{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["24(1)"],"submitter":["Yitageasu G"],"pubmed_abstract":["Background
Malaria remains a significant public health challenge, particularly in underdeveloped regions like sub-Saharan Africa, where environmental, housing, and socioeconomic factors drive its spread. This study aims to identify spatial patterns and key determinants of malaria infection among households across 19 sub-Saharan African countries to inform targeted interventions and policy strategies.Methods
A community-based cross-sectional study was conducted using recent Demographic and Health Survey (DHS) data from 19 sub-Saharan African countries, encompassing 126,424 households and 11,594 clusters. Data processing including weighting, cleaning, and analysis was carried out using Microsoft Excel and Stata version 17. Prevalence estimates and 95% confidence intervals were generated in Stata, accounting for the DHS's complex sampling design through the application of weights, clustering, and stratification. Spatial analyses, including cluster detection and Geographically Weighted Regression (GWR), Were conducted using ArcGIS version 10.7 and SaTScan™ version 10.2.Results
Malaria prevalence among households in 19 sub-Saharan African countries was 22.47% (95% CI 22.24%, 22.70%), based on weighted estimates that account for the DHS sampling design. This indicates that approximately one in five households is affected by malaria. Spatial autocorrelation was significant (Global Moran's I = 0.159; Z = 239.1; p < 0.001), confirming geographic clustering. Hot-spot analysis (Getis-Ord Gi*) highlighted hotspot zones in Benin, Burkina Faso, Togo, Uganda, Rwanda, parts of the Republic of the Congo, and Mozambique. SaTScan™ identified 34 statistically significant spatial clusters, with the most prominent situated in Ghana, Burkina Faso, Togo, and Benin; Anselin Local Moran's I further revealed intermingled high and low-risk areas. Geographically Weighted Regression showed higher malaria prevalence in rural residents; households with rudimentary or natural roofs; younger heads of household; the poorest wealth quintile; no bed-net ownership, homes using treated bed nets, and large household size (6-12 members). Conversely, risk was lower in the richest households, those headed by women, and dwellings with natural or rustic walls.Conclusion
Malaria remains highly prevalent (22.47%) in sub-Saharan Africa, with significant spatial clustering in countries like Benin, Burkina Faso, Togo, and Uganda. Key risk factors identified include rural residence, poor housing conditions, lack of bed nets, homes using treated bed nets, and lower socioeconomic status. To reduce the burden, targeted interventions such as the distribution of insecticide-treated bed nets, indoor residual spraying, health education and improved housing should focus on identified hotspot areas. Collaboration among governments, NGOs, and local communities is essential to implement these strategies effectively and meet malaria reduction goals by 2030."],"journal":["Malaria journal"],"pagination":["305"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12487587"],"repository":["biostudies-literature"],"pubmed_title":["Malaria prevalence and its determinants across 19 sub-Saharan African countries: a spatial and geographically weighted regression analysis."],"pmcid":["PMC12487587"],"pubmed_authors":["Demoze L","Yitageasu G","Tigabie M","Worede EA","Angelo AA","Birhanu A","Aweke MN","Alemu EA"],"additional_accession":[]},"is_claimable":false,"name":"Malaria prevalence and its determinants across 19 sub-Saharan African countries: a spatial and geographically weighted regression analysis.","description":"Background
Malaria remains a significant public health challenge, particularly in underdeveloped regions like sub-Saharan Africa, where environmental, housing, and socioeconomic factors drive its spread. This study aims to identify spatial patterns and key determinants of malaria infection among households across 19 sub-Saharan African countries to inform targeted interventions and policy strategies.Methods
A community-based cross-sectional study was conducted using recent Demographic and Health Survey (DHS) data from 19 sub-Saharan African countries, encompassing 126,424 households and 11,594 clusters. Data processing including weighting, cleaning, and analysis was carried out using Microsoft Excel and Stata version 17. Prevalence estimates and 95% confidence intervals were generated in Stata, accounting for the DHS's complex sampling design through the application of weights, clustering, and stratification. Spatial analyses, including cluster detection and Geographically Weighted Regression (GWR), Were conducted using ArcGIS version 10.7 and SaTScan™ version 10.2.Results
Malaria prevalence among households in 19 sub-Saharan African countries was 22.47% (95% CI 22.24%, 22.70%), based on weighted estimates that account for the DHS sampling design. This indicates that approximately one in five households is affected by malaria. Spatial autocorrelation was significant (Global Moran's I = 0.159; Z = 239.1; p < 0.001), confirming geographic clustering. Hot-spot analysis (Getis-Ord Gi*) highlighted hotspot zones in Benin, Burkina Faso, Togo, Uganda, Rwanda, parts of the Republic of the Congo, and Mozambique. SaTScan™ identified 34 statistically significant spatial clusters, with the most prominent situated in Ghana, Burkina Faso, Togo, and Benin; Anselin Local Moran's I further revealed intermingled high and low-risk areas. Geographically Weighted Regression showed higher malaria prevalence in rural residents; households with rudimentary or natural roofs; younger heads of household; the poorest wealth quintile; no bed-net ownership, homes using treated bed nets, and large household size (6-12 members). Conversely, risk was lower in the richest households, those headed by women, and dwellings with natural or rustic walls.Conclusion
Malaria remains highly prevalent (22.47%) in sub-Saharan Africa, with significant spatial clustering in countries like Benin, Burkina Faso, Togo, and Uganda. Key risk factors identified include rural residence, poor housing conditions, lack of bed nets, homes using treated bed nets, and lower socioeconomic status. To reduce the burden, targeted interventions such as the distribution of insecticide-treated bed nets, indoor residual spraying, health education and improved housing should focus on identified hotspot areas. Collaboration among governments, NGOs, and local communities is essential to implement these strategies effectively and meet malaria reduction goals by 2030.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-04T01:23:53.266Z","creation":"2026-05-03T03:13:58.621Z"},"accession":"S-EPMC12487587","cross_references":{"pubmed":["41029676"],"doi":["10.1186/s12936-025-05573-6"]}}