Geospatial Analysis of Environmental Health Risk Factors and Malaria Incidence in Nasarawa State, North Central Nigeria

Geospatial tools offer powerful capabilities for mapping environmental health risk factors and their spatial relationship with disease outcomes, enabling targeted intervention planning. This study applied geospatial analysis to examine the association between environmental risk factors and malaria incidence in Nasarawa State, North Central Nigeria. Monthly malaria case data for 2021 to 2023 were obtained from the Nasarawa State District Health Information System for all thirteen local government areas. Environmental predictors, including normalized difference vegetation index, land surface temperature, elevation, rainfall distribution, proximity to water bodies, and land use type, were extracted from satellite remote sensing data. Spatial autocorrelation analysis, Moran's Index computation, and negative binomial regression with spatial lag were conducted to identify environmental predictors of malaria incidence clusters. Results demonstrated significant spatial clustering of high malaria incidence in low-elevation areas with high normalized difference vegetation index values and close proximity to standing water bodies. Land surface temperature and rainfall lagged by two weeks showed significant positive associations with malaria incidence. Agricultural land use zones showed higher incidence rates than forest or urban land use categories. The geospatial model explained sixty-seven percent of variance in LGA-level malaria incidence, providing strong predictive utility. The study contributes an analytically rigorous geospatial framework for malaria environmental risk mapping in Nigerian states. Recommendations include satellite-data-informed targeted larviciding, ITN deployment prioritization, and environmental manipulation in high-risk zones. Keywords: geospatial analysis, malaria incidence, Nasarawa State, remote sensing, environmental risk factors.