MétaCan
Menu
Retour à la cohorte
Enregistrement W3154529326 · doi:10.1371/journal.pone.0249789

Compliance with Iron and Folic Acid Supplementation (IFAS) and associated factors among pregnant women in Sub-Saharan Africa: A systematic review and meta-analysis

2021· review· en· W3154529326 sur OpenAlexaboutno aff
Meseret Belete Fite, Kedir Teji Roba, Lemessa Oljira, Abera Kenay Tura, Tesfaye Assebe Yadeta

Notice bibliographique

RevuePLoS ONE · 2021
Typereview
Langueen
DomaineMedicine
ThématiqueIron Metabolism and Disorders
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMeta-analysisSystematic reviewMedicineEnvironmental healthFolic acidPregnancyGuidelineAnemiaMEDLINEFolic acid supplementationPediatricsInternal medicineBiologyPathology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Anemia is one of the world's leading cause of disability and the most serious global public health issues. This systematic review and meta-analysis was conducted very carefully in order to give up the pooled compliance of Iron and Folic-Acid Supplementation in Sub-Saharan Africa. METHODS: To conduct this brief systematic review and meta-analysis, a related literature search was done from different sources, PubMed Medline and Google Scholar Journals. Then IFA Supplementation related searching engine was used to make the work more meaningful and intensive. Moreover, we used modified Newcastle-Ottawa quality assessment scale for cross sectional studies to assess the quality of the study in terms of their inclusion. Then, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was followed to carry out the work in a carful manner. Finally, the pooled effect size was computed using the review manager and Compressive Meta-analysis software. RESULTS: Twenty-three studies, which encompassed 24272 pregnant women, were chosen for the analysis. From those an overall prevalence of compliance with Iron and Folic Acid Supplementation (IFAS) in pregnancy in SSA was 39.2%. However, the result from meta-analysis showed that women who were counseled on IFAS in their courses of pregnancy were 1.96 times more likely to adhere to IFAS compared to those who were not counseled [OR:1.96, 95% CI (1.76-,5.93)]. Moreover, it showed that women who had knowledge of IFAS were 2.71 times more likely to have compliance with IFAS as compared to those who had no knowledge of IFAS [OR:2.71, 95% CI (1.33,5.54)]. Also it revealed that those women who had knowledge of anemia were 5.42 times more likely to have compliance with IFAS as compared with those who had no knowledge of anemia [OR5.42, 95% CI (1.52, 19.43)]. Furthermore, women who had received fourth visit for ANC were 1.54 times more likely to have compliance with IFAS as compared to those who had not received for ANC [OR 1.54, 95% CI (0.66, 3.58.43)]. CONCLUSIONS: Our finding from this systematic review and meta-analysis shows the low case in prevalence of compliance to IFAS among pregnant women in SSA. Predictors for this includes: knowledge about anemia, knowledge about IFAS, counseling on IFAS and receiving fourth antenatal care visit were statistically correlated positively with compliance to IFAS. This demands careful appraisal of effect of prevention work for functioning policy, programs and plan nutrition intrusions for refining maternal dietary intake in gestation. Also dietary education intrusion requires to be planned to satisfy the needs of pregnant women. So we hope that the result of this study might be essential as a bridging stone for policy makers of Africa; exclusively for maternal and child health care. Finally, we recommended further studies to be conducted in the area of the study for more intensive and detailed suggestions.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,804
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0090,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,158
Tête enseignante GPT0,313
Écart entre enseignants0,155 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations66
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revuePLoS ONEMême sujetIron Metabolism and DisordersTravaux en français237 207