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1.
Infect Control Hosp Epidemiol ; 43(10): 1477-1481, 2022 10.
Article in English | MEDLINE | ID: covidwho-1338498

ABSTRACT

Using data from the National Healthcare Safety Network (NHSN), we assessed changes to intensive care unit (ICU) bed capacity during the early months of the COVID-19 pandemic. Changes in capacity varied by hospital type and size. ICU beds increased by 36%, highlighting the pressure placed on hospitals during the pandemic.


Subject(s)
COVID-19 , Pandemics , Humans , COVID-19/epidemiology , Hospital Bed Capacity , Intensive Care Units , Hospitals
2.
Infect Control Hosp Epidemiol ; 43(10): 1473-1476, 2022 10.
Article in English | MEDLINE | ID: covidwho-1303723

ABSTRACT

During March 27-July 14, 2020, the Centers for Disease Control and Prevention's National Healthcare Safety Network extended its surveillance to hospital capacities responding to COVID-19 pandemic. The data showed wide variations across hospitals in case burden, bed occupancies, ventilator usage, and healthcare personnel and supply status. These data were used to inform emergency responses.


Subject(s)
COVID-19 , Humans , United States/epidemiology , Pandemics/prevention & control , Centers for Disease Control and Prevention, U.S. , Hospitals , Delivery of Health Care
3.
Infect Control Hosp Epidemiol ; 43(1): 32-39, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1114665

ABSTRACT

OBJECTIVE: The rapid spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) throughout key regions of the United States in early 2020 placed a premium on timely, national surveillance of hospital patient censuses. To meet that need, the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN), the nation's largest hospital surveillance system, launched a module for collecting hospital coronavirus disease 2019 (COVID-19) data. We present time-series estimates of the critical hospital capacity indicators from April 1 to July 14, 2020. DESIGN: From March 27 to July 14, 2020, the NHSN collected daily data on hospital bed occupancy, number of hospitalized patients with COVID-19, and the availability and/or use of mechanical ventilators. Time series were constructed using multiple imputation and survey weighting to allow near-real-time daily national and state estimates to be computed. RESULTS: During the pandemic's April peak in the United States, among an estimated 431,000 total inpatients, 84,000 (19%) had COVID-19. Although the number of inpatients with COVID-19 decreased from April to July, the proportion of occupied inpatient beds increased steadily. COVID-19 hospitalizations increased from mid-June in the South and Southwest regions after stay-at-home restrictions were eased. The proportion of inpatients with COVID-19 on ventilators decreased from April to July. CONCLUSIONS: The NHSN hospital capacity estimates served as important, near-real-time indicators of the pandemic's magnitude, spread, and impact, providing quantitative guidance for the public health response. Use of the estimates detected the rise of hospitalizations in specific geographic regions in June after they declined from a peak in April. Patient outcomes appeared to improve from early April to mid-July.


Subject(s)
COVID-19 , Bed Occupancy , Hospitalization , Hospitals , Humans , SARS-CoV-2 , United States/epidemiology
4.
Am J Infect Control ; 49(8): 1075-1077, 2021 08.
Article in English | MEDLINE | ID: covidwho-1086738

ABSTRACT

This case study is part of a series centered on the Centers for Disease Control and Prevention/National Healthcare Safety Network (NHSN) healthcare-associated infection (HAI) surveillance definitions. This specific case study focuses on the application of the Pneumonia (PNEU), Ventilator-associated event (VAE), and Bloodstream infections (BSI) surveillance definitions to a patient with COVID-19. The intent of the case study series is to foster standardized application of the NHSN HAI surveillance definitions among Infection Preventionists (IPs) and encourage accurate determination of HAI events.


Subject(s)
COVID-19 , Catheter-Related Infections , Cross Infection , Pneumonia, Ventilator-Associated , Catheter-Related Infections/epidemiology , Catheter-Related Infections/prevention & control , Cross Infection/epidemiology , Cross Infection/prevention & control , Data Accuracy , Delivery of Health Care , Humans , Infection Control , Pneumonia, Ventilator-Associated/epidemiology , Pneumonia, Ventilator-Associated/prevention & control , SARS-CoV-2 , United States
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