Occupational low back pain prevention capacity of nurses in China: A multicenter cross-sectional study.

Introduction: Nurses have a high prevalence of occupational low back pain, especially since the outbreak of the COVID-19 pandemic, which has increased the nurses' workloads. It has brought a huge burden on nurses and their professional development. Nurses' occupational low back pain prevention capacity is the logical starting point and core of interventions to prevent its occurrence. To date, there is no study investigating it with a scientific scale. Therefore, a multicenter cross-sectional study was conducted to explore the current status of nurses' capacity in occupational low back pain prevention and its influencing factors in China. Methods: Using a two-stage, purposive and convenience mixed sampling method, 1331 nurses from 8 hospitals across 5 provinces (Hubei, Zhejiang, Shandong, Henan, and Sichuan) in the southern, western, northern, and central areas of mainland China were involved in this study. The demographic questionnaire and occupational low back pain prevention behavior questionnaire were used for data collection. The descriptive analysis, univariate analysis, and multiple stepwise linear regression were used for data analysis. Results: The results showed that the occupational low back pain prevention behavior questionnaire score was 89.00 (80.00, 103.00) [M (Q1, Q3)], which indicated that nurses' ability was at a moderate level. Participation in prevention training before, perceived stress at work, and working hours per week were predictors for nurses' occupational low back pain prevention capacity. Discussion: To improve nurses' prevention ability, nursing managers should organize various training programs, strengthen regulations to reduce nurses' workload and stress, provide a healthy workplace, and offer incentives to motivate nurses.

Indicators and prediction models for the severity of Covid-19.

OBJECTIVES: Coronavirus disease 2019 (Covid-19) is outbreaking globally. We aimed to analyse the clinical characteristics, cardiac injury, electrocardiogram and computed tomography (CT) features of patients confirmed Covid-19 and explored the prediction models for the severity of Covid-19. METHODS: A retrospective and single-centre study enrolled 98 laboratory-confirmed Covid-19 patients. Clinical data, electrocardiogram and CT features were collected and analysed using Statistical Package for the Social Sciences software. RESULTS: There were 46 males and 52 females, with a median age of 44 years, categorised into three groups, including mild, moderate and severe/critical Covid-19. The rate of abnormal electrocardiograms in severe/critical group (79%) was significantly higher than that in the mild group (17%) (P = .027), which (r = 0.392, P = .005) positively related to the severity of Covid-19 (OR: 5.71, 95% CI: 0.45-3.04, P = .008). Age older than 60 years old, comorbidities, whether had symptoms on admission, fatigue, CT features, laboratory test results such as platelet count, lymphocyte cell count, eosinophil cell count, CD3+ cell count, CD4+ cell count, CD8+ cell count, the ratio of albumin/globulin decreased and D-dimer, C-reactive protein (CRP), B-type natriuretic peptide (BNP), cardiac troponin I (cTnI) elevated were the risk factors for the increased severity of Covid-19. The logistic model, adjusted by age, lobular involvement score and lymphocyte cell count, could be applied for assessing the severity of Covid-19 (AUC, 0.903; Sensitivity, 90.9%; Specificity, 78.1%). CONCLUSIONS: Age >60 years old, chronic comorbidities, lymphocytopoenia and lobular involvement score were associated with the Covid-19 severity. The inflammation induced by Covid-19 caused myocardial injury with elevated BNP and cTnI level and abnormal electrocardiograms.