Objective To analyze the incidence and mortality of acute viral hepatitis in China, project its trends from 2022 to 2030, and provide valuable insights for the prevention and control of viral hepatitis. Methods The incidence and mortality rates of acute viral hepatitis in China from 1990 to 2021 were extracted from the Global Burden of Disease 2021 database. The change rates and the estimated annual percentage change (EAPC) for each indicator were calculated. Additionally, an autoregressive integrated moving average (ARIMA) model was used to project the incidence and mortality of acute viral hepatitis in China from 2022 to 2030. Results From 1990 to 2021, the incidence rates of acute hepatitis A (AHA), acute hepatitis B (AHB), acute hepatitis C (AHC), and acute hepatitis E (AHE) in China all showed a declining trend (EAPC=−1.980%, −2.664%, −2.078%, −1.686%; P<0.05), with a particularly marked decrease in mortality (EAPC=−11.662%, −7.411%, −12.541%, −7.504%; P<0.05). According to ARIMA model projections, the incidence rates of AHA and AHB were expected to continue declining from 2022 to 2030, while the incidence rates of AHC and AHE were expected to rise. In 2030, the projected incidence rates of AHA, AHB, AHC, and AHE were 890.425/100000, 824.158/100000, 59.202/100000, and 300.377/100000, respectively. The mortality rates of AHA, AHC, and AHE were projected to remain stable from 2022 to 2030, while the mortality rate of AHB was expected to decline. In 2030, the projected mortality rates of AHA, AHB, AHC, and AHE were 0.002/100000, 0/100000, 0.004/100000, and 0.011/100000, respectively. Conclusions From 1990 to 2021, the overall incidence and mortality of acute viral hepatitis in China showed a downward trend. However, the incidence rates of AHC and AHE may present an upward trend in the future, which suggests that the government and relevant health authorities should adjust their prevention and control strategies in a timely manner.
Objective To analyze the risk factors of type 2 diabetes mellitus and establish BP neural network model for screening of type 2 diabetes mellitus based on particle swarm optimization (PSO) algorithm. Methods Inpatients with type 2 diabetes mellitus in the Department of Endocrinology of the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between July 2021 and August 2022 were selected as the case group and healthy people in the Health Management Center of the Affiliated Hospital of Guangdong Medical University as the control group. Basic information and physical and laboratory examination indicators were collected for comparative analysis. PSO-BP neural network model, BP neural network model and logistic regression models were established using MATLAB R2021b software and the optimal screening model of type 2 diabetes mellitus was selected. Based on the optimal model, the mean impact value algorithm was used to screen the risk factors of type 2 diabetes mellitus. Results A total of 1 053 patients were included in the case group and 914 healthy peoples in the control group. Except for type of salt, family history of comorbidities, body mass index, total cholesterol, low density lipoprotein cholesterol and staple food intake (P>0.05), the other indexes showed significant differences between the two groups. The performance of the PSO-BP neural network model outperformed the BP neural network model and the logistic regression model. Based on PSO-BP neural network model, the mean impact value algorithm showed that the risk factors for type 2 diabetes mellitus were fasting blood glucose , heart rate, age , waist-arm ratio and marital status , and the protective factors for type 2 diabetes mellitus were high density lipoprotein cholestero, vegetable intake, residence, education level, fruit intake and meat intake. Conclusions There are many influencing factors of type 2 diabetes mellitus. Focus should be placed on high-risk groups and regular disease screening should be carried out to reduce the risk of type 2 diabetes. The screening model of PSO-BP neural network performs the best, and it can be extended to the early screening and diagnosis of other diseases in the future.