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

This research investigated the use of Geospatial and Path Analysis for Enhancing Malaria Control and Primary Healthcare Delivery in Low-Income Nations. Utilizing methods such as generalized linear regression (GLR), ordinary least squares (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. The GLR and OLS analyses showed a moderate model fit (Adjusted R² = 0.443), highlighting the importance of these predictors. Path analysis was used to determine both the direct and indirect effects of these variables on malaria incidence rates, leading to the creation of a new model. In this model, mean temperature showed a significant direct effect (β = 0.658) and a small indirect effect (β = 0.002779), resulting in a total effect of 0.660779. Antimalarial treatment had a strong negative direct effect (β = -0.189) with a negligible indirect effect, yielding a total effect of -0.18947. Mosquito net access demonstrated a notable direct effect (β = 0.074) and a substantial indirect effect (β = 2.5214437), culminating in a total effect of 2.59544. Total population exhibited a small direct effect (β = -0.180) and a minimal indirect effect (β = -0.0001927), leading to a total effect of -0.18019. Finally, the number of health centers showed no direct effect but a significant indirect effect (β = 1.0956237), resulting in a total effect of 1.0956237. Spatial autocorrelation revealed significant clustering of malaria rates, highlighting the need for targeted interventions. Bivariate color maps underscored the critical role of health centers in improving healthcare access and controlling malaria, suggesting that expanding health center networks in underserved regions could enhance healthcare outcomes