Assessing Regional Disparities in Bangladesh: A Comparative Cluster Analysis of Health, Education, and Demographic Indicators across Districts

Shimiown Galiver Mrong

Department of Public Health, First Capital University of Bangladesh, Chuadanga-7200, Bangladesh.

Sazin Islam *

Department of Public Health, First Capital University of Bangladesh, Chuadanga-7200, Bangladesh.

Sharmin Akter

Central Medical College, Cumilla-3500, Bangladesh.

Khondokar Shakil Ahamed

Department of Public Health, First Capital University of Bangladesh, Chuadanga-7200, Bangladesh.

Md. Azim Rana

Department of Public Health, First Capital University of Bangladesh, Chuadanga-7200, Bangladesh.

Sonia Afroz Mukta

Department of Public Health, First Capital University of Bangladesh, Chuadanga-7200, Bangladesh.

Sadia Afroz Rikta

Sociology Discipline, Khulna University, Khulna-9208, Bangladesh.

*Author to whom correspondence should be addressed.


Abstract

Background: For the development of evidence-based health policies and public health research, representative health information is essential. Often, in developing nations, studies extrapolate data from a small number of communities to the entire population, potentially leading to inaccuracies. This study utilises multivariate cluster analysis to examine regional disparities within a developing country using health indicators from the Bangladesh Multiple Indicator Cluster Survey (MICS) 2019 and demographic variables from the Bangladesh Population Census Report 2022.

Objective: The study aims to analyze disparities in socio-economic indicators across Bangladesh's districts to guide balanced development policy-making.

Methods: Indicators for the study were selected through a two-phase evaluation, retaining only those with significant variations within the dataset. The study focused on maternal, infant, and socio-demographic characteristics at a district level. The data analysis was conducted using hierarchical, kmeans, and pam clustering techniques, with the optimal number of clusters determined using a silhouette diagram. The cluster selection was validated through internal validation and stability tests.

Results: Two distinct clusters of districts showed significant disparities in health, education, and demographic indicators. The first cluster (21 districts) had lower literacy rates (45% vs 73%), school attendance (65% vs 85%), and early childhood education enrollment (25% vs 58%). This cluster also had higher rates of child stunting (40% vs 23%), wasting (16% vs 9%), maternal mortality (239 vs 140 per 100,000 live births), and unemployment (12% vs 6%) compared to the second cluster (43 districts). These findings highlight the need for targeted interventions.

Conclusion: The study demonstrates the potential for unsupervised learning techniques like cluster analysis in identifying regional disparities in developing countries. It emphasises the importance of individual district-level data in policy planning and underscores the need for targeted interventions to address specific regional health challenges.

Keywords: Clustering, literacy rates, early childhood education, nutritional indicators, maternal mortality


How to Cite

Mrong, Shimiown Galiver, Sazin Islam, Sharmin Akter, Khondokar Shakil Ahamed, Md. Azim Rana, Sonia Afroz Mukta, and Sadia Afroz Rikta. 2023. “Assessing Regional Disparities in Bangladesh: A Comparative Cluster Analysis of Health, Education, and Demographic Indicators across Districts”. Asian Journal of Language, Literature and Culture Studies 6 (3):325-35. https://journalajl2c.com/index.php/AJL2C/article/view/151.

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