Landscape Analysis of Pregnancy Exposure Registries in Low- and Middle-Income Countries: a Scoping Review
Notice bibliographique
Résumé
Abstract 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 programs 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 standardized form by two independent reviewers. Instances of discordance were resolved by a third reviewer. Identified systems were categorized by resource type. Results A total of 7,515 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 Africa, Asia, and Latin America LMICs were identified, with 36 currently in operation. These resources were grouped into six categories based on structure and approach and summarized 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 toward improving safe, world-wide access to life-saving interventions for pregnant populations. Study registration The protocol for this review has been registered with Open Science Framework (DOI: https://doi.org/10.17605/OSF.IO/FU5AT ). Article Summary: Strengths and limitations of this study This analysis documents pregnancy registries and similar systems in low- and middle-income countries for monitoring the safety of drugs and vaccines. This scoping review employed a structured search of the published scientific literature, augmented by a grey literature search, online survey and expert consultations. Some registries, particularly those without publications or accessible websites, may nevertheless have been missed in this review. Registries were not always thorough in reporting the details of their methods, strengths, and limitations in their publications.
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 | 0,036 | 0,170 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,008 |
| Bibliométrie | 0,052 | 0,053 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,007 | 0,006 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».