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.

Functional difficulty among young children in Bangladesh: An analysis of nationally representative data.

Functional difficulty in children is a crucial public health problem still undervalued in developing countries. This study explored the socio-demographic factors and anthropometry associated with children's functional difficulty in Bangladesh. Data for 2-4-year-old children, obtained from Multiple Indicator Cluster Survey 2019, were used in this study. The mixed-effects logistic regression model was used to analyse the data. Children whose mothers had functional difficulty were found to be 2.75 times more likely to have functional difficulty than children whose mothers had no functional difficulty (95% CI 1.63-4.63). Male children were more likely to experience functional difficulty than female children (OR = 1.48). Furthermore, stunting was found to be significantly associated with functional difficulty (OR = 1.50). The study also revealed that division and mother's education, specifically, children with mothers having higher secondary + education, had significant association with the outcome variable. The findings provided a vital overview of child disability in a developing country.

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.

Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.

This paper aims to demonstrate the importance of studying interactions among various sociodemographic risk factors of childhood stunting in Bangladesh with the help of an interpretable machine learning method. Data used for the analyses are extracted from the Bangladesh Demographic and Health Survey (BDHS) 2014 and pertain to a sample of 6,170 under-5 children. Social and economic determinants such as wealth, mother's decision making on healthcare, parental education are considered in addition to geographic divisions and common demographic characteristics of children including age, sex and birth order. A classification tree was first constructed to identify important interaction-based rules that characterize children with different profiles of risk for stunting. Then binary logistic regression models were fitted to measure the importance of these interactions along with the individual risk factors. Results revealed that, as individual factors, living in Sylhet division (OR: 1.57; CI: 1.26-1.96), being an urban resident (OR: 1.28; CI: 1.03-1.96) and having working mothers (OR: 1.21; CI: 1.02-1.44) were associated with higher likelihoods of childhood stunting, whereas belonging to the richest households (OR: 0.56; CI: 0.35-0.90), higher BMI of mothers (OR: 0.68 CI: 0.56-0.84) and mothers' involvement in decision making about children's healthcare with father (OR: 0.83, CI: 0.71-0.97) were linked to lower likelihoods of stunting. Importantly however, risk classifications defined by the interplay of multiple sociodemographic factors showed more extreme odds ratios (OR) of stunting than single factor ORs. For example, children aged 14 months or above who belong to poor wealth class, have lowly educated fathers and reside in either Dhaka, Barisal, Chittagong or Sylhet division are the most vulnerable to stunting (OR: 2.52, CI: 1.85-3.44). The findings endorse the need for tailored-intervention programs for children based on their distinct risk profiles and sociodemographic characteristics.

Impact of child's cognitive and social-emotional difficulties on child abuse: Does mother's justification of intimate partner violence also play a role?

BACKGROUND: Violence against children has been a persistent problem in developing nations. The adverse effects of physical violence bear a considerable impact on children's physical and psychological development resulting in both short and long-term issues. OBJECTIVE: The aim of this study was to explore whether children with cognitive and social-emotional difficulties (CSEDs) were at a higher risk of experiencing physical abuse and whether mothers' views on intimate partner violence (IPV) were also related to physical abuse against children. PARTICIPANTS AND SETTING: The Bangladesh Multiple Indicator Cluster Survey-2019 was used with a sample of 27,086 children aged 5-14. METHODS: Generalized linear modelling along with a machine learning method of classification trees was employed to investigate the important sociodemographic characteristics and identify the most vulnerable groups of children based on their likelihood of exposure to household-violence. RESULTS: Nearly 62.5 % of the children were physically abused by their mothers. Children with CSEDs were 53 % (OR 1.53; 95 % CI: 1.41, 1.67) more likely to experience physical abuse and mothers' justification of IPV was associated with a 16 % higher risk (OR 1.16; 95 % CI: 1.08, 1.26). Moreover, younger children aged 11 or below belonged to the high-risk groups of experiencing abuse. CONCLUSIONS: The findings suggest that violence against children is widespread in Bangladesh, especially in children having CSEDs. Mothers' acceptance of IPV was also associated with increased abusive practice against children. Sincere focus on these issues is imperative if Bangladesh intends to achieve the sustainable development goal 16.2 of eradicating all forms of violence against children and ensure their safe development.

Impact of COVID-19 on hospital admission of acute stroke patients in Bangladesh.

BACKGROUND: With the proposed pathophysiologic mechanism of neurologic injury by SARS CoV-2, the frequency of stroke and henceforth the related hospital admissions were expected to rise. This paper investigated this presumption by comparing the frequency of admissions of stroke cases in Bangladesh before and during the pandemic. METHODS: This is a retrospective analysis of stroke admissions in a 100-bed stroke unit at the National Institute of Neurosciences and Hospital (NINS&H) which is considerably a large stroke unit. All the admitted cases from 1 January to 30 June 2020 were considered. Poisson regression models were used to determine whether statistically significant changes in admission rates can be found before and after 25 March since when there is a surge in COVID-19 infections. RESULTS: A total of 1394 stroke patients took admission in the stroke unit during the study period. Half of the patients were older than 60 years, whereas only 2.6% were 30 years old or younger. The male to female ratio is 1.06:1. From January to March 2020, the mean rate of admission was 302.3 cases per month, which dropped to 162.3 cases per month from April to June, with an overall reduction of 46.3% in acute stroke admission per month. In those two periods, reductions in average admission per month for ischemic stroke (IST), intracerebral hemorrhage (ICH), subarachnoid hemorrhage (SAH) and venous stroke (VS) were 45.5%, 37.2%, 71.4% and 39.0%, respectively. Based on weekly data, results of Poisson regressions confirm that the average number of admissions per week dropped significantly during the last three months of the sample period. Further, in the first three months, a total of 22 cases of hyperacute stroke management were done, whereas, in the last three months, there was an 86.4% reduction in the number of hyperacute stroke patients getting reperfusion treatment. Only 38 patients (2.7%) were later found to be RT-PCR SARS Cov-2 positive based on nasal swab testing. CONCLUSION: This study revealed a more than fifty percent reduction in acute stroke admission during the COVID-19 pandemic. Whether the reduction is related to the fear of getting infected by COVID-19 from hospitalization or the overall restriction on public movement or stay-home measures remains unknown.

Hospital productivity: The role of efficiency drivers.

A major feature of health-care systems is substantial variation in hospital productivity. Hospital productivity varies widely across countries. The presence of such variation suggests potential areas for improvement, which can substantially lower health-care costs. This research aims to investigate factors that may explain variations in hospital productivity by constructing a longitudinal data (panel) on English NHS hospital trusts. It also seeks to explore possible interactions among the factors in a data-driven manner. We employ unbiased panel regression tree techniques from the machine-learning literature to explore the complex interactive structure of the data. We next use econometric panel regression to deal with individual hospital effects to identify some of the determinants of hospital productivity. The findings point to the significance of efficiency-enhancing mechanisms for hospital productivity, including measures to reduce the length of stay, increase day case (outpatient) surgery rate, and to minimize errors. Further, such measures are shaped by more fundamental factors such as the availability of human capital and management practices. Our results underscore the importance of within-hospital efficiency-enhancing mechanisms to cost-adjusted hospital productivity. Improving hospital operational processes will enhance productivity. At a deeper level, human capital and management practices are likely to be most critical.

Hospital heterogeneity: what drives the quality of health care.

A major feature of health care systems is substantial variation in health care quality across hospitals. The quality of stroke care widely varies across NHS hospitals. We investigate factors that may explain variations in health care quality using measures of quality of stroke care. We combine NHS trust data from the National Sentinel Stroke Audit with other data sets from the Office for National Statistics, NHS and census data to capture hospitals' human and physical assets and organisational characteristics. We employ a class of non-parametric methods to explore the complex structure of the data and a set of correlated random effects models to identify key determinants of the quality of stroke care. The organisational quality of the process of stroke care appears as a fundamental driver of clinical quality of stroke care. There are rich complementarities amongst drivers of quality of stroke care. The findings strengthen previous research on managerial and organisational determinants of health care quality.

Are children living on dead-end streets more active? Near-home street patterns and school-going children's time spent outdoors in Dhaka, Bangladesh.

This study aimed to investigate relationships between near-home street patterns and children's time spent outdoors (TSO). Participants were 60 (n=60) school-age Dhaka children, 7-11years old (16 girls and 44 boys) selected by a two-phase cluster sampling method. Data were collected from September 2010 to June 2011 by visiting each of 60 children's homes. Children's mean TSOs (in minutes) were reported by parents' face-to-face interviews, and near-home street pattern data were collected by systematic direct observations. The researchers also collected data on seven socio-demographic variables and three neighborhood built-environment variables. A backward selection based multiple linear regression was used to examine association between children's TSO and near-home street patterns. Results (adjusted R2=0.66 for weekdays and 0.68 for weekend) suggested that children's TSO were significantly associated with near-home street type: dead-end instead of through streets (28min on weekdays, p<0.01 and 66min on weekend, p<0.01). The width of the street, level of its branching and availability of an open space or playground near the house are also positively associated with TSO. Near-home street features significantly contribute to TSO in school-going children of Dhaka.