Factors influencing concurrent wasting, stunting, and underweight among children under five who suffered from severe acute malnutrition in low- and middle-income countries: a systematic review
Bibliographic record
Abstract
Background: Wasting, stunting, and underweight in children are complex health challenges shaped by a combination of immediate, underlying, and systemic factors. Even though copious data demonstrates that the causation routes for stunting and wasting are similar, little is known about the correlations between the diseases in low- and middle-income nations. Objective: The objective of this study is to evaluate the factors that concurrently affect wasting, stunting, and underweight in <5-year-olds with severe acute malnutrition (SAM). Method: This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched every electronic database that was available, from the medRxiv pre-print site, PubMed, MEDLINE, EMBASE, Cochrane Library, Web of Science, PsycINFO, CINAHL, Google Scholar, and Scopus, in addition to the Science Direct search engine. We considered research conducted in low- and middle-income nations on <5-year-olds with SAM. The Newcastle Ottawa Scale was used to assess the quality of the studies. Results: After screening and selecting 12 eligible studies, 1,434,207 records were included for analysis. The prevalence of factors influencing concurrent wasting, stunting, and being underweight was 26.42% in low-middle -income countries (LMI). The prevalence was higher in men, with wasting, stunting, and underweight at 14.2, 4.1, and 27.6%, respectively. Unprotected drinking water was associated with stunting [odds ratio = 0.68; 95CI (0.50, 0.92)]. Being male is another factor (aOR = 2.04, 95% CI: 1.13, 3.68). Lack of prenatal care follow-up was associated with a lower risk of wasting (OR = 2.20, 95% CI: 1.04, 4.64), while low birth weight (<2.5 kg), diarrhea, having a younger child, and being from a poor household were associated with wasting, stunting, and underweight. Other factors included body mass index (BMI) for age aOR = 2.11, 95% CI: (0.07, 0.895); maternal education: stunting [aOR = 1.52, 95% CI: (0.09, 0.89)], underweight [aOR = 1.97, 95% CI: (0.01, 0.73)], and open defecation, stunting [aOR = 1.62, 95% CI: (0.06, 0.32)], underweight [aOR = 1.92, 95% CI: (0.042, 0.257)]). Likelihood of being underweight increased with birth order (second born, aOR = 1.92, 95% CI 1.09-3.36; third born, aOR = 6.77, 95% CI 2.00-22.82). Conclusion: Inadequate dietary intake, illness, food insecurity, poor maternal and child care, poor hygiene and sanitation, and healthcare inaccessibility contribute to SAM.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".