ABSTRACT
The sudden outbreak and long-term trend of COVID-19 have brought huge attacks and uncertainty to the global economy, forcing countries to introduce various policies frequently to stimulate economic recovery. To realize sustainable development, firms established an environment-friendly economic development model by building a circular supply chain and implementing a green innovation strategy, which is expected to save resources and protect the environment by recycling resources. Based on this background, this study aims to determine the relationship between the uncertainty of economic policy, green innovation strategy, and circular supply chain performance. It divides green innovation strategies into green product innovation, green process innovation, green service innovation, and green logistics innovation to explore their different impacts on the performance of the circular supply chain. Simultaneously, the moderating effect of uncertainty of economic policy between green innovation and the performance of the circular supply chain is explored. Using survey data collected from 308 manufacturing firms in China, we empirically test the theoretical model and proposed hypotheses through the structural equation modeling approach. Our findings demonstrate that green product innovation, green process innovation, green logistics innovation, and green service innovation have a positive impact on the performance of the circular supply chain. Moreover, we also find that, contrary to our expectations, economic policy uncertainty plays a positive role in moderating the relationship between green innovation and circular supply chain performance. We believe that this paper has a clear contribution to the research on green innovation and circular supply chain management. This study provides a new perspective for the research on the integration of green innovation and circular supply chain, deepens firms' understanding of green innovation strategy and circular supply chain, and provides important implications and guidance for manufacturing firms to better manage green innovation and circular supply chain practice as well as the risk of economic policy uncertainty.
ABSTRACT
Purpose: To identify more objectively predictive factors of severe outcome among patients hospitalized for coronavirus disease 2019 (COVID-19). Patients and Methods: A retrospective cohort of 479 hospitalized patients diagnosed with COVID-19 in Hunan Province was selected. The prognostic effects of factors such as age and laboratory indicators were analyzed using the Kaplan-Meier method and Cox proportional hazards model. A prognostic nomogram model was established to predict the progression of patients with COVID-19. Results: A total of 524 patients in Hunan province with COVID-19 from December 2019 to October 2020 were retrospectively recruited. Among them, 479 eligible patients were randomly assigned into the training cohort (n = 383) and validation cohort (n = 96), at a ratio of 8:2. Sixty-eight (17.8%) and 15 (15.6%) patients developed severe COVID-19 after admission in the training cohort and validation cohort, respectively. The differences in baseline characteristics were not statistically significant between the two cohorts with regard to age, sex, and comorbidities (P > 0.05). Multivariable analyses included age, C-reactive protein, fibrinogen, lactic dehydrogenase, neutrophil-to-lymphocyte ratio, urea, albumin-to-globulin ratio, and eosinophil count as predictive factors for patients with progression to severe COVID-19. A nomogram was constructed with sufficient discriminatory power (C index = 0.81), and proper consistency between the prediction and observation, with an area under the ROC curve of 0.81 and 0.86 in the training and validation cohort, respectively. Conclusion: We proposed a simple nomogram for early detection of patients with non-severe COVID-19 but at high risk of progression to severe COVID-19, which could help optimize clinical care and personalized decision-making therapies.