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Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization

BACKGROUND: The ongoing coronavirus disease 2019 pandemic has severely affected the United States. During infectious disease outbreaks, forecasting models are often developed to inform resource utilization. Pregnancy and delivery pose unique challenges, given the altered maternal immune system and t...

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Autores principales: Putra, Manesha, Kesavan, Malavika, Brackney, Kerri, Hackney, David N., Roosa, Kimberlyn M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7182514/
https://www.ncbi.nlm.nih.gov/pubmed/32342041
http://dx.doi.org/10.1016/j.ajogmf.2020.100127
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author Putra, Manesha
Kesavan, Malavika
Brackney, Kerri
Hackney, David N.
Roosa, Kimberlyn M.
author_facet Putra, Manesha
Kesavan, Malavika
Brackney, Kerri
Hackney, David N.
Roosa, Kimberlyn M.
author_sort Putra, Manesha
collection PubMed
description BACKGROUND: The ongoing coronavirus disease 2019 pandemic has severely affected the United States. During infectious disease outbreaks, forecasting models are often developed to inform resource utilization. Pregnancy and delivery pose unique challenges, given the altered maternal immune system and the fact that most American women choose to deliver in the hospital setting. OBJECTIVE: This study aimed to forecast the first pandemic wave of coronavirus disease 2019 in the general population and the incidence of severe, critical, and fatal coronavirus disease 2019 cases during delivery hospitalization in the United States. STUDY DESIGN: We used a phenomenological model to forecast the incidence of the first wave of coronavirus disease 2019 in the United States. Incidence data from March 1, 2020, to April 14, 2020, were used to calibrate the generalized logistic growth model. Subsequently, Monte Carlo simulation was performed for each week from March 1, 2020, to estimate the incidence of coronavirus disease 2019 for delivery hospitalizations during the first pandemic wave using the available data estimate. RESULTS: From March 1, 2020, our model forecasted a total of 860,475 cases of coronavirus disease 2019 in the general population across the United States for the first pandemic wave. The cumulative incidence of coronavirus disease 2019 during delivery hospitalization is anticipated to be 16,601 (95% confidence interval, 9711–23,491) cases, 3308 (95% confidence interval, 1755–4861) cases of which are expected to be severe, 681 (95% confidence interval, 1324–1038) critical, and 52 (95% confidence interval, 23–81) fatal. Assuming similar baseline maternal mortality rate as the year 2018, we projected an increase in maternal mortality rate in the United States to at least 18.7 (95% confidence interval, 18.0–19.5) deaths per 100,000 live births as a direct result of coronavirus disease 2019. CONCLUSION: Coronavirus disease 2019 in pregnant women is expected to severely affect obstetrical care. From March 1, 2020, we forecast 3308 severe and 681 critical cases with about 52 coronavirus disease 2019–related maternal mortalities during delivery hospitalization for the first pandemic wave in the United States. These results are significant for informing counseling and resource allocation.
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spelling pubmed-71825142020-04-27 Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization Putra, Manesha Kesavan, Malavika Brackney, Kerri Hackney, David N. Roosa, Kimberlyn M. Am J Obstet Gynecol MFM Original Research BACKGROUND: The ongoing coronavirus disease 2019 pandemic has severely affected the United States. During infectious disease outbreaks, forecasting models are often developed to inform resource utilization. Pregnancy and delivery pose unique challenges, given the altered maternal immune system and the fact that most American women choose to deliver in the hospital setting. OBJECTIVE: This study aimed to forecast the first pandemic wave of coronavirus disease 2019 in the general population and the incidence of severe, critical, and fatal coronavirus disease 2019 cases during delivery hospitalization in the United States. STUDY DESIGN: We used a phenomenological model to forecast the incidence of the first wave of coronavirus disease 2019 in the United States. Incidence data from March 1, 2020, to April 14, 2020, were used to calibrate the generalized logistic growth model. Subsequently, Monte Carlo simulation was performed for each week from March 1, 2020, to estimate the incidence of coronavirus disease 2019 for delivery hospitalizations during the first pandemic wave using the available data estimate. RESULTS: From March 1, 2020, our model forecasted a total of 860,475 cases of coronavirus disease 2019 in the general population across the United States for the first pandemic wave. The cumulative incidence of coronavirus disease 2019 during delivery hospitalization is anticipated to be 16,601 (95% confidence interval, 9711–23,491) cases, 3308 (95% confidence interval, 1755–4861) cases of which are expected to be severe, 681 (95% confidence interval, 1324–1038) critical, and 52 (95% confidence interval, 23–81) fatal. Assuming similar baseline maternal mortality rate as the year 2018, we projected an increase in maternal mortality rate in the United States to at least 18.7 (95% confidence interval, 18.0–19.5) deaths per 100,000 live births as a direct result of coronavirus disease 2019. CONCLUSION: Coronavirus disease 2019 in pregnant women is expected to severely affect obstetrical care. From March 1, 2020, we forecast 3308 severe and 681 critical cases with about 52 coronavirus disease 2019–related maternal mortalities during delivery hospitalization for the first pandemic wave in the United States. These results are significant for informing counseling and resource allocation. Elsevier Inc. 2020-08 2020-04-25 /pmc/articles/PMC7182514/ /pubmed/32342041 http://dx.doi.org/10.1016/j.ajogmf.2020.100127 Text en © 2020 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Original Research
Putra, Manesha
Kesavan, Malavika
Brackney, Kerri
Hackney, David N.
Roosa, Kimberlyn M.
Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization
title Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization
title_full Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization
title_fullStr Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization
title_full_unstemmed Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization
title_short Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization
title_sort forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7182514/
https://www.ncbi.nlm.nih.gov/pubmed/32342041
http://dx.doi.org/10.1016/j.ajogmf.2020.100127
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