ResearchPad - hospital-medicine https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Availability of Telemedicine Services Across Hospitals in the United States in 2018: A Cross-sectional Study]]> https://www.researchpad.co/article/Na4349907-ae36-4fd9-a644-0f24d02113a1 <![CDATA[Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic]]> https://www.researchpad.co/article/N2e2ee2a5-a192-4ef0-bd1c-1185fc7231b2

Background:

The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.

Objective:

To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19–induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated.

Design:

Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle.

Setting:

3 hospitals in an academic health system.

Patients:

All people living in the greater Philadelphia region.

Measurements:

The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators.

Results:

Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators.

Limitations:

Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction.

Conclusion:

Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic.

Primary Funding Source:

University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.

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<![CDATA[Psychological Impact of the COVID-19 Pandemic on Health Care Workers in Singapore]]> https://www.researchpad.co/article/N8597fb65-3772-4188-9dac-424107a0fdf7 ]]> <![CDATA[Environment and Personal Protective Equipment Tests for SARS-CoV-2 in the Isolation Room of an Infant With Infection]]> https://www.researchpad.co/article/N7f3f8923-1475-4a29-8664-06bd866206f3 ]]> <![CDATA[The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application]]> https://www.researchpad.co/article/Nabb33e10-d774-4659-8b9e-22a8e152613f

Background:

A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities.

Objective:

To estimate the length of the incubation period of COVID-19 and describe its public health implications.

Design:

Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020.

Setting:

News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China.

Participants:

Persons with confirmed SARS-CoV-2 infection outside Hubei province, China.

Measurements:

Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization.

Results:

There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.

Limitation:

Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases.

Conclusion:

This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases.

Primary Funding Source:

U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

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