Objective:To investigate the trends in detection, treatment, and outcomes of rifampicin-resistant tuberculosis (TB) in Jiangsu Province from 2013 to 2023, assess the effectiveness of control policies and measures for drug-resistant TB, and provide evidence for better control of drug-resistant TB.Methods:Data and indicators related to the screening, diagnosis, treatment, and outcomes of rifampicin-resistant TB in Jiangsu Province from 2013 to 2023 were obtained from the Tuberculosis Management Information System. The Joinpoint regression method was employed to analyze the trends over this period, and annual percent change (APC) and average annual percent change (AAPC) were calculated. A comparative analysis was also conducted to evaluate the changes before and after implementing relevant policies and measures.Results:From 2013 to 2023, the number of registered rifampicin-resistant TB patients in Jiangsu Province showed a consistent upward trend (APC=AAPC=1.45%, P=0.035). The screening rates for drug resistance among new TB patients in high-risk groups and the proportion of molecular biological testing for drug resistance all exhibited increasing trends, with a notable turning point occurring in 2018. The trend of the treatment enrollment rate for rifampicin-resistant TB patients experienced a significant shift in 2020, showing a marked increase from 2013 to 2020 (APC=12.91%, P=0.008). The treatment success rate of rifampicin-resistant TB patients also showed a significant upward trend after a turning point in 2020 (APC=9.94%, P=0.004). Conclusion:From 2013 to 2023, significant progress was seen in preventing and treating rifampicin-resistant TB in Jiangsu Province, with relevant policies and measures proving to be highly effective.

Objective:To investigate the trends in detection, treatment, and outcomes of rifampicin-resistant tuberculosis (TB) in Jiangsu Province from 2013 to 2023, assess the effectiveness of control policies and measures for drug-resistant TB, and provide evidence for better control of drug-resistant TB.Methods:Data and indicators related to the screening, diagnosis, treatment, and outcomes of rifampicin-resistant TB in Jiangsu Province from 2013 to 2023 were obtained from the Tuberculosis Management Information System. The Joinpoint regression method was employed to analyze the trends over this period, and annual percent change (APC) and average annual percent change (AAPC) were calculated. A comparative analysis was also conducted to evaluate the changes before and after implementing relevant policies and measures.Results:From 2013 to 2023, the number of registered rifampicin-resistant TB patients in Jiangsu Province showed a consistent upward trend (APC=AAPC=1.45%, P=0.035). The screening rates for drug resistance among new TB patients in high-risk groups and the proportion of molecular biological testing for drug resistance all exhibited increasing trends, with a notable turning point occurring in 2018. The trend of the treatment enrollment rate for rifampicin-resistant TB patients experienced a significant shift in 2020, showing a marked increase from 2013 to 2020 (APC=12.91%, P=0.008). The treatment success rate of rifampicin-resistant TB patients also showed a significant upward trend after a turning point in 2020 (APC=9.94%, P=0.004). Conclusion:From 2013 to 2023, significant progress was seen in preventing and treating rifampicin-resistant TB in Jiangsu Province, with relevant policies and measures proving to be highly effective.

AIM: To investigate the application of two-stage estimation (TSE) on adjustment for treatment switch in oncology trials. METHODS: The theory and implementation of TSE method was described, and was applied to adjust the data from a two-arm randomized controlled trial of anti-tumor drugs. The changes of survival curves and hazard ratio of two groups after adjustment for cross-over were evaluated. In addition, the results of two-stage estimation and rank preserving structural failure time model (RPSFT) were compared. RESULTS: After adjustment for cross-over using TSE methods, the results showed that the median survival time of control group was shorter than the original one, and the hazard ratio was lower than the observed value. Moreover, TSE method showed similar results to rank preserving structural failure time model. CONCLUSION: The TSE method is relatively simple to use, reliable and has a good practice property in cross-over analysis of oncology trials. At the same time, it is necessary to pay attention to its application scopes.