Unraveling birth weight determinants: Integrating machine learning, spatial analysis, and district-level mapping

Despite a decrease in the prevalence of low birth weight (LBW) over time, its ongoing significance as a public health concern in Bangladesh remains evident. Low birth weight is believed to be a contributing factor to infant mortality, prolonged health complications, and vulnerability to non-communicable diseases. This study utilizes nationally representative data from the Multiple Indicator Cluster Surveys (MICS) conducted in 2012-2013 and 2019 to explore factors associated with birth weight. Modeling birth weight data considers interactions among factors, clustering in data, and spatial correlation. District-level maps are generated to identify high-risk areas for LBW. The average birth weight has shown a modest increase, rising from 2.93 kg in 2012-2013 to 2.96 kg in 2019. The study employs a regression tree, a popular machine learning algorithm, to discern essential interactions among potential determinants of birth weight. Findings from various models, including fixed effect, mixed effect, and spatial dependence models, highlight the significance of factors such as maternal age, household head's education, antenatal care, and few data-driven interactions influencing birth weight. District-specific maps reveal lower average birth weights in the southwestern region and selected northern districts, persisting across the two survey periods. Accounting for hierarchical structure and spatial autocorrelation improves model performance, particularly when fitting the most recent round of survey data. The study aims to inform policy formulation and targeted interventions at the district level by utilizing a machine learning technique and regression models to identify vulnerable groups of children requiring heightened attention.

From the local disparities to national realities: Mapping and multilevel modeling of catastrophic health expenditure in Bangladesh using HIES 2016

In developing nations, catastrophic health expenditures have become an all-too-common occurrence, threatening to push households into impoverishment and poverty. By analyzing the Household Income and Expenditure Survey 2016, which features a sample of 46,080 households, this study provides a comprehensive district-by-district analysis of the variation in household catastrophic health expenditures and related factors. The study utilizes a multilevel logistic regression model, which considers both fixed and random effects to identify factors associated with catastrophic health expenditure. The findings of the study indicate that districts located in the eastern and southern regions are at a significantly higher risk of experiencing catastrophic health expenditures. A potential explanation for this trend may be attributed to the high prevalence of chronic diseases in these districts, as well as their economic conditions. The presence of chronic diseases (AOR 5.45 with 95% CI: 5.14, 5.77), presence of old age person (AOR 1.50 with 95% CI: 1.39, 1.61), place of residence (AOR 1.40 with 95% CI: 1.14, 1.73) are found to be highly associated factors. Additionally, the study reveals that the thresholds used to define catastrophic health expenditures exhibit substantial variation across different regions, and differ remarkably from the threshold established by the WHO. On average, the thresholds are 23.12% of nonfood expenditure and 12.14% of total expenditure. In light of these findings, this study offers important insights for policymakers and stakeholders working towards achieving universal health coverage and sustainable development goals in Bangladesh.

Understanding the impact of COVID-19 on the informal sector workers in Bangladesh

The COVID-19 pandemic put dents on every sector of the affected countries, and the informal sector was no exception. This study is based on the quantitative analyses of the primary data of 1,867 informal workers of Bangladesh to shed light on the impact of the pandemic-induced economic crisis on this working class. The survey was conducted between 8 July and 13 August 2020 across the eight administrative divisions of the country. Analysis points out that about ninety percent of these workers faced an income and food expenditure drop during the lockdown. The effect was higher in males, particularly among the urban-centric and educated males engaged in services and sales. The findings suggest that policy support is needed for the informal workers to face such a crisis.

Efficiency Measurement on Banking Sector in Bangladesh

Nowadays banking sector in Bangladesh plays a considerable role in the economic development and business improvement, in this aspect ranking of banks is vital. In this study, an attempt has been made to rank some of the Bangladeshi Banks. Also, the most efficient bank is identified here. Data Envelopment Analysis is used for this purpose. The data from the annual reports of different banks are used in this study for the purpose of efficiency checking. In Data Envelopment Analysis two types measurement techniques are used – constant returns to scale and variable returns to scale. Since this study attempts to maximize output, that is, the operating profit, so the output oriented Data Envelopment Analysis is used here. The most efficient bank is identified here by the highest efficiency score obtained by that specific bank. Dhaka Univ. J. Sci. 61(1): 1-5, 2013 (January) DOI: http://dx.doi.org/10.3329/dujs.v61i1.15088

A stochastic model for early identification of infectious disease epidemics with application to measles cases in Bangladesh

In this article, a stochastic modeling approach was employed for the detection of epidemics in advance that was based on a negative binomial model with 2 components: an endemic component and an epidemic component. This study used monthly measles cases from January 2000 to August 2009 collected from the Expanded Program on Immunization, Bangladesh. General optimization routines provided the maximum likelihood estimates with corresponding standard errors. The negative binomial model with both seasonal endemic and epidemic components was shown to provide adequate fit with no measles epidemic during September 2008 to August 2009.