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中国防痨杂志 ›› 2025, Vol. 47 ›› Issue (4): 416-424.doi: 10.19982/j.issn.1000-6621.20240530

• 论著 • 上一篇    下一篇

2020—2023年吉林省利福平耐药肺结核患者诊治延迟现状及影响因素分析

姜雪, 白云龙, 马建军, 安源, 杨帆, 赵庆龙()   

  1. 吉林省结核病防治科学研究院,长春 130103
  • 收稿日期:2024-11-27 出版日期:2025-04-10 发布日期:2025-04-02
  • 通信作者: 赵庆龙,Email:jlcdczql@126.com

Status and influencing factors of diagnosis and treatment delay of rifampicin resistant pulmonary tuberculosis patients, Jilin Province, 2020—2023

Jiang Xue, Bai Yunlong, Ma Jianjun, An Yuan, Yang Fan, Zhao Qinglong()   

  1. Jilin Provincial Institute of Tuberculosis Prevention and Treatment, Changchun 130103,China
  • Received:2024-11-27 Online:2025-04-10 Published:2025-04-02
  • Contact: Zhao Qinglong,Email:jlcdczql@126.com

摘要:

目的: 分析吉林省2020—2023年利福平耐药肺结核患者诊治延迟现状及影响因素,为吉林省制定利福平耐药肺结核防控措施提供依据。方法: 从“中国疾病预防控制信息系统”子系统“结核病管理信息系统”中导出吉林省2020年1月1日至2023年12月31日登记的利福平耐药肺结核患者病案信息,延迟率随时间变化趋势采用$χ^{2}_{趋势}$检验,发生诊治延迟的影响因素采用logistic回归模型进行分析。结果: 1931例利福平耐药肺结核患者就诊延迟率、确诊延迟率和治疗延迟率分别为50.7%(979/1931)、12.7%(245/1931)和25.4%(491/1931)。2020—2023年耐药患者确诊延迟率呈波动上升趋势($χ^{2}_{趋势}$=12.353,P<0.001),治疗延迟率呈波动下降趋势($χ^{2}_{趋势}$=33.459,P<0.001)。多因素分析结果显示,患者发现途径为推介(OR=0.443, 95%CI: 0.241~0.817)和复治患者(OR=0.818, 95%CI: 0.680~0.984)是发生就诊延迟的保护因素;有并发症(OR=1.312, 95%CI: 1.080~1.721)和耐多药患者(OR=1.252, 95%CI: 1.035~1.515)是发生就诊延迟的危险因素。患者发现途径为转诊(OR=2.184, 95%CI: 1.568~3.042)和追踪(OR=1.946, 95%CI: 1.390~2.724)是发生确诊延迟的危险因素;分子生物学检测(OR=0.140, 95%CI: 0.072~0.273)和省市级机构(OR=0.072, 95%CI: 0.049~0.107)是发生确诊延迟的保护因素。分子生物学检测(OR=0.420, 95%CI: 0.136~0.501)是发生治疗延迟的保护因素;复治患者(OR=1.259,95%CI: 1.019~1.555)和流动人口(OR=1.907, 95%CI: 1.275~2.852)是发生治疗延迟的危险因素。结论: 吉林省利福平耐药肺结核患者诊治延迟水平较高,为了进一步减少延迟现象,应针对高危因素采取相应的防控措施。

关键词: 结核, 利福平, 药物耐受性, 因素分析,统计学, 吉林省

Abstract:

Objective: To analyze diagnosis and treatment delay and their related influencing factors among rifampicin-resistant tuberculosis (RR-TB) patients in Jilin Province, aiming to provide a reference basis for developing prevention and control measures in Jilin Province. Methods: Medical records on RR-TB patients from 2020 to 2023 were extracted from the Tuberculosis Management Information System, a subsystem of the China Disease Prevention and Control Information System. Trends of diagnosis and treatment delay rates over time were analyzed using trend chi-square test, and logistic regression model was used to examine the related influencing factors. Results: Among 1931 RR-TB patients, their health-care seeking delay rate, definitive diagnosis delay rate and treatment delay rate were 50.7% (979/1931), 12.7% (245/1931) and 25.4% (491/1931), respectively. From 2020 to 2023, the rate of definitive diagnosis delay increased ($χ^{2}_{trend}$=12.353,P<0.001), while the rate of treatment delay decreased ($χ^{2}_{trend}$=33.459,P<0.001). Logistic regression model showed that patients being detected by primary care units recommending to TB designated hospital (OR=0.443, 95%CI:0.241-0.817) and patients being relapse (OR=0.818, 95%CI:0.680-0.984) were protective factors for health-care seeking delay, comorbidity (OR=1.312, 95%CI:1.080-1.721) and multi-drug resistance (OR=1.252, 95%CI:1.035-1.515) were risk factors for health-care seeking delay. Patients were detected through general hospital referring to TB designated hospital (OR=2.184, 95%CI:1.568-3.042) and TB designated hospital tracing (OR=1.946, 95%CI:1.390-2.724) were risk factors for definitive diagnosis delay, molecular biological rapid testing (OR=0.140, 95%CI:0.072-0.273)and diagnosed in provincial or municipal medical institutions (OR=0.072, 95%CI:0.049-0.107) were protective factors for definitive diagnosis delay. Molecular biological rapid detection (OR=0.420, 95%CI:0.136-0.501) was a protective factor for treatment delay, relapse patients (OR=1.259, 95%CI:1.019-1.555) and floating population (OR=1.907, 95%CI:1.275-2.852) were risk factors for treatment delay. Conclusion: The risks of diagnosis and treatment delays in Jilin Province were relatively high. In order to further reduce these delay, corresponding prevention and control measures for high-risk factors should be taken.

Key words: Tuberculosis, Rifampin, Drug tolerance, Factor analysis, statistical, Jilin Province

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