Prevalence and Determinants of Undernutrition among Under-Five Children in Nigeria: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Child undernutrition is a key public health issue that both causes and contributes to disease and death. Undernutrition accounts for 45% of under-five deaths globally most of which occur in Low- and Middle-income countries (LMIC). Malnutrition has a substantial and long-lasting effect on individuals, families, communities and the entire nation. This study aimed to assess the prevalence and determinants of undernutrition in under-five children in Nigeria. METHODOLOGY: This systematic review was done following the Cochrane library guidelines. A search of literature written in English language and published between 2000 and 2022 was done using PubMed, CINAHL, MEDLINE and ProQuest databases. The initial search resulted in 760 studies. These were exported to End note version 9 to remove duplicates. Titles and abstracts were screened for studies that met the inclusion criteria. Finally, 11 studies that met the inclusion criteria were thoroughly assessed and data that were relevant to this systematic review were captured. The study findings were analyzed using descriptive statistics. RESULTS: The prevalence of undernutrition was between 1.0% and 43.3%. The highest prevalence of underweight, wasting and stunting were 43.3%, 29.3% and 41%, respectively. Factors associated with undernutrition were age, sex, birth order, recent acute diarrhoea and acute respiratory infection, maternal literacy level, maternal income <$20 and socio-economic class among others. CONCLUSION: Under-five undernutrition is a huge public health issue in Nigeria. Prevalence of undernutrition varies widely across geo-political zone with a myriad of associated risk factors. Multi-level and multidisciplinary interventions are required to sustainably address the determinants of under-five undernutrition.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".