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

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.

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

Figure 1.

Treatment outcomes in pulmonary tuberculosis patients, 2009-2016. (A) In smear-positive pulmonary tuberculosis patients. (B) In smear-negative and unknown pulmonary tuberculosis patients. Others include those who discontinued treatment because of side effects.

Figure 2.

Annual spatial clusters of poor treatment outcomes in Shanghai from 2009 to 2016 (A) 2009, 2 spatial clusters, (B) 2010, 0 spatial clusters, (C) 2011, 2 spatial clusters, (D) 2012, 3 spatial clusters, (E) 2013, 1 spatial cluster, (F) 2014, 1 spatial cluster, (G) 2015, 2 spatial clusters, and (H) 2016, 1 spatial cluster. The red circles are the significant spatial clusters identified by the Kulldorff spatial scan statistic. The black dots are the 38 designated tuberculosis hospitals.

Figure 3.

Straight-line distance from the residential addresses to the tuberculosis hospitals of pulmonary tuberculosis patients in Shanghai in 2009-2016. (A) Distance from the residential address to the treatment hospital. (B) Distance from the residential address to the nearest designated tuberculosis hospital. For legibility, only patients in 2016 were used to draw this figure.

Figure 4.

Flow of tuberculosis patients from the district of the residential address to the district of the treatment hospital, and to the treatment hospital in Shanghai, 2009-2016. A Sankey diagram was used, with the thickness of each link representing the number of patients.