Application of Multivariate Binary Logistic Regression Grouped Outlier and Spatial Statistics to Identify Villages Having Unusual Health-Seeking Habits for Childhood Malaria in Malawi

Gracious Hamuza, National Statistical Office
Tsirizani Kaombe, University of Malawi
Emmanuel Singogo, University of Northern Carolina

Early diagnosis and prompt treatment of malaria in young children are crucial for prevention of serious stages of the disease. In areas where general delayed treatment-seeking habits are observed, targeted campaigns and interventions can be implemented. Using 2021 Malawi Malaria Indicator Survey data, we applied multivariate binary logistic regression diagnostics and spatial statistics to identify traditional authorities having caregivers with outlying health-seeking behaviour for childhood malaria. Findings indicated traditional authorities Vuso Jere, Kampingo Sibande, Ngabu, and Dzoole were outliers in the model. Most mothers in these authorities sought treatment within twenty-four hours of the onset of malaria symptoms in the children, which was uncommon in the study population. The findings suggest that spatial statistics and multivariate regression residuals can be combined to identify communities that perform well in the fight against malaria and whose practices can be replicated in areas where progress is lagging.