Landscape analysis of pregnancy exposure registries in low- and middle-income countries: a scoping review
Notice bibliographique
Résumé
INTRODUCTION: Drug and vaccine safety information relevant to pregnant individuals is typically insufficient, especially so for persons living in low- and middle-income countries (LMICs). Pregnancy exposure registries (PERs) and similar systems are used to monitor medical products safety. A better understanding of the landscape of PERs in LMICs can support medicines regulatory system strengthening and preparation for new vaccine and drug introductions. OBJECTIVES: To identify PERs and related health data collection platforms in LMICs that systematically record pregnancy exposures to medical products and pregnancy outcomes to inform how future efforts, such as new vaccine introductions and treatment programmes, can better support maternal populations in these countries. DESIGN: Scoping review based on methodology outlined in the Joanna Briggs Institute manual for scoping reviews. DATA SOURCES: Electronic search of Medline/PubMed, Embase, CINAHL and Global Index Medicus in June 2022, and key informants via online survey in July 2022 and interviews. ELIGIBILITY CRITERIA: Eligible resources included registries, surveillance systems and databases that collect information on exposures to medical products during pregnancy and on subsequent maternal, perinatal and neonatal outcomes in populations located entirely or partially in LMICs. Eligible records were published from January 2000 through June 2022. DATA EXTRACTION AND SYNTHESIS: Search results were screened and data extracted using a standardised form by two independent reviewers. Instances of discordance were resolved by a third reviewer. Identified systems were categorised by resource type. RESULTS: A total of 7515 records from electronic searches were screened, with 396 selected for full-text review and 47 additional records obtained from other sources. From these, 45 data collection systems located in African, Asian and Latin American LMICs were identified, with 36 currently in operation. These resources were grouped into six categories based on structure and approach and summarised according to key features, strengths and weaknesses. CONCLUSIONS: This scoping review identified several resources in LMICs dedicated to drug and vaccine safety in pregnancy, but findings indicate that more investment will be needed to ensure such efforts are widespread and sustainable. Understanding the current landscape of such resources in these settings is an important step towards improving safe, world-wide access to life-saving interventions for pregnant populations. TRIAL REGISTRATION NUMBER: The protocol for this review has been registered with Open Science Framework (https://doi.org/10.17605/OSF.IO/FU5AT).
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».