Factors associated with childhood undernutrition in Bangladesh: a scoping review with implications for modeling

Abstract Background Childhood undernutrition remains highly prevalent in low- and middle-income countries and continues to be a major public health concern, contributing to increased morbidity, mortality, and impaired cognitive development. Although many studies have examined factors associated with childhood undernutrition, methodological and analytical gaps remain. This scoping review aimed to synthesize evidence on factors associated with childhood undernutrition in Bangladesh and identify methodological and analytical gaps to inform future research and predictive modeling. Methods We used the Population-Concept-Context framework to define the eligibility criteria and structure the review question. The population included children under 5 years of age, the concept focused on factors associated with undernutrition, including stunting, wasting, and underweight, and the context was Bangladesh. Studies with data collection periods between 2000 and 2025 were included. PubMed, MEDLINE, Web of Science, Scopus, and CINAHL were searched using keywords developed from the research questions. Supplementary searching, including Google Scholar, manual reference checking, and citation tracking, was also conducted. Results A total of 128 studies were included. Most studies used cross-sectional designs and secondary datasets. Logistic regression was the most commonly used analytical method, while six studies applied machine learning approaches. Across the included studies, child age, birth weight, maternal education, maternal nutritional status, and household socioeconomic status were among the factors most frequently reported to be associated with childhood undernutrition. Morbidity-related factors, feeding practices, antenatal care, sanitation indicators, food insecurity measures, regional variation, and household size were also reported as associated factors across studies. Conclusions Multiple factors associated with childhood undernutrition in Bangladesh were identified; however, most studies relied on conventional analytical approaches with limited attention to prediction, validation, and advanced modeling techniques. Future studies may benefit from incorporating advanced analytical techniques, improved feature engineering, and external validation to strengthen predictive modeling. Integrating environmental and contextual data may further support future research and policy planning on childhood undernutrition.