Spatial Distribution, Environmental Associations and Geographical Mapping of Microscopy-confirmed Malaria Cases among Children under Five in Nigeria
Charles Olalekan Collins *
Department of Applied Geoinformatics, University of Salzburg, Salzburg, Austria.
Ekata Leo Oni
Department of Applied Geoinformatics, University of Salzburg, Salzburg, Austria.
Miracle Owhorji
Department of Applied Geoinformatics, University of Salzburg, Salzburg, Austria.
*Author to whom correspondence should be addressed.
Abstract
Aims: This study assessed the prevalence and geographical distribution of microscopy-confirmed malaria among children aged 6–59 months in Nigeria, examined selected environmental associations and spatial clustering, modelled cluster-level malaria probability, and identified geographical areas with comparatively higher model-predicted probabilities.
Study Design: Cross-sectional secondary analysis of nationally representative survey data integrated with environmental and geospatial datasets.
Place and Duration of Study: Nigeria; analysis used data from the 2021 Nigeria Malaria Indicator Survey and environmental datasets corresponding to the survey period.
Methodology: Data for 10,655 children from 567 survey clusters were analysed using survey-weighted prevalence estimation and complex-samples logistic regression. Cluster locations were linked with environmental data, and Local Moran’s I was used to identify statistically significant spatial clusters and outliers. Model-predicted probabilities were aggregated to cluster level and used to identify intervention-priority areas.
Results: National malaria prevalence was 22.3%, ranging from 2.6% in Lagos to 49.2% in Kebbi. Prevalence was higher in rural than urban areas (26.7% versus 10.5%; P < .001). Annual rainfall was associated with malaria in one alternative model, while elevation and built-up land coverage showed inverse associations. Local Moran’s I identified 193 significant clusters and outliers, including 45 High–High hotspots and 107 Low–Low coldspots. Model-predicted probabilities of malaria across the survey clusters ranged from 1.7% to 37.3%; 272 of 567 clusters (48.0%) were classified as higher categories of model-predicted probability. Bauchi and Jigawa had the highest intervention-priority percentages (87.5% each), followed by Ebonyi (84.6%), Katsina (82.4%) and Kebbi (80.0%).
Conclusion: Childhood malaria risk in Nigeria shows marked geographical variation. The identified hotspots and priority areas can support population-level surveillance, resource allocation and targeted malaria control. Given the model’s modest predictive performance and displacement of DHS cluster coordinates, the estimates should not be interpreted as individual-level clinical predictions.
Keywords: Childhood malaria, environmental risk factors, spatial clustering, epidemiology, spatial analysis, Nigeria