Sumonkanti Das | Shahjalal University of Science and Technology (original) (raw)

Papers by Sumonkanti Das

Child indicators research, Jun 21, 2024

The family lives of children and their early childhood development outcomes are attributable to t... more The family lives of children and their early childhood development outcomes are attributable to the level of socioeconomic disadvantage and relative isolation. This study aims to investigate how the disadvantage of the local area (i.e., socioeconomic indexes for areas (SEIFA)) and the remoteness (i.e., accessibility/remoteness index of Australia (ARIA)) contribute to improved prevalence estimates of child development vulnerability in statistical areas level 3 (SA3) and 4 (SA4) across Australia. Data from the 2018 Australian Early Development Census (AEDC) has been used. The study included 308,953 children involved in the AEDC 2018 where one-in-ten of them were considered to be developmentally vulnerable, nationally. We developed models in a hierarchical Bayesian framework at the SA3 level using SEIFA and ARIA indices as covariates to account for spatial and unobserved heterogeneity. The performances of developed models are examined based on the consistency at SA3, SA4, and state level. The results reveal that SEIFA makes a significant contribution to explaining the spatial variation in childhood development vulnerability across small domains in Australia. Further, the inclusion of the ARIA score improves the model performance and provides better accuracy, particularly in remote and very remote regions. In these regions, the spatial model fails to distinguish the remoteness characteristics. The chosen non-spatial model accounting for heterogeneity at higher hierarchies performs best. The utilization of socioeconomic disadvantage and geographic remoteness of the finer level domains helps to explain the geographic variation in child development vulnerability, particularly in sparsely populated remote regions in Australia.

Tobacco induced diseases, Feb 23, 2024

INTRODUCTION Despite that the smoking prevalence has considerably declined in Australia after suc... more INTRODUCTION Despite that the smoking prevalence has considerably declined in Australia after successful public health strategies over many decades, smoking is still the leading cause of preventable diseases and death in Australia. These declines have not occurred consistently across all geographical-demographic domains. In order to provide an evidence base for monitoring the trend towards the goal of reducing smoking across all domains in Australia, this study aims to estimate trends of smoking prevalence for small domains cross-classified by seven age groups (18-24, 25-29, 30-39, 40-49, 50-59, 60-69, and ≥70 years), two genders, and eight states and territories over twenty years (2001-2021). METHODS Direct estimates of smoking prevalence for the target small domains were calculated from the micro-data of the Australian National Health Surveys conducted in seven rounds during 2001-2021. The obtained direct estimates were then used as input for developing time-series models expressed in a hierarchical Bayesian structure as a form of small-area estimation. The developed models borrow cross-sectional, temporal, and spatial strength in such a way that they can interpolate smoking levels in the non-survey years for all detailed level small domains. Smoothed trends of smoking prevalence for higher aggregation levels are obtained by aggregation of the detailed level trend predictions. RESULTS Model-based small area estimators provide consistent and reasonable smoothed trends at both detailed and higher aggregation levels. Results show that the national-level trend exhibits a steeper linear decline over the study period, from 24% in 2001 to 12% in 2021, with a considerable gender difference of around 5% over the period, with males reporting a higher prevalence. Improved model-based estimates at the state level and by age also show steady declines in trends except for the Northern Territory (still above 20%) and older age groups 60-69 and ≥70 years (declining trends remain stable after 2012). CONCLUSIONS The findings of the study identify the geographical-demographic groups that had poor improvement over the period 2001-2021, and are still behind the target of achieving lower smoking prevalence. These, in turn, will help health researchers and policymakers deliver targeted programs to the most vulnerable, enabling the nation to meet its health goals in a timely way.

Research Square (Research Square), Apr 19, 2023

Scientific Reports, Dec 6, 2023

Childhood chronic undernutrition, known as stunting, remains a critical public health problem glo... more Childhood chronic undernutrition, known as stunting, remains a critical public health problem globally. Unfortunately while the global stunting prevalence has been declining over time, as a result of concerted public health efforts, there are areas (notably in sub-Saharan Africa and South Asia) where progress has stagnated. These regions are also resource-poor, and monitoring progress in the fight against chronic undernutrition can be problematic. We propose geostatistical modelling using data from existing demographic surveys supplemented by remote-sensed information to provide improved estimates of childhood stunting, accounting for spatial and non-spatial differences across regions. We use two study areas-Bangladesh and Ghana-and our results, in the form of prevalence maps, identify communities for targeted intervention. For Bangladesh, the maps show that all districts in the southeastern region are identified to have greater risk of stunting, while in Ghana the greater northern region had the highest prevalence of stunting. In countries like Bangladesh and Ghana with limited resources, these maps can be useful diagnostic tools for health planning, decision making and implementation. Motivation There are significant public health challenges faced by low and middle income countries as a result of childhood chronic undernutrition known as stunting 1. Stunting is associated with poorer cognitive function and impairments in physical and metabolic development, and leads to increased likelihood of cardiovascular and other chronic diseases in later life 2,3. In addition, stunting has been linked to poorer social and economic outcomes, such as lower educational attainment and reduced productivity 4. Some of these effects appear to be epigenetic, passing from one generation to the next 5. The prevalence of stunting globally is 170 million, being 22 per cent of the worldwide population of underfive year old children. But practically all (98 per cent) of stunted children are located in Sub-Saharan Africa and south Asia 6,7. Although the global prevalence of stunting declined steeply from 47 per cent in 1980 to 33 per cent in 2000, this declining trend has not been observed across all regions 8. Like many processes underpinned by various demographic, social, and geographic factors, there is significant spatial variability in the prevalence of stunting, especially sub-nationally. These regional and between-country differences underline the importance of monitoring global levels and trends of stunting in children. Sustainable Development Goal (SDG) goal 2) is dedicated to reducing all forms

PLOS ONE

Objective Food security is an important policy issue in India. As India recently ranked 107th out... more Objective Food security is an important policy issue in India. As India recently ranked 107th out of 121 countries in the 2022 Global Hunger Index, there is an urgent need to dissect, and gain insights into, such a major decline at the national level. However, the existing surveys, due to small sample sizes, cannot be used directly to produce reliable estimates at local administrative levels such as districts. Design The latest round of available data from the Household Consumer Expenditure Survey (HCES 2011–12) done by the National Sample Survey Office of India used stratified multi-stage random sampling with districts as strata, villages as first stage and households as second stage units. Setting Our Small Area Estimation approach estimated food insecurity prevalence, gap, and severity of each rural district of the Eastern Indo-Gangetic Plain (EIGP) region by modeling the HCES data, guided by local covariates from the 2011 Indian Population Census. Participants In HCES, 5915 (344...

Spatial Demography

Bangladesh has experienced a rapid national decline in fertility in recent decades, however, fert... more Bangladesh has experienced a rapid national decline in fertility in recent decades, however, fertility rates vary considerably at the sub-national level (i.e., division). These variations are expected to be more pronounced at lower levels of geography (e.g., district level). However, routinely conducted demographic health surveys are designed for national estimates and do not have adequate samples to produce reliable estimate of fertility rates at lower levels of administrative units, particular when considering district level age-specific fertility rates. Data extracted from the Bangladesh Demographic Health Survey 2014 are used to derive direct estimates of age-specific fertility rates and associated smoothed standard errors. These are used as inputs for developing a small area model, which is expressed in a hierarchical Bayesian framework and fitted by Markov Chain Monte Carlo simulation. The model accounts for variation at different levels—women age-group, division, and district...

BMC Public Health, May 18, 2022

The International Journal of Biostatistics

District-representative data are rarely collected in the surveys for identifying localised dispar... more District-representative data are rarely collected in the surveys for identifying localised disparities in Bangladesh, and so district-level estimates of undernutrition indicators – stunting, wasting and underweight – have remained largely unexplored. This study aims to estimate district-level prevalence of these indicators by employing a multivariate Fay–Herriot (MFH) model which accounts for the underlying correlation among the undernutrition indicators. Direct estimates (DIR) of the target indicators and their variance–covariance matrices calculated from the 2019 Bangladesh Multiple Indicator Cluster Survey microdata have been used as input for developing univariate Fay–Herriot (UFH), bivariate Fay–Herriot (BFH) and MFH models. The comparison of the various model-based estimates and their relative standard errors with the corresponding direct estimates reveals that the MFH estimator provides unbiased estimates with more accuracy than the DIR, UFH and BFH estimators. The MFH model-...

ITACOSM 2019 - Survey and Data Science, May 6, 2019

This report describes the time series models developed for the mobility trend estimation project ... more This report describes the time series models developed for the mobility trend estimation project carried out by Statistics Netherlands in collaboration with KiM/Rijkswaterstaat. First, direct estimates along with standard error estimates are obtained for each year in the period 1999-2017 from the microdata of the Dutch Travel Survey for a detailed cross-classification by person characteristics sex and age class and trip leg characteristics mode and purpose. Consequently, these direct estimates are smoothed by modeling them using multi-level time series models that account for influential outliers as well as for the redesigns of the survey within the Ɵme span considered. Two target variables are modeled in this way: the number of trip legs per person per day and the distance traveled per trip leg. The models are specified in a hierarchical Bayesian framework and esƟmated using a Markov Chain Monte Carlo simulation method. From the model outputs smooth trend esƟmates can be computed at various aggregation levels for the mean number of trip legs per person per day and the mean distance traveled per trip leg, as well as for derived quantities such as the mean distance per person per day. We discuss the model building and evaluation processes as well as the results based on the fitted models.

Poverty is a complex phenomenon and most of the developing countries are struggling to overcome t... more Poverty is a complex phenomenon and most of the developing countries are struggling to overcome the problem. Small area estimation offers help to allocate resources efficiently to address poverty at lower administrative level. This study used data from Census 2011 and Household Income and Expenditure Survey (HIES)-2010. Using ELL and M-Quantile methods, this study identified Rangpur division as the poorest one where Kurigram is the poorest district. Finally, considering both upper and lower poverty lines this study identified the poverty estimates at upazila level of Rangpur division using ELL and M-Quantile methods. The analyses found that 32% of the households were absolute poor and 19% were extremely poor in rural Bangladesh. Among the upazilas under Rangpur division Rajarhat, Ulipur, Char Rajibpur, Phulbari, Chilmari, Kurigram Sadar, Nageshwari, and Fulchhari Upazilas have been identified as the poorest upazilas.

Description of data: Distribution of mothers by household (HH) wealth status, motherâ s highest l... more Description of data: Distribution of mothers by household (HH) wealth status, motherâ s highest level education status and residential place, BDHS 2011. (PDF 185 kb)

Background: The study attempts to develop an ordinal logistic regression (OLR) model to identify ... more Background: 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. Methods: Based on weight-for-age anthropometric index (Z-score) child nutrition status is categorized into three groups-severely undernourished (<-3.0), moderately undernourished (-3.0 to-2.01) and nourished (≥-2.0). Since nutrition status is ordinal, an OLR model-proportional odds model (POM) can be developed instead of two separate BLR models to find predictors of both malnutrition and severe malnutrition if the proportional odds assumption satisfies. The assumption is satisfied with low p-value (0.144) due to violation of the assumption for one co-variate. So partial proportional odds model (PPOM) and two BLR models have also been developed to check the applicability of the OLR model. Graphical test has also...

Journal of Physics: Conference Series, 2021

In this study, a systematic analysis of fresh and sea water fishes, poultry and their feeds and s... more In this study, a systematic analysis of fresh and sea water fishes, poultry and their feeds and sediments was conducted using research reactor based neutron activation analysis (NAA) technique with the aim to determine elemental status with special emphasis on arsenic and chromium contamination. Sixty nine samples in different categories (fresh water fishes from three ponds, and corresponding sediments, sea water fishes from Bay of Bengal, chicken from five farms, their feeds) were analyzed through a series of NAA experiments to determine the translocations of toxic elements from feed to fish, sediments and birds and their excretion through litters. The analytical results revealed that some of the locally produced feeds for poultry and fish are highly contaminated with chromium. The flesh of both the fresh water fish and poultry are free from arsenic contamination. The sea fish contains high arsenic concentration. However, the major form of seafood arsenic as arsenobetaine that is c...

Asia-Pacific Population Journal, 2009

... Better knowledge and skills enable women to improve the way they care for and feed their infa... more ... Better knowledge and skills enable women to improve the way they care for and feed their infants. by Sumonkanti Das, Md. Zakir Hossain and Mossamet Kamrun Nesa* Malnutrition is a persistent problem for both children and mother throughout the world. ...

Computers, Environment and Urban Systems, 2017

Aim To determine if the addition of inert fillers to a bioactive dental restorative composite mat... more Aim To determine if the addition of inert fillers to a bioactive dental restorative composite material affects its degree of conversion (DC), polymerization shrinkage (PS), and microhardness (HV). Methods Three amorphous calcium phosphate (ACP)based composite resins: without added fillers (0-ACP), with 10% of barium-glass fillers (Ba-ACP), and with 10% of silica fillers (Si-ACP), as well as commercial control (Ceram•X, Dentsply DeTrey) were tested in laboratory conditions. The amount of ACP (40%) and the composition of the resin mixture (based on ethoxylated bisphenol A dimethacrylate) was the same for all ACP materials. Fourier transform infrared spectroscopy was used to determine the DC (n = 40), 20 min and 72 h after polymerization. Linear PS and Vickers microhardness (n = 40) were also evaluated. The results were analyzed by paired samples t test, ANOVA, and oneway repeated measures ANOVA with Student-Newman-Keuls or Tukey's post-hoc test (P = 0.05). Results The addition of barium fillers significantly increased the DC (20 min) (75.84 ± 0.62%) in comparison to 0-ACP (73.92 ± 3.08%), but the addition of silica fillers lowered the DC (71.00 ± 0.57%). Ceram•X had the lowest DC (54.93 ± 1.00%) and linear PS (1.01 ± 0.24%) but the highest HV (20.73 ± 2.09). PS was significantly reduced (P < 0.010) in both Ba-ACP (1.13 ± 0.25%) and Si-ACP (1.17 ± 0.19%) compared to 0-ACP (1.43 ± 0.21%). HV was significantly higher in Si-ACP (12.82 ± 1.30) than in 0-ACP (10.54 ± 0.86) and Ba-ACP (10.75 ± 0.62) (P < 0.010). Conclusion Incorporation of inert fillers to bioactive remineralizing composites enhanced their physical-mechanical performance in laboratory conditions. Both added fillers reduced the PS while maintaining high levels of the DC. Silica fillers additionally moderately improved the HV of ACP composites.

BMC Nutrition, 2017

Background: Logistic regression analysis is widely used to explore the determinants of child maln... more Background: Logistic regression analysis is widely used to explore the determinants of child malnutrition status mainly for nominal response variable and non-linear relationship of interval-scale anthropometric measure with nominal-scale predictors. Multiple classification analysis relaxes the linearity assumption and additionally prioritizes the predictors. Main objective of the study is to show how does multiple classification analysis perform like linear and logistic regression analyses for exploring and ranking the determinants of child malnutrition. Methods: Anthropometric data of under-5 children are extracted from the 2011 Bangladesh Demographic and Health Survey. The analysis is carried out considering several socioeconomic , demographic and environmental explanatory variables. The Height-forage Z-score is used as the anthropometric measure from which malnutrition status (stunting: below −2.0 Z-score) is identified. Results: The fitted multiple classification analysis models show similar results as linear and logistic models. Children age, birth weight and birth interval; mother's education and nutrition status; household economic status and family size; residential place and regional settings are observed as the significant predictors of both Height-forage Z-score and stunting. Child, household, and mother level variables have been ranked as the first three significant groups of predictors by multiple classification analysis. Conclusions: Detecting and ranking the determinants of child malnutrition through Multiple classification analysis might help the policy makers in priority-based decision-making.

Journal of the Royal Statistical Society: Series A (Statistics in Society), 2017

The method of Elbers, Lanjouw and Lanjouw (ELL) is the small area estimation method developed by ... more The method of Elbers, Lanjouw and Lanjouw (ELL) is the small area estimation method developed by the World Bank for poverty mapping and is widely used in developing countries. However, it has been criticized because of its assumption of negligible between-area variability when used to calculate small area poverty estimates. In particular, the mean-squared errors (MSEs) of these estimates are significantly underestimated when this between-area variability cannot be adequately explained by the model covariates. A method of MSE estimation for ELL-type estimates is proposed which is robust to significant unexplained between-area variability. Simulation results show that the method proposed performs better than standard ELL MSE estimators when the area homogeneity assumption is violated. An application to a Bangladesh poverty mapping study provides some empirical evidence for this robustness.