Evaluation and analysis of anxiety and depression symptoms for college students during COVID-19 pandemic.

BACKGROUND: The mental health of students is affected by COVID-19. We aim to evaluate the anxiety and depression symptoms among college students during COVID-19 pandemic, analyze the influence factors that contribute to college students' anxiety and depression symptoms, and provide some suggestions for improving the mental health of college students. METHODS: With 179 college students participating, an online questionnaire consisting of a general questionnaire, Generalized Anxiety Disorder (GAD-7), and Patient Health Questionnaire (PHQ-9) was conducted in universities in Shanghai. The anxiety and depression symptoms among college students were evaluated using GAD-7 and PHQ-9 scales, and influence factors were analyzed using an unordered multi-class Logistic regression model. RESULTS: The reliability and validity of the GAD-7 and PHQ-9 scales were good (reliability ≥ 0.9, validity = 100%). The incidence of anxiety was 32.4%, of which were 23.5%, 8.4%, and 0.6% in mild, moderate, and severe, respectively; and the incidence of depression was 46.40%, of which in mild, moderate, moderate to severe, and severe were 28.5%, 10.1%, 7.3%, and 0.6%, respectively. Multivariate analysis revealed that male students with strong psychological quality, who were not easily affected by the COVID-19 pandemic, who received less negative or false information, and who had a strong grasp of psychology and related knowledge were less likely to suffer from mild or moderate anxiety symptoms [OR (95% CI) 0.18 (0.04, 0.81), 0.12 (0.05, 0.33), 0.23 (0.06, 0.89) and 0.07 (0.01, 0.74)]. Furthermore, college students who were not affected by the COVID-19 pandemic were less likely to suffer from mild, moderate, and moderate to severe depression symptoms [OR (95% CI) 0.23 (0.08, 0.65), 0.22 (0.05, 0.93), 0.10 (0.02, 0.54)]. CONCLUSION: The GAD-7 and PHQ-9 scales are suitable for evaluating anxiety and depression symptoms in college students. The COVID-19 pandemic was associated with a high incidence of anxiety and depression symptoms among college students, although gender and mental state fluctuations during the pandemic, negative and false information, and exposure to psychology and related courses were the main influencing factors.

Correlation Analysis between Urban Elements and COVID-19 Transmission Using Social Media Data.

The outbreak of the COVID-19 has become a worldwide public health challenge for contemporary cities during the background of globalization and planetary urbanization. However, spatial factors affecting the transmission of the disease in urban spaces remain unclear. Based on geotagged COVID-19 cases from social media data in the early stage of the pandemic, this study explored the correlation between different infectious outcomes of COVID-19 transmission and various factors of the urban environment in the main urban area of Wuhan, utilizing the multiple regression model. The result shows that most spatial factors were strongly correlated to case aggregation areas of COVID-19 in terms of population density, human mobility and environmental quality, which provides urban planners and administrators valuable insights for building healthy and safe cities in an uncertain future.

Flap Reconstruction of the Oropharyngeal Defect After Tumor Resection via Combined Transcervical and Transoral Approach in Patients With HPV-Positive and -Negative Oropharyngeal Squamous Cell Carcinoma.

Objective: To investigate a novel surgical approach of combined transcervical parapharyngeal space (PPS) with the transoral approach to dissect oropharyngeal cancer. Methods: 31 patients who were pathologically diagnosed with oropharyngeal cancer and had undergone surgical treatment in Beijing Tongren Hospital during June 2018 and December 2020 were enrolled. All patients were squamous cell carcinoma patients. There were 25 males and 6 females, and the age ranged between 44 and 70 years old. The number of patients with T1, T2, T3, and T4 stage disease was 8, 15, 8, and 0, respectively, according to the American Joint Committee on Cancer staging method, 8th edition. After the dissection of the submandibular and cervical lymph nodes, the parapharyngeal space was exposed, and the parapharyngeal space lymph node and the outer borderline of the tumor were dissected, and then the inner borderline of the tumor was dissected via a transoral approach; the tumor was dissected en bloc, and the defects were reconstructed with the flap from the neck through the parapharyngeal space. Results: Among the patients enrolled, 21 were HPV positive and 10 were HPV negative. 8 patients were free of lymph node metastasis. The tumor resection margins were negative in all 31 patients. Safe and sufficient excision of tumors was feasible by this new surgical approach, avoiding complications associated with mandibulotomy or lip-splitting. All patients had no obvious dysfunctions of swallowing and voice. By the time of this follow-up, none died caused by OPSCC, and only two patients suffered from local recurrence. The 3-year survival rate is 100%, and the 3-year recurrence-free survival rate is 84.58%. Conclusion: The surgical approach of combined transcervical parapharyngeal space with the transoral approach was effective and safe. On this basis, this approach has the advantage of fewer postoperative complications and better functional results.

Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility

Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from 'Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies.

The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States

(1) Background: Human mobility between geographic units is an important way in which COVID-19 is spread across regions. Due to the pressure of epidemic control and economic recovery, states in the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating epidemic policies. (2) Methods: We utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (excluding the District of Colombia) with daily new cases at the county level from 22 January 2020 to 20 August 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation test and stepwise OLS regression with socioeconomic factors. (3) Results: The K-means clustering divided the time-varying spatial autocorrelation curves of the 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with the variables of median age, population density, and proportions of international immigrants and highly educated population, but negatively correlated with the birth rate. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and highly educated population proportion. (4) Conclusions: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population;high-density populated states need to strengthen regional mobility restrictions;and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model.

BACKGROUND: Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. METHODS: Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. RESULTS: Based on the US county-level COVID-19 data from 22 January (T1) to 20 August (T212) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T212. INTERPRETATION: Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.

Impact of the 2019 Novel Coronavirus Disease (COVID-19) Epidemic on Radiotherapy-Treated Patients with Cancer: A Single-Center Descriptive Study.

INTRODUCTION: The present study investigated hospitalization data of patients receiving radiotherapy at Anhui Cancer Hospital during the COVID-19 epidemic and analyzed the impact of the epidemic on the clinical data of radiotherapy patients to provide references for the feasibility and safety of radiotherapy at other medical institutions. METHODS: The present study performed a retrospective analysis of hospitalization data of patients undergoing radiotherapy at the Radiation Department (from January 5 to March 19, 2020 according to the Chinese lunar calendar), who were defined as the epidemic group. Hospitalization data for patients undergoing radiotherapy during the same period in 2019 were used as the control group for comparison with the epidemic group in terms of sex, age, distribution of various cancer types, hospitalization costs, average length of stay, completion rate of radiotherapy, treatment mode, and purpose of radiotherapy. RESULTS: A total of 79 and 115 patients received radiotherapy in the epidemic group and control group, respectively. The number of patients who received radiotherapy declined 31.3% during the epidemic period. The number of head and neck cancer patients who received radiotherapy was 36 (45.57%) in the epidemic group and 32 (27.83%) in the control group, which was a significant difference (χ2 =6.476, P=0.011). The proportions of patients with other types of cancer decreased, with no significant difference between the two groups (P>0.05). No significant differences between the two groups were found in terms of other hospitalization data (P>0.05). CONCLUSION: The total number of patients who received radiotherapy decreased during the epidemic period, but the proportion of head and neck cancer increased. The epidemic had no significant effect on other hospitalization data. While strengthening prevention and control measures, we should actively perform radiotherapy to ensure that cancer patients receive timely and safe treatment.