Burden and determinants of malnutrition among pregnant women in Africa: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Malnutrition in pregnancy remains unacceptably high across all regions of Africa though promising progresses have been made globally. Primary studies might not be sufficient to portrait a comprehensive picture of malnutrition during pregnancy and its main risk factors. Therefore, we intended to review the burden of malnutrition, for this specific review implies to protein energy malnutrition, during pregnancy in Africa to present its magnitude and determinant factors. METHODS: We did a systematic review of observational studies published from January 1/2008 to January 31/2018. The CINAHL(EBSCO), MEDLINE (via Ovid), Emcare, PubMed databases and Google scholar were searched. Articles quality was assessed using the Newcastle-Ottawa Scale and studies with fair to good quality were included. We pooled malnutrition prevalence and an odds ratio estimates for risk factors after checking for heterogeneity and publication bias. This review has been registered in Prospero with a protocol number CRD42018114949. RESULT: 23 studies involving 20,672 pregnant women were included. Using a random effect model, the overall pooled prevalence of malnutrition among pregnant women in Africa was 23.5% (95%CI: 17.72-29.32; I2 = 98.5%). Based on the current review pooled odds ratio finding; rural residency (POR = 2.6%; 95%CI: 1.48-4.65; I2 = 0%), low educational status of partners (POR = 1.7%; 95%CI: 1.19-2.53; I2 = 54.8%), multiple pregnancy (POR = 2.15%; 95%CI: 1.27-3.64; I2 = 0%) and poor nutritional indicators (POR = 2.03%; 95%CI: 1.72-2.4, I2 = 0%) were positively determine maternal malnutrition. On contrary, better household economic status (POR = 0.47%; 95%CI: 0.36-0.62; I2 = 24.2%) negatively determine maternal malnutrition. CONCLUSION: A significant number of the pregnant population in Africa are suffering of malnutrition, above 10% of the standard acceptable malnutrition rate. Thus, efforts should be renewed to ensure a proper and widespread implementation of programs that would address issues identified in the current review to reduce the burden of malnutrition.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".