Abstract
INTRODUCTION: This study aimed to evaluate the epidemiological trends and spatial-temporal distribution of hepatitis C virus (HCV) in mainland China. METHODS: HCV monthly incidence surveillance data from 2004 to 2018 was mainly available from the Public Health Sciences Data Center of China. Five models (Bayesian age-period-cohort, BAPC; Auto-regressive integrated moving average, Auto_arima; Exponential smoothing, ETS; Prophet; Earth) were used to forecast the incidence. Temporal-spatial scanning analysis was conducted to visualize the grading of hepatitis C incidence in mainland China. RESULTS: This study observed 180 months of data from January 2004 and December 2018. A total of 2,278,280 hepatitis C cases and 1771 deaths were reported, with an average annual reported incidence rate of 11.24/100,000. The BAPC model indicated that the indexes (mean absolute error, MAE; mean absolute percentage error, MAPE; mean squared error, MSE; root mean square error, RMSE) in the BAPC model were better than those of the other models (4.33 × 10(- 6), 0.03, 2.34 × 10(- 11), 4.84 × 10(- 6)), and annual reported HCV incidence in mainland China would remain at a high level of 17.92/100,000 until 2030. Spatial-temporal aggregation analysis indicated that the time range of the first-class aggregation area was from January 1, 2012 to December 31, 2018, and the aggregation area was in Xinjiang, Qinghai, Gansu, Tibet, Ningxia, Inner Mongolia, Sichuan, Shaanxi, Shanxi, Chongqing, Hebei, Beijing, Henan and Yunnan provinces (relative risk, RR = 2.15, log-likelihood ratio, LLR = 129946.01, P < 0.001). CONCLUSIONS: In summary, this study showed that the incidence of HCV in mainland China continued to be high, and BAPC model suggested that this trend will continue until at least 2030. In order to effectively control the HCV epidemic, it was necessary to strengthen blood safety management, expand screening for high-risk population, improve public awareness of HCV, and provide timely and effective antiviral treatment in potentially high-risk areas.