Projected changes and burden of type 2 diabetes in China from 1990 to 2021: an analysis of GBD 2021 data - Junhao MA; Guangqiang MIAO; Guixiao WANG; Shen LI; Liping YANG (Zhongguo gonggong weisheng)
ObjectiveTo analyze the disease burden and trends of type 2 diabetes mellitus (T2DM) in China from 1990 to 2021, and to predict the T2DM disease burden from 2022 to 2026, providing data support and a scientific basis for the development and evaluation of diabetes prevention and control strategies. MethodsData on the disease burden of T2DM in China were obtained from the Global Burden of Disease Study 2021 database. The trends in the prevalence, mortality, and disability-adjusted life years (DALYs) rates of T2DM in China from 1990 to 2021 were analyzed. Autoregressive integrated moving average (ARIMA) models, neural network autoregression (NNAR) models, and combined ARIMA-NNAR models for the prevalence, mortality, and DALYs rates of T2DM in China were established based on data from 1990 to 2016 (training set). Model evaluation was performed using data from 2017 to 2021 (test set). The mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) were used to compare the fitting and prediction performance of the models, and the best model was selected to predict the disease burden of T2DM in China from 2022 to 2026. ResultsBy calculating the rate of change of T2DM in China from 1990 to 2021, the prevalence, mortality, and DALYs rates of T2DM in China in 2021 showed a significant upward trend compared with 1990, and the disease burden was higher in males than in females. The dynamic trends of the predicted values of the ARIMA model, NNAR model, and combined ARIMA-NNAR model were basically consistent with the actual situation, but the values of each evaluation index of the combined prediction model showed that the prediction effect was better than that of the single prediction model. The prevalence, mortality, and DALYs rates of T2DM in China from 2022 to 2026 predicted by the ARIMA-NNAR model were 8 353.71/105, 8 565.00/105, 8 776.30/105, 8 987.60/105, and 9 198.90/105; 12.55/105, 12.78/105, 12.99/105, 13.20/105, and 13.41/105; and 826.79/105, 846.22/105, 864.49/105, 881.86/105, and 898.54/105, respectively. ConclusionsThe combined ARIMA-NNAR model has a good effect in predicting the disease burden of T2DM in China and can provide a reference for short-term prediction of the T2DM disease burden. The prevalence, mortality, and DALYs rates of T2DM in China will continue to increase from 2022 to 2026, indicating that the disease burden of T2DM in China will continue to increase, and the situation of disease prevention and control remains severe.