SARS-CoV-Related Pandemic Outbreaks and Mental Disorder Risk.

ABSTRACT: This study aimed to quantify the association between exposure to pandemic outbreaks and psychological health via a comprehensive meta-analysis. Literature retrieval, study selection, and data extraction were completed independently and in duplicate. Effect-size estimates were expressed as odds ratio (OR) with 95% confidence interval (CI). Data from 22 articles, involving 40,900 persons, were meta-analyzed. Overall analyses revealed a significant association of exposing to SARS-CoV-related pandemics with human mental health (OR, 1.32; 95% CI, 1.24-1.40; p < 0.001). Subgroup analyses showed that anxiety (OR, 1.37; 95% CI, 1.19-1.58; p < 0.001), depression (OR, 1.28; 95% CI, 1.15-1.42; p < 0.001), posttraumatic stress (OR, 1.36; 95% CI, 1.17-1.58; p < 0.001), and psychological distress (OR, 1.25; 95% CI, 1.11-1.40; p < 0.001) were all obviously related to pandemic diseases. In the context of infectious disease outbreaks, the mental health of general populations is clearly vulnerable. Therefore, all of us, especially health care workers, need special attention and psychological counseling to overcome pandemic together.

Risk profiles of severe illness in children with COVID-19: a meta-analysis of individual patients.

BACKGROUND: We prepared a meta-analysis on case reports in children with COVID-19, aiming to identify potential risk factors for severe illness and to develop a prediction model for risk assessment. METHODS: Literature retrieval, case report selection, and data extraction were independently completed by two authors. STATA software (version 14.1) and R programming environment (v4.0.2) were used for data handling. RESULTS: This meta-analysis was conducted based on 52 case reports, including 203 children (96 boys) with COVID-19. By severity, 26 (12.94%), 160 (79.60%), and 15 (7.46%) children were diagnosed as asymptomatic, mild/moderate, and severe cases, respectively. After adjusting for age and sex, 11 factors were found to be significantly associated with the risk of severe illness relative to asymptomatic or mild/moderate illness, especially for dyspnea/tachypnea (odds ratio, 95% confidence interval, P: 6.61, 4.12-9.09, <0.001) and abnormal chest X-ray (3.33, 1.84-4.82, <0.001). A nomogram modeling age, comorbidity, cough, dyspnea or tachypnea, CRP, and LDH was developed, and prediction performance was good as reflected by the C-index. CONCLUSIONS: Our findings provide systematic evidence for the contribution of comorbidity, cough, dyspnea or tachypnea, CRP, and LDH, both individually and jointly, to develop severe symptoms in children with asymptomatic or mild/moderate COVID-19. IMPACT: We have identified potential risk factors for severe illness in children with COVID-19. We have developed a prediction model to facilitate risk assessment in children with COVID-19. We found the contribution of five risk factors to develop severe symptoms in children with asymptomatic or mild/moderate COVID-19.

Predicting severe or critical symptoms in hospitalized patients with COVID-19 from Yichang, China.

OBJECTIVES: We aimed to identify potential risk factors for severe or critical coronavirus disease 2019 (COVID-19) and establish a prediction model based on significant factors. METHODS: A total of 370 patients with COVID-19 were consecutively enrolled at The Third People's Hospital of Yichang from January to March 2020. COVID-19 was diagnosed according to the COVID-19 diagnosis and treatment plan released by the National Health and Health Committee of China. Effect-size estimates are summarized as odds ratio (OR) and 95% confidence interval (CI). RESULTS: 326 patients were diagnosed with mild or ordinary COVID-19, and 44 with severe or critical COVID-19. After propensity score matching and statistical adjustment, eight factors were significantly associated with severe or critical COVID-19 (p <0.05) relative to mild or ordinary COVID-19. Due to strong pairwise correlations, only five factors, including diagnostic delay (OR, 95% CI, p: 1.08, 1.02 to 1.17, 0.048), albumin (0.82, 0.75 to 0.91, <0.001), lactate dehydrogenase (1.56, 1.14 to 2.13, 0.011), white blood cell (1.27, 1.08 to 1.50, 0.004), and neutrophil (1.40, 1.16 to 1.70, <0.001), were retained for model construction and performance assessment. The nomogram model based on the five factors had good prediction capability and accuracy (C-index: 90.6%). CONCLUSIONS: Our findings provide evidence for the significant contribution of five independent factors to the risk of severe or critical COVID-19, and their prediction was reinforced in a nomogram model.

Cancer associates with risk and severe events of COVID-19: A systematic review and meta-analysis.

Evidence is mounting to indicate that cancer patients may have more likelihood of having coronavirus disease 2019 (COVID-19) but lack consistency. A robust estimate is urgently needed to convey appropriate information to the society and the public, in the time of ongoing COVID-19 pandemic. We performed a systematic review and meta-analysis through a comprehensive literature search in major databases in English and Chinese, and two investigators conducted publication selection and data extraction independently. A meta-analysis was used to obtain estimates of pooled prevalence of cancer in patients with COVID-19 and determine the association of cancer with severe events, after assessment of potential heterogeneity, publication bias, and correction for the estimates when necessary. Total 38 studies comprising 7094 patients with COVID-9 were included; the pooled prevalence of cancer was estimated at 2.3% (95% confidence limit [CL] [0.018, 0.029]; P < .001) overall and 3.2% (95% CL [0.023, 0.041]; P < .001) in Hubei province; the corresponding estimates were 1.4% and 1.9% after correction for publication bias; cancer was significantly associated with the events of severe cases (odds ratio [OR] = 2.20, 95% CL [1.53, 3.17]; P < .001) and death (OR = 2.97, 95% CL [1.48, 5.96]; P = .002) in patients with COVID-19, there was no significant heterogeneity and a minimal publication bias. We conclude that cancer comorbidity is associated with the risk and severe events of COVID-19; special measures should be taken for individuals with cancer.