Role of iron in the reduction of anemia among women of reproductive age in low-middle income countries: insights from systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Iron deficiency anemia is a common public health issue among women of reproductive age (WRA) because it can result in adverse maternal and birth outcomes. Although studies are undertaken to assess iron efficacy, some gaps and limitations in the existing literature need to be addressed. To fill the gaps, we conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) assessing the role of iron in reducing anemia among WRA in low-middle-income countries (LMICs). Methods A comprehensive search strategy was used to search Medline through PubMed, Embase, and Science Direct for RCTs published between 2000 and 2020. The primary outcome was the mean change in hemoglobin level. We used standardized mean differences and their respective 95% CI to estimate the pooled effect. We used I 2 statistics and Egger’s test to assess heterogeneity and publication bias, respectively. This review was carried out in accordance with revised guidelines based on the Preferred Reporting Items for Systematic Review and Meta-analysis. Results The findings showed that iron therapy improved hemoglobin and ferritin levels, though the results varied across studies. An overall pooled effect estimate for the role of iron therapy in improving the hemoglobin levels among WRA was -0.71 (95% CI: -1.27 to -0.14) ( p = 0.008). Likewise, the overall pooled effect estimate for the role of iron therapy in improving the ferritin levels among WRA was -0.76 (95% CI: -1.56 to 0.04) ( p = 0.04). The heterogeneity (I 2 ) across included studies was found to be statistically significant for studies assessing hemoglobin (Q = 746.93, I 2 = 97.59%, p = 0.000) and ferritin level (Q = 659.95, I 2 = 97.88%, p = 0.000). Conclusion Iron therapy in any form may reduce anemia’s burden and improve hemoglobin and ferritin levels, indicating improvement in iron-deficiency anemia. More evidence is required, however, to assess the morbidity associated with iron consumption, such as side effects, work performance, economic outcomes, mental health, and adherence to the intervention, with a particular focus on married but non-pregnant women planning a pregnancy in the near future. Trial registration Registered with PROSPERO and ID is CRD42020185033.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.009 | 0.007 |
| 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".