Structural equation modelling (SEM) for malaria prevalence and risk factors in Uganda

Area of Science:

• Epidemiology
• Public Health
• Biostatistics

Background:

• Malaria significantly affects children under five in sub-Saharan Africa, with complex contributing factors.
• Traditional analyses struggle to capture the multifaceted relationships influencing malaria transmission.
• Structural Equation Modelling (SEM) offers a robust approach to understanding these intricate pathways.

Purpose of the Study:

• To explore latent and observed predictors of child malaria prevalence using SEM.
• To provide a comprehensive understanding of the underlying factors influencing child malaria.
• To identify key pathways affecting malaria transmission in young children.

Main Methods:

• Utilized secondary data from the 2018-2019 Uganda Malaria Indicator Survey (MIS).
• Constructed a SEM framework with latent variables: Socioeconomic Status (SES), Environment, Maternal Health, and Child Health.
• Assessed model fit using standard indices (CFI, TLI, RMSEA, SRMR).

Main Results:

• Child Health showed the strongest positive association with malaria status (β=0.22, p<0.001).
• Environmental factors exhibited a marginal negative association (β=-0.36, p=0.056).
• Socioeconomic Status and Maternal Health were not statistically significant predictors.

Conclusions:

• SEM effectively disentangles complex factors influencing child malaria.
• Child health status and environmental factors are critical in malaria prevalence.
• Findings support integrated interventions targeting child health and environmental exposures to reduce malaria burden.