Effect of and Interventions in Prevention and Management of Maternal Anemia in the Advent of COVID-19
Bibliographic record
Abstract
Abstract Background There were many unknowns for pregnant women during the COVID-19 pandemic. Most of these could have been silent however lethal and anemic conditions could escalate the worsening of pregnancy outcomes. Existing evidence indicate that, array of factors is associated with the ability of compromising maternal anemia, some directly and others indirectly. Objective This review aimed at ascertaining the pooled effect of several anemia interventions. Specifically, the aim of this study was to establish if pregnancy status is associated with COVID-19 severity characterized by a cytokine storm. Methods We searched the Google Scholar, PubMed, Scopus, Web of Science, and Embase databases to studies suitable for inclusion in this meta-analysis. Studies examining women of reproductive age on any maternal anemia intervention were included. The risk of bias was assessed using the Cochrane risk of bias tool. Review Manager 5.4.1 was used to calculate rate ratios (RRs) with 95% CIs, which were depicted using forest plots. Quantitative variables were summarized in total numbers and percentages. The effect on prevention, control, management and or treatment of anemia was calculated and compared between the intervention and the comparator arms. Heterogeneity was evaluated with the Cochran Q statistic and Higgins test. Results A total of 11 articles including data for 6,129 were included. With sensitivity analysis, the interventions had a utility of 39% on maternal anemia prevention and management (random effects model RR 0.61, 95% CI 0.43, 0.87; P = 0.006) (χ 2 6=286.98, P<.00001; I 2 =97%). All the interventions against maternal anemia showed an effect of 17% (fixed-effect model RR 0.83, 95% CI 0.79-0.88; P<.00001) (χ 2 4=2.93, P=0.57; I 2 =0%). Education to pregnant women showed a 28% effect (RR 0.72 95% CI 0.58, 0.89), medicinal administration 19% (RR 0.81 95% CI 0.73, 0.90), iron supplementation 17% (RR 0.83 95% CI 0.75, 0.92) and I.V Ferric Carboxy-maltose 15% (RR 0.85 95% CI 0.74, 0.97) (I 2 = 0%). Interventions in African region had a higher (16%) and significant effect compared to other regions (fixed-effects model RR 0.84, 95% CI 0.79-0.89; P<.001) (χ 2 5=176.53, P<.00001; I 2 =97%). Multiple center studies had a significant predictive effect (16%) compared to single center studies (fixed-effects model RR 0.84, 95% CI 0.79-0.89; P<.00001)(χ 2 5=176.53, P<.00001; I 2 =97%). The year 2020 recorded the highest effect of maternal anemia interventions at 28% (random-effects model RR 0.72, 95% CI 0.67-0.78; P<.00001) (χ 2 3=167.34, P<.00001; I 2 =98%) Conclusion In the advent of COVID-19, maternal anemia interventions were compromised demonstrated by a low effectiveness trend from the year 2020 to the year 2022. During this period, even the most effective and recommended interventions against maternal anemia were somehow affected.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".