Spatial determinants of disability prevalence in myanmar: A district-level spatial regression analysis

Abstract

Myanmar, a country with a significant number of persons with disability, needs an understanding of the variation in disability prevalence and its determinants for efficient intervention strategies. This study aims to identify geographic patterns and spatial determinants of disability prevalence across Myanmar's districts. This cross-sectional spatial analysis utilized data from Myanmar's 2019 Inter-Censual Survey covering all 80 districts. Getis-Ord Gi* statistics were employed to discover spatial patterns and identify district-level clusters of disability prevalence. Beyond traditional spatial autocorrelation assessment, we applied comparative spatial regression modeling through Spatial Error Model (SEM) and Spatial Lag Model (SLM) to identify determinants of geographic disparities in disability prevalence. Our analytical framework distinguished between demographic vulnerabilities, technological protective factors, and district-level socioeconomic indicators. Heat mapping revealed substantial geographic variation in disability prevalence across Myanmar (ranging from 4.9 to 24.8 per 100 population). Gi* statistics identified eight hotspots and ten cold spots (p < 0.05), demonstrating non-random distribution of disability prevalence. The optimal SEM (R² = 0.579) identified child dependency ratio (β = 0.135, p < 0.001) and old dependency ratio (β = 0.665, p < 0.001) as key determinants. Myanmar's disability landscape is characterized by significant district-level disparities. Demographic vulnerabilities, particularly child and elderly dependency ratios, constitute primary drivers of these disparities. These findings provide an evidence-based geographic framework for addressing disability disparities through targeted intervention and resource allocation rather than uniform approaches