No abstract

The current study aims to illustrate male to female suicide rate ratios in the world and explore the correlations between female labour force participation rates (FLPR) and suicide rates of both genders. Further, whether the relationship of FLPR and suicide rates vary according to the human capabilities of a given country are examined. Using suicide data obtained from the World Health Organization Statistical Information System, suicide gender ratios of 70 countries are illustrated. Based on the level of Human Development Index (HDI) and FLPR, the Bayesian Information Criteria (BIC) was used to determine the optimal number of clusters of those countries. Graphic illustrations of FLPR and gender-specific suicide rates, stratified by each cluster were presented, and Pearson's correlation coefficients calculated. Three clusters are identified, there was no correlation between FLPR and suicide rates in the first cluster where both the HDI and FLPR were the highest (Male: r = 0.29, P = 0.45; Female: r = 0.01, P = 0.97); whereas in Cluster 2, higher level of FLPR corresponded to lower suicide rates in both genders, although the statistical significance was only found in females (Male: r = -0.32, P = 0.15; Female: r = -0.48, P = 0.03). In Cluster 3 countries where HDI/FLPR were relatively lower, increased FLPR was associated with higher suicide rates for both genders (Male: r = 0.32, P = 0.04; Female: r = 0.32, P = 0.05). The relationship between egalitarian gender norms and suicide rates varies according to national context. A greater egalitarian gender norms may benefit both genders, but more so for women in countries equipped with better human capabilities. Although the beneficial effect may reach a plateau in countries with the highest HDI/FLPR; whereas in countries with relatively lower HDI/FLPR, increased FLPR were associated with higher suicide rates.

Background: The detection of large vessel occlusion (LVO) plays a critical role in the diagnosis and treatment of acute ischemic stroke (AIS). Identifying LVO in the pre-hospital setting or early stage of hospitalization would increase the patients' chance of receiving appropriate reperfusion therapy and thereby improve neurological recovery.

Background This is the first study on prognostication in an entire cohort of laboratory-confirmed COVID-19 patients in the city of Hong Kong. Prognostic tool is essential in the contingency response for the next wave of outbreak. This study aims to develop prognostic models to predict COVID-19 patients’ clinical outcome on day 1 and day 5 of hospital admission. Methods We did a retrospective analysis of a complete cohort of 1037 COVID-19 laboratory-confirmed patients in Hong Kong as of 30 April 2020, who were admitted to 16 public hospitals with their data sourced from an integrated electronic health records system. It covered demographic information, chronic disease(s) history, presenting symptoms as well as the worst clinical condition status, biomarkers’ readings and Ct value of PCR tests on Day-1 and Day-5 of admission. The study subjects were randomly split into training and testing datasets in a 8:2 ratio. Extreme Gradient Boosting (XGBoost) model was used to classify the training data into three disease severity groups on Day-1 and Day-5. Results The 1037 patients had a mean age of 37.8 (SD ± 17.8), 53.8% of them were male. They were grouped under three disease outcome: 4.8% critical/serious, 46.8% stable and 48.4% satisfactory. Under the full models, 30 indicators on Day-1 and Day-5 were used to predict the patients’ disease outcome and achieved an accuracy rate of 92.3% and 99.5%. With a trade-off between practical application and predictive accuracy, the full models were reduced into simpler models with seven common specific predictors, including the worst clinical condition status (4-level), age group, and five biomarkers, namely, CRP, LDH, platelet, neutrophil/lymphocyte ratio and albumin/globulin ratio. Day-1 model’s accuracy rate, macro-/micro-averaged sensitivity and specificity were 91.3%, 84.9%/91.3% and 96.0%/95.7% respectively, as compared to 94.2%, 95.9%/94.2% and 97.8%/97.1% under Day-5 model. Conclusions Both Day-1 and Day-5 models can accurately predict the disease severity. Relevant clinical management could be planned according to the predicted patients’ outcome. The model is transformed into a simple online calculator to provide convenient clinical reference tools at the point of care, with an aim to inform clinical decision on triage and step-down care.