使用倾向得分匹配法评估南京市耐多药结核病的影响因素

许可; 丁松宁; 杨晨; 王荣; 苗瑞芬; 张大卫; 张洪英; 李晨

doi:10.16462/j.cnki.zhjbkz.2022.03.008

使用倾向得分匹配法评估南京市耐多药结核病的影响因素

doi: 10.16462/j.cnki.zhjbkz.2022.03.008

210003 南京，南京市疾病预防控制中心慢性传染病防制科

基金项目:

南京市卫健委医学重点科技发展项目 ZKX2019048

详细信息

通讯作者:
李晨, E-mail: 24143179@qq.com

中图分类号: R52; R521
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出版历程
- 收稿日期: 2021-03-29
- 修回日期: 2021-06-08
- 网络出版日期: 2022-03-17
- 刊出日期: 2022-03-10

Evaluating the influential factors of multidrug-resistant tuberculosis using a propensity score matching method

Department of Chronic Infectious Disease Prevention and Control, Nanjing Municipal Center for Disease Control and Prevention, Nanjing 210003, China

Funds:

Medical Key Science and Technology Development Project of Nanjing Municipal Health Commission ZKX2019048

More Information

Corresponding author: LI Chen, E-mail: 24143179@qq.com

摘要

摘要: 目的本研究旨在评价南京市耐多药结核病(multidrug-resistance tuberculosis, MDR-TB)的影响因素，为降低MDR-TB的发病率提供科学依据。方法对南京市2013年1月1日―2020年12月31日登记管理的6 649例MDR-TB可疑者进行筛查。通过中国疾病预防控制中心结核病管理系统查询患者一般信息，通过电子病历和电话调查跟踪患者治疗管理情况。描述279例MDR-TB和6 370例非MDR-TB的一般人口学特征，使用倾向得分匹配法1∶1匹配筛选出279例非MDR-TB。采用配对资料条件Logistic回归分析模型分析MDR-TB的影响因素。结果条件Logistic回归分析模型分析结果显示，既往二线抗结核药物使用史和抗结核药物不良反应史是MDR-TB发生的危险因素(OR=2.39, 95% CI: 1.46~3.93, P < 0.001; OR=3.90, 95% CI: 2.45~6.21, P < 0.001)，初治患者、规律服药是MDR-TB发生的保护因素(OR=1.55, 95% CI: 1.02~2.34, P=0.038; OR=2.63, 95% CI: 1.69~4.07, P < 0.001)。结论 MDR-TB对患者自身、家庭以及社会的危害极大，临床医疗机构和CDC要加强协作，提高患者的早期发现率和规范治疗管理率，从而控制MDR-TB的传播。
- 结核分枝杆菌 /
- 耐多药结核病 /
- 倾向得分匹配法 /
- 影响因素
Abstract: Objective The aim of present study is to evaluate the influential factors of multidrug-resistance tuberculosis (MDR-TB) in Nanjing, and to provide scientific evidence for reducing the incidence rate of MDR-TB. Methods To screening 6 649 suspected MDR-TB cases who registered and managed in Nanjing from January 1, 2013 to December 31, 2020. The general information of the patients was checked through the Tuberculosis (TB) management system of the Chinese Center for Disease Control and Prevention, and the treatment management conditions of the patients were tracked through electronic medical records and telephone investigation. A total of 279 MDR-TB and 6 370 non-MDR-TB were described from general demographic characteristic. A total of 279 non-MDR-TB with 1∶1 matched were select using a propersity score matching method. Conditional Logistic regression model was used to analyze single and multiple factors influential of MDR-TB. Results Conditional Logistic regression results showed that previous history of second-line drugs and adverse reactions of anti-tuberculosis drugs were risk factors for MDR-TB (OR=2.39, 95% CI: 1.46-3.93, P < 0.001; OR=3.90, 95% CI: 2.45-6.21, P < 0.001), Initial treatment patient and regular medication were protective factors for MDR-TB (OR=1.55, 95% CI: 1.02-2.34, P=0.038; OR=2.63, 95% CI: 1.69-4.07, P < 0.001). Conclusions MDR causes a huge of harm to patients, their families and the society. Clinical medical institution and CDC should strengthen cooperation to improve the detection rate of TB and the standard treatment management rate of patients, so as to control the spread of MDR epidemic.
- Mycobacterium tuberculosis /
- Multidrug-resistance tuberculosis /
- Propensity score matching method /
- Influential factors

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图 1 匹配前后倾向得分直方图

Figure 1. Histograms of propensity scores before and after matching.

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表 1 匹配前两组患者一般人口学特征比较

Table 1. Comparison of general demographic characteristics between the two groups before matching

人口学特征	耐多药[n(%)] ^a	非耐多药[n(%)]	χ²值	P值
性别			10.45	0.001
男	222(4.7)	4 497(95.3)
女	57(3.0)	1 873(97.0)
年龄(岁)			25.73	< 0.001
< 20	4(2.8)	137(97.2)
20~ < 40	78(4.3)	1 740(95.7)
40~ < 60	103(6.2)	1 566(93.8)
≥60	94(3.1)	2 927(96.9)
户籍			3.32	0.068
本地	187(3.9)	4 589(96.1)
流动人口	92(4.9)	1 781(95.1)
职业			80.39	< 0.001
工人	136(2.8)	4 669(97.2)
其他	143(7.8)	1 701(92.2)
合计	279(4.2)	6 370(95.8)
注：^a：n表示耐多药组例数，(%)表示耐多药率。

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表 2 匹配后耐多药组和非耐多药组间单因素分析

Table 2. Univariate analysis between the MDR group and non-MDR group after matching

分析因素	耐多药(n=279)	非耐多药(n=279)	χ²值	P值
既往二线抗结核药物史			17.50	< 0.001
一线	192	234
一线和二线	87	45
治疗分类
初治患者	171	205	9.43	0.002
复治患者	108	74
规律服药
是	151	217	34.76	< 0.001
否	128	62
药物不良反应			45.02	< 0.001
有	145	68
无	134	211

下载: 导出CSV

表 3 匹配后耐多药组和非耐多药组条件Logistic回归分析模型分析

Table 3. Condition Logistic regression analysis between the MDR-resistant group and non-MDR-resistant group after matching

变量	β值	P值	OR值(95% CI)
既往使用二线抗结核药物	0.87	< 0.001	2.39(1.46~3.93)
初治患者	0.44	0.038	1.55(1.02~2.34)
规律服药	0.97	< 0.001	2.63(1.69~4.07)
抗结核药物不良反应	1.36	< 0.001	3.90(2.45~6.21)

下载: 导出CSV

参考文献(14)

[1]	杨海涛, 陆伟, 竺丽梅. 耐药结核病的治疗与控制[M]. 北京: 军事医学科学出版社, 2014: 6-7, 145. Yang HT, Lu W, Zhu LM. Treatment and control of drug-resistant tuberculosis[M]. Beijing: Military Medical Science Press, 2014: 6-7, 145.
[2]	Daley CL, Caminero JA. Management of Multidrug-Resistant Tuberculosis[J]. Semin Respir Crit Care Med, 2018, 39(3): 310-324. DOI: 10.1055/s-0038-1661383.
[3]	World Health Organization. Global tuberculosis report 2018[R]. World Health Organization, 2018.
[4]	中国疾病预防控制中心专家组. 中国结核病预防控制工作技术规范(2020版)[M]. 北京: 人民卫生出版社, 2020: 18-19. Chinese Center for Disease Control and Prevention Expert Group. Technical Specifications for Tuberculosis prevention and control in China (2020 edition)[M]. Beijing: People's Medical Press, 2020: 18-19.
[5]	Zhang Y, Yew WW. Mechanisms of drug resistance in Mycobacterium tuberculosis: update 2015[J]. Int J Tuberc Lung Dis, 2015, 19(11): 1276-1289. DOI: 10.5588/ijtld.15.0389.
[6]	王坚杰, 周美兰, 杜义祥等. 武汉市耐药/耐多药结核病影响因素分析[J]. 中华疾病控制杂志, 2017, 21(2): 164-167. DOI:10.16462/j.cnki.zhjbkz.201 7.02.014. Wang JJ, Zhou ML, Du YX, et al. Analysis on the influential factors of drug resistance and multidrug resistant tuberculosis in Wuhan City[J]. Chin J Dis Control Prev, 2017, 21(2): 164 - 167. DOI: 10.16462/j.cnki.zhjbkz.2017.02.014.
[7]	Gaude GS, Hattiholli J, Kumar P. Risk factors and drug-resistance patterns among pulmonary tuberculosis patients in northern Karnataka region, India[J]. Niger Med J, 2014, 55(4): 327-332. DOI: 10.4103/0300-1652.137194.
[8]	Matteelli A, Centis R, D'Ambrosio L, et al. WHO strategies for the programmatic management of drug-resistant tuberculosis[J]. Expert Rev Respir Med, 2016, 10(9): 991-1002. DOI:10.1080/1747634 8.2016.1199278.
[9]	Sun G, Luo T, Yang CG, et al. Dynamic population changes in Mycobacterium tuberculosis during acquisition and fixation of drug resistance in patients[J]. J Infect Dis, 2012, 206(11): 1724-1733. DOI: 10.1093/infdis/jis601.
[10]	Misombo-Kalabela A, Nguefack-Tsague G, Kalla GC, et al. Risk factors for multidrug-resistant tuberculosis in the city of Kinshasa in the Democratic Republic of Congo[J]. Pan Afr Med J, 2016, 23: 157. DOI: 10.11604/pamj.2016.23.157.6137.
[11]	Stosic M, Vukovic D, Babic D, et al. Risk factors for multi- drug- resistant tuberculosis among tuberculosis patients in Serbia: a case-control study[J]. BMC Public Health, 2018, 18(1): 1114. DOI: 10.1186/s12889-018-6021-5.
[12]	Tupasi TE, Garfin AM, Kurbatova EV, et al. Factors associated with loss to follow- up during treatment for multidrug- resistant tuberculosis, the Philippines, 2012-2014[J]. Emerg Infect Dis, 2016, 22(3): 491-502. DOI: 10.3201/eid2203.151788.
[13]	Cox HS, Niemann S, Ismailov G, et al. Risk of acquired drug resistance during short-course directly observed treatment of tuberculosis in an area with high levels of drug resistance[J]. Clin Infect Dis, 2007, 44(11): 1421-1427. DOI: 10.1086/517536.
[14]	杨小猛, 赵丹, 陈倩瑜, 等. 结核分枝杆菌L型感染对结核病患者耐药状况的影响[J]. 临床肺科杂志, 2015, 20(3): 426-429. DOI:10.3969 /j.issn.1009-6663.2015.03.013. Yang XM, Zhao D, Chen QY, et al. Effect of mycobacterium tuberculosis L form infection on drug resistant state in tuberculosis patients[J]. Clin J Pulmonary Med, 2015, 20(3): 426-429. DOI:10.3969 /j.issn.1009-6663.2015.03.013.