Birth weight and nutritional status of children under five in sub-Saharan Africa
Bibliographic record
Abstract
INTRODUCTION: Over the past three decades, undernutrition has become a major cause of morbidity and mortality among children under five years globally. Low birth weight has been identified as a risk factor for child morbidity and mortality, especially among children under five years in sub-Saharan Africa. There is, however, a paucity of empirical literature establishing the association between low birth weight and undernutrition in sub-Saharan Africa. We examined the association between birth weight and nutritional status of children under five in sub-Saharan Africa. METHODS: Our analyses were performed on a weighted sample of 110,497 children under five years from 32 countries in sub-Saharan Africa. Data were obtained from the Demographic and Health Surveys conducted from 2010 to 2019. We reported the prevalence of low birth weight and nutritional status (stunting, wasting, and underweight) for all the 32 countries using percentages. We used multilevel binary logistic regression to examine the association between birth weight and nutritional status (stunting, wasting, and underweight) of the children, controlling for covariates. The results of the regression analyses were presented using adjusted odds ratios (aOR) with 95% confidence intervals. Statistical significance was set at p<0.05. RESULTS: The prevalence of low birth weight was 5.4%, with the highest (13.1%) and lowest (0.9%) reportedin South Africa and Chad, respectively. The pooled prevalence of wasting, underweight, and stunting were 8.1%, 17.0%, and 31.3%, respectively. Niger had the highest prevalence of wasting (21.5%) and underweight (37.1%), whereas Burundi had the highest prevalence of stunting (51.7%). We found that children with low birth weight were more likely to be stunted [aOR = 1.68, 95% CI = 1.58-1.78], underweight [aOR = 1.82, 95% CI = 1.70-1.94], and wasted [aOR = 1.35, 95% CI = 1.20-1.38] after controlling for covariates. CONCLUSION: Our study has demonstrated that low birth weight is a key determinant of undernutrition among children under five in sub-Saharan Africa. Policymakers need to give special attention to improving the nutritional status of children under-five years in sub-Saharan Africa by implementing measures aimed at enhancing the weight of children. To accelerate progress towards the achievement of the Sustainable Development Goal 3.2 target of ending preventable deaths of newborns and under-five by 2030, it is imperative for countries in sub-Saharan Africa to intensify interventions aimed at improving maternal and child nutrition. Specific nutrition interventions such as dietary modification counselling should prioritized.
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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 | 0.000 |
| 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".