Directed acyclic graph helps to understand the causality of malnutrition in under-5 children born small for...

Small-for-gestational age (SGA) is a causal factor for malnutrition (undernutrition). The available evidence on this causal relationship is based on observational studies and suffers from confounding and collider biases. This study aimed to construct a theoretical causal model to estimate the effect of SGA on malnutrition in children aged less than 5 years.

For the causal model, we designated term-SGA status as the exposure variable and malnutrition at 6 months to 5 years of age (diagnosed by World Health Organization criteria) as the outcome variable. Causal estimands were formulated for three stakeholders. A "rapid narrative review" methodology was adopted for literature synthesis. Studies (observational and randomized) listing the causal factors of malnutrition in children aged less than 5 years from the Indian subcontinent were eligible. Four databases (PubMed, Scopus, Web of Science, and ProQuest) were searched and restricted to the last 10 years (search date: December 15, 2023). Information about the causal factors (covariates) of malnutrition and study characteristics was extracted from the article abstracts. Next, a causal model in the form of a directed acyclic graph (DAG) (DAGitty software) was constructed by connecting exposure, outcome, and covariate nodes using the sequential causal criteria of temporality, face validity, recourse to theory, and counterfactual thought experiments.

The search yielded 4818 records, of which 342 abstracts were included. Most of the studies were conducted in India (39%) and Bangladesh (27%). The literature synthesis identified 81 factors that were grouped into 17 nodes, referring to 5 domains: socioeconomic, parental, child-related, environmental, and political. The DAG identified 12 different minimal sufficient adjustment sets (conditioning sets for regression analysis) to estimate the total effect of SGA on malnutrition.

We offer an evidence-based causal diagram that will minimize bias due to improper selection of factors in studies focusing on malnutrition in term-SGA infants. The DAG and adjustment sets will facilitate the design and data analysis of future studies.