ArticleBMJ paediatrics open2024
Statistical modelling of determinants of child stunting using secondary data and Bayesian networks: a UKRI Global Challenges Research Fund (GCRF) Action Against Stunting Hub protocol paper.
Article in BMJ paediatrics open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Effect of eggs on Maya child development and growth: the Saqmolo' Project randomized clinical trial.Lancet regional health. Americas · 2026Article
- Addressing the poverty-nutrition gap for SDG 1 and SDG 2: a Bayesian geo-additive analysis of spatial heterogeneity and nonlinear maternal risks in childhood stunting in India (NFHS-4 and NFHS-5).Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionSeveral factors have been implicated in child stunting, but the precise determinants, mechanisms of action and causal pathways remain poorly understood. The objective of this study is to explore causal relationships between the various determinants of child stunting. METHODS AND ANALYSIS: The study will use data compiled from national health surveys in India, Indonesia and Senegal, and reviews of published evidence on determinants of child stunting. The data will be analysed using a causal Bayesian network (BN)-an approach suitable for modelling interdependent networks of causal relationships. The model's structure will be defined in a directed acyclic graph and illustrate causal relationship between the variables (determinants) and outcome (child stunting). Conditional probability distributions will be generated to show the strength of direct causality between variables and outcome. BN will provide evidence of the causal role of the various determinants of child stunning, identify evidence gaps and support in-depth interrogation of the evidence base. Furthermore, the method will support integration of expert opinion/assumptions, allowing for inclusion of the many factors implicated in child stunting. The development of the BN model and its outputs will represent an ideal opportunity for transdisciplinary research on the determinants of stunting. ETHICS AND DISSEMINATION: Not applicable/no human participants included.
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