Complete sources of cluster variation on the risk of under-five malaria in Uganda: a multilevel-weighted mixed effects logistic regression model approach

Abstract

Background: Malaria, a major cause of mortality worldwide is linked to a web of determinants ranging from individual to contextual factors. This calls for examining the magnitude of the effect of clustering within malaria data. Regrettably, researchers usually ignore cluster variation on the risk of malaria and also apply final survey weights in multilevel modelling instead of multilevel weights. This most likely produces biased estimates, misleads inference and lowers study power. The objective of this study was to determine the complete sources of cluster variation on the risk of under-five malaria and risk factors associated with under-five malaria in Uganda. Methods: This study applied a multilevel-weighted mixed effects logistic regression model to account for both individual and contextual factors. Results: Every additional year in a child’s age was positively associated with malaria infection (AOR = 1.42; 95% CI 1.33–1.52). Children whose mothers had at least a secondary school education were less likely to suffer from malaria infection (AOR = 0.53; 95% CI 0.30–0.95) as well as those who dwelled in households in the two highest wealth quintiles (AOR = 0.42; 95% CI 0.27–0.64). An increase in altitude by 1 m was negatively associated with malaria infection (AOR = 0.98; 95% CI 0.97–0.99). About 77% of the total variation in the positive testing for malaria was attributable to differences between enumeration areas (ICC = 0.77; p < 0.001). Conclusions: Interventions towards reducing the burden of under-five malaria should be prioritized to improve individual-level characteristics compared to household-level features. Enumeration area (EA) specific interventions may be more effective compared to household specific interventions.