationship and influence of economic indicators on the total reserve of Bangladesh using a suitable statistical model. METHODS AND MATERIALS: To meet the objective of this study, the secondary data set was extracted from the World Bank's website which is openly accessible over the period 1976 to 2020. Moreover, the model used the appropriate splines to describe the non-linearity. The performance of the model was evaluated by the Akaike information criterion (AIC), Bayesian information criterion (BIC), and adjusted R-square. RESULTS: The total reserve of Bangladesh gradually increased since 2001, and it reached its peak in 2020 which was 43172 billion US dollars. The data were first utilized to build a multiple linear regression model as a base model, but it was later found that the model has severe multicollinearity problems, with a maximum value of VIF for GNI of 499.63. Findings revealed that total debt, inflation, import, and export are showing a non-linear relationship with the total reserve in Bangladesh. Therefore, the authors applied the Generalized Additive Model (GAM) model to take advantage of the nonlinear relationship between the reserve and the selected covariates. The overall response, which is linearly tied to the net foreign asset in the GAM model, will change by 14.43 USD for every unit change in the net foreign asset. It is observed that the GAM model performs better than the multiple linear regression. CONCLUSION: A non-linear relationship is observed between the total reserve and different economic indicators of Bangladesh. The authors believed that this study will be beneficial to the government, monetary authorities also to the people of the country to better understand the economy.
Developing Countries , Government , Humans , Bangladesh , Bayes Theorem
BACKGROUND: Potato is a staple food and a main crop of Bangladesh. Climate plays an important role in different crop production all over the world. Potato production is influenced by climate change, which is occurring at a rapid pace according to time and space. OBJECTIVE: The main objective of this research is to observe the variation in potato production based on the discrepancy of the variability in the spatial and temporal domains. The research is based on secondary data on potato production from different parts of Bangladesh and five major climate variables for the last 17 years ending with 2020. METHODS: Bayesian Spatial-temporal modelling for linear, analysis of variance (ANOVA), and auto-Regressive models were used to find the best-fitted model compared with the independent Error Bayesian model. The Watanabe-Akaike information criterion (WAIC) and Deviance Information Criterion (DIC) were used as the model choice criteria and the Markov Chain Monte Carlo (MCMC) method was implemented to generate information about the prior and posterior realizations. RESULTS: Findings revealed that the ANOVA model under the Spatial-temporal framework was the best model for all model choice and validation criteria. Results depict that there is a significant impact of spatial and temporal variation on potato yield rate. Besides, the windspeed does not show any influence on potato production, however, temperature, humidity, rainfall, and sunshine are important components of potato yield rate in Bangladesh. CONCLUSION: It is evident that there is a potential impact of climate change on potato production in Bangladesh. Therefore, the authors believed that the findings will be helpful to the policymakers or farmers in developing potato varieties that are resilient to climate change to ensure the United Nations Sustainable Development Goal of zero hunger.
Climate Change , Solanum tuberosum , Bayes Theorem , Bangladesh , Markov Chains
