Analysis of Mycobacterium tuberculosis drug susceptibility test results and its spatial distribution characteristics in Dali Bai Autonomous Prefecture from 2017 to 2019
10.3760/cma.j.cn114452-20210301-00131
- VernacularTitle:大理白族自治州2017至2019年结核分枝杆菌药敏试验结果分析及空间分布特征
- Author:
Ran CHEN
1
;
Jinglan ZHANG
;
Lixian WU
Author Information
1. 大理白族自治州疾病预防控制中心检验中心，大理 671000
- Keywords:
Mycobacterium tuberculosis;
Antitubercular agents;
Drug sensitivity test;
Spatial autocorrelation analysis
- From:
Chinese Journal of Laboratory Medicine
2021;44(11):1064-1069
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Analyze the drug resistance of Mycobacterium tuberculosis (MTB) to commonly used anti-tuberculosis drugs and its spatial distribution in Dali Bai Autonomous Prefecture from 2017 to 2019, which would provid a reference for the treatment of tuberculosis and the prevention and control of drug-resistant tuberculosis. Methods:A total of 1 013 Mycobacterial strains were isolated from sputum samples in the tuberculosis laboratories of the designated People′s hospital of 12 counties (cities) of Dali Bai Autonomous Prefecture from January 2017 to December 2019. Proportional method was used to conduct drug susceptibility tests and strain identification of 6 anti-tuberculosis drugs. Further used ArcMap10.2 and GeoDa1.14 software to visualize the map display and spatial autocorrelation analysis of the drug resistance of MTB.Results:From 2017 to 2019, the drug resistance rates of MTB in Dali Prefecture were 10.33%(28/271), 10.35%(55/531) and 30.00%(51/170), respectively, showing an rising trend ( χ2=26.62, P<0.05). Among 1 030 samples, 972 strains (95.95%) was MTB and 41 strains (4.05%) was non-tuberculous Mycobacterium (NTM). The total resistance rate of 972 strains of MTB was 13.79% (134/972), of which the single resistance rate was 6.59% (64/972), the multi-drug resistance rate was 4.84% (47/972), and the poly-drug resistance rate was 2.06% (20/972), the rate of extensive drug resistance is 0.31% (3/972). There are 25 combinations of drug resistance patterns. The detection rate of NTM was 4% (41/1 013), among which Midu County had the highest detection rate (0.89%, 9/1 013). The spatial distribution showed that the number of MTB resistant strains among counties and cities had a negative spatial correlation (Moran′s I value was -0.367, P<0.05). It shows that there is no clustering of drug resistance among counties and cities, and the resistance is serious in individual counties and cities. Yongping County and Nanjian Yi Autonomous County had low and high aggregation, and Yunlong County had high and low aggregation. Conclusions:The drug resistance of MTB showed an rising trend in Dali Bai Autonomous Prefecture from 2017 to 2019. The number of drug-resistant strains among regions was not randomly distributed, the regional difference was large, and spatial autocorrelation analysis provided theoretical clues and basis for the formulation of drug resistance prevention and control measures for tuberculosis in the whole state.
