Poverty and childhood malnutrition: Evidence-based on a nationally representative survey of Bangladesh
Bibliographic record
Abstract
BACKGROUND: Malnutrition contributes to children's morbidity and mortality, and the situation undermines the economic growth and development of Bangladesh. Malnutrition is associated with lower levels of education that decrease economic productivity and leads to poverty. The global burden of malnutrition continues to be unacceptably high amid social and economic growth, including in Bangladesh. Therefore, identifying the factors associated with childhood malnutrition and poverty is necessary to stop the vicious cycle of malnutrition leaded poverty. METHODS: The study utilized the 2017-18 Bangladesh Demographic and Health Survey (BDHS), accumulating 7,738 mother-child pairs. Associations between potential risk factors and nutritional status were determined using chi-square tests, and multivariate logistic regression models were utilized on significant risk factors to measure their odds ratio (OR) with their 95% confidence intervals (CI). RESULTS: The prevalence of moderate and severe wasting was 7.0% and 1.8%, respectively, whereas the prevalence of moderate and severe stunting was 19.2% and 8.0%, while 16.4% and 3.6% of children were moderately and severely underweight. Children from the poorest and poor households were suffering from at least one form of malnutrition. Adjusted ORs were estimated by controlling socio-economic and demographic risk factors, such as poor maternal body mass index, parents' lower education level, use of unhygienic toilet, child age in months, and recent experience of diarrhea and fever. The pattern was almost similar for each malnutrition status (i.e., stunting, underweight, and wasting) in the poorest and poor households. CONCLUSION: Bangladesh achieved the Millennium Development Goals, focusing primarily on health-related indicators and working to achieve the Sustainable Development Goals. Even considering this success, the prevalence of malnutrition and poverty in same household remains relatively high compared to other developing countries. Therefore, the study recommends the implementation of nationwide systematic measures to prevent poverty and malnutrition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".