Secular trends in low birth weight and child undernutrition in West Africa: evidence from complex nationwide surveys, 1985–2019
Bibliographic record
Abstract
Abstract Objective: We present prevalence estimates and secular trends of stunting, wasting, underweight, and anaemia among children under 5 years of age and low birth weight (LBW) over the period 1985–2019 in West Africa (WA). Design: Analysis of Demographic and Health Survey (DHS) and World Bank data. DerSimonian–Laird random effect model with the Knapp–Hartung adjustment to the standard error was used to derive overall prevalence estimates. We used fixed effect ordinary least square regression models with cluster robust standard error to conduct time trends analyses. Setting: West Africa. Participants: Children aged 0 to 59 months. Results: Three distinct periods (1986–1990, 1993–1996 and 1997–2000) of sharp increases in prevalence of all outcomes was observed. After the year 2000, prevalence of all outcomes except LBW started to decline with some fluctuations. LBW prevalence showed a steady increase after 2000. We observed a decline in prevalence of stunting (β = –0·20 %; 95 % CI –0·43 %, 0·03 %), log-wasting (β = –0·02 %; 95 % CI –0·02 %, –0·01 %), log-underweight (β = –0·02 %; 95 % CI –0·03 %, –0·01 %) anaemia (β = –0·44; 95 % CI –0·55 %, –0·34 %), and an increase in LBW (β = 0·06 %; 95 % CI –0·10 %, 0·22 %) in WA over the period. Pooled prevalence of stunting, wasting, underweight, anaemia and LBW in WA for the period 1985–2019 was 26·1 %, 16·4 %, 22·7 %, 76·2 % and 11·3 %, respectively. Conclusions: Child undernutrition prevalence varied greatly between countries and the year cohorts. We observed marginal reductions in prevalence of all outcomes except anaemia where the reductions were quite striking and LBW where an increase was noted. There is the need for more rigorous and sustained targeted interventions in WA.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".