Quantifying Disease Burden in Low-Resource Settings: Statistical Insights and Approaches with Model-Based Geostatistical Modelling.

Michael Chipeta, African Institute for Development Policy (AFIDEP)
Jeremot Masoambeta, National Statistics Office
McEwen Khundi, African Institute for Development Policy (AFIDEP)

This paper emphasises the challenge of quantifying disease burden in low- and middle-income countries (LMICs) due to limited access to reliable disease registries. The study introduces Generalised Linear Geostatistical Modelling (GLGM) as a robust method to estimate disease burden in LMICs by integrating statistical modelling with geospatial analysis. GLGM overcomes data limitations by incorporating alternative sources like hospital records (i.e., routine health management information systems (HMIS) data) and population-based surveys (i.e., demographic and health surveys (DHS)). It enables precise disease mapping at administrative and pixel levels, among others, identifying hotspots, informing interventions, and aiding resource allocation. The method’s application to mapping childhood anaemia prevalence in Malawi demonstrates its utility in leveraging available data for targeted public health strategies. GLGM emerges as a valuable tool for policymakers addressing the challenges of incomplete disease registries in LMICs, facilitating informed decision-making in resource-constrained environments.