Public Health Interventions' Effect on Hospital Use in Patients With COVID-19 : Comparative Study
©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..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:6 |
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| Enthalten in: |
JMIR public health and surveillance - 6(2020), 4 vom: 23. Dez., Seite e25174 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Xiaofeng [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 05.01.2021 Date Revised 30.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.2196/25174 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM318808331 |
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| 520 | |a ©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. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Comparative Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Observational Study | |
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| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a China | |
| 650 | 4 | |a United States | |
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| 650 | 4 | |a hospital | |
| 650 | 4 | |a implementation | |
| 650 | 4 | |a intervention | |
| 650 | 4 | |a observational | |
| 650 | 4 | |a prediction | |
| 650 | 4 | |a public health | |
| 650 | 4 | |a use | |
| 700 | 1 | |a Ren, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Kattan, Michael W |e verfasserin |4 aut | |
| 700 | 1 | |a Jehi, Lara |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Zhenshun |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Kuangnan |e verfasserin |4 aut | |
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