Zhang Ruixin, Mi Hongfei, He Tingjuan, Ren Shuhao, Zhang Renyan, Xu Liansheng, Wang Mingzhai, Su Chenghao
School of Public Health, Xiamen University, Xiamen City, Fujian Province, China.
Department of Public Health, Zhongshan Hospital (Xiamen), Fudan University, Xiamen City, Fujian Province, China.
Infect Dis Model. 2024 Aug 6;9(4):1276-1288. doi: 10.1016/j.idm.2024.08.001. eCollection 2024 Dec.
This study aims to analyze the trend of Hepatitis B incidence in Xiamen City from 2004 to 2022, and to select the best-performing model for predicting the number of Hepatitis B cases from 2023 to 2027.
Data were obtained from the China Information System for Disease Control and Prevention (CISDCP). The Joinpoint Regression Model analyzed temporal trends, while the Age-Period-Cohort (APC) model assessed the effects of age, period, and cohort on hepatitis B incidence rates. We also compared the predictive performance of the Neural Network Autoregressive (NNAR) Model, Bayesian Structural Time Series (BSTS) Model, Prophet, Exponential Smoothing (ETS) Model, Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Hybrid Model, selecting the model with the highest performance to forecast the number of hepatitis B cases for the next five years.
Hepatitis B incidence rates in Xiamen from 2004 to 2022 showed an overall declining trend, with rates higher in men than in women. Higher incidence rates were observed in adults, particularly in the 30-39 age group. Moreover, the period and cohort effects on incidence showed a declining trend. Furthermore, in the best-performing NNAR(10, 1, 6)[12] model, the number of new cases is predicted to be 4271 in 2023, increasing to 5314 by 2027.
Hepatitis B remains a significant issue in Xiamen, necessitating further optimization of hepatitis B prevention and control measures. Moreover, targeted interventions are essential for adults with higher incidence rates.
本研究旨在分析2004年至2022年厦门市乙型肝炎发病率的趋势，并选择预测2023年至2027年乙型肝炎病例数的最佳模型。
数据来自中国疾病预防控制信息系统（CISDCP）。Joinpoint回归模型分析时间趋势，年龄-时期-队列（APC）模型评估年龄、时期和队列对乙型肝炎发病率的影响。我们还比较了神经网络自回归（NNAR）模型、贝叶斯结构时间序列（BSTS）模型、Prophet、指数平滑（ETS）模型、季节性自回归积分滑动平均（SARIMA）模型和混合模型的预测性能，选择性能最高的模型预测未来五年的乙型肝炎病例数。
2004年至2022年厦门市乙型肝炎发病率总体呈下降趋势，男性发病率高于女性。成年人发病率较高，尤其是30-39岁年龄组。此外，时期和队列对发病率的影响呈下降趋势。此外，在性能最佳的NNAR(10, 1, 6)[12]模型中，预计2023年新发病例数为4271例，到2027年增至5314例。
乙型肝炎在厦门仍然是一个重要问题，需要进一步优化乙型肝炎防控措施。此外，针对发病率较高的成年人进行有针对性的干预至关重要。
