Spatial analysis of tuberculosis treatment outcome in Shanghai: implications for tuberculosis control

Zhang, Jing; Shen, Xin; Yang, Chongguang; Chen, Yue; Guo, Juntao; Wang, Decheng; Zhang, Jun; Lynn, Henry; Hu, Yi; Pan, Qichao; Zhang, Zhi jie

doi:2022.0.0.e2022045

Original article

Epidemiology and Health 2022;e2022045.
DOI: https://doi.org/10.4178/epih.e2022045 [Accepted] Published online May 1, 2022.

Spatial analysis of tuberculosis treatment outcome in Shanghai: implications for tuberculosis control

Jing Zhang^1,2

, Yue Chen⁵

¹Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
²Department of Population and Quantitative Health Science, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
³Division of TB and HIV/AIDS Prevention, Shanghai Municipal Center for Disease Control and Prevention, China; Shanghai Institutes of Preventive Medicine, China
⁴Department of Epidemiology of Microbial Diseases, School of Public Health, Yale University, CT 06510, USA
⁵School of Epidemiology, Public Health and Preventive Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Canada
⁶Medical Science College, China Three Gorges University, Yichang, China

Correspondence Zhi jie Zhang ,Email: zhj_zhang@fudan.edu.cn

Received: Sep 14, 2021 Accepted after revision: May 1, 2022

Abstract

Objectives:
Tuberculosis (TB) treatment outcome is a key indicator in the assessment of a TB control program. We aimed to identify spatial factors associated with TB treatment outcome, and to provide additional insights into TB control from a geographical perspective.
Method:
We collected data from the electronic TB surveillance system in Shanghai, China and included pulmonary TB patients registered for the period from January 1, 2009 to December 31, 2016. We examined the association of physical accessibility to hospitals, autoregression term and random hospital effects with treatment outcomes in logistic regression models after adjusting for demographic, clinical and treatment factors.
Results:
Of the 53475 pulmonary TB patients, 49002 (91.6%) had a successful treatment outcome. The success rate increased from 89.3% in 2009 to 94.4% in 2016. The successful treatment outcome rate varied among hospitals from 78.6% to 97.8%, and there were 12 spatial clusters of poor treatment outcome during the 8-year study period. The best fit model incorporated the spatial factors. Both the random hospital effects and autoregression terms had significant impacts on TB treatment outcome, ranking sixth and tenth, respectively, in terms of statistical importance among 14 factors. However, number of bus stations around home is the least important variable in the model.
Conclusions:
Spatial autocorrelation and hospital effects are associated with TB treatment outcome in Shanghai. In highly-integrated cities like Shanghai, physical accessibility is not related to treatment outcome. Governments need to pay more attention to the mobility of patients and different success rate of treatment among hospitals.

Keywords: Tuberculosis; Treatment outcome; Spatial autocorrelation; Hospital effect; Physical accessibility; China