Bioversity International, Montpellier, France
Department of Cognitive Science, Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.
BMJ Paediatr Open. 2024 Mar 22;8(Suppl 1):e001983. doi: 10.1136/bmjpo-2023-001983.
Several 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.
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.
Not applicable/no human participants included.
有几个因素与儿童发育迟缓有关，但确切的决定因素、作用机制和因果途径仍知之甚少。本研究的目的是探讨儿童发育迟缓的各种决定因素之间的因果关系。
该研究将使用印度、印度尼西亚和塞内加尔的国家健康调查汇编的数据，以及对儿童发育迟缓决定因素的已发表证据进行审查。使用因果贝叶斯网络（BN）进行数据分析 - 这是一种适合于建模因果关系相互依存网络的方法。该模型的结构将在有向无环图中定义，并说明变量（决定因素）和结果（儿童发育迟缓）之间的因果关系。条件概率分布将生成，以显示变量和结果之间直接因果关系的强度。BN 将提供儿童发育迟缓各种决定因素的因果作用的证据，确定证据差距，并支持对证据基础的深入审查。此外，该方法将支持专家意见/假设的整合，允许纳入许多与儿童发育迟缓有关的因素。BN 模型的开发及其输出将为发育迟缓决定因素的跨学科研究提供理想的机会。
不适用/不涉及人类参与者。
