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. 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. 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. 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.

Background: Limited access to water caused by its scarcity is becoming one of the most serious problems facing several developing countries including East Africa. For this, understanding the spatial distribution of limited access to water source service is more pertinent. The purpose of this study is to analyze the spatial distribution of limited access to water source services in East Africa countries and seeks to inform the development and implementation of targeted interventions and policies in East Africa.Methods: This study analyzed data from recent demographic and health surveys conducted in 12 East African nations between 2012 and 2023. Data were gathered from 206,748 households. Global spatial autocorrelation was performed to analyze whether the pattern of limited access to drinking water service is clustered, dispersed, or random across the study areas. Once a positive global autocorrelation was confirmed, a local spatial autocorrelation analysis (Getis-OrdGi* statistics) was employed to detect local clusters. ArcGIS Pro 2.8.0 was used to map the clusters and Kulldorff SaTScan version 10.0.2 software using Bernoulli model were used for spatial scan statistical tests. The geostatistical ordinary kriging spatial interpolation technique was used to predict for unsampled areas based on sampled clusters.Result: Limited access to drinking water service were spatially clustered in the study area (Moran’s I: 0.16) (p < 0.0000). 40 significant clusters of which, one primary cluster and 39 secondary clusters were identified. The first spatial window which contains primary clusters was located in Uganda and Rwanda. The other 39 secondary spatial clusters were observed in Malawi, Mozambique, Kenya, Ethiopia and small part of Madagascar. Cold spot clusters were identified Madagascar, Zimbabwe, Tanzania, Comoros, Zambia and some parts of Kenya.Conclusion: The result of this study revealed the existence of spatial variation of limited access to drinking water services in East Africa. The variation was observed both within and across the countries. Engaging practitioners, researchers, policymakers, and local communities through multi-stakeholder platforms will establish effective management strategy, which will increase water access to the community.

Under-5 children’s diarrhea is a significant public health threat, and the World Health Organization (WHO) reported that it is the second leading cause of children’s death worldwide. In this study area, little is known about the spatiotemporal distribution of under-5 children’s diarrhea incidence rates. Therefore, this study was conducted to assess the spatiotemporal variation under-5 diarrhea incidence and associated meteorological factors in the Central Gondar Zone. The data for under-5 diarrhea were obtained from Central Gondar Zone Health Department diarrhea reports from January 2013 to December 2022. Climatic variables were obtained from the West Amhara Meteorological Agency. Every district was covered and given a geocode. The spatial data were created in ArcGIS 10.8.1. Global and local spatial autocorrelation were used to detect hot spots. The Poisson model, which applies the Kulldorff methods and SaTScan™9.6, was used to analyze the purely temporal, spatial, and space-time clusters. Spearman correlation and bivariate and multivariable negative binomial regressions were used to analyze the relationships between under-5 diarrhea cases and climatic factors. This study revealed spatial variation in the incidence of under-5 diarrhea, where Dembia, Gondar Zuria, and Chilga districts and Gondar Zuria, East Dembia, and Lay Armacho districts were high-rate spatial clusters between 2013 and 2018 and between 2019 and 2022, respectively. A temporal scan statistic identified 2014–2016 and 2020–2021 as risk periods across all districts. Spatiotemporal scan statistics revealed high-rate clusters in Dembia, Gondar Zuria, Chilga, Wegera, Alefa, Lay Armacho, and West Belesa between 2013 and 2018, and Gondar City, Gondar Zuria, East Dembia, Lay Armacho, and Alefa between 2019 and 2022. This study also revealed positive correlations between the number of individuals with under-5 diarrhea and the average monthly temperature at 0 and 2 lag months, with values of 1.0209 (1.0034–1.0387) and 1.0202 (1.0022–1.0385), respectively. In addition, there was a negative correlation between the number of under-5 diarrhea events and the average monthly rainfall at 0 and 2 lag months, with values of 0.999 (0.9985–0.9996) and 0.9992 (0.9987–0.9997), respectively. In conclusion, there has been spatiotemporal variability in the incidence of diarrhea in children under-5 years of age in the Central Gondar Zone. The mean monthly temperature and rainfall were positively and negatively related to the incidence of diarrhea in children under-5 years of age. Season-specific intervention strategies should be developed to reduce under-5 morbidity and mortality.

Solid fuel use is a major contributor to indoor air pollution and poses significant public health risks in low-income countries. However, regional variation and its determinants in East Africa remain poorly understood. This study aimed to assess the spatial distribution and key predictors of solid fuel utilization among households in East Africa. We analyzed data from 218,282 households across 12 East African countries using Demographic and Health Survey (2012–2023) data. Spatial analyses were conducted using ArcGIS V.10.7.1 and SaTScan™ 10.0.2, while multilevel logistic regression was applied to identify significant individual- and community-level factors. The AIC, BIC, and Deviance were used to assess the model’s fitness. Solid fuel use was highly prevalent at 88.5% (95% CI: 88.4–88.7%), with hotspots identified in Uganda, Burundi, Rwanda, and Malawi; some parts of Mozambique and Ethiopia. Significant predictors were female household head, 30–40 age of household head, large family size, education level, marital status, no mobile phone, wealth index, no electricity access, rural residence, and no media exposure. Solid fuel dependence remains widespread in East Africa, with marked regional disparities. Expanding access to affordable clean energy and targeted public health policies are urgently needed to reduce reliance on solid fuels and achieve equitable energy access.

Under-five children’s diarrhea is a significant public health threat and the World Health Organization (WHO) reported it as the second leading cause of children’s death worldwide. In this study area, little is known about the spatiotemporal distribution of under-5 diarrhea incidence. This study was therefore, conducted among all districts in the Central Gondar zone to assess the spatial, temporal, and spatiotemporal variation in diarrhea incidence among under-five children in the Central Gondar zone. The data for children under 5 years of age with diarrhea was obtained from Central Gondar Zone Health Department diarrhea reports from January 2019 to December 2022. All districts were included and geo-coded. The spatial data were created in ArcGIS 10.8.1. Global and local spatial autocorrelation were used to detect hot spots and cold spots. The Poisson model was generated by applying the Kulldorff method in SaTScan™9.6 to analyse the the purely temporal, spatial, and space-time clusters. The study revealed spatial variation of under-5 diarrhea where Gondar City, Gondar Zuria, East Dembia, and Lay Armacho districts were the high-rate spatial clusters during the study period. A year search window for temporal scan statistic identified 01 January 2020-30 December 2021 as risk periods across all districts. Spatiotemporal scan statistics detected high-rate clusters at Gondar City, Gondar Zuria, East Dembia, Lay Armacho, and Alefa between 2019 and 2022. In conclusion, there has been a spatial, temporal, and spatiotemporal variability of under-5 children’s diarrhea in the Central Gondar Zone. Interventional and preventive strategies should be developed and given priority to the areas that has been detected as a hotspot in this study to reduce the mortality and morbidity of under 5 children.

Background: By the end of 2024, an estimated 123.2 million people were forcibly displaced due to conflict and persecution, with 80% residing in low- and middle-income countries. Scabies, a neglected tropical disease affecting around 200 million people globally and causing over 455 million new cases annually, disproportionately affects displaced populations due to overcrowding, poor hygiene, and limited healthcare access. However, global evidence on its prevalence among these groups remains inconsistent. This study aimed to estimate the global pooled prevalence of scabies among forcibly displaced populations. Methods: Systematic searches were conducted in PubMed, Epistemonikos, ScienceDirect, Scopus, and Embase for studies published up to May 28, 2025, supplemented by hand-searches using Google and Google Scholar to capture any additional relevant studies. Data were extracted using Microsoft Excel 2021 and analyzed with STATA version 17. We conducted a systematic review and meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies that met the predefined inclusion criteria were included. The Newcastle-Ottawa Scale assessed study quality. A random-effects model was used to calculate pooled prevalence. Heterogeneity was measured using the Galbraith plot and the I ² statistic, while publication bias with funnel plots and Egger’s regression test. The protocol was registered with PROSPERO (CRD420251067217). Results: Fourteen studies with 76,664 participants were included. The global pooled prevalence of scabies was 15% (I ² = 99.87%). Prevalence varies by region, study setting, population group, and data collection method. Prevalence was highest in Asia (25%), followed by Europe (13%) and Africa (7%). Refugees showed the highest prevalence (23%), compared to IDPs (13%), asylum seekers (2%), and refugee-asylum seekers (2%). Among population groups, prevalence was highest in combined adult–child populations (66%), patients (24%), and school-aged children (15%), women and children under 15 years (6%), the general population (2%), and unaccompanied minors (1%). Studies using both clinical exams and Questionnaires reported a higher prevalence (27%) than those using clinical exams alone (14%) and interviews or physical examination only (3% and 2%, respectively). Conclusion: Scabies poses a significant health (15%) burden among forcibly displaced populations. Integrated interventions, including screening, treatment, WASH programs, and improved surveillance, are urgently needed to reduce transmission and improve health outcomes in displacement settings.