Childhood underweight in Ethiopia: modelling non-linear risk factors and geographic hotspots using Bayesian...

Area of Science:

• Child Health and Nutrition
• Public Health
• Spatial Epidemiology

Background:

• Underweight in children under five is a critical global health issue, defined by a weight-for-age z-score below -2SD from WHO Child Growth Standards.
• Identifying risk factors and high-prevalence areas is essential for targeted interventions to reduce childhood malnutrition.

Purpose of the Study:

• To examine the influence of socio-demographic, geographical, and flexible metrical covariates on childhood underweight in Ethiopia.
• To identify specific communities at high risk of underweight for focused public health strategies.

Main Methods:

• Utilized cross-sectional data from the 2016 Ethiopian Demographic and Health Survey (EDHS) involving 10,641 children.
• Employed a Bayesian geoadditive Gaussian regression model with Markov chain Monte Carlo (MCMC) simulation for robust analysis.
• Incorporated inverse distance weighting (IDW) for spatial analysis and identification of underweight hotspots.

Main Results:

• Non-linear relationships were observed between child underweight and covariates including child age, maternal age, and maternal Body Mass Index (BMI).
• Maternal BMI exhibited an inverted U-shaped association with child underweight, with both lower and higher BMIs linked to increased risk.
• Significant spatial heterogeneity was identified, with western, central, and eastern regions identified as hotspots for childhood underweight.

Conclusions:

• Findings underscore the need for integrated socio-demographic and community-based programs within Ethiopian policy to address childhood malnutrition.
• Targeted interventions in identified hotspot regions are recommended to combat childhood underweight effectively.