Importance of oil shocks and the GCC macroeconomy: A structural VAR analysis

This study is an endeavour to analyse the influence of oil price shocks on the macroeconomy of the Gulf Cooperation Council (GCC) member countries (Bahrain, Kuwait, KSA, Oman, Qatar and UAE). By employing a structural Vector auto-regression (SVAR) model for period 1980–2016, our key findings suggest that there are significant positive effects of oil price shocks on the GDP, inflation and trade balance of those countries. The findings, however, show substantial heterogeneities in the responses of the GCC members to oil shocks, which suggests the presence of idiosyncrasies in the underlying structure of their economies and differences in the degree to which these economies are dependent on oil revenues. In terms of inflation, there are also major differences in the intensity of the impact of oil shocks on the overall price, which implies that the GCC monetary policies might face a different genre of challenges to attain price stability in the face of those shocks. Our findings have profound policy implications in terms of diversifying the economies of the GCC countries and efforts to decrease the sole dependence on oil revenues

Development of a Prediction Model for COVID-19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases: Results From the Global Rheumatology Alliance Registry

OBJECTIVE: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. METHODS: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. RESULTS: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. CONCLUSION: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression