Objective To compare the accuracy of the seasonal time series decomposition method and autoregressive integrated moving average (ARIMA) in the prediction of incidence of tuberculosis(TB) in order to facilitate early-warning.Methods The seasonal decomposition model and ARIMA model were constructed by SPSS20.0 software based on time series of monthly TB incidence between January 2005 and December 2014 in Urumqi,China.The obtained models were used to forecast the monthly incidence in 2015 and compared with the actual incidence respectively.Results Between 2005 and 2014,the incidence of TB was higher during March,April and May in Urumqi.A linear fitting model and a cubic curve fitting model were constructed by the time series seasonal decomposition method.The mean absolute percentage error (MAPE) of each predicted monthly incidence in 2015 was 18.75% and 92.25%,respectively.The predicted values of the linear model were lower than actual values and the predicted values of the cubic curve model were higher than actual values.An ARIMA (2,1,1) (1,1,0)12 fitting model was established by ARIMA method.The MAPE of each predicted monthly incidence in 2015 was 9.46% and there were no significant differences between the predicted and actual values.Conclusion The ARIMA method is better than the seasonal decomposition method for predicting the monthly incidence of TB in Urumqi.

Objective: To analyze the association between recombinant solute carrier family 11, member 1 ( SLC11A1 ) rs17235409 polymorphism and treatment failure of pulmonary tuberculosis, so as to provide the basis for the prevention and treatment of pulmonary tuberculosis. Methods: The patients with pulmonary tuberculosis registered for treatment at the Urumqi Center for Disease Control and Prevention in 2019 was recruited and collected demographic, clinical and treatment information from National Infectious Diseases Reporting System. The polymorphism of SLC11A1 rs17235409 was detected by multiple ligase chain reaction and Hardy-Weinberg balance test was performed. The multivariate logistic regression analysis was conducted for the association between rs17235409 and the treatment outcome of tuberculosis. Results: A total of 731 cases of pulmonary tuberculosis patients were enrolled, and 37 cases failed, with a failure rate of 5.06%. The failure rate of the patients with G/A was 8.55%, with G/G was 4.23%. The results of multivariate logistic regression analysis showed that the patients with G/A were more likely to fail in the treatment than those with G/G ( OR=2.213, 95%CI: 1.041-4.702 ). The males with G/A were more likely to fail in the treatment than those with G/G ( OR=2.547, 95%CI: 1.021-6.356 ). Conclusion The rs17235409 polymorphism of SLC11A1 is associated with the failure of tuberculosis treatment, and the patients with G/A are more likely to fail.

Objective: To investigate the delay in identification, healthcare-seeking, and definitive diagnosis of tuberculosis among students in Urumqi City from 2010 to 2019, and to identify the influencing factors, so as to provide insights into tuberculosis control among students. Methods: The demographic and diagnosis data of tuberculosis patients in Urumqi City from 2010 to 2019 were captured from the Tuberculosis Information Management System of Chinese Disease Control and Prevention Information System. The delay in identification, healthcare-seeking and definitive diagnosis of tuberculosis was analyzed among students, and the factors affecting the delay in identification, healthcare-seeking and definitive diagnosis of tuberculosis were identified using a multivariable logistic regression model. Results: A total of 996 tuberculosis cases were identified among students in Urumqi City from 2010 to 2019. There were 702 students with delay in identification of tuberculosis (70.48%), 500 students with delay in healthcare-seeking (55.22%) and 534 students with delay in definitive diagnosis (53.61%). Multivariable logistic regression analysis identified active identification (OR=0.116, 95%CI: 0.032-0.420) as a factor affecting delay in identification of tuberculosis, women (OR=1.424, 95%CI: 1.104-1.836), non-local household registration (OR=1.311, 95%CI: 1.016-1.694) and active identification (OR=0.232, 95%CI: 0.064-0.848) as factors affecting delay in healthcare-seeking, and active identification (OR=0.143, 95%CI: 0.032-0.644) as a factor affecting delay in definitive diagnosis of tuberculosis among students. Conclusions There is a high proportion of delay in identification, healthcare-seeking and definitive diagnosis of tuberculosis among students in Urumqi City from 2010 to 2019, and female and non-locally household-registered students were at a high risk of delay in healthcare-seeking for tuberculosis. Active detection and screening of tuberculosis should be reinforced.