Contribution of psychological immunity dimensions in predicting psychological flow during coronavirus crisis among health workers in Kuwait

This study was conducted to evaluate the contribution of Psychological Immunity dimensions in predicting Psychological Flow, and the impact of two demographic factors among health workers in Kuwait. This study was performed on random sample consisted of 90 doctor and nurses were chosen from three coronavirus quarries in Kuwait. Psychological Immunity, and Psychological Flow scales were developed, validated and used current study. Psychological Immunity and psychological Flow are found to be at high levels. Furthermore, all Psychological Immunity dimensions are found to be predictors of Psychological Flow, especially optimism, and self-confidence which were found as the most important predictors. None of the demographic factors was found to be a significant predictor of the Psychological Immunity and Psychological Flow among health workers. The study suggests to stimulate awareness in health sector associations about the importance of optimism and self-confidence in protecting mental health during difficult situations.

Clinical outcomes of cancer patients with COVID-19: A systematic review and metaanalysis

Background: Patients with coronavirus disease 2019 (COVID-19) and cancer have worse clinical outcomes compared to those without cancer. Primary studies have examined this population, but most had small sample sizes and conflicting results. Prior meta-analyses exclude most US and European data or only examine mortality. The present meta-analysis evaluates the prevalence of several clinical outcomes in cancer patients with COVID-19, including new emerging data from Europe and the US. Methods: A systematic search of PubMED, medRxiv, JMIR and Embase by two independent investigators included peer-reviewed papers and preprints up to July 8, 2020. The primary outcome was mortality. Other outcomes were ICU and non-ICU admission, mild, moderate and severe complications, ARDS, invasive ventilation, stable, and clinically improved rates. Study quality was assessed through the Newcastle-Ottawa scale. Random effects model was used to derive prevalence rates, their 95% confidence intervals (CI) and 95% prediction intervals (PI). Results: Thirty-four studies (N = 4,371) were included in the analysis. The mortality prevalence rate was 25.2% (95% CI: 21.1-29.7;95% PI: 9.8-51.1;I2= 85.4), with 11.9% ICU admissions (95% CI: 9.2-15.4;95% PI: 4.3- 28.9;I2= 77.8) and 25.2% clinically stable (95% CI: 21.1-29.7;95% PI: 9.8-51.1;I2= 85.4). Furthermore, 42.5% developed severe complications (95% CI: 30.4-55.7;95% PI: 8.2- 85.9;I2= 94.3), with 22.7% developing ARDS (95% CI: 15.4-32.2;95% PI: 5.8-58.6;I2= 82.4), and 11.3% needing invasive ventilation (95% CI: 6.7-18.4;95% PI: 2.3-41.1;I2= 79.8). Post-follow up, 49% clinically improved (95% CI: 35.6-62.6;95% PI: 9.8-89.4;I2= 92.5). All outcomes had large I2 , suggesting high levels of heterogeneity among studies, and wide PIs indicating high variability within outcomes. Despite this variability, the mortality rate in cancer patients with COVID-19, even at the lower end of the PI (9.8%), is higher than the 2% mortality rate of the non-cancer with COVID-19 population, but not as high as what other meta-analyses conclude, which is around 25%. Conclusions: Patients with cancer who develop COVID-19 have a higher probability of mortality compared to the general population with COVID-19, but possibly not as high as previous studies have shown. A large proportion of them developed severe complications, but a larger proportion recovered. Prevalence of mortality and other outcomes published in prior meta-analyses did not report prediction intervals, which compromises the clinical utilization of such results.