Asian Journal of Probability and Statistics

This study employed a cross-sectional, geo-statistical research design to examine spatial patterns of disease burden and healthcare accessibility in rural Northwestern Nigeria. Secondary health data obtained from routine health records and geospatial data on healthcare facilities were integrated with population and administrative boundary data. The analysis covered multiple rural districts within the study region, with observations aggregated at the local administrative level. Geo-statistical techniques, including spatial autocorrelation analysis, spatial regression modeling, and cluster detection methods, were applied to assess the relationship between disease burden and healthcare accessibility while accounting for spatial dependence. The results revealed significant spatial clustering of disease burden and pronounced geographic disparities in access to healthcare services, with higher disease prevalence observed in areas located farther from healthcare facilities. Spatial regression models demonstrated that geographic accessibility was a significant predictor of disease burden after controlling for spatial effects. The findings highlight the importance of incorporating spatial analysis into public health planning and demonstrate how geo-statistical tools can provide evidence-based insights for identifying underserved communities. These results have practical implications for guiding equitable healthcare resource allocation and strengthening health systems in rural and underserved settings.