Prevalence, types and determinants of anemia among pregnant women in Sudan: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Anemia during pregnancy is a public health problem especially in developing countries and it is associated with maternal and perinatal adverse outcomes. There is no meta-analysis on anemia during pregnancy in Sudan. The current systemic review and meta-analysis was conducted to assess the prevalence, types and determinant of anemia during pregnancy in Sudan. METHODS: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was followed. The databases (PubMed, Cochrane Library, Google Scholar, CINAHL, and African Journals Online) were searched using; anemia, pregnancy related anemia and Sudan. Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) and Modified Newcastle - Ottawa quality assessment scale were used for critical appraisal of studies. The pooled Meta logistic regression was computed using OpenMeta Analyst software. RESULTS: = 0.465) between the anemic and no anemic women. Malaria was investigated in six studies. Pregnant women who had malaria infection during pregnancy were 1.94 times more likely to develop anemia than women who had no malaria infection (OR = 1.94, 95% CI =1.33-2.82). Six (37.5%) studies investigated type of anemia. The pooled prevalence of iron deficiency anemia (IDA) among pregnant women in Sudan was 13.6% (95% CI = 8.9-18.2). CONCLUSION: There is a high prevalence of anemia among pregnant in the different region of Sudan. While age and parity have no association with anemia, malaria infection was associated with anemia. Interventions to promote the strengthening of antenatal care, and access and adherence to nutrition, and malaria preventive measures are needed to reduce the high level of anemia among pregnant women in Sudan.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| 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".