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

ABSTRACT

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, 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. These formed the basis for the development of an operational framework for maternal and perinatal research during epidemics. 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 future epidemics.