Geospatial and Path Analysis for Enhancing Malaria Control and Primary Healthcare Delivery in Low-Income Nations: A Case Study of Uganda

Abstract

Using generalized linear regression, OLS regression, and spatial autocorrelation (Moran's I), the study identified key factors influencing malaria incidence rates: mean temperature, antimalarial treatment, mosquito net access, total population, and total health centers (Adjusted R² = 0.443). Path analysis quantified direct and indirect effects — mean temperature showed a total effect of 0.66; mosquito net access 2.60; health centers exerted their influence entirely indirectly (1.10). Spatial autocorrelation revealed significant clustering of malaria rates. Bivariate maps underscored the critical role of health centers, suggesting that expanding health center networks in underserved regions could enhance healthcare outcomes.