# Institutional quality, aid flows, and malaria burden: a geospatial analysis of sub-Saharan Africa

## TL;DR

This study explores how institutional quality and aid affect malaria in sub-Saharan Africa, finding that local factors are more important than regional ones.

## Contribution

The novel contribution is using geospatial analysis to show localized factors drive malaria burden despite aid and institutional quality.

## Key findings

- Malaria burden reduction in sub-Saharan Africa is spatially clustered.
- Health worker density and institutional effectiveness are positively linked to malaria burden.
- Local conditions have a stronger influence on malaria than cross-border effects.

## Abstract

Malaria remains a major public health challenge in sub-Saharan Africa, accounting for approximately 95 percent of global malaria deaths despite extensive interventions. Significant disparities persist in disease burden across countries, with some achieving remarkable progress while others experiencing persistently high transmission rates, suggesting factors beyond resource availability influence disease control effectiveness. This study examines the relationship between institutional quality, development aid flows, and malaria burden across 38 sub-Saharan African countries during 2010–2022.

The analysis employed malaria cases and deaths per 1,000 population as malaria burden measures. Key explanatory variables included development assistance for health per capita, government effectiveness indices and health worker density as institutional quality indicators, alongside control variables for intervention coverage, climatic factors, and socioeconomic conditions. Data was sourced from the World Health Organization, United Nations Development Program, Institute for Health Metrics and Evaluation, Malaria Atlas project, Demographic and Health surveys, and World Bank databases. Spatial econometric models, including Spatial Durbin Models, Spatial Lag of X, and Spatial Durbin Error Models, were used to account for spatial autocorrelation and cross-border transmission effects, while fixed-effects models with Driscoll-Kraay standard errors provided baseline estimates.

The study found overall malaria burden reduction across sub-Saharan Africa. Malaria cases and deaths demonstrated significant spatial autocorrelation annually, indicated by Moran's I statistics. Findings revealed that increased health worker density, enhanced institutional effectiveness, and higher aid levels are positively associated with the burden. These effects persisted with lagged values of health workers and government effectiveness. The geospatial analysis reveals that the malaria burden is driven more by local conditions with limited spillover effects from neighbouring countries.

Findings highlight that while malaria burden has generally declined across sub-Saharan Africa, it is spatially clustered. There is need for localized health systems strengthening alongside targeted regional coordination.

The online version contains supplementary material available at 10.1186/s12936-025-05592-3.