Trend in the incidence of hepatitis A in mainland China from 2004 to 2017: a joinpoint regression analysis.

BACKGROUND: China has experienced a continuous decreasing trend in the incidence of hepatitis A in recent years. Temporal trend analyses are helpful in exploring the reasons for the changing trend. Thus, this study aims to analyse the incidence trend of viral hepatitis A by region and age group in mainland China from 2004 to 2017 to evaluate the effectiveness of prevention and control measures. METHODS: Data on hepatitis A and population information were collected and analysed with a joinpoint regression model. Annual percentage changes (APCs) and average annual percentage changes (AAPCs) were estimated for the whole country and for each region and age group. RESULTS: From 2004 to 2017, the seasonality and periodicity of hepatitis A case numbers were obvious before 2008 but gradually diminished from 2008 to 2011 and disappeared from 2012-2017. The national incidence of hepatitis A (AAPC = - 12.1%) and the incidence rates for regions and age groups showed decreasing trends, with differences in the joinpoints and segments. Regarding regions, the hepatitis A incidence in the western region was always the highest among all regions, while a nonsignificant rebound was observed in the northeastern region from 2011 to 2017 (APC = 14.2%). Regarding age groups, the hepatitis A incidence showed the fastest decrease among children (AAPC = - 15.3%) and the slowest decrease among elderly individuals (AAPC = - 6.6%). Among all segments, the hepatitis A incidence among children had the largest APC value in 2007-2017, at - 20.4%. CONCLUSION: The national annual incidence of hepatitis A continually declined from 2004 to 2017 and the gaps in hepatitis A incidence rates across different regions and age groups were greatly narrowed. Comprehensive hepatitis A prevention and control strategies, including the use of routine vaccination during childhood in mainland China, especially the implementation of the national Expanded Program on Immunization (EPI) in 2008, resulted in substantial progress from 2004 to 2017. However, gaps remain. Regular monitoring and analysis of hepatitis A epidemic data and prompt adjustment of hepatitis A prevention and control strategies focusing on children, elderly individuals and those living in certain regions are recommended.

Predicting coronary heart disease in Chinese diabetics using machine learning.

Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients complicated with CHD for understanding the occurrence of diabetes/CHD comorbidity. In this study, by analyzing the risk of CHD in more than 300,000 diabetes patients in southwest China, an artificial intelligence (AI) model was proposed to predict the risk of diabetes/CHD comorbidity. Firstly, we statistically analyzed the distribution of four types of features (basic demographic information, laboratory indicators, medical examination, and questionnaire) in comorbidities, and evaluated the predictive performance of three traditional machine learning methods (eXtreme Gradient Boosting, Random Forest, and Logistic regression). In addition, we have identified nine important features, including age, WHtR, BMI, stroke, smoking, chronic lung disease, drinking and MSP. Finally, the model produced an area under the receiver operating characteristic curve (AUC) of 0.701 on the test samples. These findings can provide personalized guidance for early CHD warning for diabetic populations.

A gender specific risk assessment of coronary heart disease based on physical examination data.

Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its prevention and management. Physical examination data has the advantages of wide coverage, large capacity, and easy collection. Therefore, here we report a gender-specific cascading system for risk assessment of CHD based on physical examination data. The dataset consists of 39,538 CHD patients and 640,465 healthy individuals from the Luzhou Health Commission in Sichuan, China. Fifty physical examination characteristics were considered, and after feature screening, ten risk factors were identified. To facilitate large-scale CHD risk screening, a CHD risk model was developed using a fully connected network (FCN). For males, the model achieves AUCs of 0.8671 and 0.8659, respectively on the independent test set and the external validation set. For females, the AUCs of the model are 0.8991 and 0.9006, respectively on the independent test set and the external validation set. Furthermore, to enhance the convenience and flexibility of the model in clinical and real-life scenarios, we established a CHD risk scorecard base on logistic regression (LR). The results show that, for both males and females, the AUCs of the scorecard on the independent test set and the external verification set are only slightly lower (<0.05) than those of the corresponding prediction model, indicating that the scorecard construction does not result in a significant loss of information. To promote CHD personal lifestyle management, an online CHD risk assessment system has been established, which can be freely accessed at http://lin-group.cn/server/CHD/index.html .