Epidemiol Health.
2022;44(1):e2022045. 10.4178/epih.e2022045.
Spatial analysis of tuberculosis treatment outcomes in Shanghai: implications for tuberculosis control
- Affiliations
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- 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, USA
- 3Division of TB and HIV/AIDS Prevention, Shanghai Municipal Center for Disease Control and Prevention and Shanghai Institutes of Preventive Medicine, Shanghai, China
- 4Department of Epidemiology of Microbial Diseases, School of Public Health, Yale University, New Haven, CT, USA
- 5School of Epidemiology, Public Health and Preventive Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- 6Medical Science College, China Three Gorges University, Yichang, China
- KMID: 2541180
- DOI: http://doi.org/10.4178/epih.e2022045
Abstract
OBJECTIVES
Tuberculosis (TB) treatment outcomes are a key indicator in the assessment of TB control programs. We aimed to identify spatial factors associated with TB treatment outcomes, and to provide additional insights into TB control from a geographical perspective.
METHODS
We collected data from the electronic TB surveillance system in Shanghai, China and included pulmonary TB patients registered from January 1, 2009 to December 31, 2016. We examined the associations of physical accessibility to hospitals, an autoregression term, and random hospital effects with treatment outcomes in logistic regression models after adjusting for demographic, clinical, and treatment factors.
RESULTS
Of the 53,475 pulmonary TB patients, 49,002 (91.6%) had successful treatment outcomes. 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 outcomes during the 8-year study period. The best-fit model incorporated spatial factors. Both the random hospital effects and autoregression terms had significant impacts on TB treatment outcomes, ranking 6th and 10th, respectively, in terms of statistical importance among 14 factors. The number of bus stations around the home was the least important variable in the model.
CONCLUSIONS
Spatial autocorrelation and hospital effects were associated with TB treatment outcomes in Shanghai. In highly-integrated cities like Shanghai, physical accessibility was not related to treatment outcomes. Governments need to pay more attention to the mobility of patients and different success rates of treatment among hospitals.
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