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This paper examines the daily return series of five main indexes, including Shanghai Stock Exchange Composite Index(SSE), Shenzhen Stock Exchange Component Index(SZSE), Shanghai Shenzhen 300 Index(SHSE-SZSE300), Small and Medium Enterprise Board Index(SME), and ChiNext Index(ChiNext), in different boards of Chinese stock market from 2000 to 2018 by multifractal detrended fluctuation analysis (MF-DFA). The return series exhibit significant multifractal properties on the whole time scale and ChiNext has the lowest multifractal properties among the five indexes, indicating the highest market efficiency. The multifractal properties of the five indexes are due to both long-range correlation and fat-tail characteristics of non-Gaussian probability density function, and these two factors have different effects on the multifractality of five indexes. The crosscorrelations among different boards of Chinese stock market reflect the internal linkages between different boards. This paper also aims to compare the multifractality degrees of Main-Board, Small and Medium Enterprise Board(SME Board), and Growth Enterprises Market Board(GEM Board) in three sub-samples divided by the 2015 stock market crash and to study its effects on efficiency and risk of these boards in China's stock market in each sub-sample, from the statistical and fractal perspectives, which has theoretical and practical significance in the application of Effective Market Hypothesis (EMH) in China's stock market. The findings of the study may also provide important implications for further study on the dynamic mechanism and efficiency in stock market and help regulators and policymakers effectively control the market risk and achieve more effective resource allocation. • Returns in different boards of Chinese stock market exhibit significant nonlinear multifractal properties. • The whole sample from 2000 to 2018 is divided into three sub-samples according to the 2015 stock market crash. • The efficiencies of Chinese stock market experienced volatilities in sub-samples. • The efficiencies of Chinese stock market are affected differently by the 2015 stock market crash. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to explore the association between the Chinese visceral adiposity index (CVAI) and cardiometabolic multimorbidity in middle-aged and older Chinese adults., Methods: The data used in this study were obtained from a national cohort, the China Health and Retirement Longitudinal Study (CHARLS, 2011-2018 wave). The CVAI was measured using previously validated biomarker estimation formulas, which included sex, age, body mass index, waist circumference, triglycerides, and high-density lipoprotein cholesterol. The presence of two or more of these cardiometabolic diseases (diabetes, heart disease, and stroke) is considered as cardiometabolic multimorbidity. We used Cox proportional hazard regression models to examine the association between CVAI and cardiometabolic multimorbidity, adjusting for a set of covariates. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used to show the strength of the associations. We also conducted a subgroup analysis between age and sex, as well as two sensitivity analyses. Receiver operator characteristic curves (ROC) were used to test the predictive capabilities and cutoff value of the CVAI for cardiometabolic multimorbidity., Results: A total of 9028 participants were included in the final analysis, with a mean age of 59.3 years (standard deviation: 9.3) and women accounting for 53.7% of the sample population. In the fully-adjusted model, compared with participants in the Q1 of CVAI, the Q3 (HR = 2.203, 95% CI = 1.039 - 3.774) and Q4 of CVAI (HR = 3.547, 95% CI = 2.100 - 5.992) were associated with an increased risk of cardiometabolic multimorbidity. There was no evidence of an interaction between the CVAI quartiles and sex or age in association with cardiometabolic multimorbidity ( P > 0.05). The results of both sensitivity analyses suggested that the association between CVAI and cardiometabolic multimorbidity was robust. In addition, the area under ROC and ideal cutoff value for CVAI prediction of cardiometabolic multimorbidity were 0.685 (95% CI = 0.649-0.722) and 121.388., Conclusion: The CVAI is a valid biomarker with good predictive capability for cardiometabolic multimorbidity and can be used by primary healthcare organizations in the future for early warning, prevention, and intervention with regard to cardiometabolic multimorbidity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ye, Zhang, Han, Wang, Lu and Zhang.)

Background: Chickenpox is an old but easily neglected infectious disease. Although chickenpox is preventable by vaccines, vaccine breakthroughs often occur, and the chickenpox epidemic is on the rise. Chickenpox is not included in the list of regulated communicable diseases that must be reported and controlled by public and health departments; therefore, it is crucial to rapidly identify and report varicella outbreaks during the early stages. The Baidu index (BDI) can supplement the traditional surveillance system for infectious diseases, such as brucellosis and dengue, in China. The number of reported chickenpox cases and internet search data also showed a similar trend. BDI can be a useful tool to display the outbreak of infectious diseases., Objective: This study aimed to develop an efficient disease surveillance method that uses BDI to assist in traditional surveillance., Methods: Chickenpox incidence data (weekly from January 2017 to June 2021) reported by the Yunnan Province Center for Disease Control and Prevention were obtained to evaluate the relationship between the incidence of chickenpox and BDI. We applied a support vector machine regression (SVR) model and a multiple regression prediction model with BDI to predict the incidence of chickenpox. In addition, we used the SVR model to predict the number of chickenpox cases from June 2021 to the first week of April 2022., Results: The analysis showed that there was a close correlation between the weekly number of newly diagnosed cases and the BDI. In the search terms we collected, the highest Spearman correlation coefficient was 0.747. Most BDI search terms, such as "chickenpox," "chickenpox treatment," "treatment of chickenpox," "chickenpox symptoms," and "chickenpox virus," trend consistently. Some BDI search terms, such as "chickenpox pictures," "symptoms of chickenpox," "chickenpox vaccine," and "is chickenpox vaccine necessary," appeared earlier than the trend of "chickenpox virus." The 2 models were compared, the SVR model performed better in all the applied measurements: fitting effect, R 2 =0.9108, root mean square error (RMSE)=96.2995, and mean absolute error (MAE)=73.3988; and prediction effect, R 2 =0.548, RMSE=189.1807, and MAE=147.5412. In addition, we applied the SVR model to predict the number of reported cases weekly in Yunnan from June 2021 to April 2022 using the same period of the BDI. The results showed that the fluctuation of the time series from July 2021 to April 2022 was similar to that of the last year and a half with no change in the level of prevention and control., Conclusions: These findings indicated that the BDI in Yunnan Province can predict the incidence of chickenpox in the same period. Thus, the BDI is a useful tool for monitoring the chickenpox epidemic and for complementing traditional monitoring systems., (©Zhaohan Wang, Jun He, Bolin Jin, Lizhi Zhang, Chenyu Han, Meiqi Wang, Hao Wang, Shuqi An, Meifang Zhao, Qing Zhen, Shui Tiejun, Xinyao Zhang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.05.2023.)

The rate of brucellosis, a zoonotic disease, has rapidly increased in humans brucellosis(HB) in recent years. In 1950-2018, a total of 684,380 HB cases (median 2274/year (interquartile range (IQR) 966-8325)) were reported to the National Infectious Disease Surveillance System in mainland China. The incidence of HB peaked in 2014 (4.32/100,000), and then showed a downward trend; we predict that it will maintain a steady downward trend in 2019-2020. Since 2015, the incidence of HB has shown opposite trends in the north and south of China; rates in the north have fallen and rates in the south have increased. In 2004-2018, the most significant increases in incidence of HB were in Yunnan (IQR 0.002-0.463/100,000), Hubei (IQR 0.000-0.338/100,000), and Guangdong (IQR 0.015-0.350/100,000). The areas where HB occurs have little overlap with areas with high per capita GDP in China. The "high-high" clusters of HB are located in northeastern China (Inner Mongolia, Heilongjiang, Jilin, Liaoning, Ningxia, Shanxi, and Gansu), and the "low-low" clusters of HB are located in southern China (Yunnan, Jiangxi, Shanghai, Guangxi, Guangdong, Zhejiang, Guizhou, and Hunan). In recent years, the incidence of HB in China has been controlled to some extent, but the incidence of HB has increased in southern China, and the disease has spread geographically in China from north to south. Further research is needed to address this change and to continue to explore the relationship between the incidence of HB and relevant factors.