Vitamin D Deficiency Among Pregnant Women in Sub‐Saharan Africa: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Vitamin D deficiency (VDD) during pregnancy is linked to adverse maternal and fetal outcomes. Deficiency may result from low cutaneous synthesis, poor dietary intake, or metabolic disruptions. In Sub-Saharan Africa (SSA), diverse climates, diets, and health systems may influence VDD prevalence, yet comprehensive data remains limited. OBJECTIVE: To estimate the pooled proportion of VDD among pregnant women in SSA. METHODS: A systematic review and meta-analysis were conducted on studies reporting VDD among pregnant women in SSA. Databases searched included PubMed, Scopus, Science Direct, HINARI, Google, and Google Scholar without restrictions on language or study period. Study quality was assessed with the Newcastle-Ottawa Scale. Heterogeneity was examined using Cochrane's Q and I² statistics. Publication bias was evaluated using Egger's test at a 5% significance level. A random-effects model was used to estimated the pooled proportion. RESULTS: Thirty observational studies with 6853 pregnant women were included. Reported proportion ranged from 99.2% in Sudan to 1.6% in Zimbabwe. The pooled proportion of VDD was 34.8% (95% CI: 20.75, 48.76) with significant heterogeneity (I² = 99.83%, p < 0.001). Subgroup analysis showed the highest proportion in East Africa at 45.65% (95% CI: 17.68-73.63) and the lowest in Southern Africa at 13.83% (95% CI: 2.99-24.67). Most studies were high-quality, facility-based, and predominantly single-center. CONCLUSION: VDD is common among pregnant women in SSA, particularly in East Africa, and may worsen maternal and neonatal health outcomes. Public health strategies, such as nutrition education and supplementation programs alongside food fortification policies are needed to reduce vitamin D deficiency.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".