An Online Solution Focused Brief Therapy for Adolescent Anxiety during the COVID-19 pandemic: A randomized controlled trial

In this randomized clinical trial, we investigated the efficacy of an online solution focused brief therapy (SFBT) for adolescents' anxiety symptoms during the COVID-19 period. Eligible participants were between the ages of 11 and 18 years, scored a 10 or above on the Generalized Anxiety Disorder-7 (GAD-7). The results found that compared to adolescents who did not receive any treatment, the intervention yielded significant results in alleviating adolescents' anxiety and depressive symptoms while promoting problem oriented coping strategies at immediate post-intervention. The therapeutic benefit has persisted, as shown in our results from the 1-month follow-up.

An online solution focused brief therapy for adolescent anxiety : A randomized controlled trial.

In this randomized clinical trial, we investigated the efficacy of an online solution focused brief therapy (SFBT) for adolescents' anxiety symptoms during the COVID-19 period. Eligible participants were between the ages of 11 and 18 years, scored a 10 or above on the Generalized Anxiety Disorder-7 (GAD-7). The results found that compared to adolescents who did not receive any treatment, the intervention yielded significant results in alleviating adolescents' anxiety and depressive symptoms while promoting problem oriented coping strategies at immediate post-intervention. The therapeutic benefit has persisted, as shown in our results from the 1-month follow-up.

Demographic and clinical feature disparity between progress and non-progress patients with vitiligo after COVID-19 vaccination: A cross-sectional study.

There have been several case reports regarding newly developed vitiligo following the coronavirus disease 19 (COVID-19) vaccination. However, the relationship between COVID-19 vaccine and vitiligo progression remains unclear. To explore the relationship between COVID-19 vaccine and vitiligo progression and its potential influencing factors, A cross-sectional study was conducted on 90 patients with vitiligo who received inactivated COVID-19 vaccination. Detailed information covering demographic characteristics (age and sex), vitiligo clinical features (disease subtypes, duration, stage and comorbidities) and disease activity was collected through an electronic questionnaire. Ninety patients with vitiligo included 44.4% males, with an average age of 38.1 years (standard deviation, SD = 15.0). Patients were divided into progress group (29, 32.2%) and normal group (61, 67.8%) based on whether they experienced vitiligo progression after inactivated COVID-19 vaccination. 41.3% of patients in the progress group experienced vitiligo progression within 1 week after vaccination, and disease progression mainly occurred after the first dose inoculation (20, 69.0%). Logistic regression revealed that patients aged <45 years (odds ratio (OR) was 0.87, 95% confidence interval (CI): 0.34-2.22) and male patients (OR = 0.84, 95% CI: 0.34-2.05) had lower risk for vitiligo progression, while patients with segmental vitiligo (SV) subtype (OR = 1.68, 95% CI: 0.53-5.33), with <5 years disease duration (OR = 1.32, 95% CI: 0.51-3.47) had higher risk for vitiligo progression after COVID-19 vaccination, but without statistical significance. Over 30% patients experienced vitiligo progression after inactivated COVID-19 vaccination, and female patients, elder age, shorter disease duration and SV subtype are potential risk factors for vitiligo progression.

Modelling COVID-19 epidemic with confirmed cases-driven contact tracing quarantine.

The pandemic of novel coronavirus disease 2019 (COVID-19) has been a severe threat to public health. The policy of close contract tracing quarantine is an effective strategy in controlling the COVID-19 epidemic outbreak. In this paper, we developed a mathematical model of the COVID-19 epidemic with confirmed case-driven contact tracing quarantine, and applied the model to evaluate the effectiveness of the policy of contact tracing and quarantine. The model is established based on the combination of the compartmental model and individual-based model simulations, which results in a closed-form delay differential equation model. The proposed model includes a novel form of quarantine functions to represent the number of quarantine individuals following the confirmed cases every day and provides analytic expressions to study the effects of changing the quarantine rate. The proposed model can be applied to epidemic dynamics during the period of community spread and when the policy of confirmed cases-driven contact tracing quarantine is efficient. We applied the model to study the effectiveness of contact tracing and quarantine. The proposed delay differential equation model can describe the average epidemic dynamics of the stochastic-individual-based model, however, it is not enough to describe the diverse response due to the stochastic effect. Based on model simulations, we found that the policy of contact tracing and quarantine can obviously reduce the epidemic size, however, may not be enough to achieve zero-infectious in a short time, a combination of close contact quarantine and social contact restriction is required to achieve zero-infectious. Moreover, the effect of reducing epidemic size is insensitive to the period of quarantine, there are no significant changes in the epidemic dynamics when the quarantine days vary from 7 to 21 days.

Unfolding and modeling the recovery process after COVID lockdowns.

Lockdown is a common policy used to deter the spread of COVID-19. However, the question of how our society comes back to life after a lockdown remains an open one. Understanding how cities bounce back from lockdown is critical for promoting the global economy and preparing for future pandemics. Here, we propose a novel computational method based on electricity data to study the recovery process, and conduct a case study on the city of Hangzhou. With the designed Recovery Index, we find a variety of recovery patterns in main sectors. One of the main reasons for this difference is policy; therefore, we aim to answer the question of how policies can best facilitate the recovery of society. We first analyze how policy affects sectors and employ a change-point detection algorithm to provide a non-subjective approach to policy assessment. Furthermore, we design a model that can predict future recovery, allowing policies to be adjusted accordingly in advance. Specifically, we develop a deep neural network, TPG, to model recovery trends, which utilizes the graph structure learning to perceive influences between sectors. Simulation experiments using our model offer insights for policy-making: the government should prioritize supporting sectors that have greater influence on others and are influential on the whole economy.

The Effects of COVID-19 Lockdown on Air Pollutant Concentrations across China: A Google Earth Engine-Based Analysis.

To overcome the spread of the severe COVID-19 outbreak, various lockdown measures have been taken worldwide. China imposed the strictest home-quarantine measures during the COVID-19 outbreak in the year 2020. This provides a valuable opportunity to study the impact of anthropogenic emission reductions on air quality. Based on the GEE platform and satellite imagery, this study analyzed the changes in the concentrations of NO2, O3, CO, and SO2 in the same season (1 February-1 May) before and after the epidemic control (2019-2021) for 16 typical representative cities of China. The results showed that NO2 concentrations significantly decreased by around 20-24% for different types of metropolises, whereas O3 increased for most of the studied metropolises, including approximately 7% in megacities and other major cities. Additionally, the concentrations of CO and SO2 showed no statistically significant changes during the study intervals. The study also indicated strong variations in air pollutants among different geographic regions. In addition to the methods in this study, it is essential to include the differences in meteorological impact factors in the study to identify future references for air pollution reduction measures.

From fears to recession? Time‐frequency risk contagion among stock and credit default swap markets during the COVID pandemic

This study uses a high-dimensional time-frequency volatility spillover model to examine risk interactions across 32 global stock and credit default swap (CDS) markets during the coronavirus (COVID-19) pandemic. Our empirical results mainly show that cross-market risk spillovers between these two types of markets are rare over the whole crisis period. Adding CDS assets to the stock portfolio would significantly decrease the overall risk spillovers in the mixed portfolio. Then, the time-frequency spillover and rolling window analyses confirm this finding and provide further evidence of frequency heterogeneity of risk spillovers during the pandemic. The outbreak of the COVID pandemic only sharply increases short-term risk spillovers between the stock and sovereign CDS market but exerts insignificant impacts on the medium and long-term cross-market risk spillovers. Moreover, our results show that developed countries are net transmitters in the stock markets. In contrast, the emerging markets account for the most risk spillovers in sovereign CDS markets over the sample period, emphasizing the heterogenous adverse impacts of the pandemic across various countries and markets. Finally, we find that bailouts implemented by the US and EU central banks, though with heterogeneous impacts across frequencies, gradually enhance the confidence and resilience of investors in these markets.

New-onset COVID-19-related diabetes: an early indicator of multi-organ injury and mortally of SARS-CoV-2 infection.

Objective: The pandemic of 2019 coronavirus (SARS-CoV-2) disease (COVID-19) has imposed a severe public health burden worldwide. Most patients with COVID-19 were mild. Severe patients progressed rapidly to critical condition including acute respiratory distress syndrome (ARDS), multi-organ failure and even death. This study aims to find early multi-organ injury indicators and blood glucose for predicting mortality of COVID-19. Methods: Fasting blood glucose (FBG) ≥7.0 mmol/L for two times during hospitalization and without a history of diabetes were defined as new-onset COVID-19-related diabetes (CRD). Indicators of injuries for multiple organs, including the lung, heart, kidney and liver, and glucose homeostasis were specifically analyzed for predicting death. Results: A total of 120 patients with a severity equal to or greater than Moderate were hospitalized. After excluding patients with history of diabetes, chronic heart, kidney, and liver disease, 69 patients were included in the final analysis. Of the 69 patients, 23 were Moderate, 20 were Severe, and 26 were Critical (including 16 deceased patients). Univariable analysis indicated that CRD, lactate dehydrogenase (LDH), hydroxybutyrate dehydrogenase (HBDH), creatine kinase (CK) and creatinine (Cr) were associated with death. Multivariable analysis indicated that CRD was an independent predictor for death (HR = 3.75, 95% CI 1.26-11.15). Abnormal glucose homeostasis or CRD occurred earlier than other indicators for predicting poor outcomes. Indicators of multiple organ injury were in parallel with the expression patterns of ACE2 (the SARS-CoV-2 receptor) in different organs including pancreatic islet. Conclusions: New-onset COVID-19-related diabetes is an early indicator of multi-organ injury and predictor for poor outcomes and death in COVID-19 patients. As it is easy to perform for clinical practices and self-monitoring, glucose testing will be helpful for predicting poor outcomes to facilitate appropriate intensive care.

Association between intimate partner violence and prenatal anxiety and depression in pregnant women: a cross-sectional survey during the COVID-19 epidemic in Shenzhen, China.

OBJECTIVES: Intimate partner violence (IPV) against women remains a major global public health problem with harmful consequences for individuals and society. People's lifestyles have been greatly affected by the COVID-19 pandemic. This study investigated the prevalence of and relationship between IPV and anxiety and depression in pregnant Chinese women during the pandemic. DESIGN: Cross-sectional study. SETTING: This investigation was conducted in Shenzhen City, Guangdong Province, China from 15 September to 15 December 2020. PARTICIPANTS: A total of 3434 pregnant women were screened with the Abuse Assessment Screen Questionnaire to evaluate IPV and General Anxiety Disorder and Patient Health Questionnaire to evaluate symptoms of anxiety and depression, respectively. Pregnant women with perinatal health records at Shenzhen District Maternity and Child Healthcare Hospitals who consented to participate were enrolled. Women with psychotic disorders such as schizophrenia, mania or substance dependence and pregnant women who refused to participate were excluded. Data were analysed with the χ2 test and by logistic regression analysis. RESULTS: The prevalence of IPV among pregnant women was 2.2%. Mental violence was the most common type of violence (2.2%), followed by physical (0.6%) and sexual (0.7%) violence. The prevalence of anxiety and depression symptoms was 9.8% and 6.9%, respectively. After adjusting for covariates, there was a statistically significant association between IPV and prenatal anxiety (OR=4.207, 95% CI: 2.469 to 7.166) and depression (OR=3.864, 95% CI: 2.095 to 7.125). CONCLUSIONS: IPV increased the risk of prenatal anxiety and depression in pregnant women in China during the COVID-19 pandemic. Efforts should be made by the government and civil society to promote long-lasting antenatal interventions to ensure the safety and protect the mental health of pregnant women.

COVID-19 vaccine acceptance among cold-chain workers in Shenzhen, China: A cross-sectional survey.

Given the high level of exposure to SARS-CoV-2, cold-chain workers are considered priority vaccination groups. To date, many studies have reported on the willingness within distinct populations to be vaccinated against COVID-19, whereas it has not been reported among cold-chain workers worldwide. To address this void, we conducted a cross-sectional survey to gather general information, COVID-19-related knowledge, attitudes, and practices (KAP), and willingness to receive a COVID-19 vaccine among cold-chain workers in Shenzhen, China. Binary logistic analyses were conducted to explore the associations between COVID-19-related KAP factors and the willingness for COVID-19 vaccination. Among 244 cold-chain workers, 76% indicated that they were willing to be vaccinated. Knowledge about SARS-CoV-2, comprehending the most effective prevention, understanding the transmission routes, and recognizing the priority vaccination groups were positively associated with willingness to be vaccinated against COVID-19. Regarding attitude factors, perceiving the social harmfulness and severity of COVID-19 were related to a higher willingness to vaccination. Participants considering themselves a priority group for COVID-19 vaccination were more likely to get vaccinated. For practice factors, attaining more knowledge and higher self-reported compliance with maintaining adequate ventilation were also positively associated with the dependent variable. Agreement on the importance of COVID-19 vaccination was the most frequent reason for accepting the COVID-19 vaccine; additionally, concerns about side effects and insufficient understanding of efficacy were the main factors contributing to vaccine refusal. Enhancing KAP levels related to COVID-19 helps promote vaccine acceptance. Health authorities should promptly implement educational activities following the updated vaccine status among cold-chain workers.

[Design and Development of Cloud Platform of Emergency COVID-19 Nucleic Acid Detection].

According to the characteristics of short time and large amount of samples for out of hospital emergency nucleic acid detection, this study introduces an out of hospital emergency nucleic acid detection cloud platform system, which realizes the functions of rapid identification of the detected person and one-to-one correspondence with the samples, and real-time upload of the detection results to Zhejiang Government service network for quick viewing and statistics, so as to complete the task of national nucleic acid screening efficiently and accurately that we must provide information support.

Influenza Vaccination Coverage among Older Adults with Hypertension in Shenzhen, China: A Cross-Sectional Survey during the COVID-19 Pandemic.

BACKGROUND: Older individuals with hypertension are at a high risk of being infected with influenza. However, there have been few studies investigating the influenza vaccination status among older people with hypertension. The present work aimed to estimate the vaccination coverage and determine the predictors of seasonal influenza vaccinations among hypertensive patients aged over 60 years in Shenzhen, China. METHOD: The study used data from an online cross-sectional survey that was conducted in Shenzhen City, China, in October 2020. Frequencies and proportions of all the variables including sociodemographic characteristics and health-related information were described and tabulated based on the influenza vaccination status. Bivariate and multivariable logistic regression analyses were used to identify independent predictors associated with the influenza vaccination. RESULTS: A total of 5216 older people with hypertension aged above 60 years were recruited. Overall, only 4.7% had received an influenza vaccine in the latest influenza season. Using the action toward being vaccinated as the primary outcome, the multivariable regression analysis showed that participants aged over 80 years (aOR 2.957, 95% CI: 1.784-4.900), obtaining higher education levels (aOR 1.424, 95% CI: 1.060-1.914 for high school, aOR 1.681, 95% CI: 1.066-2.650 for college or above), living with a partner (aOR 1.432, 95% CI: 1.068-1.920), using a family doctor (aOR 2.275, 95% CI: 1.744-2.968), and taking a physical examination 1-2 and ≥3 times each year (aOR 2.107, 95% CI: 1.601-2.772 and aOR 2.118, 95% CI: 1.083-4.143, respectively) were more likely to be vaccinated. In contrast, smokers had less likelihood of having the influenza vaccination than non-smokers (aOR 1.829, 95% CI: 1.208-2.767). CONCLUSIONS: The coverage rate of influenza vaccinations is far away from optimistic among older adults with hypertension. Additional works should be undertaken immediately to improve the influenza vaccination status.

Knowledge, Attitudes, and Practices Toward COVID-19 Among Chinese Teachers, Shenzhen: An Online Cross-sectional Study During the Global Outbreak of COVID-19.

Background: Adequate understanding and precautionary behaviors are of vital importance to contain the spread of coronavirus disease 2019 (COVID-19). To date, the knowledge, attitudes, and practices (KAP) toward COVID-19 among different populations have been reported, whereas such information is unavailable in teachers. We aimed to investigate the KAP of teachers associated with COVID-19 during the global outbreak. Methods: A large-scale population-based survey was conducted to gather information on COVID-19-related KAP among Chinese teachers using a self-administered questionnaire. We received 10,658 responses in April 2020, out of which 8,248 were enrolled in the final analysis. Participants responded to a self-administered questionnaire concerning demographic characteristics and KAP associated with COVID-19. Results: This work included 4,252 (51.6%) teachers in kindergartens, 2,644 (32.1%) teachers in primary schools, and 1,352 (16.4%) teachers in secondary schools. The knowledge level (mean: 4.46 out of seven points) was relatively lower than the levels of attitudes (mean: 3.27 out of four points) and practices (mean: 4.29 out of five points) toward COVID-19. Knowledge scores significantly varied by the collected demographic variables except education worksite (p < 0.05), whereas practice scores significantly differed in age groups (p < 0.05), education level (p < 0.001), education worksite (p < 0.001), and years of teaching (p < 0.001). The multivariate logistic analysis indicated that poor knowledge related to COVID-19 was common among men, younger, and less-educated teachers. In contrast, female teachers and those with higher education levels tend to have good practices against COVID-19. Conclusion: The present work suggested the knowledge gaps regarding COVID-19 were needed to be corrected immediately in teachers. Given the critical role of teachers in the education system, health authorities should take gender, age, and education level into account when developing suitable health interventions.

Rational optimization of a human neutralizing antibody of SARS-CoV-2.

SARS-CoV-2 has caused a worldwide epidemic of coronavirus disease 19 (COVID-19). Antibody drugs present an effective weapon for tens of millions of COVID-19 patients. Antibodies disrupting the interactions between the receptor-binding domain (RBD) of SARS-CoV-2 S protein and the angiotensin converting enzyme 2 (ACE2) effectively block SARS-CoV-2 cell entry into host cells. In order to rapidly develop more potent neutralizing antibodies, we utilized virtual scanning mutageneses and molecular dynamics simulations to optimize the antibody of P2B-2F6 isolated from single B cells of SARS-CoV-2 infected patients. Two potent P2B-2F6 mutants, namely H:V106R and H:V106R/H:P107Y, were found to possess higher binding affinities with the RBD domain of SARS-CoV-2 than others. Polar interactions are preferred near 106 and 107 paratope residues of the heavy chain. The mutations also increase the hydrogen-bonding network formed between the antibody and the RBD. Notably, the optimized antibodies possess potential neutralizing activity against the alarming SARS-CoV-2 variant of N501Y. This study provides insights into structure-based optimization of antibodies with higher affinity to the antigen. We hope that our proposed antibody mutants could contribute to the development of improved therapies against COVID-19.

Prevalence and contributory factors of anxiety and depression among pregnant women in the post-pandemic era of COVID-19 in Shenzhen, China.

BACKGROUND: Pregnant women are emotionally vulnerable and have suffered great psychological impacts. Following the coronavirus disease 2019 (COVID-19) outbreak, a study was undertaken of the prevalence of, and factors contributing to, symptoms of anxiety and depression among pregnant women in Shenzhen, China. METHODS: A cross-sectional study on pregnant women was conducted from September to December 2020 in Shenzhen, using a random-recruit method. The General Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9) surveys were used to evaluate symptoms of anxiety and depression. A multivariate logistic regression model was developed to explore factors potentially associated with symptoms of anxiety and depression during pregnancy. RESULTS: A total of 3,434 pregnant women aged 15 to 59 years were enrolled. Symptoms of anxiety and depression were present in 9.8% and 6.9%, respectively. Logistic regression analysis using a stepwise procedure revealed that an increased risk of symptoms of anxiety and depression was associated with unmarried/divorced/widowed, unemployed, received professional psychological counseling, family dysfunction, the first trimester of pregnancy, pregnancy complications and vaginal bleeding, unplanned pregnancy, decline in household income and disputes between partners caused by the COVID-19 pandemic, consumption of alcoholic drinks by women and their partners, smoking, lack of exercise and sedentary lifestyle. Women with education from junior high school through college were less likely to experience symptoms of prenatal depression. CONCLUSIONS: Our study revealed factors associated with psychological symptoms among pregnant women in the post-COVID-19-pandemic era. These results should help to update guidance for psychological interventions for pregnant women during the period of COVID-19.

Efficient management strategy of COVID-19 patients based on cluster analysis and clinical decision tree classification.

Early classification and risk assessment for COVID-19 patients are critical for improving their terminal prognosis, and preventing the patients deteriorate into severe or critical situation. We performed a retrospective study on 222 COVID-19 patients in Wuhan treated between January 23rd and February 28th, 2020. A decision tree algorithm has been established including multiple factor logistic for cluster analyses that were performed to assess the predictive value of presumptive clinical diagnosis and features including characteristic signs and symptoms of COVID-19 patients. Therapeutic efficacy was evaluated by adopting Kaplan-Meier survival curve analysis and cox risk regression. The 222 patients were then clustered into two groups: cluster I (common type) and cluster II (high-risk type). High-risk cases can be judged from their clinical characteristics, including: age > 50 years, chest CT images with multiple ground glass or wetting shadows, etc. Based on the classification analysis and risk factor analysis, a decision tree algorithm and management flow chart were established, which can help well recognize individuals who needs hospitalization and improve the clinical prognosis of the COVID-19 patients. Our risk factor analysis and management process suggestions are useful for improving the overall clinical prognosis and optimize the utilization of public health resources during treatment of COVID-19 patients.

Heightened Willingness toward Pneumococcal Vaccination in the Elderly Population in Shenzhen, China: A Cross-Sectional Study during the COVID-19 Pandemic.

BACKGROUND: Elderly population is considered at high risk for pneumococcal diseases. The pneumococcal vaccine coverage presents extremely low among elderly people in China. However, the serious event of COVID-19 drives interest in the pneumococcal vaccine, prompting us investigating the willingness to accept the 23-valent pneumococcal polysaccharide vaccine (PPSV23) and influencing factors among people aged over 60 years during the COVID-19 pandemic. METHODS: A cross-sectional study was employed using a self-administered questionnaire in Shenzhen City of China, elaborating the willingness toward PPSV23 in the elderly persons. Binomial logistic analyses were performed to estimate the influencing factors using odds ratios (ORs) and 95% confidence interval (CI). RESULTS: Among 15,066 respondents, 91.5% presented a positive attitude toward PPSV23. Logistic analyses suggested the influencing factors included knowledge about pneumonia (adjusted OR [aOR] 1.391, 95%CI 1.214-1.593), perception of the seriousness of pneumonia (aOR 1.437, 95%CI 1.230-1.680) and preventing way for pneumonia (aOR 1.639, 95%CI 1.440-1.865), worried about getting pneumonia (aOR 2.751, 95%CI 2.444-3.096), understanding vaccine policy (aOR 1.774, 95%CI 1.514-2.079), and influenza vaccine (aOR 3.516 and 95%CI 2.261-5.468) and PPSV23 histories (aOR 3.199, 95%CI 1.492-6.860). CONCLUSIONS: The interest surge in pneumococcal vaccine coincided with the COVID-19 outbreak, foreshadowing higher demand for pneumococcal vaccine in the near future.