Potential Factors Associated with Undernutrition in Children Under 5 years and its Patterns in Bangladesh: Insights from Four Cross-Sectional Studies
Bibliographic record
Abstract
Abstract Background: Undernutrition in children under 5 years of age is one of the most important measurements of assessing the excellence of human life as it confirms healthy growth in early childhood. The main objective of this study is to understand the potential patterns and factors of undernutrition in children under 5 years of age in Bangladesh from 2007 to 2017. Methods: Four cross-sectional rounds of data from the Bangladesh Demographic and Health Survey (2007, 2011, 2014, and 2017) were used for this study. The ordinal logistic regression model was applied to explore the association between selected factors and undernutrition status (stunting, wasting, and underweight). Results: For stunting, the age of the child emerged as a significant factor. Between 2007 and 2017, older children (12–59 months) consistently exhibited a higher likelihood of severe stunting. In 2017, children aged 48–59 months had an odds ratio (OR) of 1.61 [confidence interval (CI): 1.34, 1.94] compared to those aged 0–11 months, a decrease from 3.04 (CI: 2.50, 3.71) in 2007. Regional disparities were notable, with children from the Sylhet division increasingly at risk. The OR for Sylhet rose from 1.02 (CI: 0.85, 1.22) in 2007 to 1.66 (CI: 1.35, 2.04) in 2017. In addition, socioeconomic factors shifted over time: OR for the poorest households decreased from 1.45 (CI: 1.21, 1.74) in 2007 to 1.18 (CI: 0.99, 1.42) in 2017, while children of mothers with higher education saw a consistent effect, with OR of 0.55 (CI: 0.38, 0.77) in 2007 and 0.49 (CI: 0.37, 0.66) in 2017. Over the survey years 2007, 2014, and 2017, girls were consistently less likely to be wasted than boys. In 2007, OR for girls was 0.86 (CI: 0.74, 0.99), slightly decreasing to 0.81 (CI: 0.68, 0.96) in 2017. Children of mothers with higher education were also less likely to be wasted compared to those of mothers without education. OR for children of mother with higher education was 0.79 (CI: 0.52, 1.20) in 2007, decreasing to 0.51 (CI: 0.35, 0.75) in 2017. In 2017, children aged 48–59 months had an OR of 1.84 (CI: 1.51, 2.24) for being underweight compared to those aged 0–11 months, slightly lower than the OR of 2.10 (CI: 1.75, 2.53) in 2007. In addition, in 2017, children from the richest families had OR of 0.68 (CI: 0.53, 0.87), consistent with that of 0.65 (CI: 0.52, 0.80) observed in 2007. Conclusion: These findings underscore the importance of targeted interventions addressing the specific needs of older children, those from low-income families, and regions with persistent disparities. Strengthening educational initiatives, enhancing nutritional programs, and promoting equitable healthcare access are crucial for sustaining the progress in reducing undernutrition in children under 5 years in Bangladesh.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".