Intervention Effect of New Tuberculosis Control Model on Tuberculosis Incidence in Xinjiang

References

• World Health Organization. Global tuberculosis report 2022; 2022: 68. Available from: https://www.who.int/teams/global-tuberculosis-programme/TB-reports/global-tuberculosis-report-2022. Accessed November 30, 2023.
   Google Scholar
• Ming-zhe W, Le W, Aierken Z, Fei H, Xin-qi W. Epidemiological characteristics of common patients with pulmonary tuberculosis in Xinjiang from 2011 to 2020. J Prevent Med Inform. 2022;38(10):1334–1339+1345.
   Google Scholar
• Abudula Z, Xinqi W, Zhen Z, et al. Epidemiological characteristics of pulmonary tuberculosis in Xinjiang, 2015–2019. Dis Surveillance. 2021;36(11):1138–1141. doi:10.3784/jbjc.202107010375
   Google Scholar
• Xin-qi W, Yipaer A, Sen-lu W, Nian-qiang L, Zhen Z, Xi-hong Y. Preliminary discussion and prospect of mode of tuberculosis prevention and control in Xinjiang. Bull Dis Contr Prevent. 2022;37(02):11–16. doi:10.13215/j.cnki.jbyfkztb.2112021
   Google Scholar
• Miller CJ, Smith SN, Pugatch M. Experimental and quasi-experimental designs in implementation research. Psychiatry Res. 2020;283:112452. doi:10.1016/j.psychres.2019.06.027
   PubMed Web of Science ®Google Scholar
• Ewusie JE, Soobiah C, Blondal E, Beyene J, Thabane L, Hamid JS. Methods, applications and challenges in the analysis of interrupted time series data: a scoping review. J Multidiscip Healthc. 2020;13:411–423. doi:10.2147/jmdh.S241085
   PubMed Web of Science ®Google Scholar
• Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions. a tutorial. Int J Epidemiol. 2017;46(1):348–355. doi:10.1093/ije/dyw098
   PubMed Web of Science ®Google Scholar
• Degli Esposti M, Spreckelsen T, Gasparrini A, et al. Can synthetic controls improve causal inference in interrupted time series evaluations of public health interventions? Int J Epidemiol. 2020;49(6):2010–2020. doi:10.1093/ije/dyaa152
   Web of Science ®Google Scholar
• Long Q, Guo L, Jiang W, Huan S, Tang S. Ending tuberculosis in China: health system challenges. Lancet Public Health. 2021;6(12):e948–e953. doi:10.1016/s2468-2667(21)00203-6
   PubMed Web of Science ®Google Scholar
• Si-qing Z. Joinpoint regression model and its application in epidemic trend analysis of infectious diseases Chinese. J Health Statist. 2019;36(5):787–791.
   Google Scholar
• Zhang Y, Liu J, Han X, et al. Long-term trends in the burden of inflammatory bowel disease in China over three decades: a joinpoint regression and age-period-cohort analysis based on GBD 2019. Front Public Health. 2022;10:994619. doi:10.3389/fpubh.2022.994619
   PubMed Web of Science ®Google Scholar
• Xiao H, Augusto O, Wagenaar BH. Reflection on modern methods: a common error in the segmented regression parameterization of interrupted time-series analyses. Int J Epidemiol. 2021;50(3):1011–1015. doi:10.1093/ije/dyaa148
   PubMed Web of Science ®Google Scholar
• Jiang H, Feng X, Lange S, Tran A, Manthey J, Rehm J. Estimating effects of health policy interventions using interrupted time-series analyses: a simulation study. BMC Med Res Methodol. 2022;22(1):235. doi:10.1186/s12874-022-01716-4
   PubMed Web of Science ®Google Scholar
• Linden A. Challenges to validity in single-group interrupted time series analysis. J Eval Clin Pract. 2017;23(2):413–418. doi:10.1111/jep.12638
   PubMed Web of Science ®Google Scholar
• Turner SL, Forbes AB, Karahalios A, Taljaard M, McKenzie JE. Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study. BMC Med Res Methodol. 2021;21(1):181. doi:10.1186/s12874-021-01364-0
   PubMed Web of Science ®Google Scholar
• He X, Cao M, Mahapatra T, et al. Burden of tuberculosis in Xinjiang between 2011 and 2015: a surveillance data-based study. PLoS One. 2017;12(11):e0187592. doi:10.1371/journal.pone.0187592
   PubMed Web of Science ®Google Scholar
• Burke RM, Nliwasa M, Feasey HRA, et al. Community-based active case-finding interventions for tuberculosis: a systematic review. Lancet Public Health. 2021;6(5):e283–e299. doi:10.1016/s2468-2667(21)00033-5
   PubMed Web of Science ®Google Scholar
• Bohlbro AS, Hvingelby VS, Rudolf F, Wejse C, Patsche CB. Active case-finding of tuberculosis in general populations and at-risk groups: a systematic review and meta-analysis. Eur Respir J. 2021;58(4):2100090. doi:10.1183/13993003.00090-2021
   PubMed Web of Science ®Google Scholar
• Sinha P, Lönnroth K, Bhargava A, et al. Food for thought: addressing undernutrition to end tuberculosis. Lancet Infect Dis. 2021;21(10):e318–e325. doi:10.1016/s1473-3099(20)30792-1
   PubMed Web of Science ®Google Scholar
• Ockenga J, Fuhse K, Chatterjee S, et al. Tuberculosis and malnutrition: the European perspective. Clin Nutr. 2023;42(4):486–492. doi:10.1016/j.clnu.2023.01.016
   PubMed Web of Science ®Google Scholar
• Yuen CM, Amanullah F, Dharmadhikari A, et al. Turning off the tap: stopping tuberculosis transmission through active case-finding and prompt effective treatment. Lancet. 2015;386(10010):2334–2343. doi:10.1016/s0140-6736(15)00322-0
   PubMed Web of Science ®Google Scholar
• Mugwagwa T, Stagg HR, Abubakar I, White PJ. Comparing different technologies for active TB case-finding among the homeless: a transmission-dynamic modelling study. Sci Rep. 2018;8(1):1433. doi:10.1038/s41598-018-19757-5
   PubMedGoogle Scholar
• Chen JO, Qiu YB, Rueda ZV, et al. Role of community-based active case finding in screening tuberculosis in Yunnan province of China. Infect Dis Poverty. 2019;8(1):92. doi:10.1186/s40249-019-0602-0
   PubMed Web of Science ®Google Scholar
• André E, Rusumba O, Evans CA, et al. Patient-led active tuberculosis case-finding in the Democratic Republic of the Congo. Bull World Health Organ. 2018;96(8):522–530. doi:10.2471/blt.17.203968
   PubMed Web of Science ®Google Scholar
• Xiaohan X, Chunquan O. An introduction to interrupted time series design in evaluating the effect of public health interventions and its application. Chin J Health Statist. 2023;40(1):41–44. doi:10.11783/j.issn.1002-3674.2023.01.009
   Google Scholar
• Štelemėkas M, Manthey J, Badaras R, et al. Alcohol control policy measures and all-cause mortality in Lithuania: an interrupted time-series analysis. Addiction. 2021;116(10):2673–2684. doi:10.1111/add.15470
   PubMed Web of Science ®Google Scholar
• Kibuule D, Rennie TW, Ruswa N, Mavhunga F, Verbeeck RK. Effectiveness of community-based DOTS strategy on tuberculosis treatment success rates in Namibia. Int J Tuberculosis Lung Dis. 2019;23(4):441–449. doi:10.5588/ijtld.17.0785
   PubMed Web of Science ®Google Scholar
• Palmer KR, Tanner M, Davies-Tuck M, et al. Widespread implementation of a low-cost telehealth service in the delivery of antenatal care during the COVID-19 pandemic: an interrupted time-series analysis. Lancet. 2021;398(10294):41–52. doi:10.1016/s0140-6736(21)00668-1
   PubMed Web of Science ®Google Scholar
• Sørup S, Englund H, Laake I, et al. Revaccination with measles-mumps-rubella vaccine and hospitalization for infection in Denmark and Sweden – an interrupted time-series analysis. Vaccine. 2022;40(11):1583–1593. doi:10.1016/j.vaccine.2021.01.028
   PubMed Web of Science ®Google Scholar