Ethnic Disparities in Coronavirus Disease 2019 after the Implementation of Universal Screening in Hartford, Connecticut

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Objective The aim of this article was to estimate the prevalence of coronavirus disease 2019 (COVID-19) in Connecticut, examine racial/ethnic disparities, and assess pregnancy outcomes in pregnant women following the implementation of universal screening for the virus.

Materials and methods This is a retrospective cohort study of all obstetric patients admitted to our labor and delivery unit during the first 4 weeks of implementation of universal screening of COVID-19. Viral studies were performed in all neonates born to mothers with severe acute respiratory syndrome coronavirus 2. We calculated the prevalence of COVID-19, compared the baseline characteristics and pregnancy outcomes between those who tested positive and negative for the virus, and determined the factors associated with COVID-19.

Results A total of 10 (4.6%) of 220 women screened positive for the virus. All were asymptomatic. Week 1 had the highest prevalence of infection, nearing 8%. No neonates were infected. Hispanics were more likely to test positive (odds ratio: 10.23; confidence interval: [2.71–49.1], p = 0.001). Obstetric and neonatal outcomes were similar between the groups (p > 0.05).

Conclusion Although the rate of asymptomatic COVID-19 was low, ethnic disparities were present with Hispanics being more likely to have the infection.

Key Points

4.6% of pregnant women in labor and delivery tested positive for COVID-19 while being asymptomatic.

Hispanic women were more likely to test positive for severe acute respiratory syndrome coronavirus 2.

Pregnancy outcomes were similar between COVID-19 positive and negative women.

No vertical transmission was detected.

Keywords coronavirus - COVID-19 - SARS-CoV-2 - pregnancy - ethnic disparities - obstetric outcomes - neonates - labor and delivery Publication History

Received: 17 August 2020

Accepted: 08 October 2021

Publication Date:
22 December 2021 (online)

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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