ABSTRACT
Many urban residents have recently lost their jobs due to the COVID-19 pandemic, which has made employment vulnerability in cities attained attention. It is thus important to explore the relationship between urbanization and employment. This study quantitatively analyzes spatiotemporal evolution and data correlation of urbanization and vulnerable employment, and explores the role urbanization plays in vulnerable employment by using historical data on 163 countries in the period 1991-2019 to test the theoretical hypothesis. The results show: It's clearly observed that there is a high correlation between the rate of urbanization and that of vulnerable employment, and the examples of G7 and BRICs are for it. The estimated urbanization yields a negative and statistically significant regression coefficient (-0.168), indicating that urbanization has a negative effect on vulnerable employment. If the urbanization rate increased by 1 %, the rate of vulnerable employment decreased by 0.168 %. The rural-urban sector conversion and changes in employment relationship driven by urbanization account for this. Countries with different income groups or populations have reacted differently to the rise in urbanization. Vulnerable employment in higher-income countries has been more significantly affected by the rise in urbanization, and more populous countries are more sensitive to it as well. These findings provide evidence for how urbanization promotes employment and decent work.
ABSTRACT
Many urban residents have recently lost their jobs due to the COVID-19 pandemic, which has made employment vulnerability in cities attained attention. It is thus important to explore the relationship between urbanization and employment. This study quantitatively analyzes spatiotemporal evolution and data correlation of urbanization and vulnerable employment, and explores the role urbanization plays in vulnerable employment by using historical data on 163 countries in the period 1991–2019 to test the theoretical hypothesis. The results show: It's clearly observed that there is a high correlation between the rate of urbanization and that of vulnerable employment, and the examples of G7 and BRICs are for it. The estimated urbanization yields a negative and statistically significant regression coefficient (−0.168), indicating that urbanization has a negative effect on vulnerable employment. If the urbanization rate increased by 1 %, the rate of vulnerable employment decreased by 0.168 %. The rural–urban sector conversion and changes in employment relationship driven by urbanization account for this. Countries with different income groups or populations have reacted differently to the rise in urbanization. Vulnerable employment in higher-income countries has been more significantly affected by the rise in urbanization, and more populous countries are more sensitive to it as well. These findings provide evidence for how urbanization promotes employment and decent work.
ABSTRACT
BACKGROUND: Prior studies reported that 5 ~ 32% COVID-19 patients were critically ill, a situation that poses great challenge for the management of the patients and ICU resources. We aim to identify independent risk factors to serve as prediction markers for critical illness of SARS-CoV-2 infection. METHODS: Fifty-two critical and 200 non-critical SARS-CoV-2 nucleic acid positive patients hospitalized in 15 hospitals outside Wuhan from January 19 to March 6, 2020 were enrolled in this study. Multivariable logistic regression and LASSO logistic regression were performed to identify independent risk factors for critical illness. RESULTS: Age older than 60 years, dyspnea, respiratory rate > 24 breaths per min, leukocytosis > 9.5 × 109/L, neutrophilia > 6.3 × 109/L, lymphopenia < 1.1 × 109/L, neutrophil-to-lymphocyte ratio > 3.53, fibrinogen > 4 g/L, d-dimer > 0.55 µg/mL, blood urea nitrogen > 7.1 mM, elevated aspartate transaminase, elevated alanine aminotransferase, total bilirubin > 21 µM, and Sequential Organ Failure Assessment (SOFA) score ≥ 2 were identified as risk factors for critical illness. LASSO logistic regression identified the best combination of risk factors as SOFA score, age, dyspnea, and leukocytosis. The Area Under the Receiver-Operator Curve values for the risk factors in predicting critical illness were 0.921 for SOFA score, 0.776 for age, 0.764 for dyspnea, 0.658 for leukocytosis, and 0.960 for the combination of the four risk factors. CONCLUSIONS: Our findings advocate the use of risk factors SOFA score ≥ 2, age > 60, dyspnea and leukocytosis > 9.5 × 109/L on admission, alone or in combination, to determine the optimal management of the patients and health care resources.