Modeling ICD-10 Morbidity and Multidimensional Poverty as a Spatial Network: Evidence from Thailand

2026-01-06

arXiv.org

AI Summary

This study investigates the spatial association mechanisms and regional spillover effects between disease burden and multidimensional poverty across Thailand’s 76 provinces. Treating health and poverty as interdependent attributes within a spatial network—a novel approach—it integrates spatial econometric methods (Moran’s I, LISA, and the spatial Durbin model) with social network analysis to construct an interprovincial interaction network. The findings reveal significant high–high spatial clustering of digestive, respiratory, and musculoskeletal disorders in specific regions. Moreover, spillover effects from neighboring provinces—particularly in dimensions such as living conditions and healthcare accessibility—often outweigh the impact of local deprivation, underscoring the critical need for cross-regional collaborative governance. These results provide a new empirical foundation for designing network-structure-informed intervention strategies to address health-poverty linkages.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Health and poverty in Thailand exhibit pronounced geographic structuring, yet the extent to which they operate as interconnected regional systems remains insufficiently understood. This study analyzes ICD-10 chapter-level morbidity and multidimensional poverty as outcomes embedded in a spatial interaction network. Interpreting Thailand's 76 provinces as nodes within a fixed-degree regional graph, we apply tools from spatial econometrics and social network analysis, including Moran's I, Local Indicators of Spatial Association (LISA), and Spatial Durbin Models (SDM), to assess spatial dependence and cross-provincial spillovers. Our findings reveal strong spatial clustering across multiple ICD-10 chapters, with persistent high-high morbidity zones, particularly for digestive, respiratory, musculoskeletal, and symptom-based diseases, emerging in well-defined regional belts. SDM estimates demonstrate that spillover effects from neighboring provinces frequently exceed the influence of local deprivation, especially for living-condition, health-access, accessibility, and poor-household indicators. These patterns are consistent with contagion and contextual influence processes well established in social network theory. By framing morbidity and poverty as interdependent attributes on a spatial network, this study contributes to the growing literature on structural diffusion, health inequality, and regional vulnerability. The results highlight the importance of coordinated policy interventions across provincial boundaries and demonstrate how network-based modeling can uncover the spatial dynamics of health and deprivation.

Problem

• spatial network
• ICD-10 morbidity
• multidimensional poverty
• spatial dependence
• regional spillovers

Innovation

• spatial network
• multidimensional poverty
• ICD-10 morbidity
• spatial spillovers
• Spatial Durbin Model