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Original Article
Spatial analysis of tuberculosis treatment outcomes in Shanghai: implications for tuberculosis control
Jing Zhang, Xin Shen, Chongguang Yang, Yue Chen, Juntao Guo, Decheng Wang, Jun Zhang, Henry Lynn, Yi Hu, Qichao Pan, Zhijie Zhang
Epidemiol Health. 2022;44:e2022045.   Published online May 1, 2022
DOI: https://doi.org/10.4178/epih.e2022045
  • 7,638 View
  • 359 Download
AbstractAbstract AbstractSummary PDFSupplementary Material
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.
Summary
Key Message
Tuberculosis treatment outcomes, a key indicator in the assessment of TB control programs, were associated with spatial autocorrelation and hospital effects in Shanghai; however, they were not associated with physical accessibility to hospitals.
Methods
Network meta-analysis: application and practice using Stata
Sungryul Shim, Byung-Ho Yoon, In-Soo Shin, Jong-Myon Bae
Epidemiol Health. 2017;39:e2017047.   Published online October 27, 2017
DOI: https://doi.org/10.4178/epih.e2017047
  • 38,886 View
  • 1,420 Download
  • 274 Web of Science
  • 281 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using Stata software under frequentist framework. The NMA tries to synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions—similarity, transitivity, and consistency—should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates publication bias or effect modifiers for a valid inference from results. The synthesized evidences through five steps would be very useful to evidence-based decision-making in healthcare. Thus, NMA should be activated in order to guarantee the quality of healthcare system.
Summary
Korean summary
같은 치료 목적을 가진 여러 치료법들 간의 효과를 상호 비교한 결과들을 통합하기 위해 개발된 네트워크 메타분석 (NMA) 결과는 의사결정의 우선순위를 정하는데 있어 가장 상위의 과학적 설득력을 갖는다. STATA 통계프로그램에서 제공하는 빈도주의 틀에 기반한 NMA를 수행하는데 점검해야 할 가정들과 실제 적용 과정을 소개한다.

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Original Article
Visceral leishmaniasis treatment outcome and its determinants in northwest Ethiopia
Getachew Mebrahtu Welay, Kefyalew Addis Alene, Berihun Assefa Dachew
Epidemiol Health. 2017;39:e2017001.   Published online December 28, 2016
DOI: https://doi.org/10.4178/epih.e2017001
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AbstractAbstract PDF
Abstract
OBJECTIVES
Poor treatment outcomes of visceral leishmaniasis (VL) are responsible for the high mortality rate of this condition in resource-limited settings such as Ethiopia. This study aimed to identify the proportion of poor VL treatment outcomes in northwest Ethiopia and to evaluate the determinants associated with poor outcomes.
METHODS
A hospital-based retrospective study was conducted among 595 VL patients who were admitted to Kahsay Abera Hospital in northwest Ethiopia from October 2010 to April 2013. Data were entered into Epi Info version 7.0 and exported to SPSS version 20 for analysis. Bivariate and multivariate logistic regression models were fitted to identify the determinants of VL treatment outcomes. Adjusted odds ratio (aORs) with 95% confidence intervals (CIs) were used, and p-values <0.05 were considered to indicate statistical significance.
RESULTS
The proportion of poor treatment outcomes was 23.7%. Late diagnosis (≥29 days) (aOR, 4.34; 95% CI, 2.22 to 8.46), severe illness at admission (inability to walk) (aOR, 1.63; 95% CI, 1.06 to 2.40) and coinfection with VL and human immunodeficiency virus (HIV) (aOR, 2.72; 95% CI, 1.40 to 5.20) were found to be determinants of poor VL treatment outcomes.
CONCLUSIONS
Poor treatment outcomes, such as death, treatment failure, and non-adherence, were found to be common. Special attention must be paid to severely ill and VL/HIV-coinfected patients. To improve VL treatment outcomes, the early diagnosis and treatment of VL patients is recommended.
Summary

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