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

Sally Sonia Simmons, John Elvis Hagan, Imanol L. Nieto-Gonzalez and Thomas Schack

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: East Africa; cause-specific mortality; deep learning; diabetes; heterogeneous graph transformers; hypertensive heart disease; ischaemic heart disease; metabolic risk factors; non-communicable diseases; socio-demographic index; spatial predictive patterns; stroke (search for similar items in EconPapers)
JEL-codes: J1 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2026-08-20
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Published in Information, 20, August, 2026, 17(8). ISSN: 2078-2489

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