Micronutrient Supplementation for Pregnant and Lactating Women to Improve Maternal and Infant Nutritional Status in Low- and Middle-Income Countries: Protocol for a Systematic Review and Meta-analysis
Notice bibliographique
Résumé
Background Two billion people in low- and middle-income countries (LMICs) are deficient in key nutrients. Nutritional deficiencies worsen during pregnancy, causing adverse outcomes for the mother and the fetus, with consequences after pregnancy. These effects may be mitigated by providing micronutrient supplementation to women during pregnancy and lactation. However, the effects of micronutrient supplementation on the nutritional status of pregnant and lactating women and that of their infants remain largely unclear in LMICs. Objective The purpose of this systematic review and meta-analysis is to determine the effects of single, double, or multiple micronutrient supplements during pregnancy or lactation on maternal and infant nutritional status in LMICs. Methods Randomized controlled trials of single, double, or combinations of micronutrients assessing effects on the maternal (serum, plasma, and breastmilk) and infant (serum and plasma) nutritional status will be included. MEDLINE (through PubMed), EMBASE, CENTRAL (through Cochrane Library), and the World Health Organization (WHO) library database will be used to identify relevant published studies, starting from the inception of each database until February 28, 2022. The Cochrane Risk of Bias Tool will be used to assess the risk of bias in the included studies. The selection of studies, data extraction, and risk of bias assessment will be carried out independently by 2 reviewers. A narrative summary will be provided of all the included studies. Meta-analyses will be performed whenever possible, and the heterogeneity of effects will be evaluated using I2, subgroup analyses, and metaregression. The certainty of the evidence for each outcome will be assessed using the GRADE (Grading of Recommendation, Assessment, Development, and Evaluation) approach. Results We will conduct meta-analyses using Stata software (version 16, StataCorp) and present both a narrative and systematic summary of all studies included in this review in text and table form. For continuous outcomes, effect estimates will be expressed as mean differences and standardized mean differences, while for binary outcomes, they will be expressed as risk ratios, rate ratios, hazards ratios, or odds ratios, all with 95% CIs and comparing the intervention group with the control group. When studies for an outcome are adequately consistent with respect to intervention, comparator, and definition of the outcome, a random-effects, inverse variance-weighted meta-analysis will be conducted. We will provide a narrative synthesis for outcomes with insufficient data or extreme heterogeneity. Conclusions This review will provide evidence upon which to base policy and programming for women in LMICs to supplement micronutrients in pregnancy and lactation. Detailed results disaggregated by variables such as maternal age, sex of infant, duration, and dose of intervention may also help policy makers, researchers, practitioners, and government agencies to adopt more effective maternal and child health policies and programs in LMICs. The review will also identify any gaps in the existing evidence. Trial Registration PROSPERO CRD42022308715; https://tinyurl.com/y33cxekr. International Registered Report Identifier (IRRID) PRR1-10.2196/40134
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 | 0,055 | 0,073 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,005 |
| Méta-épidémiologie (sens large) | 0,025 | 0,032 |
| Bibliométrie | 0,012 | 0,012 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,008 | 0,007 |
| Science ouverte | 0,006 | 0,005 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,044 | 0,004 |
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 ».