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Spatial epidemiology of malaria by bed net utilization in 19 sub-Saharan African countries: a DHS-based study
Gelila Yitageasu1, Lidetu Demoze2
1Department of Environmental and Occupational Health and Safety, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia. gelilayitageasu1@gmail.com.
Background:
Malaria remains a major public health concern in Sub-Saharan Africa, where prevention efforts such as insecticide-treated nets (ITNs) play a critical role in reducing transmission. However, malaria prevalence varies across geographic areas and household contexts, including differences in mosquito bed net utilization (treated, untreated, or absent), as well as other environmental and socioeconomic determinants. Understanding the spatial distribution of malaria and how it varies across households with different bed net utilization patterns can provide descriptive insights to support malaria control programs. This study aimed to describe and map the spatial distribution of malaria among households with different mosquito bed net utilization categories (no net, treated net, and untreated net) across 19 sub-Saharan African countries.
Method:
A community-based cross-sectional study was conducted using the most recent Demographic and Health Survey (DHS) data from 19 sub-Saharan African countries. The analysis included 126,424 individuals who underwent malaria rapid diagnostic testing across 11,594 survey clusters. Malaria infection status was measured at the individual level, while spatial analyses were conducted at the cluster (enumeration area) level, the primary sampling unit used in DHS surveys. Data were cleaned, weighted, and analyzed using Stata version 17, accounting for the DHS complex sampling design (sampling weights, clustering, and stratification). Malaria prevalence and corresponding 95% confidence intervals were estimated. Spatial patterns of malaria distribution were explored using ArcGIS version 10.7 and SaTScan™ version 10.0.2.
Result:
The pooled malaria prevalence across 19 sub-Saharan African countries was 24.0% (95% CI 18-30%), with regional variation observed: Central Africa recorded the highest prevalence at 26% (95% CI 18-34%), followed by West Africa at 25% (95% CI 15-35%), and East Africa at 20% (95% CI 10-31%). Regarding bed net usage, 49.17% (95% CI 48.89-49.45%) reported no bed net, 48.06% (95% CI 47.78-48.34%) used insecticide-treated nets (ITNs), and 2.77% (95% CI 2.68-2.86%) used untreated bed nets. Malaria prevalence was highest among no bed net users with 32.75%, followed by 28.91% among untreated bed net users and 13.23% among ITN users. Spatial analysis indicated significant spatial clustering of malaria cases (Global Moran's I = 0.159, Z-score = 239.06, p ≤ 0.001). Hotspot areas identified by the Getis-Ord Gi* statistic included Benin, Burkina Faso, Togo, Uganda, Rwanda, and parts of Republic of the Congo and Mozambique. SaTScan analysis detected 34 significant malaria clusters overall, with 23 clusters among no bed net users, 25 among ITN users, and 12 among untreated bed net users.
Conclusion:
Despite ongoing malaria control efforts, malaria prevalence remains substantial across sub-Saharan Africa, with notable regional and spatial disparities observed across areas with different bed net utilization patterns. These descriptive spatial findings highlight geographic heterogeneity in malaria burden and may help inform geographically targeted malaria control strategies. Strengthening access to insecticide-treated nets, improving community awareness, and prioritizing identified hotspot areas may contribute to strengthening malaria control efforts toward achieving global malaria reduction targets by 2030.
