Effect of and Interventions in Prevention and Management of Maternal Anemia in the Advent of COVID-19
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,017 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».