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 BackgroundIron deficiency anemia is a common public health problem problem among women of reproductive age (WRA) as it is can lead to unfavorable maternal and birth outcomes. Although studies are undertaken to assess the efficacy of iron, there are some gaps and limitations in the existing studies that need to be addressed. To address the gaps, we undertook a systematic review and meta-analysis to assess the existing evidence regarding the role of iron in decreasing anemia among WRA in low-middle-income countries.MethodsPubMed, Embase, and Science Direct were systematically searched using a comprehensive search strategy for randomized controlled trials published between 2000 to 2020. Mean change in hemoglobin level was assessed as a primary outcome. We performed a meta-analysis to estimate the pooled effect of all studies using standardized mean differences and their respective 95% CI. We assessed heterogeneity and publication bias using I2 statistics and Egger’s test respectively. This review was conducted with the help of updated guidelines based on the Preferred Reporting Items for Systematic Review and Meta-analysisResultsGenerally, the results demonstrated a favorable effect of iron therapy in improving hemoglobin levels with variation across studies. An overall pooled effect estimate for the role of iron therapy in decreasing the burden of anemia among WRA was -0.51 (95% CI: -1.03 to 0.01) (p = 0.04). Likewise, iron therapy improved ferritin levels and decreased the prevalence of anemia. The heterogeneity across included studies was found to be statistically significant as indicated by the parameters of heterogeneity (Q = 1191.60, I2 = 98.24%, p = 0.000). ConclusionIron therapy in any form may reduce the burden of anemia and improve the hemoglobin and ferritin levels, indicating improvement in iron-deficiency anemia. However, more evidence is needed to assess the morbidity associated with iron consumption such as side effects, work performance, economic outcomes, mental health, and compliance to the intervention with a special focus on married but non-pregnant women planning a pregnancy in near future. A systematic review and Meta-analysis 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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".