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Peer-reviewedCC BY 4.0PLOS One

Child malnutrition in the Democratic Republic of the Congo: A Bayesian Gaussian regression analysis using the 2023–24 demographic and health survey

  • Zacharie Tsala Dimbuene1
  • Hugues Sampasa-Kanyinga2
  1. 1School of Population and Development Sciences, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
  2. 2Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of Congo
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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.

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Cite

BibTeXRIS
BibTeX
@article{dimbuene2026child,
  title = {{Child malnutrition in the Democratic Republic of the Congo: A Bayesian Gaussian regression analysis using the 2023–24 demographic and health survey}},
  author = {Zacharie Tsala Dimbuene and Hugues Sampasa-Kanyinga},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0360056},
  url = {https://doi.org/10.1371/journal.pone.0360056},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Child malnutrition in the Democratic Republic of the Congo: A Bayesian Gaussian regression analysis using the 2023–24 demographic and health survey
AU  - Zacharie Tsala Dimbuene
AU  - Hugues Sampasa-Kanyinga
PY  - 2026
DA  - 2026/10/08
JO  - PLOS One
DO  - 10.1371/journal.pone.0360056
UR  - https://doi.org/10.1371/journal.pone.0360056
AB  - 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.
SN  - 1932-6203
ER  -