Sleep disturbance and anxiety symptoms among asymptomatic COVID-19 carriers in Shanghai, China: the mediating role of entrapment and defeat.

BACKGROUND: The COVID-19 pandemic increases the risk of psychological problems, especially for the infected population. Sleep disturbance and feelings of defeat and entrapment are well-documented risk factors of anxiety symptoms. Exploring the psychological mechanism of the development of anxiety symptoms is essential for effective prevention. This study aimed to examine the mediating effects of entrapment and defeat in the association between sleep disturbance and anxiety symptoms among asymptomatic COVID-19 carriers in Shanghai, China. METHODS: A cross-sectional study was conducted from March to April, 2022. Participants were 1,283 asymptomatic COVID-19 carriers enrolled from the Ruijin Jiahe Fangcang Shelter Hospital, Shanghai (59.6% male; mean age = 39.6 years). Questionnaire measures of sleep disturbance, entrapment, defeat, anxiety symptoms, and background characteristics were obtained. A mediation model was constructed to test the mediating effects of entrapment and defeat in the association between sleep disturbance and anxiety symptoms. RESULTS: The prevalence rates of sleep disturbance and anxiety symptoms were 34.3% and 18.8%. Sleep disturbance was positively associated with anxiety symptoms (OR [95%CI] = 5.013 [3.721-6.753]). The relationship between sleep disturbance and anxiety symptoms (total effect: Std. Estimate = 0.509) was partially mediated by entrapment (indirect effect: Std. Estimate = 0.129) and defeat (indirect effect: Std. Estimate = 0.126). The mediating effect of entrapment and defeat accounted for 50.3% of the association between sleep disturbance and anxiety symptoms. CONCLUSION: Sleep disturbance and anxiety symptoms were prevalent among asymptomatic COVID-19 carriers. Entrapment and defeat mediate the association between sleep disturbance and anxiety symptoms. More attention is needed to monitoring sleep conditions and feelings of defeat and entrapment to reduce the risk of anxiety.

Narcissistic Enough to Challenge: The Effect of Narcissism on Change-Oriented Organizational Citizenship Behavior.

During the COVID-19 pandemic, organizations need to effectively manage changes, and employees need to proactively adapt to these changes. The present research investigated when and how individual employees' narcissism was related to their change-oriented organizational citizenship behavior. Specifically, based on a trait activation perspective, this research proposed the hypotheses that individual employees' narcissism and environmental uncertainty would interactively influence employees' change-oriented organizational citizenship behavior via felt responsibility for constructive change; furthermore, the effect of narcissism on change-oriented organizational citizenship behavior via felt responsibility for constructive change would be stronger when the environmental uncertainty prompted by the COVID-19 pandemic was high rather than low. Two studies were conducted to test these hypotheses: an online survey of 180 employees in mainland China (Study 1) and a field study of 167 leader-follower dyads at two Chinese companies (Study 2). The current research reveals a bright side of narcissism, which has typically been recognized as a dark personality trait, and enriches the understanding of the antecedents of change-oriented organizational citizenship behavior. This research can also guide organizations that wish to stimulate employee proactivity.

Evaluation of the Rapid Antigen Detection Test for Diagnosing SARS-CoV-2 during the COVID-19 Pandemic: Experience from a Centralized Isolation Site in Shanghai, China.

Rapid and reliable diagnosis is important for the management of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid antigen detection test (RADT) is a rapid, inexpensive, and easy method. Several studies have reported that RADTs performed well in many countries; however, very few studies have been reported in China. In this study, we assessed the performance of the RADT (Ediagnosis COVID-19 antigen test kit). This study was conducted in a centralized isolation site in Shanghai and enrolled 716 patients with COVID-19 and 203 noninfected participants. Nasopharyngeal swabs from all participants were collected on the same day and tested using the RADT and real-time reverse transcription-PCR (RT-PCR). The performance of the RADT was evaluated in different scenarios, such as threshold cycle (CT) values, symptomatic phase, and symptoms on the day of testing. The results demonstrated that the sensitivity for patients with CT values lower than 20 was 96.55% (95% confidence interval [CI], 87.05 to 99.4). The sensitivities were 78.4% (95% CI, 69.96 to 85.05) for participants within 5 days after the first RT-PCR-positive result and 90.77% (95% CI, 80.34 to 96.19) within 5 days after symptom onset. Moreover, the sensitivity of the RADT was more than 80% for patients with symptoms on the day of testing, including fever (89.29%), cough (86.84%), stuffy nose (92.59%), runny nose (92%), sore throat (81.25%), and muscle pain (80.77%), especially for those with upper respiratory tract symptoms. The specificity of the RADT was good in all scenarios. During the SARS-CoV-2 epidemic, Ediagnosis performed excellently in individuals with a higher viral load (evidenced by lower CT values), individuals in the early symptomatic phase, and especially those with upper respiratory tract symptoms. IMPORTANCE RADTs have demonstrated excellent performance in many counties for screening SARS-CoV-2 infection, but very few studies have been conducted in China. The performance of RADTs is largely related to different real-life scenarios. In our study, the performance of the RADT was evaluated in different scenarios, such as CT values, symptomatic phase, and symptoms on the day of testing. The results demonstrated that Ediagnosis (an RADT made in China) performed excellently for individuals with a higher viral load (evidenced by lower CT values), individuals in the early symptomatic phase, and especially those with upper respiratory tract symptoms.

Health inspector ratings of Asian restaurants during the early COVID-19 pandemic

The COVID-19 pandemic has devastated the restaurant industry, with Asian restaurants having perhaps suffered the most, as many reported business losses well before shelter-in-place orders were announced. Media outlets argue that this decline in business reflects biases that are linked to the China- and food-related origin of COVID-19. However, discrimination against Asian Americans and their cuisine is not new, as it is rooted in a long and history of assimilation and racism. Overlooked in this body of literature, as well as in conversations on the impacts of COVID-19 on Asian restaurants, is the role of how government institutions shape these biases against a cuisine that has hundreds of years of history in the US yet remains distinctly ‘foreign’. In this study, we use 3-years of New York City restaurant health inspection data to examine trends in citation scores before and after the onset of the news of the COVID-19 pandemic. Using a synthetic control approach, we find that Asian restaurants uniquely received more citations after news of the pandemic became pervasive in the US. We end by discussing the implications of this finding for the history of Asian cuisine in the US, theoretical frameworks to understand assimilation, and the restaurant industry. [ FROM AUTHOR]

Social Stigma and Depression among Asymptomatic COVID-19 Carriers in Shanghai, China: The Mediating Role of Entrapment and Decadence.

INTRODUCTION: Since the advent of 2019 novel coronavirus (COVID-19), the coexistence between social stigma and depression symptoms (depression hereafter) in COVID-19 patients has been mentioned, but the mechanisms involved remains unclear. This study aimed to explore how the stigma affects depression during the mid-pandemic period. METHODS: A cross-sectional survey using non-probability sampling was conducted among asymptomatic COVID-19 carriers in Shanghai, China (April 2022). An online questionnaire was used to obtain information on demographic characteristics and psychological traits. Logistic regression and path analysis were performed to analyze the depression risk factors and examine the mediation model, respectively. RESULTS: A total of 1283 participants (59.6% men) were involved in this study, in which 44.7% of carriers reported having depression. Univariate analyses found that education level (OR 0.575; 95% CI 0.448-0.737) and doses of vaccine (OR 1.693; 95% CI 1.042-2.750), were significantly associated with depression among asymptomatic carriers. The association between social stigma and depression was fully mediated by their feelings of entrapment and decadence (indirect effect = 0.204, p < 0.001; direct effect = -0.059, p = 0.058). The mediating role of entrapment between stigma and depression was moderated by age group (estimate = 0.116, p = 0.008). CONCLUSION: Mental health issues resulting from the COVID-19 pandemic are increasingly apparent in China and require urgent attention and responses. These findings provide new perspectives for the early prevention of depression in asymptomatic carriers.

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.

BACKGROUND: Heart failure (HF) is a common disease and a major public health problem. HF mortality prediction is critical for developing individualized prevention and treatment plans. However, due to their lack of interpretability, most HF mortality prediction models have not yet reached clinical practice. OBJECTIVE: We aimed to develop an interpretable model to predict the mortality risk for patients with HF in intensive care units (ICUs) and used the SHapley Additive exPlanation (SHAP) method to explain the extreme gradient boosting (XGBoost) model and explore prognostic factors for HF. METHODS: In this retrospective cohort study, we achieved model development and performance comparison on the eICU Collaborative Research Database (eICU-CRD). We extracted data during the first 24 hours of each ICU admission, and the data set was randomly divided, with 70% used for model training and 30% used for model validation. The prediction performance of the XGBoost model was compared with three other machine learning models by the area under the curve. We used the SHAP method to explain the XGBoost model. RESULTS: A total of 2798 eligible patients with HF were included in the final cohort for this study. The observed in-hospital mortality of patients with HF was 9.97%. Comparatively, the XGBoost model had the highest predictive performance among four models with an area under the curve (AUC) of 0.824 (95% CI 0.7766-0.8708), whereas support vector machine had the poorest generalization ability (AUC=0.701, 95% CI 0.6433-0.7582). The decision curve showed that the net benefit of the XGBoost model surpassed those of other machine learning models at 10%~28% threshold probabilities. The SHAP method reveals the top 20 predictors of HF according to the importance ranking, and the average of the blood urea nitrogen was recognized as the most important predictor variable. CONCLUSIONS: The interpretable predictive model helps physicians more accurately predict the mortality risk in ICU patients with HF, and therefore, provides better treatment plans and optimal resource allocation for their patients. In addition, the interpretable framework can increase the transparency of the model and facilitate understanding the reliability of the predictive model for the physicians.

Clinical characteristics and outcomes of lung cancer patients with COVID-19: A systematic review and meta-analysis protocol.

BACKGROUND: COVID-19 is spreading rapidly worldwide, and the population is generally susceptible to SARS-CoV-2, especially those with cancer. Hence, our study aims to design a protocol for a systematic review and meta-analysis of the clinical characteristics and prognoses of lung cancer patients with COVID-19. METHODS: The protocol is prepared following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The literature will be searched in Embase, Pubmed, the Cochrane Library, LitCovid, and CNKI for potentially eligible articles. The quality of the articles will be used in the Newcastle-Ottawa Quality Assessment Scale (NOS) and Cochrane Handbook for Systematic Reviews of Interventions. Statistical analysis will be performed through RevMan 5 software. This review protocol has been registered in PROSPERO (CRD42022306866). DISCUSSION: To clarify whether COVID-19 affects the clinical symptoms and prognoses of lung cancer patients. Further study is needed to establish the best evidence-based for the management of lung cancer patients with COVID-19. CONCLUSION: The definitive conclusion will be important to physicians effectively manage lung cancer patients with COVID-19.

Identifying Drug-Induced Liver Injury Associated With Inflammation-Drug and Drug-Drug Interactions in Pharmacologic Treatments for COVID-19 by Bioinformatics and System Biology Analyses: The Role of Pregnane X Receptor

Of the patients infected with coronavirus disease 2019 (COVID-19), approximately 14–53% developed liver injury resulting in poor outcomes. Drug-induced liver injury (DILI) is the primary cause of liver injury in COVID-19 patients. In this study, we elucidated liver injury mechanism induced by drugs of pharmacologic treatments against SARS-CoV-2 (DPTS) using bioinformatics and systems biology. Totally, 1209 genes directly related to 216 DPTS (DPTSGs) were genes encoding pharmacokinetics and therapeutic targets of DPTS and enriched in the pathways related to drug metabolism of CYP450s, pregnane X receptor (PXR), and COVID-19 adverse outcome. A network, constructed by 110 candidate targets which were the shared part of DPTSGs and 445 DILI targets, identified 49 key targets and four Molecular Complex Detection clusters. Enrichment results revealed that the 4 clusters were related to inflammatory responses, CYP450s regulated by PXR, NRF2-regualted oxidative stress, and HLA-related adaptive immunity respectively. In cluster 1, IL6, IL1B, TNF, and CCL2 of the top ten key targets were enriched in COVID-19 adverse outcomes pathway, indicating the exacerbation of COVID-19 inflammation on DILI. PXR-CYP3A4 expression of cluster 2 caused DILI through inflammation-drug interaction and drug-drug interactions among pharmaco-immunomodulatory agents, including tocilizumab, glucocorticoids (dexamethasone, methylprednisolone, and hydrocortisone), and ritonavir. NRF2 of cluster 3 and HLA targets of cluster four promoted DILI, being related to ritonavir/glucocorticoids and clavulanate/vancomycin. This study showed the pivotal role of PXR associated with inflammation-drug and drug-drug interactions on DILI and highlighted the cautious clinical decision-making for pharmacotherapy to avoid DILI in the treatment of COVID-19 patients.

Development and Validation of a Nomogram for Predicting the Risk of Coronavirus-Associated Acute Respiratory Distress Syndrome: A Retrospective Cohort Study.

Background: Since the outbreak of coronavirus disease (COVID-19) in December 2019 in Wuhan, it has spread rapidly worldwide. We aimed to establish and validate a nomogram that predicts the probability of coronavirus-associated acute respiratory distress syndrome (CARDS). Methods: In this single-centre, retrospective study, 261 patients with COVID-19 were recruited using positive reverse transcription-polymerase chain reaction tests for severe acute respiratory syndrome coronavirus 2 in Tongji Hospital at Huazhong University of Science and Technology (Wuhan, China). These patients were randomly distributed into the training cohort (75%) and the validation cohort (25%). The factors included in the nomogram were determined using univariate and multivariate logistic regression analyses based on the training cohort. The area under the receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, and decision curve analysis (DCA) were used to evaluate the efficiency of the nomogram in the training and validation cohorts. Results: Independent predictive factors, including fasting plasma glucose, platelet, D-dimer, and cTnI, were determined using the nomogram. In the training cohort, the AUC and concordance index were 0.93. Similarly, in the validation cohort, the nomogram still showed great distinction (AUC: 0.92) and better calibration. The calibration plot also showed a high degree of agreement between the predicted and actual probabilities of CARDS. In addition, the DCA proved that the nomogram was clinically beneficial. Conclusion: Based on the results of laboratory tests, we established a predictive model for acute respiratory distress syndrome in patients with COVID-19. This model shows good performance and can be used clinically to identify CARDS early. Trial Registration: Ethics committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (No.:(2020) Linlun-34th).

Narcissistic Enough to Challenge: The Effect of Narcissism on Change-Oriented Organizational Citizenship Behavior

During the COVID-19 pandemic, organizations need to effectively manage changes, and employees need to proactively adapt to these changes. The present research investigated when and how individual employees’ narcissism was related to their change-oriented organizational citizenship behavior. Specifically, based on a trait activation perspective, this research proposed the hypotheses that individual employees’ narcissism and environmental uncertainty would interactively influence employees’ change-oriented organizational citizenship behavior via felt responsibility for constructive change;furthermore, the effect of narcissism on change-oriented organizational citizenship behavior via felt responsibility for constructive change would be stronger when the environmental uncertainty prompted by the COVID-19 pandemic was high rather than low. Two studies were conducted to test these hypotheses: an online survey of 180 employees in mainland China (Study 1) and a field study of 167 leader–follower dyads at two Chinese companies (Study 2). The current research reveals a bright side of narcissism, which has typically been recognized as a dark personality trait, and enriches the understanding of the antecedents of change-oriented organizational citizenship behavior. This research can also guide organizations that wish to stimulate employee proactivity.

"What's Going to Happen to Us?" Cultivating Partnerships with Immigrant Families in an Adverse Political Climate

With the increasing numbers of immigrant and refugee students across the US K--12 system, the xenophobia of the current political climate, and the effects of COVID19 on the immigrant community, it is critical to examine schools that serve immigrant students and their families. Drawing on case studies of two public high schools that exclusively serve immigrant students, authors Adriana Villavicencio, Chandler Patton Miranda, Jia-Lin Liu, and Hua-Yu Sebastian Cherng examine how educators frame the current political context and how this frame informs their collective approach to engaging with and supporting families. The study finds that these schools shifted norms of parental engagement by proactively forging relationships with families, cultivating alliances with community partners, and mediating within families around challenges related to work and higher education to benefit the communities they serve. In so doing, these school actors have shifted the norms of parental engagement to center the perspectives, voices, and experiences of immigrant families.

Hypophosphatemia is an independent risk factor for AKI among hospitalized patients with COVID-19 infection.

BACKGROUND: This study sought to investigate incidence and risk factors for acute kidney injury (AKI) in hospitalized COVID-19. METHODS: In this retrospective study, we enrolled 823 COVID-19 patients with at least two evaluations of renal function during hospitalization from four hospitals in Wuhan, China between February 2020 and April 2020. Clinical and laboratory parameters at the time of admission and follow-up data were recorded. Systemic renal tubular dysfunction was evaluated via 24-h urine collections in a subgroup of 55 patients. RESULTS: In total, 823 patients were enrolled (50.5% male) with a mean age of 60.9 ± 14.9 years. AKI occurred in 38 (40.9%) ICU cases but only 6 (0.8%) non-ICU cases. Using forward stepwise Cox regression analysis, we found eight independent risk factors for AKI including decreased platelet level, lower albumin level, lower phosphorus level, higher level of lactate dehydrogenase (LDH), procalcitonin, C-reactive protein (CRP), urea, and prothrombin time (PT) on admission. For every 0.1 mmol/L decreases in serum phosphorus level, patients had a 1.34-fold (95% CI 1.14-1.58) increased risk of AKI. Patients with hypophosphatemia were likely to be older and with lower lymphocyte count, lower serum albumin level, lower uric acid, higher LDH, and higher CRP. Furthermore, serum phosphorus level was positively correlated with phosphate tubular maximum per volume of filtrate (TmP/GFR) (Pearson r = 0.66, p < .001) in subgroup analysis, indicating renal phosphate loss via proximal renal tubular dysfunction. CONCLUSION: The AKI incidence was very low in non-ICU patients as compared to ICU patients. Hypophosphatemia is an independent risk factor for AKI in patients hospitalized for COVID-19 infection.

Public attitudes toward COVID-19 vaccines on English-language Twitter: A sentiment analysis.

OBJECTIVE: To identify themes and temporal trends in the sentiment of COVID-19 vaccine-related tweets and to explore variations in sentiment at world national and United States state levels. METHODS: We collected English-language tweets related to COVID-19 vaccines posted between November 1, 2020, and January 31, 2021. We applied the Valence Aware Dictionary and sEntiment Reasoner tool to calculate the compound score to determine whether the sentiment mentioned in each tweet was positive (compound ≥ 0.05), neutral (-0.05 < compound < 0.05), or negative (compound ≤ -0.05). We applied the latent Dirichlet allocation analysis to extract main topics for tweets with positive and negative sentiment. Then we performed a temporal analysis to identify time trends and a geographic analysis to explore sentiment differences in tweets posted in different locations. RESULTS: Out of a total of 2,678,372 COVID-19 vaccine-related tweets, tweets with positive, neutral, and negative sentiments were 42.8%, 26.9%, and 30.3%, respectively. We identified five themes for positive sentiment tweets (trial results, administration, life, information, and efficacy) and five themes for negative sentiment tweets (trial results, conspiracy, trust, effectiveness, and administration). On November 9, 2020, the sentiment score increased significantly (score = 0.234, p = 0.001), then slowly decreased to a neutral sentiment in late December and was maintained until the end of January. At the country level, tweets posted in Brazil had the lowest sentiment score of -0.002, while tweets posted in the United Arab Emirates had the highest sentiment score of 0.162. The overall average sentiment score for the United States was 0.089, with Washington, DC having the highest sentiment score of 0.144 and Wyoming having the lowest sentiment score of 0.036. CONCLUSIONS: Public sentiment on COVID-19 vaccines varied significantly over time and geography. Sentiment analysis can provide timely insights into public sentiment toward the COVID-19 vaccine and guide public health policymakers in designing locally tailored vaccine education programs.

Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis.

BACKGROUND: The COVID-19 vaccine is considered to be the most promising approach to alleviate the pandemic. However, in recent surveys, acceptance of the COVID-19 vaccine has been low. To design more effective outreach interventions, there is an urgent need to understand public perceptions of COVID-19 vaccines. OBJECTIVE: Our objective was to analyze the potential of leveraging transfer learning to detect tweets containing opinions, attitudes, and behavioral intentions toward COVID-19 vaccines, and to explore temporal trends as well as automatically extract topics across a large number of tweets. METHODS: We developed machine learning and transfer learning models to classify tweets, followed by temporal analysis and topic modeling on a dataset of COVID-19 vaccine-related tweets posted from November 1, 2020 to January 31, 2021. We used the F1 values as the primary outcome to compare the performance of machine learning and transfer learning models. The statistical values and P values from the Augmented Dickey-Fuller test were used to assess whether users' perceptions changed over time. The main topics in tweets were extracted by latent Dirichlet allocation analysis. RESULTS: We collected 2,678,372 tweets related to COVID-19 vaccines from 841,978 unique users and annotated 5000 tweets. The F1 values of transfer learning models were 0.792 (95% CI 0.789-0.795), 0.578 (95% CI 0.572-0.584), and 0.614 (95% CI 0.606-0.622) for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. Specifically, tweets containing positive behavioral intentions increased significantly in December 2020. In addition, we selected tweets in the following categories: positive attitudes, negative attitudes, positive behavioral intentions, and negative behavioral intentions. We then identified 10 main topics and relevant terms for each category. CONCLUSIONS: Overall, we provided a method to automatically analyze the public understanding of COVID-19 vaccines from real-time data in social media, which can be used to tailor educational programs and other interventions to effectively promote the public acceptance of COVID-19 vaccines.

A validation study of UCSD-Mayo risk score in predicting hospital-acquired acute kidney injury in COVID-19 patients.

INTRODUCTION: Acute kidney injury (AKI) in coronavirus disease 2019 (COVID-19) patients is associated with poor prognosis. Early prediction and intervention of AKI are vital for improving clinical outcome of COVID-19 patients. As lack of tools for early AKI detection in COVID-19 patients, this study aimed to validate the USCD-Mayo risk score in predicting hospital-acquired AKI in an extended multi-center COVID-19 cohort. METHODS: Five hundred seventy-two COVID-19 patients from Wuhan Tongji Hospital Guanggu Branch, Wuhan Leishenshan Hospital, and Wuhan No. Ninth Hospital was enrolled for this study. Patients who developed AKI or reached an outcome of recovery or death during the study period were included. Predictors were evaluated according to data extracted from medical records. RESULTS: Of all patients, a total of 44 (8%) developed AKI. The UCSD-Mayo risk score achieved excellent discrimination in predicting AKI with the C-statistic of 0.88 (95%CI: 0.84-0.91). Next, we determined the UCSD-Mayo risk score had good overall performance (Nagelkerke R2 = 0.32) and calibration in our cohort. Further analysis showed that the UCSD-Mayo risk score performed well in subgroups defined by gender, age, and several chronic comorbidities. However, the discrimination of the UCSD-Mayo risk score in ICU patients and patients with mechanical ventilation was not good which might be resulted from different risk factors of these patients. CONCLUSIONS: We validated the performance of UCSD-Mayo risk score in predicting hospital-acquired AKI in COVID-19 patients was excellent except for patients from ICU or patients with mechanical ventilation.

Physicians' Perspectives of Telemedicine During the COVID-19 Pandemic in China: Qualitative Survey Study.

BACKGROUND: Generalized restriction of movement due to the COVID-19 pandemic, together with unprecedented pressure on the health system, has disrupted routine care for non-COVID-19 patients. Telemedicine should be vigorously promoted to reduce the risk of infections and to offer medical assistance to restricted patients. OBJECTIVE: The purpose of this study was to understand physicians' attitudes toward and perspectives of telemedicine during and after the COVID-19 pandemic, in order to provide support for better implementation of telemedicine. METHODS: We surveyed all physicians (N=148), from October 17 to 25, 2020, who attended the clinical informatics PhD program at West China Medical School, Sichuan University, China. The physicians came from 57 hospitals in 16 provinces (ie, municipalities) across China, 54 of which are 3A-level hospitals, two are 3B-level hospitals, and one is a 2A-level hospital. RESULTS: Among 148 physicians, a survey response rate of 87.2% (129/148) was attained. The average age of the respondents was 35.6 (SD 3.9) years (range 23-48 years) and 67 out of 129 respondents (51.9%) were female. The respondents come from 37 clinical specialties in 55 hospitals located in 14 provinces (ie, municipalities) across Eastern, Central, and Western China. A total of 94.6% (122/129) of respondents' hospitals had adopted a telemedicine system; however, 34.1% (44/129) of the physicians had never used a telemedicine system and only 9.3% (12/129) used one frequently (≥1 time/week). A total of 91.5% (118/129) and 88.4% (114/129) of physicians were willing to use telemedicine during and after the COVID-19 pandemic, respectively. Physicians considered the inability to examine patients in person to be the biggest concern (101/129, 78.3%) and the biggest barrier (76/129, 58.9%) to implementing telemedicine. CONCLUSIONS: Telemedicine is not yet universally available for all health care needs and has not been used frequently by physicians in this study. However, the willingness of physicians to use telemedicine was high. Telemedicine still has many problems to overcome.

Comparing tweet sentiments in megacities using machine learning techniques: In the midst of COVID-19.

COVID-19 was announced by the World Health Organization as a pandemic on March 11, 2020. Not only has COVID-19 struck the economy and public health, but it also has deep influences on people's feelings. Twitter, as an active social media, is a great database where we can investigate people's sentiments during this pandemic. By conducting sentiment analysis on Tweets using advanced machine learning techniques, this study aims to investigate how public sentiments respond to the pandemic from March 2 to May 21, 2020 in New York City, Los Angeles, London, and another six global mega-cities. Results showed that across cities, negative and positive Tweet sentiment clustered around mid-March and early May, respectively. Furthermore, positive sentiments of Tweets from New York City and London were positively correlated with stricter quarantine measures, although this correlation was not significant in Los Angeles. Meanwhile, Tweet sentiments of all three cities did not exhibit a strong correlation with new cases and hospitalization. Last but not least, we provide a qualitative analysis of the reasons behind differences in correlations shown above, along with a discussion of the polarizing effect of public policies on Tweet sentiments. Thus, the results of this study imply that Tweet sentiment is more sensitive to quarantine orders than reported statistics of COVID-19, especially in populous megacities where public transportation is heavily relied upon, which calls for prompt and effective quarantine measures during contagious disease outbreaks.

Understanding Behavioral Intentions Toward COVID-19 Vaccines: Theory-Based Content Analysis of Tweets.

BACKGROUND: Acceptance rates of COVID-19 vaccines have still not reached the required threshold to achieve herd immunity. Understanding why some people are willing to be vaccinated and others are not is a critical step to develop efficient implementation strategies to promote COVID-19 vaccines. OBJECTIVE: We conducted a theory-based content analysis based on the capability, opportunity, motivation-behavior (COM-B) model to characterize the factors influencing behavioral intentions toward COVID-19 vaccines mentioned on the Twitter platform. METHODS: We collected tweets posted in English from November 1-22, 2020, using a combination of relevant keywords and hashtags. After excluding retweets, we randomly selected 5000 tweets for manual coding and content analysis. We performed a content analysis informed by the adapted COM-B model. RESULTS: Of the 5000 COVID-19 vaccine-related tweets that were coded, 4796 (95.9%) were posted by unique users. A total of 97 tweets carried positive behavioral intent, while 182 tweets contained negative behavioral intent. Of these, 28 tweets were mapped to capability factors, 155 tweets were related to motivation, 23 tweets were related to opportunities, and 74 tweets did not contain any useful information about the reasons for their behavioral intentions (κ=0.73). Some tweets mentioned two or more constructs at the same time. Tweets that were mapped to capability (P<.001), motivation (P<.001), and opportunity (P=.03) factors were more likely to indicate negative behavioral intentions. CONCLUSIONS: Most behavioral intentions regarding COVID-19 vaccines were related to the motivation construct. The themes identified in this study could be used to inform theory-based and evidence-based interventions to improve acceptance of COVID-19 vaccines.

Anemia is associated with severe illness in COVID-19: A retrospective cohort study.

Anemia commonly aggravates the severity of respiratory diseases, whereas thus far, few studies have elucidated the impact of anemia on coronavirus disease 2019 (COVID-19). The aim of this study was to evaluate the clinical characteristics of patients with anemia, and to further explore the relationship between anemia and the severity of COVID-19. In this single-center, retrospective, observational study, a total of 222 confirmed patients admitted to Wuhan Ninth Hospital from 1 December 2019 to 20 March 2020 were recruited, including 79 patients with anemia and 143 patients without anemia. Clinical characteristics, laboratory findings, disease progression and prognosis were collected and analyzed. Risk factors associated with the severe illness in COVID-19 were established by univariable and multivariable logistic regression models. In our cohort, compared to patients without anemia, patients with anemia were more likely to have one or more comorbidities and severe COVID-19 illness. More patients demonstrated elevated levels of C-reactive protein (CRP), procalcitonin (PCT) and creatinine in anemia group. Levels of erythrocyte sedimentation rate, D-dimer, myoglobin, T-pro brain natriuretic peptide (T-pro-BNP) and urea nitrogen in patients with anemia were significantly higher than those without. In addition, the proportion of patients with dyspnea, elevated CRP, and PCT was positively associated with the severity of anemia. The odd ratio of anemia related to the severe condition of COVID-19 was 3.47 (95% confidence interval [CI]: 1.02-11.75; P = .046) and 3.77 (95% CI: 1.33-10.71; P = .013) after adjustment for baseline date and laboratory indices, respectively. Anemia is an independent risk factor associated with the severe illness of COVID-19, and healthcare professionals should be more sensitive to the hemoglobin levels of COVID-19 patients on admission. Awareness of anemia as a risk factor for COVID-19 was of great significance.

The management of coronavirus disease 2019 (COVID-19).

In December 2019, a novel coronavirus causing severe acute respiratory disease occurred in Wuhan, China. It is an emerging infectious disease with widespread and rapid infectiousness. The World Health Organization declared the coronavirus outbreak to be a public health emergency of international concern on 31 January 2020. Severe COVID-19 patients should be managed and treated in a critical care unit. Performing a chest X-ray/CT can judge the severity of the disease. The management of COVID-19 patients includes epidemiological risk and patient isolation; treatment entails general supportive care, respiratory support, symptomatic treatment, nutritional support, psychological intervention, etc. The prognosis of the patients depends upon the severity of the disease, the patient's age, the underlying diseases of the patients, and the patient's overall medical condition. The management of COVID-19 should focus on early diagnosis, immediate isolation, general and optimized supportive care, and infection prevention and control.