Spatiotemporal characteristics and the epidemiology of tuberculosis from 2004 to 2017 in China by the nationwide surveillance system

• Published in: BMC Public Health 2020
• Main Authors: Zuo, Zhongbao, Wang, Miaochan, Cui, Huaizhong, Wang, Ying, Wu, Jing, Jianjiang Qi, Pan, Kenv, Sui, Dongming, Liu, Pengtao, Xu, Aifang
• Format: Text Resource
• Language: English
• Subjects: Epidemiology; Provinces; Regression analysis; Time series; Tuberculosis
• DOI: 10.21203/rs.3.rs-17053/v3

Background China has always been one of the countries with the most serious Tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of Tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model. Methods The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the spatial autocorrelation. The relative importance component of TB was detected by the multivariate time series model. Results We included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model (P=0.0001) with an annual average percent change (AAPC) of -3.3 (95% CI: -4.3 to -2.2, P70 cases per 100,000) were influenced by the autoregressive component for the past fourteen years. Conclusion In a word, China still has a high TB incidence. However, the incidence ra