ABSTRACT
Background: The COVID-19 pandemic is a significant health threat. Health care worker (HCWs) are at a significant risk of infection which may cause high levels of psychological distress. The aim of this study was to investigate the psychological impact of the COVID-19 on HCWs and factors which were associated with these stresses during the first outbreak in Shanghai. Methods: Between February 9 and 21, 2020, a total of 3,114 frontline HCWs from 26 hospitals in Shanghai completed an online survey. The questionnaire included questions on their sociodemographic characteristics, 15 stress-related questions, and General Health Questionnaire-12 (GHQ-12). Exploratory factor analysis was applied to the 15 stress-related questions which produced four distinct factors for evaluation. Multiple linear regression models were performed to explore the association of personal characteristics with each score of the four factors. Binary logistic analysis was used to explain the association of personal characteristics and these four factors with the GHQ-12. Results: There were 2,691 valid surveys received. The prevalence of emotional distress (defined as GHQ-12 ≥ 12) was noted in 47.7% (95%CI:45.7-49.6%) HCWs. Females (OR = 1.43, 95%CI:1.09-1.86) were more likely to have a psychological distress than males. However, HCWs who work in secondary hospitals (OR = 0.71, 95% CI:0.58-0.87) or had a no contact history (OR = 0.45, 95%CI: 0.35-0.58) were less likely to suffer psychological distress. HCWs who were nurses, married, and had a known contact history were highly likely to have anxiety. HCWs working at tertiary hospitals felt an elevated anxiety regarding the infection, a lack of knowledge, and less protected compared to those who worked at secondary hospitals. Conclusions: Our study shows that the frontline HCWs had a significant psychosocial distress during the COVID-19 outbreak in Shanghai. HCWs felt a lack of knowledge and had feelings of being not protected. It is necessary for hospitals and governments to provide additional trainings and psychological counseling to support the first-line HCWs.
Subject(s)
COVID-19 , Pandemics , China/epidemiology , Cross-Sectional Studies , Disease Outbreaks , Female , Health Personnel , Humans , Male , SARS-CoV-2ABSTRACT
The aim is to diagnose COVID-19 earlier and to improve its treatment by applying medical technology, the “COVID-19 Intelligent Diagnosis and Treatment Assistant Program (nCapp)” based on the Internet of Things. Terminal eight functions can be implemented in real-time online communication with the “cloud” through the page selection key. According to existing data, questionnaires, and check results, the diagnosis is automatically generated as confirmed, suspected, or suspicious of 2019 novel coronavirus (2019-nCoV) infection. It classifies patients into mild, moderate, severe or critical pneumonia. nCapp can also establish an online COVID-19 real-time update database, and it updates the model of diagnosis in real time based on the latest real-world case data to improve diagnostic accuracy. Additionally, nCapp can guide treatment. Front-line physicians, experts, and managers are linked to perform consultation and prevention. nCapp also contributes to the long-term follow-up of patients with COVID-19. The ultimate goal is to enable different levels of COVID-19 diagnosis and treatment among different doctors from different hospitals to upgrade to the national and international through the intelligent assistance of the nCapp system. In this way, we can block disease transmission, avoid physician infection, and epidemic prevention and control as soon as possible.
ABSTRACT
Background: The outbreak of COVID-19 has led to international concern. We aimed to establish an effective screening strategy in Shanghai, China, to aid early identification of patients with COVID-19. Methods: We did a multicentre, observational cohort study in fever clinics of 25 hospitals in 16 districts of Shanghai. All patients visiting the clinics within the study period were included. A strategy for COVID-19 screening was presented and then suspected cases were monitored and analysed until they were confirmed as cases or excluded. Logistic regression was used to determine the risk factors of COVID-19. Findings: We enrolled patients visiting fever clinics from Jan 17 to Feb 16, 2020. Among 53â617 patients visiting fever clinics, 1004 (1·9%) were considered as suspected cases, with 188 (0·4% of all patients, 18·7% of suspected cases) eventually diagnosed as confirmed cases. 154 patients with missing data were excluded from the analysis. Exposure history (odds ratio [OR] 4·16, 95% CI 2·74-6·33; p<0·0001), fatigue (OR 1·56, 1·01-2·41; p=0·043), white blood cell count less than 4â×â109 per L (OR 2·44, 1·28-4·64; p=0·0066), lymphocyte count less than 0·8â×â109 per L (OR 1·82, 1·00-3·31; p=0·049), ground glass opacity (OR 1·95, 1·32-2·89; p=0·0009), and having both lungs affected (OR 1·54, 1·04-2·28; p=0·032) were independent risk factors for confirmed COVID-19. Interpretation: The screening strategy was effective for confirming or excluding COVID-19 during the spread of this contagious disease. Relevant independent risk factors identified in this study might be helpful for early recognition of the disease. Funding: National Natural Science Foundation of China.