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Spatial predictive patterns of cause-specific mortality: evidence from East Africa

Author

Listed:
  • Simmons, Sally Sonia
  • Hagan, John Elvis
  • Nieto-Gonzalez, Imanol L.
  • Schack, Thomas

Abstract

(1) Background: Whether spatial predictive patterns in non-communicable disease mortality persist after accounting for socio-demographic development and biomarkers remains understudied in East Africa. (2) Methods: This study used heterogeneous graph transformer (HGT) models and other techniques to model spatial patterns in cause- and sex/age-specific mortality (hypertensive heart disease [HHD], ischaemic heart disease [IHD], stroke, and diabetes), incorporating risk factors and socio-demographic development (SDI), using data from the Global Burden of Disease (GBD) study, 1990–2023, across Burundi, Kenya, Rwanda, Tanzania, and Uganda. (3) Results: HGT achieved higher performance than OLS spatial lag benchmarks (R2 0.948–0.970 vs. 0.194–0.376). Spatial predictive patterns were disease-specific. Stroke was the only disease with consistent positive spatial structure (SDI-only: 0.645%, 95% CI [0.380, 0.907]), with spatial structure strengthening after 2015. HHD exhibited severe and stable degradation (Risk-only: −137.892%, 95% CI [−181.908, −96.380]), driven by the interaction between metabolic risk covariates and geographic adjacency. Diabetes showed consistently severe degradation (SDI + Risk: −201.941%, 95% CI [−257.349, −150.082]). IHD patterns were weak and unstable. Sex disaggregation revealed stronger stroke spatial signals, indicating latent sex-specific patterns masked by aggregation. GBD measurement uncertainty contributed less than 0.025% of result variance, with model randomness dominating. (4) Conclusions: Spatial predictive patterns in NCD mortality in East Africa are disease-specific. Stroke shows emerging cross-border spatial structure after 2015, while HHD and diabetes reflect country-specific determinants. Sex-disaggregated graph construction reveals latent spatial heterogeneity invisible to aggregate models, supporting disease-specific, sex-stratified regional health strategies.

Suggested Citation

  • Simmons, Sally Sonia & Hagan, John Elvis & Nieto-Gonzalez, Imanol L. & Schack, Thomas, 2026. "Spatial predictive patterns of cause-specific mortality: evidence from East Africa," LSE Research Online Documents on Economics 140878, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:140878
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    References listed on IDEAS

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    1. Vladimir Canudas-Romo, 2010. "Three measures of longevity: Time trends and record values," Demography, Springer;Population Association of America (PAA), vol. 47(2), pages 299-312, May.
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    4. Simmons, Sally Sonia & Hagan Jr, John Elvis & Schack, Thomas, 2025. "Generative data modelling for diverse populations in Africa: insights from South Africa," LSE Research Online Documents on Economics 129032, London School of Economics and Political Science, LSE Library.
    5. Alan Piper, 2023. "What Does Dynamic Panel Analysis Tell Us About Life Satisfaction?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 376-394, June.
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    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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