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Record 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 on 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

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsnot available
FundersMedical 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
KeywordsPreparednessPandemicPopulationMedicineHealth careDiseasePolitical scienceEnvironmental healthPublic relationsInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.155
GPT teacher head0.463
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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