ABSTRACT
Introduction The current field of research on the impact of COVID-19 on mental health was mostly limited to the evaluation of the first round of the epidemic, few reports focused on the impact of the re-emergence of COVID-19. This study aimed to investigate the mental health literacy and status of residents during the re-outbreak of COVID-19 in China. Methods The basic information sheet, health literacy survey scale, physical health questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7), insomnia severity index (ISI), and Alzheimer dementia 8 (AD8) were applied to evaluate the mental health literacy, mental health status and elderly cognitive function, and χ2 test was applied for analysis of the difference between different groups. Results A total of 2,306 participants were involved in this study, of which 734 people completed the mental health literacy survey. The qualified rate of mental health literacy was 6.4%. The difference is statistically significant. A total of 1,015 people completed the survey of mental health status, the prevalence of depressive symptoms was 8.87%, the monthly income of different families (χ2 = 13.96, P = 0.01), the self-assessed health status (χ2 = 128.56, P < 0.05), the presence or absence of chronic diseases (χ2 = 4.78, P = 0.03), among all which the difference was statistically significant;the prevalence of anxiety symptoms was 3.84%, different regions (χ2 = 12.26, P < 0.05), occupations (χ2 = 11.65, P < 0.05), household monthly income (χ2 = 12.65, P = 0.01), self-rated health status (χ2 = 151.11, P < 0.05), and chronic diseases (χ2 = 7.77, P = 0.01), among all which the differences were statistically significant. The prevalence of insomnia symptoms was 7.98%, different age (χ2 = 18.45, P < 0.05), region (χ2 = 5.11, P = 0.02), monthly household income (χ2 = 12.68 P = 0.01), and self-assessed health status (χ2 = 91.71, P < 0.05), in which there was a statistically significant difference between those with or without chronic diseases (χ2 = 3 3.25, P < 0.05). A total of 557 elderly people over 65 years old completed the cognitive dysfunction screening, in which the prevalence of cognitive dysfunction was 17.41%, and the difference was statistically significant at the different self-assessed health status (χ2 = 96.24, P < 0.05) and with or without chronic diseases (χ2 = 107.09, P < 0.05). Conclusion The mental health literacy and status of residents have not improved significantly during the second outbreak of the epidemic, indicating that under the normalization of epidemic prevention and control, more attention should be paid to the mental health of residents, and targeted health education and psychological intervention should be carried out to avoid relative adverse events.
ABSTRACT
Background: Given that 2019 novel coronavirus (COVID-19) spreads rapidly, it is critical to make rapid and accurate detection of COVID-19 patients towards containment of SARS-CoV-2 virus. At present, COVID-19 patients are mainly identified through viral nuclear acid testing (NAT). However, factors such as time for patients being tested, experience of test operators, and specimen’s preparation, might affect the accuracy of testing results. The purpose of this study was to use different classification and feature selection methods to improve the diagnostic accuracy of COVID-19 patients. Methods: We utilized seven machine learning algorithms for assisting diagnosis of COVID-19 by developing a non-NAT algorithm. In order to reduce the number of input features while maintaining the models’ performance so as to decrease the cost and time consumption, we adopted three algorithms, such as Chi-square test, variance analysis, and feature importance tests to identify the optimal feature sets. Findings: The XGBoost and RF models displayed the best performance for COVID-19 detection, with the highest accuracy rate more than 0·96. The accuracy of RF model was 0·968 when using only ten hematological features and body temperature. Interpretation: Ten blood features and body temperature can fairly accurately determine whether a suspected patient is infected with COVID-19. Our model can improve the diagnostic accuracy of COVID-19 and reduce the spread. Funding: This work is supported by grants from the National Key Research and Development Program of China under Grant 2017YFE0123600, the Natural Science Foundation of China (81873931, 81974382 and 81773104), the Frontier Exploration Program of Huazhong University of Science and Technology (2015TS153), and the Major Scientific and Technological Innovation Projects in Hubei Province (2018ACA136).Declaration of Interests: All the authors stated that the paper had never been published elsewhere, and that there were no competing economic interests.Ethics Approval Statement: The collection, use, and retrospective analysis of chest CT images, CFs and SARS-CoV-2 nucleic acid PCR results of patients were approved by the institutional ethical committees of HUST-UH (IRB ID: [2020] IEC(A001)).