Report of five nurses infected with severe acute respiratory syndrome coronavirus 2 during patient care: case series

Gheysarzadeh, Ali; Sadeghifard, Nourkhoda; Safari, Maryam; Balavandi, Firooz; Falahi, Shahab; Kenarkoohi, Azra; Tabesh, Hamed

doi:10.1016/j.nmni.2020.100694

Cited by 24 publications

(17 citation statements)

References 8 publications

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“…Hence, application of efficient sanitization procedures is critical to minimize aerosol transmission of SARS-CoV-2. In another study, Gheysarzadeh et al (2020) reported about five nurses infected with SARS-CoV-2 during patient care in Shahid Mustafa Khomeini Hospital which is a good agreement with the results of the present experiment.…”

Section: Resultssupporting

confidence: 91%

Hospital indoor air quality monitoring for the detection of SARS-CoV-2 (COVID-19) virus

Kenarkoohi

Noorimotlagh

Falahi

et al. 2020

Science of The Total Environment

140

128

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On December 31, 2019, the novel human coronavirus (COVID-19) was identified in Wuhan, China and swiftly spread in all nations and territories around the globe. There is much debate about the major route of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmissions. So, more evidence is required to determine the potential pathway of transmission of SARS-CoV-2 including airborne transmission. Therefore, we examined the potential aerosol transmission of the virus through hospital wards indoor air by confirmed COVID-19 patients on May 7, 2020. In order to capture airborne SARS-CoV-2, the liquid impinger biosampler was used to take fourteen air samples in different wards of the indoor air of the hospital. The specific primer and probe real-time reverse transcriptase-polymerase chain reaction (RT-PCR) were applied to detect viral genomes of the SARS-CoV-2 virus in positive air samples. Accordingly, we found two positive air samples (in the ICU) out of 14 ones taken from different wards with confirmed COVID-19 patients. The results revealed the possibility of airborne transmission of SARS-CoV-2 though more studies are required to determine the role of actual mechanisms such as cough, sneeze, normal breathing and speaking in the emission of airborne size carrier aerosols. Likewise, more quantitative analyses are needed to estimate airborne viability of SARS-CoV-2 in the carrier aerosols.

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Section: Resultssupporting

confidence: 91%

Hospital indoor air quality monitoring for the detection of SARS-CoV-2 (COVID-19) virus

Kenarkoohi

Noorimotlagh

Falahi

et al. 2020

Science of The Total Environment

140

128

View full text Add to dashboard Cite

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“…Nurses are primarily at risk among frontline health workers due to direct contact with infected patients. 41 The Minnesota Department of Health recommends that health workers with high-risk exposures stay in quarantine for 14 days and follow some safety protocols to keep themselves, patients, and co-workers safe. 42 Although nurses have to care for patients in an epidemic situation, many have concerns about being infected or transmitting the infection to others.…”

Section: Results Of the Neural Network Modelmentioning

confidence: 99%

Predicting COVID-19 Cases Among Nurses Using Artificial Neural Network Approach

Namdar¹,

Shafiekhani²,

Teymori³

et al. 2022

CIN: Computers, Informatics, Nursing

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We designed a forecasting model to determine which frontline health workers are most likely to be infected by COVID-19 among 220 nurses. We used multivariate regression analysis and different classification algorithms to assess the effect of several covariates, including exposure to COVID-19 patients, access to personal protective equipment, proper use of personal protective equipment, adherence to hand hygiene principles, stressfulness, and training on the risk of a nurse being infected. Access to personal protective equipment and training were associated with a 0.19- and 1.66-point lower score in being infected by COVID-19. Exposure to COVID-19 cases and being stressed of COVID-19 infection were associated with a 0.016- and 9.3-point higher probability of being infected by COVID-19. Furthermore, an artificial neural network with 75.8% (95% confidence interval, 72.1-78.9) validation accuracy and 76.6% (95% confidence interval, 73.1-78.6) overall accuracy could classify normal and infected nurses. The neural network can help managers and policymakers determine which frontline health workers are most likely to be infected by COVID-19.

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“…In a study conducted in Iran, it was reported that the nurses had sufficient knowledge and skills; however, they were infected since they did not take sufficient preventive measures during the initial period of the pandemic. 27 A person carrying the virus and exhibiting no symptoms can wander through departments within the hospital, spreading the disease unknowingly. Therefore, all employees are at risk, regardless of the department they are employed in.…”

Section: Table V Results Of the Multiple Regression Analysis About The Effect Of Ies-r On Dass-21mentioning

confidence: 99%

Evaluation of the Changes in Mental Health State at the Beginning of COVID-19 in Hospital Personnel

et al. 2021

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Objective: In the present study, it was aimed to determine the changes in the mental states of hospital personnel at the beginning of COVID-19 pandemic, and the factors affecting these changes. Methods: The research sample consisted of 299 hospital personnel in Turkey, who could be reached with the online method. The data were collected using the “Information Form”, which was prepared in line with the literature and the pandemic process, “The Impact of Event Scale (IES)” and the “Depression-Anxiety-Stress Scale (DAS-21)”. Results: It was found that the sociodemographic characteristics, medical history, and the characteristics related to COVID-19 were effective on the participants in terms of their mental states and the way they perceived traumatic experiences. According to the multiple linear regression analyses, it was found that hyper arousal and reexperiencing predicted stress, anxiety, and depression with a rate of 58.5%, 59.0% and 60.6%, respectively. Conclusion: At the beginning of the pandemic, it was observed that the responses of frontline hospital employees to traumatic experiences affected their mental states. Psychiatric nurses should take an active role in the development and implementation of psychosocial support programs for hospital personnel. Bangladesh J Medicine July 2022; 33(1) : 19-26

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Report of five nurses infected with severe acute respiratory syndrome coronavirus 2 during patient care: case series

Cited by 24 publications

References 8 publications

Hospital indoor air quality monitoring for the detection of SARS-CoV-2 (COVID-19) virus

Hospital indoor air quality monitoring for the detection of SARS-CoV-2 (COVID-19) virus

Predicting COVID-19 Cases Among Nurses Using Artificial Neural Network Approach

Evaluation of the Changes in Mental Health State at the Beginning of COVID-19 in Hospital Personnel

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