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

Pratana Kukieattikool, Kittiya Ku-kiattikun, Anukool Noymai, Navaporn Surasvadi, Jantakarn Makma, Pubodin Pornratchpum, Watcharakon Noothong, Chainarong Amornbunchornvej

TL;DR

This paper investigates how ICD-10 morbidity across Thailand's provinces co-varies with multidimensional poverty within a spatial network. By treating provinces as fixed-degree nodes connected via a 7-nearest-neighbors matrix and estimating Spatial Durbin Models, the authors quantify local versus spillover effects of poverty on morbidity across multiple ICD-10 chapters. They find strong spatial clustering, regional health belts, and widespread spillovers, with neighbor poverty often explaining more variation in morbidity than local deprivation. The study highlights the importance of region-wide, network-informed public health policy and provides a transferable framework for analyzing health–poverty dynamics in other settings.

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.

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

TL;DR

This paper investigates how ICD-10 morbidity across Thailand's provinces co-varies with multidimensional poverty within a spatial network. By treating provinces as fixed-degree nodes connected via a 7-nearest-neighbors matrix and estimating Spatial Durbin Models, the authors quantify local versus spillover effects of poverty on morbidity across multiple ICD-10 chapters. They find strong spatial clustering, regional health belts, and widespread spillovers, with neighbor poverty often explaining more variation in morbidity than local deprivation. The study highlights the importance of region-wide, network-informed public health policy and provides a transferable framework for analyzing health–poverty dynamics in other settings.

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.
Paper Structure (26 sections, 8 equations, 7 figures, 3 tables)

This paper contains 26 sections, 8 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Socioeconomic Disadvantage as a Common Driver of Spatial Morbidity Patterns
  4. Spatial epidemiology and clustering of health outcomes
  5. Spatial dependence, spillovers, and poverty traps
  6. Spatial poverty and health in Thailand and Southeast Asia
  7. Data
  8. ICD-10 morbidity
  9. Multidimensional poverty indicators (TPMAP)
  10. Methods
  11. Spatial weights matrix
  12. Exploratory spatial analysis
  13. Global Moran's I and permutation test
  14. Local Moran's I (LISA)
  15. Visualization
  16. ...and 11 more sections

Figures (7)

  • Figure 1: Maps of Respiratory system diseases (C7) (left) ICD-10 chapter ratios, (middle) Z-score of local Moran’s I, (right) LISA map of C7
  • Figure 2: Maps of Diseases of the digestive system (C2) (left) ICD-10 chapter ratios, (middle) Z-score of local Moran’s I, (right) LISA map of C2
  • Figure 3: Maps of Musculoskeletal & connective tissue diseases (C5) (left) ICD-10 chapter ratios, (middle) Z-score of local Moran’s I, (right) LISA map of C5
  • Figure 4: Maps of Symptoms & abnormal findings (NEC) (C12) (left) ICD-10 chapter ratios, (middle) Z-score of local Moran’s I, (right) LISA map of C12
  • Figure 5: Spatial patterns of the first principal component (PC1) of ICD-10 morbidity. (Left) PC1 value map showing the composite morbidity gradient. (Middle) Local Moran’s z-scores indicating spatial autocorrelation. (Right) LISA clusters identifying High–High regions in the North/Central area and Low–Low regions in the South.
  • ...and 2 more figures