Prevalence and Determinants of the Double Burden of Malnutrition at Household Level: A Systematic Review
Bibliographic record
Abstract
The persistent problem of undernutrition and the emerging prevalence of overnutrition hints at a new term for the double burden of malnutrition among children globally. This study aims to provide a review of the published studies concerning the prevalence of double burden of malnutrition at the household level and the associated factors. Articles were identified from the electronic databases of PubMed, Nature, SAGE, Scopus, and SpringerLink, using the same search terms for all. A total of fourteen articles were eligible and sixteen sets of prevalence values were obtained. Most articles were published in 2018 – 2020. Fourteen articles used secondary data from the Demographic and Health Survey. Most articles studied under five children and mothers 15-49 years. Mother’s nutritional status was identified using BMI, while for children height for age z-score was commonly used. The reported prevalence of double burden at the household level varied from 1.0 to 28.0% by country. Frequently assessed factors observed that older children and older mothers were likely to develop a household double burden of malnutrition. A negative association was found when households possessed access to mass media. Overall, the media should have been channels for health promotion. Intervention concerning the nutrition of mothers and children at the household level is required to be intensified through nutrition-specific and nutrition-sensitive programs.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".