Basic determinants of child growth in sub-Saharan Africa: cross-sectional survey analysis of positive deviants in poor households
Bibliographic record
Abstract
Abstract Background Childhood malnutrition is a significant public health problem confronting countries across the globe. Nonetheless, recent evidence suggests a downward trend in undernutrition among children globally. Despite the progress made at the global level, sub-Saharan Africa did not experience significant improvement in the past decades. The objective of this study was to investigate the basic determinants associated with linear growth among children under 5 years living in poor households. Methods The study used nationally representative cross-sectional survey data from Ghana, Kenya, Democratic Republic of Congo (DRC), Nigeria and Mozambique. The participants are children aged 0–59 months (N=24,264) living in poor households. The DHS obtained information on children through face-to-face interviews with mothers. The height of the children was also measured and used to compute the height-for-age Z-scores (HAZ). In this study, HAZ is categorised into HAZ>-2 standard deviations (SD) (not stunted/better growth) and HAZ<-2 SD (stunted/poor growth). Results A unit change in maternal years of education was associated with increased odds of better growth among children living in poor households in DRC [adjusted odds ratio (aOR)= 1.03, 95% CI=1.01,1.07)], Ghana (aOR=1.06, 95% CI=1.01,1.11), Kenya (aOR=1.03, 95% CI= 1.01, 1.05) and Nigeria (aOR=1.08, 95%=1.06,1.10). Maternal antenatal attendance of at least four visits was associated positively with better child growth in DRC (aOR=1.32, 95% CI=1.05, 1.67) and Ghana (aOR=1.67, 95% CI=1.19, 2.33). In Ghana, Mozambique and DRC, breastfeeding was associated significantly with the likelihood of better linear growth when only socio-demographic correlates were included in the models but disappeared after the inclusion of child-level covariates. In Nigeria, normal maternal weight was associated with increased odds (aOR=1.24, 95% CI=1.08, 1.43) of positive growth among children living in poor households, so was overweight (aOR=1.51, 95% CI= 1.24, 1.83). In all the countries except Ghana, child biological factors such as sex and age were associated with reduced odds of better growth. Conclusions The socio-demographic factors included in this analysis have the potential to promote linear growth of children under 5 years living in poor households. Interventions aimed at fostering linear growth among children living in poverty should target at enhancing these factors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".