Novel Coronavirus Pandemic Impacts Children and Adolescents' Psychological Well-Being in Heavily Hit Chinese Provinces (preprint)

AbstractsIn light of the novel coronaviruss (COVID-19s) threat to public health worldwide, we sought to elucidate COVID-19s impacts on the mental health of children and adolescents in China. Through online self-report questionnaires, we aimed to discover the psychological effects of the pandemic and its associated risk factors for developing mental health symptoms in young people. We disseminated a mental health survey through online social media, WeChat, and QQ in the five Chinese provinces with the most confirmed cases of COVID-19 during the late stage of the country-wide lockdown. We used a self-made questionnaire that queried children and adolescents aged 6 to 18 on demographic information, psychological status, and other lifestyle and COVID-related variables. A total of 17,740 children and adolescents with valid survey data participated in the study. 10,022 (56.5%), 11,611 (65.5%), 10,697 (60.3%), 6,868 (38.7%), and 6,225 (35.1%) participants presented, respectively, more depressive, anxious, compulsive, inattentive, and sleep-related problems compared to before the outbreak of COVID-19. High school students reported a greater change in depression and anxiety than did middle school and primary school students. Despite the fact that very few children (0.1%) or their family members (0.1%) contracted the virus in this study, the psychological impact of the pandemic was clearly profound. Fathers anxiety appeared to have the strongest influence on a childrens psychological symptoms, explaining about 33% of variation in the childs overall symptoms. Other factors only explained less than 2% of the variance in symptoms once parents anxiety was accounted for. The spread of COVID-19 significantly influenced the psychological state of children and adolescents. It is clear that children and adolescents, particularly older adolescents, need mental health support during the pandemic. The risk factors we uncovered suggest that reducing fathers anxiety is particularly critical to addressing young peoples mental health disorders in this time.

Severity-associated markers and assessment model for predicting the severity of COVID-19: a retrospective study in Hangzhou, China (preprint)

Background: The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19.Methods: 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively.Results: Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values <0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914-0.943) and 0.827 (95% CI, 0.716-0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845-0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score.Conclusions: Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.