Peer-reviewed article
Child malnutrition in the Democratic Republic of the Congo: A Bayesian Gaussian regression analysis using the 2023–24 demographic and health survey
Zacharie Tsala Dimbuene, Hugues Sampasa‐Kanyinga ·
Summary
Written by AI, not yet AICS's view · claude-sonnet-5-5This study uses the 2023–24 Demographic and Health Survey to look at stunting (low height for age) among children under five in the Democratic Republic of the Congo. It uses a statistical model to examine how child age, maternal education and geography relate to stunting. Stunting differs a lot between the 26 provinces, with Kwilu a major hotspot. Younger children had higher stunting risk in the model, and maternal education showed a weak, uneven association. The authors conclude that targeted, context-specific interventions are needed.
Key points
- Substantial regional disparities in stunting exist across the 26 provinces of the DRC.
- Kwilu province is a major hotspot with very high stunting levels.
- Several other provinces have medium to high stunting prevalence, so stunting is widespread nationally.
- Child age shows a strong negative trend with stunting risk, with risk decreasing sharply as age increases.
- Maternal education has a weaker, nonlinear association, with small effects and greater uncertainty at higher schooling levels.
Limitations
Only the abstract is available, so sample size, exact effect sizes and data quality cannot be assessed. The analysis examines associations in survey data only and cannot show causes or whether any intervention works.
Abstract
Background Child stunting remains a major public health challenge in the Democratic Republic of the Congo (DRC), reflecting persistent inequalities in child nutrition and health outcomes. This study examines the distribution and spatial variation of height-for-age z-scores (HAZ) among children under five, while also assessing the influence of selected demographic and socioeconomic factors. Methods Data were obtained from the 2023–2024 Demographic and Health Survey (DHS-8). A structured additive regression model was then applied to assess the effects of child age and maternal education, allowing for nonlinear relationships through smooth functions. To capture geographic heterogeneity, spatial effects were decomposed into structured (spatially correlated) and unstructured (area-specific random) components, enabling the identification of regional clustering and residual province-level variation in stunting. Results Geographic analysis reveals substantial regional disparities in stunting prevalence across the 26 provinces of the DRC. The province of Kwilu emerges as a major hotspot with very high levels of stunting, while several provinces fall within medium to high prevalence categories, indicating that stunting is a widespread issue across the country. Model results highlight important nonlinear relationships for certain predictors. Child’s age shows a strong negative trend with stunting risk: younger children have higher log-odds of stunting, and the risk decreases sharply with age. Maternal education displays a weaker and nonlinear association, with generally small effects across most education levels and greater uncertainty at higher levels of schooling. Spatial modeling further demonstrates that geographic factors significantly contribute to variation in stunting outcomes. Both structured spatial effects, indicating regional clustering, and unstructured spatial effects, reflecting province-specific deviations, contribute to the observed patterns. The combined spatial effect suggests persistent geographic disparities even after adjusting for covariates, although uncertainty varies across regions. Conclusions The findings underscore the importance of geographic context and early childhood factors in shaping stunting outcomes in the DRC, highlighting the need for targeted, context-specific interventions to address persistent nutritional inequalities.