Geographically weighted logistic regression model for identifying risk factors for malaria infection among under-5 children in Ghana

Abstract

Malaria remains a significant public health challenge globally, particularly affecting children under-5 years of age due to their underdeveloped immune systems. Identifying the risk factors associated with malaria infection in this vulnerable group is crucial for improving policy formulation and creating effective training programs. However, there is limited information on how the relationship between malaria risk and associated factors varies across different regions, especially among children in Ghana. This is important because understanding these spatial variations can enhance targeted interventions including area remediation and resource allocation. To address this gap, a geographically weighted logistic regression (GWLR) model was developed to identify spatially varying risk factors for malaria infection among children under five in Ghana. The model was built on the premise that the relationship between malaria and potential risk factors is not uniform across geographic areas. Data from the Ghana Malaria Indicator Survey collected through the demographic and health survey program were used for analysis. The study found that the GWLR model fit the data better than the conventional binary logistic regression (BLR) model, based on the information criterion used and mode evaluation metrics. The results indicated that risk factors for malaria such as a child's age, anaemia status, dwellings sprayed, place of residence, electricity access, NHIS (National Health Insurance Scheme) coverage, age of the household head, and household wealth index, were non-stationary across the study area. These findings underscore the importance of spatially tailored interventions to reduce malaria risk in children under-5. The results contribute to the body of literature on malaria risk factors and provide valuable insights for Ghana's national malaria control policies, potentially enhancing the effectiveness of future public health strategies.