Epidemiology of drug-resistant tuberculosis in Chongqing, China: A retrospective observational study from 2010 to 2017.

China is one of the top 30 countries with high multidrug-resistant tuberculosis (MDR-TB) and rifampin-resistant tuberculosis (RR-TB) burden. Chongqing is a southwest city of China with a large rural population. A retrospective observational study has been performed based on routine tuberculosis (TB) surveillance data in Chongqing from 2010 to 2017. The MDR/RR-TB notification rate increased from 0.03 cases per 100,000 population in 2010 to 2.1 cases per 100,000 population in 2017. The extensively drug-resistant TB (XDR-TB) notification rate has increased to 0.09 cases per 100,000 population in 2017. There was a decreasing detection gap between the number of notified MDR/RR-TB cases and the estimate number of MDR/RR-TB cases in new TB cases. The treatment success rate of MDR/RR-TB was 59% (95% confidence interval [CI], 53%-65%) in this period. Despite the progress achieved, the prevalence of MDR/RR-TB was still high facing challenges including detection gaps in new TB cases, the regional disparity, and the high risk for MDR/RR-TB in elderly people and farmers. Sustained government financing and policy support should be guaranteed in the future.

[Applications of multiple seasonal autoregressive integrated moving average (ARIMA) model on predictive incidence of tuberculosis].

OBJECTIVE: To discuss the application of multiple seasonal autoregressive integrated moving average (ARIMA) predictive model of time series and to establish a predictive incidence model of tuberculosis. METHODS: Parameters of the model were estimated using conditional least squares method according to the data of tuberculosis incidence and the averaged population in a district in Chongqing from 1993 to 2004. In a structure determined according to criteria of residual un-correlation and conclusion, ARIMA predictive model was established and the order of model was confirmed by Akaike's Information Criterion (AIC, for short) and Schwartz's Bayesian Information Criterion (SBC or BIC, for short). RESULTS: There were significant differences of the fitted multiple seasonal moving-average coefficients with the nonseasonal and the seasonal moving-average coefficients being 0.84076 and 0.46602 respectively. The estimated variance was 0.088589, AIC = 19.75979, SBC = 23.28219. Autocorrelation check of residuals of model was white-noise residual. ARIMA(0,1,1)(0,1,1)4NOINT seemed to be the most appropriate model by chi2 test. CONCLUSION: The multiple seasonal ARIMA model can be used to forecast for tuberculosis incidence with high prediction and precision in a short-term.