The study attempts to develop an ordinal logistic regression (OLR) model to identify the determinants of child malnutrition instead of developing traditional binary logistic regression (BLR) model using the data of Bangladesh Demographic and Health Survey 2004.

This study explores the impact of financial inclusion on financial resilience in Bangladesh, using World Bank data on global financial inclusions. It finds that respondents with financial accounts are more resilient than those without accounts. The chances of being financially resilient are around 1.4 times higher for account holders than their counterparts. There was also a significant relationship between gender and financial resilience; males are 1.4 times more resilient than females when other covariates are considered in the regression model.

Malnutrition is a persistent problem for both children and mother throughout the world. In developing countries malnutrition is an important root of infant and young child mortality, morbidity and reduced life span. It is considered that if malnutrition cannot be reduced and prevented, it will be impossible to achieve many of the Millennium Development Goals (MDGs) including the goals on extreme poverty and hungry, primary education, child mortality, and incidence of infectious diseases. The World Summit for Children in 1990 recognized malnutrition as a contributing factor in half of all deaths occurring among young children. The nutrition goals for the decade 1990-2000 include reduction of both moderate and severe protein-energy malnutrition among children under five years of age by one half of the 1990 levels (UNICEF, 1990). However, the reduction of child malnutrition by half in a decade was one of the most ambitious goals set by the various summits convened during the 1990s. As a result, all of the nutrition goals were not successfully achieved during the period 1990-2000. As a step towards building a strong foundation for attaining the internally agreed development goals, including the MDGs, a consistent set of intermediate targets and benchmarks during the course of the decade (2000-2010) were set to help the unmet goals (UNICEF, 2003; United Nations, 2001). One of the most important goals regarding nutrition during the period 2000-2010 was the one on reducing child malnutrition among children aged under five by at least one third of the 2000 levels, with special attention paid to children under two years of age—especially reduction of stunted and underweighted children by at least one third during the period 2000-2010 (UNICEF, 2002).

The existence of excess zeros in the distribution of antenatal care (ANC) visits in Bangladesh raises the research question of whether there are two separate generating processes in taking ANC and the frequency of ANC. Thus the main objective of this study is to identify a proper count regression model for the number of ANC visits by pregnant women in Bangladesh covering the issues of overdispersion, zero-inflation, and intra-cluster correlation with an additional objective of determining risk factors for ANC use and its frequency. The data have been extracted from the nationally representative 2014 Bangladesh Demographic and Health Survey, where 22% of the total 4493 women did not take any ANC during pregnancy. Since these zero ANC visits can be either structural or sampling zeros, two-part zero-inflated and hurdle regression models are investigated along with the standard one-part count regression models. Correlation among response values has been accounted for by incorporating cluster-specific random effects in the models. The hurdle negative binomial regression model with cluster-specific random intercepts in both the zero and the count part is found to be the best model according to various diagnostic tools including likelihood ratio and uniformity tests. The results show that women who have poor education, live in poor households, have less access to mass media, or belong to the Sylhet and Chittagong regions are less likely to use ANC and also have fewer ANC visits. Additionally, women who live in rural areas, depend on family members’ decisions to take health care, and have unintended pregnancies had fewer ANC visits. The findings recommend taking both cluster-specific random effects and overdispersion and zero-inflation into account in modelling the ANC data of Bangladesh. Moreover, safe motherhood programmes still need to pay particular attention to disadvantaged and vulnerable subgroups of women.

Micro-level statistics on child undernutrition are highly prioritized by stakeholders for measuring and monitoring progress on the sustainable development goals. In this regard district-representative data were collected in the Bangladesh Multiple Indicator Cluster Survey 2019 for identifying localised disparities. However, district-level estimates of undernutrition indicators - stunting, wasting and underweight - remain largely unexplored. This study aims to estimate district-level prevalence of these indicators as well as to explore their disparities at sub-national (division) and district level spatio-demographic domains cross-classified by children sex, age-groups, and place of residence. Bayesian multilevel models are developed at the sex-age-residence-district level, accounting for cross-sectional, spatial and spatio-demographic variations. The detailed domain-level predictions are aggregated to higher aggregation levels, which results in numerically consistent and reasonable estimates when compared to the design-based direct estimates. Spatio-demographic distributions of undernutrition indicators indicate south-western districts have lower vulnerability to undernutrition than north-eastern districts, and indicate significant inequalities within and between administrative hierarchies, attributable to child age and place of residence. These disparities in undernutrition at both aggregated and disaggregated spatio-demographic domains can aid policymakers in the social inclusion of the most vulnerable to meet the sustainable development goals by 2030.

Food insecurity is an important and persistent social issue in Bangladesh. Existing data based on socio-economic surveys produce divisional and nationally representative food insecurity estimates but these surveys cannot be used directly to generate reliable district level estimates. We deliberate small area estimation (SAE) approach for estimating the food insecurity status at district level in Bangladesh by combining Household Income and Expenditure Survey 2010 with the Bangladesh Population and Housing Census 2011. The food insecurity prevalence, gap and severity status have been determined based on per capita calorie intake with a threshold of 2122 kcal per day, as specified by the Bangladesh Bureau of Statistics.The results show that the food insecurity estimates generated from SAE are precise and representative of the spatial heterogeneity in the socioeconomic conditions than do the direct estimates. The maps showing the food insecurity indicators by district indicate that a number of districts in northern and southern parts are more vulnerable in terms of all indicators. These maps will guide the government, international organizations, policymakers and development partners for efficient resource allocation.