Relationship between health literacy and COVID-19 knowledge: A cross-sectional study.

Background: Health literacy (HL) is a protective factor for some chronic diseases. However, its role in the Coronavirus Disease 2019 (COVID-19) pandemic has not been clarified. This study aims to explore the association between HL and COVID-19 knowledge among residents in Ningbo. Methods: A total of 6,336 residents aged 15-69 years in Ningbo were selected by multi-stage stratified random sampling method. The "Health Literacy Questionnaire of Chinese Citizens (2020)" was used to evaluate the relationship between COVID-19 knowledge and HL. Chi-square test, Mann-Whitney U test and logistic regression were used to analyze the data. Results: The HL and COVID-19 knowledge levels of Ningbo residents were 24.8% and 15.7%, respectively. After adjusting for confounding factors, people with adequate HL were the more likely to have adequate COVID-19 knowledge compared with those with limited HL (OR = 3.473, 95% CI = 2.974-4.057, P <0.001). Compared with the limited HL group, the adequate HL group had a higher rate of COVID-19 knowledge, a more positive attitude, and a more active behavior. Conclusion: COVID-19 knowledge is significantly associated with HL. Improving HL may influence people's knowledge about COVID-19, thereby changing people's behaviors, and finally combating the pandemic.

Prevalence and risk factors of depression and anxiety among Chinese adults who received SARS-CoV-2 vaccine - A cross-sectional survey.

BACKGROUND: Direct data reflecting the psychological problems during the nationwide SARS-CoV-2 vaccination campaign are scarce in China. The aim of this study was to assess the prevalence of depression and anxiety and investigate the associated risk factors after vaccination against SARS-CoV-2 among Chinese adults. METHODS: We conducted a web-based cross-sectional survey from June to July 2021. A structured questionnaire including the Patient Health Questionnaire-9(PHQ-9) and Generalized Anxiety Disorder-7(GAD-7) was used to investigated depression and anxiety symptoms. After excluding 223 ineligible records, a total of 6984 participants were included in our final analysis. Multivariable logistic regression analysis was used to examined the potential factors associated with depression or anxiety. RESULTS: Our data indicated that the overall prevalence of depression and anxiety was assessed at 19.39 % and 9.74 %, respectively. Participants who had vaccinated the second dose were more likely to have depressive symptoms (20.95 % vs.16.40 %) and anxiety symptoms (10.38 % vs. 8.51 %) than who had vaccinated the first dose. Multivariable logistic regression analysis indicated female gender, being healthcare worker, college or above and planning a pregnancy were all independently linked to depression or anxiety. LIMITATIONS: The present study was based on an online survey. CONCLUSION: The present study confirmed the presence of depression and anxiety among Chinese adults who received SARS-COV-2 vaccine, as well as the potential influencing factors. Additional attention and psychological support should be directed at these high-risk groups during SARS-CoV-2 vaccination campaign.

A Multivariate Model for Predicting the Progress of COVID-19 Using Clinical Data besides Chest CT Scan

Objective. Computed tomography (CT) scan is a method to predict the progression and prognosis of COVID-19. It is not sufficient merely to measure the prognosis of COVID-19 without other clinical methods. The purpose of this study was to investigate the association between the CT scan and clinical laboratory indicators as well as clinical manifestations. Method. A total of 335 patients were enrolled from January 26, 2020, to February 26, 2020, in Shandong province and Huanggang city. Demographic and clinical characteristics, laboratory variables, and the data from the CT scans were collected for analysis. Scatter plot analysis and correlation analysis were used to calculate the relationship between CT evaluation and other indicators. Multivariable linear regression analysis was used to establish a model for diagnostic and prognostic prediction. Age, CRP, LDH, and lymphocyte counts as independent variables were selected to develop a predictive model, and the results from the CT scans to reflect the degree of lung injury were taken as the dependent variable. Result. The median age was 44 years (IQR: 34–56);among them, 188 (56%) were male. Severe patients were older (56 vs. 40, P < 0.001). There were statistically significant differences in lymphocyte counts, platelet counts, C-reactive protein (CRP), lactate dehydrogenase (LDH), procalcitonin (PCT), and creatine kinase (CK) between the general patients and severe patients. We found that, without effective antiviral treatment, mild patients had a 6-day interval from symptom onset to CRP elevation, but in severe patients, CRP started to increase from day 2. Lung injury score from a chest CT scan and incidence of acute respiratory distress syndrome (ARDS) were significantly higher in severe patients than in mild patients. Lung injury score from a chest CT scan was closely correlated with CRP (rs = 0.704, P < 0.01), and they reflected the severity of the disease. The receiver operating curve (ROC) value of the injury score from the chest CT scan was 0.854 (95% CI: 0.808–0.901), and the area under the curve (AUC) value of CRP was 0.823 (95% CI: 0.769–0.878). Conclusion. The results from CRP and chest CT scans were indicators of the severity of COVID-19. Combining patient age, CRP, LDH, and lymphocyte counts, we developed a model that could help to predict lung injury/function of patients with COVID-19. [ABSTRACT FROM AUTHOR] Copyright of Scientific Programming is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

COVID-19-CT-CXR: A Freely Accessible and Weakly Labeled Chest X-Ray and CT Image Collection on COVID-19 From Biomedical Literature.

The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature, including those that report findings on radiographs. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. Because a large portion of figures in COVID-19 articles are not CXR or CT, we designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved deep-learning (DL) performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza, another common infectious respiratory illness that may present similarly to COVID-19, and fine-tuned a baseline deep neural network to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We fine-tuned an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared 15 clinical symptoms and 20 clinical findings of COVID-19 versus those of influenza to demonstrate the disease differences in the scientific publications. Our database is unique, as the figures are retrieved along with relevant text with fine-grained descriptions, and it can be extended easily in the future. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR.

COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature.

The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. We also designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared clinical symptoms and clinical findings of COVID-19 vs. those of influenza to demonstrate the disease differences in the scientific publications. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR.