MétaCan
Menu
Retour à la cohorte
Enregistrement W4388978533 · doi:10.1101/2023.11.24.23298980

Maternal and perinatal health research during emerging and ongoing epidemic threats: a landscape analysis and expert consultation

2023· preprint· en· W4388978533 sur OpenAlexfundno aff
Mercedes Bonet, Magdalena Babinska, Pierre Buekens, Shivaprasad S. Goudar, Beate Kampmann, Marian Knight, Dana Meaney‐Delman, Smaragda Lamprianou, Flor M. Muñoz, Andy Stergachis, Cristiana M. Toscano, Joycelyn Bhatia, Sarah Chamberlain, Usman Chaudhry, Jacqueline W. Mills, Emily Serazin, Hannah Short, Asher J Steene, Michael Wahlen, Olufemi T. Oladapo

Notice bibliographique

RevuemedRxiv · 2023
Typepreprint
Langueen
DomaineMedicine
ThématiqueCOVID-19 Impact on Reproduction
Établissements canadiensnon disponible
Organismes subventionnairesMedical Research CouncilUniversity of Cape TownHospital for Sick ChildrenMonash UniversityUnited States Agency for International DevelopmentUniversity of BirminghamUniversity of OxfordCanadian Institutes of Health ResearchUniversity of WashingtonImperial College LondonCenters for Disease Control and PreventionCentre Hospitalier Universitaire VaudoisUNICEFYale UniversityKhon Kaen UniversityEmory UniversityJohns Hopkins UniversityBill and Melinda Gates Foundation
Mots-clésPreparednessPandemicPopulationMedicineHealth careDiseasePolitical scienceEnvironmental healthPublic relationsInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)

Résumé

récupéré en direct d'OpenAlex

Summary Introduction Pregnant women and their offspring are often at increased direct and indirect risks of adverse outcomes during epidemics and pandemics. A coordinated research response is paramount to ensure that this group is offered at least the same level of disease prevention, diagnosis, treatment, and care as the general population. We conducted a landscape analysis and held expert consultations to identify research efforts relevant to pregnant women affected by disease outbreaks, highlight gaps and challenges, and propose solutions to addressing them in a coordinated manner. Methods Literature searches were conducted from 1 January 2015 to 22 March 2022 using Web of Science, Google Scholar, and PubMed augmented by key informant interviews. Findings were reviewed and Quid analysis was performed to identify clusters and connectors across research networks followed by two expert consultations. Results Ninety-four relevant research efforts were identified. Although well-suited to generating epidemiological data, the entire infrastructure to support a robust research response remains insufficient, particularly for use of medical products in pregnancy. Limitations in global governance, coordination, funding, and data-gathering systems have slowed down research responses. Conclusion Leveraging current research efforts while engaging multinational and regional networks may be the most effective way to scale up maternal and perinatal research preparedness and response. The findings of this landscape analysis and proposed operational framework will pave the way for developing a roadmap to guide coordination efforts, facilitate collaboration, and ultimately promote rapid access to countermeasures and clinical care for pregnant women and their offspring in the future. Funding UNDP–UNFPA–UNICEF–WHO–World Bank Special Programme of Research, Development and Research Training in Human Reproduction, WHO, and Bill and Melinda Gates Foundation. Research in context Evidence before this study Previous epidemics and pandemics highlighted the dearth of preparedness and response for maternal and perinatal health, resulting in access to countermeasures being delayed for this group, despite pregnant women and their offspring often being identified as at increased risk of severe disease outcomes. Based on this experience, we first searched PubMed from 1 January 2015 to 22 March 2022 with no language restrictions to identify any landscape analyses evaluating research efforts pertaining to pregnant women facing ongoing and emerging epidemic threats. Those efforts were defined as persistent data generation or aggregation exercises, including single studies, networks, and collaborations. As many of them struggled to secure and sustain baseline funding, it could be potentially beneficial to have them covered by some form of a global coordination mechanism to help improve their coherence. Multiple commentary articles discussing the need for harmonization of research and preparedness planning to avoid maternal and perinatal exclusion from potential preventative and treatment interventions in future epidemics/pandemics were identified, with most focusing on the lessons that can be learned from the COVID-19 pandemic. Evaluation of existing literature and scoping reviews identified studies which have evaluated gaps in approaches for alleviating gender inequality in future public health emergencies and the impacts of the COVID-19 pandemic on maternal and perinatal health services. None of them, however, have specifically focused on current research efforts in maternal and perinatal health that can be utilised in context of emerging and ongoing epidemic threats, or have proposed a framework for harmonizing future research efforts. Added value of this study This study provides a comprehensive overview of existing research efforts relevant to maternal and perinatal health in future outbreak, epidemic or pandemic situations. We summarise the key areas of focus of research efforts, identifying current gaps and areas in which the existing infrastructure is insufficient, and proposing an operational framework for improving conduct of maternal and perinatal heath research related to emerging and ongoing epidemic threats. Implications of all the available evidence The available evidence indicates that while current research efforts are well-suited to collecting maternal and perinatal epidemiological data, some gaps remain. They include limitations in global governance, coordination, funding, and data-gathering systems. The proposed operational framework developed based on the findings of this study will allow for development of a roadmap for guiding efforts and coordinating research to maximise access to countermeasures and clinical care for pregnant women and their offspring in during emerging and ongoing epidemic threats future outbreak, epidemic, and pandemic situations.

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)0,003
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,939

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
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 Opus0,155
Tête enseignante GPT0,463
Écart entre enseignants0,308 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations3
Publié2023
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revuemedRxivMême sujetCOVID-19 Impact on ReproductionTravaux en français237 207