Objective We aimed to systematically identify the possible risk factors responsible for severe cases. Methods We searched PubMed, Embase, Web of science and Cochrane Library for epidemiological studies of confirmed COVID-19, which include information about clinical characteristics and severity of patients’ disease. We analyzed the potential associations between clinical characteristics and severe cases. Results We identified a total of 41 eligible studies including 21060 patients with COVID-19. Severe cases were potentially associated with advanced age (Standard Mean Difference (SMD) = 1.73, 95% CI: 1.34–2.12), male gender (Odds Ratio (OR) = 1.51, 95% CI:1.33–1.71), obesity (OR = 1.89, 95% CI: 1.44–2.46), history of smoking (OR = 1.40, 95% CI:1.06–1.85), hypertension (OR = 2.42, 95% CI: 2.03–2.88), diabetes (OR = 2.40, 95% CI: 1.98–2.91), coronary heart disease (OR: 2.87, 95% CI: 2.22–3.71), chronic kidney disease (CKD) (OR = 2.97, 95% CI: 1.63–5.41), cerebrovascular disease (OR = 2.47, 95% CI: 1.54–3.97), chronic obstructive pulmonary disease (COPD) (OR = 2.88, 95% CI: 1.89–4.38), malignancy (OR = 2.60, 95% CI: 2.00–3.40), and chronic liver disease (OR = 1.51, 95% CI: 1.06–2.17). Acute respiratory distress syndrome (ARDS) (OR = 39.59, 95% CI: 19.99–78.41), shock (OR = 21.50, 95% CI: 10.49–44.06) and acute kidney injury (AKI) (OR = 8.84, 95% CI: 4.34–18.00) were most likely to prevent recovery. In summary, patients with severe conditions had a higher rate of comorbidities and complications than patients with non-severe conditions. Conclusion Patients who were male, with advanced age, obesity, a history of smoking, hypertension, diabetes, malignancy, coronary heart disease, hypertension, chronic liver disease, COPD, or CKD are more likely to develop severe COVID-19 symptoms. ARDS, shock and AKI were thought to be the main hinderances to recovery.

Background Workplace violence (WPV) is a serious issue for healthcare workers and leads to many negative consequences. Several studies have reported on the prevalence of WPV in China, which ranges from 42.2 to 83.3%. However, little information is available regarding the correlates of WPV among healthcare workers and the differences across the different levels of hospitals in China. This study aimed to explore the correlates of WPV and career satisfaction among healthcare workers in China. Methods A self-designed WeChat-based questionnaire was used that included demographic and occupational factors. The Chinese version of the Workplace Violence Scale was used to measure WPV. Career satisfaction was assessed using two questions about career choices. Descriptive analyses, chi-square tests and multivariate logistic regressions were used. Results A total of 3706 participants (2750 nurses and 956 doctors) responded to the survey. Among the 3684 valid questionnaires, 2078 (56.4%) reported at least one type of WPV in the last year. Multivariate logistic regressions revealed that male sex, shift work, bachelor’s degree education, a senior professional title, working more than 50 h per week and working in secondary-level hospitals were risk factors associated with WPV. Healthcare workers who had experienced higher levels of WPV were less likely to be satisfied with their careers. Conclusions WPV remains a special concern for the Chinese healthcare system. Interventions to reduce WPV should be implemented by health authorities to create a zero-violence practice environment.

Introduction: Workplace violence (WPV) against healthcare providers has severe consequences and is underreported worldwide. The aim of this study was to present the features, causes, and outcomes of serious WPV against healthcare providers in China.Method: We searched for serious WPV events reported online and analyzed information about time, location, people, methods, motivations, and outcomes related to the incident.Result: Serious WPV reported online in China (n = 379) were mainly physical (97%) and often involved the use of weapons (34.5%). Doctors were victims in most instances (81.1%). Serious WPV mostly happened in cities (90.2%), teaching hospitals (87.4%), and tertiary hospitals (67.9%) and frequently in Emergency Department (ED), Obstetrics and Gynecology Department (OB-GYN), and pediatric departments; it was most prevalent in the months of June, May, and February. Rates of serious WPV increased dramatically in 2014 and decreased after 2015, with death (12.8%), severe injury (6%), and hospitalization (24.2%) being the major outcomes. A law protecting healthcare providers implemented in 2015 may have helped curb the violence.Conclusion: Serious WPV in China may stem from poor patient–doctor relationships, overly stressed health providers in highly demanding hospitals, poorly educated/informed patients, insufficient legal protection, and poor communication. Furthering knowledge about WPV and working toward curtailing its presence in healthcare settings are crucial to increasing the safety and well-being of healthcare workers.

Abstract:With the increasing use of mobile GPS (global positioning system) devices, a large volume of trajectory data on users can be produced. In most existing work, trajectories are usually divided into a set of stops and moves. In trajectories, stops represent the most important and meaningful part of the trajectory; there are many data mining methods to extract these locations. DBSCAN (density-based spatial clustering of applications with noise) is a classical density-based algorithm used to find the high-density areas in space, and different derivative methods of this algorithm have been proposed to find the stops in trajectories. However, most of these methods required a manually-set threshold, such as the speed threshold, for each feature variable. In our research, we first defined our new concept of move ability. Second, by introducing the theory of data fields and by taking our new concept of move ability into consideration, we constructed a new, comprehensive, hybrid feature-based, density measurement method which considers temporal and spatial properties. Finally, an improved DBSCAN algorithm was proposed using our new density measurement method. In the Experimental Section, the effectiveness and efficiency of our method is validated against real datasets. When comparing our algorithm with the classical density-based clustering algorithms, our experimental results show the efficiency of the proposed method.