ABSTRACT
We evaluated the distributions of dental splatters and the corresponding control measure effects with high-speed videography and laser diffraction. Most of the dental splatters were small droplets (<50 µm). High-volume evacuation combined with a suction air purifier could clear away most of the droplets and aerosols.
ABSTRACT
BACKGROUND: Reliable and detailed nationwide data on the prevalence and distribution of mental disorders among healthcare workers in China during the coronavirus disease 2019 (COVID-19) outbreak are scarce. METHODS: We did a cross-sectional online survey from March 2 to 2 April 2020 and a total of 19,379 healthcare workers from 25 provinces participated. Depression, anxiety and post-traumatic stress disorder (PTSD) were assessed by the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7) and PTSD Checklist for DSM-5 (PCL-5), respectively. RESULTS: The age-standardized prevalence of depression, anxiety and PTSD was 15.5%, 12.7% and 5.2%, respectively. Frontline workers had higher prevalence estimates than non-frontline workers (depression: 18.2% vs. 13.9%; anxiety: 14.7% vs. 11.6%; PTSD: 6.1% vs. 4.6%). Subgroups who were nurses, were married or had dependent children reported higher prevalence of depression, anxiety and PTSD. Despite of the large variations, the prevalence of mental disorders was lowest in East China, followed by Middle China, and highest in West China. CONCLUSION: Healthcare workers faced enormous stress not only from the direct risk presented by the COVID-19 outbreak, but also from the profound changes in their professional practice. Prevalence of adverse psychological outcomes has a significant association with geographically distribution of health resources and regional economic level. Sufficient medical resource may be a protective factor to mental health condition of healthcare personnel when such a public health emergency happened.
Subject(s)
COVID-19 , Mental Disorders , Stress Disorders, Post-Traumatic , Anxiety , China/epidemiology , Cross-Sectional Studies , Health Personnel , Humans , Mental Disorders/epidemiology , Prevalence , SARS-CoV-2 , Stress Disorders, Post-Traumatic/epidemiologyABSTRACT
BACKGROUND: Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. METHODS: Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). RESULTS: The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. CONCLUSIONS: The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.