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Protocol for a sequential, prospective meta-analysis to describe coronavirus disease 2019 (COVID-19) in the pregnancy and postpartum periods

Authors :
Emily R. Smith
Erin Oakley
Siran He
Rebecca Zavala
Kacey Ferguson
Lior Miller
Gargi Wable Grandner
Ibukun-Oluwa Omolade Abejirinde
Yalda Afshar
Homa Ahmadzia
Grace Aldrovandi
Victor Akelo
Beth A. Tippett Barr
Elisa Bevilacqua
Justin S. Brandt
Natalie Broutet
Irene Fernández Buhigas
Jorge Carrillo
Rebecca Clifton
Jeanne Conry
Erich Cosmi
Camille Delgado-López
Hema Divakar
Amanda J. Driscoll
Guillaume Favre
Valerie Flaherman
Christopher Gale
Maria M. Gil
Christine Godwin
Sami Gottlieb
Olivia Hernandez Bellolio
Edna Kara
Sammy Khagayi
Caron Rahn Kim
Marian Knight
Karen Kotloff
Antonio Lanzone
Kirsty Le Doare
Christoph Lees
Ethan Litman
Erica M. Lokken
Valentina Laurita Longo
Laura A. Magee
Raigam Jafet Martinez-Portilla
Elizabeth McClure
Torri D. Metz
Deborah Money
Edward Mullins
Jean B. Nachega
Alice Panchaud
Rebecca Playle
Liona C. Poon
Daniel Raiten
Lesley Regan
Gordon Rukundo
Jose Sanin-Blair
Marleen Temmerman
Anna Thorson
Soe Thwin
Jorge E. Tolosa
Julia Townson
Miguel Valencia-Prado
Silvia Visentin
Peter von Dadelszen
Kristina Adams Waldorf
Clare Whitehead
Huixia Yang
Kristian Thorlund
James M. Tielsch
Source :
PLoS ONE, Vol 17, Iss 6 (2022)
Publication Year :
2022
Publisher :
Public Library of Science (PLoS), 2022.

Abstract

We urgently need answers to basic epidemiological questions regarding SARS-CoV-2 infection in pregnant and postpartum women and its effect on their newborns. While many national registries, health facilities, and research groups are collecting relevant data, we need a collaborative and methodologically rigorous approach to better combine these data and address knowledge gaps, especially those related to rare outcomes. We propose that using a sequential, prospective meta-analysis (PMA) is the best approach to generate data for policy- and practice-oriented guidelines. As the pandemic evolves, additional studies identified retrospectively by the steering committee or through living systematic reviews will be invited to participate in this PMA. Investigators can contribute to the PMA by either submitting individual patient data or running standardized code to generate aggregate data estimates. For the primary analysis, we will pool data using two-stage meta-analysis methods. The meta-analyses will be updated as additional data accrue in each contributing study and as additional studies meet study-specific time or data accrual thresholds for sharing. At the time of publication, investigators of 25 studies, including more than 76,000 pregnancies, in 41 countries had agreed to share data for this analysis. Among the included studies, 12 have a contemporaneous comparison group of pregnancies without COVID-19, and four studies include a comparison group of non-pregnant women of reproductive age with COVID-19. Protocols and updates will be maintained publicly. Results will be shared with key stakeholders, including the World Health Organization (WHO) Maternal, Newborn, Child, and Adolescent Health (MNCAH) Research Working Group. Data contributors will share results with local stakeholders. Scientific publications will be published in open-access journals on an ongoing basis.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
19326203
Volume :
17
Issue :
6
Database :
Directory of Open Access Journals
Journal :
PLoS ONE
Publication Type :
Academic Journal
Accession number :
edsdoj.f09a66f04519419585e20561b2eede0c
Document Type :
article