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Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study

BACKGROUND: Different states in the United States had different nonpharmaceutical public health interventions during the COVID-19 pandemic. The effects of those interventions on hospital use have not been systematically evaluated. The investigation could provide data-driven evidence to potentially i...

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Autores principales: Wang, Xiaofeng, Ren, Rui, Kattan, Michael W, Jehi, Lara, Cheng, Zhenshun, Fang, Kuangnan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759508/
https://www.ncbi.nlm.nih.gov/pubmed/33315585
http://dx.doi.org/10.2196/25174
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author Wang, Xiaofeng
Ren, Rui
Kattan, Michael W
Jehi, Lara
Cheng, Zhenshun
Fang, Kuangnan
author_facet Wang, Xiaofeng
Ren, Rui
Kattan, Michael W
Jehi, Lara
Cheng, Zhenshun
Fang, Kuangnan
author_sort Wang, Xiaofeng
collection PubMed
description BACKGROUND: Different states in the United States had different nonpharmaceutical public health interventions during the COVID-19 pandemic. The effects of those interventions on hospital use have not been systematically evaluated. The investigation could provide data-driven evidence to potentially improve the implementation of public health interventions in the future. OBJECTIVE: We aim to study two representative areas in the United States and one area in China (New York State, Ohio State, and Hubei Province), and investigate the effects of their public health interventions by time periods according to key interventions. METHODS: This observational study evaluated the numbers of infected, hospitalized, and death cases in New York and Ohio from March 16 through September 14, 2020, and Hubei from January 26 to March 31, 2020. We developed novel Bayesian generalized compartmental models. The clinical stages of COVID-19 were stratified in the models, and the effects of public health interventions were modeled through piecewise exponential functions. Time-dependent transmission rates and effective reproduction numbers were estimated. The associations of interventions and the numbers of required hospital and intensive care unit beds were studied. RESULTS: The interventions of social distancing, home confinement, and wearing masks significantly decreased (in a Bayesian sense) the case incidence and reduced the demand for beds in all areas. Ohio’s transmission rates declined before the state’s “stay at home” order, which provided evidence that early intervention is important. Wearing masks was significantly associated with reducing the transmission rates after reopening, when comparing New York and Ohio. The centralized quarantine intervention in Hubei played a significant role in further preventing and controlling the disease in that area. The estimated rates that cured patients become susceptible in all areas were small (<0.0001), which indicates that they have little chance to get the infection again. CONCLUSIONS: The series of public health interventions in three areas were temporally associated with the burden of COVID-19–attributed hospital use. Social distancing and the use of face masks should continue to prevent the next peak of the pandemic.
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spelling pubmed-77595082020-12-31 Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study Wang, Xiaofeng Ren, Rui Kattan, Michael W Jehi, Lara Cheng, Zhenshun Fang, Kuangnan JMIR Public Health Surveill Original Paper BACKGROUND: Different states in the United States had different nonpharmaceutical public health interventions during the COVID-19 pandemic. The effects of those interventions on hospital use have not been systematically evaluated. The investigation could provide data-driven evidence to potentially improve the implementation of public health interventions in the future. OBJECTIVE: We aim to study two representative areas in the United States and one area in China (New York State, Ohio State, and Hubei Province), and investigate the effects of their public health interventions by time periods according to key interventions. METHODS: This observational study evaluated the numbers of infected, hospitalized, and death cases in New York and Ohio from March 16 through September 14, 2020, and Hubei from January 26 to March 31, 2020. We developed novel Bayesian generalized compartmental models. The clinical stages of COVID-19 were stratified in the models, and the effects of public health interventions were modeled through piecewise exponential functions. Time-dependent transmission rates and effective reproduction numbers were estimated. The associations of interventions and the numbers of required hospital and intensive care unit beds were studied. RESULTS: The interventions of social distancing, home confinement, and wearing masks significantly decreased (in a Bayesian sense) the case incidence and reduced the demand for beds in all areas. Ohio’s transmission rates declined before the state’s “stay at home” order, which provided evidence that early intervention is important. Wearing masks was significantly associated with reducing the transmission rates after reopening, when comparing New York and Ohio. The centralized quarantine intervention in Hubei played a significant role in further preventing and controlling the disease in that area. The estimated rates that cured patients become susceptible in all areas were small (<0.0001), which indicates that they have little chance to get the infection again. CONCLUSIONS: The series of public health interventions in three areas were temporally associated with the burden of COVID-19–attributed hospital use. Social distancing and the use of face masks should continue to prevent the next peak of the pandemic. JMIR Publications 2020-12-23 /pmc/articles/PMC7759508/ /pubmed/33315585 http://dx.doi.org/10.2196/25174 Text en ©Xiaofeng Wang, Rui Ren, Michael W Kattan, Lara Jehi, Zhenshun Cheng, Kuangnan Fang. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 23.12.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Wang, Xiaofeng
Ren, Rui
Kattan, Michael W
Jehi, Lara
Cheng, Zhenshun
Fang, Kuangnan
Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study
title Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study
title_full Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study
title_fullStr Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study
title_full_unstemmed Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study
title_short Public Health Interventions’ Effect on Hospital Use in Patients With COVID-19: Comparative Study
title_sort public health interventions’ effect on hospital use in patients with covid-19: comparative study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759508/
https://www.ncbi.nlm.nih.gov/pubmed/33315585
http://dx.doi.org/10.2196/25174
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