Author: Dionne-Odom, Jodie; Klipstein, Sigal
Title: The Impact of Epidemiology on Prenatal and Fertility Care during the COVID-19 Pandemic Cord-id: vd7nsg3t Document date: 2021_2_10
ID: vd7nsg3t
Snippet: The emergence of the novel coronavirus COVID-19 presented the field of reproductive medicine with many challenges due to an absence of data to guide clinical decision making and inform patient counseling and management in the early days of the pandemic. Epidemiological studies rapidly filled in key gaps in our understanding of the susceptibility of reproductive aged women to the virus, transmission dynamics during pregnancy and lactation, and the effect of infection during the prenatal, pregnanc
Document: The emergence of the novel coronavirus COVID-19 presented the field of reproductive medicine with many challenges due to an absence of data to guide clinical decision making and inform patient counseling and management in the early days of the pandemic. Epidemiological studies rapidly filled in key gaps in our understanding of the susceptibility of reproductive aged women to the virus, transmission dynamics during pregnancy and lactation, and the effect of infection during the prenatal, pregnancy and post-partum periods. This data guided the development of clinical guidelines written by the American Society for Reproductive Medicine as patients and clinicians navigated reproductive decisions during a time of uncertainty. We present a review of epidemiologic studies published between March and December 2020 that directly informed prenatal and fertility care during the COVID-19 pandemic. Despite a significant increase in our knowledge base over the past year, many questions remain about the impact of COVID-19 on conception, pregnancy, fetal development, and lactation. In the future, a commitment toward inclusion of pregnant persons and those attempting pregnancy in the design of observational and interventional trials is necessary to gain earlier insights about outcomes and assist providers and patients in making data driven decisions.
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