Background: Parkinson’s disease is a disabling degenerative disease of the central nervous system that occurs mainly in elderly people. The changes in the incidence and mortality of Parkinson’s disease at the national level in China over the past three decades have not been fully explored.Methods: Research data were obtained from the Global Burden of Disease 2019 study. The trends of crude and age-standardized incidence and mortality rates by gender of Parkinson’s disease in China were analyzed with the age-period-cohort model and the Joinpoint regression analysis. The effects of age, time period, and birth cohort on the incidence and mortality of Parkinson’s disease were estimated. The gender- and age-specific incidence and mortality rates of Parkinson’s disease from 2020 to 2030 were projected using the Bayesian age-period-cohort model with integrated nested Laplace approximations.Results: From 1990 to 2019, the annual percentage change of the age-standardized incidence rate was 0.8% (95% CI: 0.7%–0.8%) for males and 0.2% (95% CI, 0.2–0.2%) for females. And the age-standardized mortality rate for males was 2.9% (95% CI: 2.6%–3.2%) and 1.8% (95% CI: 1.5%–2.1%) for females. The results of the age-period-cohort analysis suggested that the risk and burden of Parkinson’s disease continued to increase for the last several decades. Projection analysis suggested that the overall Parkinson’s disease incidence will continue to increase for the next decades. It was projected that China would have 4.787 million Parkinson’s patients by the year 2030, however, the mortality of Parkinson’s disease for both genders in China may keep decreasing.Conclusion: Though the mortality risk may decrease, Parkinson’s disease continues to become more common for both genders in China, especially in the senior-aged population. The burden associated with Parkinson’s disease would continue to grow. Urgent interventions should be implemented to reduce the burden of Parkinson’s disease in China.

Colorectal cancer is among the leading causes of cancer worldwide. Processed meat was known to be positively associated with a higher risk of gastrointestinal cancer. This study focused on the long-time trends of colorectal cancer mortality attributable to high processed meat intake in China from 1990 to 2019 and the projection for the next decade based on data obtained from the Global Burden of Disease 2019 study. We used an age-period-cohort model to fit the long-time trend. The joinpoint model was conducted to estimate the average and annual change of the attributable mortality. The Bayesian age-period-cohort model was used to project the crude attributable mortality from 2020 to 2030. An upward trend in colorectal cancer mortality attributable to high processed meat intake was observed for both sexes in China from 1990 to 2019, with an overall net drift of 4.009% for males and 2.491% for females per year. Projection analysis suggested that the burden of colorectal cancer incidence and mortality would still be high. Our findings suggested that colorectal cancer death attributable to high processed meat intake is still high in China, and elderly males were at higher risk. Gradually decreasing the intake of processed meat could be an effective way to reduce colorectal cancer mortality.

Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599–0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients.