结核分枝杆菌多重耐药的流行病学特征及患者治疗依从性的影响因素分析
收稿日期: 2025-03-18
修回日期: 2025-04-22
录用日期: 2025-11-12
网络出版日期: 2025-11-18
基金资助
福建省自然科学基金面上项目（2023J 011363）
Epidemiological Characteristics of Multi-Drug Resistant Mycobacterium Tuberculosis and Analysis of Factors Affecting Patient Treatment Adherence
Received date: 2025-03-18
Revised date: 2025-04-22
Accepted date: 2025-11-12
Online published: 2025-11-18
目的：研究分析结核分枝杆菌多重耐药的流行病学特征，并探寻影响患者治疗依从性的因素。方法：收集我院2020年2月至2024年2月共265例（265株）结核分枝杆菌多重耐药患者，研究患者临床结核病症状出现的时间分布特征及季节分布特征；对患者进行问卷调查，收集患者的年龄、性别、婚姻状况、文化程度等资料，并对患者的服药信息进行记录，进行依从性评估，采用Logistic回归分析影响患者依从性的因素。结果：265例患者中，4月出现临床结核病症占比最高（16.60%），9月占比最低（3.40%），春季出现结核病症的例数最多，为111例（41.89%）；其次是冬季，为81例（30.57%）。265株结核分枝杆菌均对两种以上检测药物呈现耐药性，总体耐药率为100%，其中耐药率较高的药品为氟诺酮类和对氨基水杨酸，耐药率为70.57%，其次为异烟肼（59.62%）。高依从组>60岁患者、饮酒史占比低于低依从组（P<0.05），规律运动、参加过结核病治疗培训、结核病核心知识知晓占比高于低依从组（P<0.05），性别、婚姻状况、教育程度、年收入、合并慢性病、接种卡介苗、吸烟史、家庭住址与医院距离组间比较差异无统计学意义（P>0.05）。Logistics回归分析发现，年龄≥60岁及饮酒史会降低患者治疗依从性，规律运动、参加过结核病治疗培训以及知晓结核病知识的患者可提高治疗依从性。结论：分枝杆菌多重耐药患者中，临床结核病症在4月出现占比最高，9月最低，春、冬季相对更为高发。在治疗依从性方面，年龄>60岁及饮酒史会降低患者治疗依从性，规律运动、参加过结核病治疗培训以及知晓结核病知识高则提高治疗依从性。
关键词： ; 结核分枝杆菌；多重耐药；流行病学；治疗依从性；影响因素
左杨斌
,
陈春喜
.
结核分枝杆菌多重耐药的流行病学特征及患者治疗依从性的影响因素分析
Objective： To investigate the epidemiological characteristics of multi-drug resistant mycobacterium tuberculosis （MDR-TB） and identify factors influencing patient treatment adherence.Methods： A total of 265 MDR-TB patients （265 strains） were selected between February 2020 and February 2024. The temporal and seasonal distribution of clinical tuberculosis symptoms was examined. Patients completed a questionnaire collecting information on age， sex， marital status， education level， and medication adherence. Adherence was assessed， and factors influencing adherence were analyzed.Results： The analysis revealed that April had the highest proportion of clinical tuberculosis symptom onset （16.60%）， while September had the lowest （3.40%）. Spring had the highest number of cases （111 cases， 41.89%）， followed by winter （81 cases， 30.57%）， indicating a seasonal peak in spring and winter. 265 strains of divergent tuberculosis showed resistant to more than two tested drugs， and the overall resistance rate was 100%， among which the drugs with high resistance rate were flunolone and aminosalicylic acid， with a resistance rate of 70.57%， followed by isoniazid （59.62%）.Patients in the high-adherence group were younger （≥60 years）， less likely to have a history of alcohol consumption， more likely to exercise regularly， more likely to have participated in TB treatment training， and had greater awareness of core TB knowledge compared to the low-adherence group （P<0.05）. No significant differences were observed between the groups in terms of sex， marital status， education level， income， chronic comorbidities， Bacillus Calmette-Guérin （BCG） vaccination， smoking history， or the distance from their home to the hospital （P>0.05）. Logistic regression analysis indicated that age ≥60 years and a history of alcohol consumption increased the risk of poor adherence， while regular exercise， participation in TB treatment training， and knowledge of core TB information reduced the risk of poor adherence.Conclusion： Among MDR-TB patients， the highest proportion of clinical symptom onset occurred in April， the lowest in September， with spring and winter showing relatively higher incidence. For treatment adherence， age >60 years and alcohol consumption were associated with increased risk of poor adherence， whereas regular exercise， participation in TB treatment training， and greater awareness of core TB knowledge were associated with reduced risk.
［1］ Hall MB， Lima L， Coin LJM， et al. Drug resistance prediction for mycobacterium tuberculosis with reference graphs［J］.Microb Genom，2023，9（8）：e001081.
［2］ Al Mahrouqi S， Gadalla A， Al Azri S， et al. Drug resistant mycobacterium tuberculosis in Oman：resistance-conferring mutations and lineage diversity［J］.Peer J，2022，10（2）：e13645.
［3］ CRyPTIC Consortium. Quantitative measurement of antibiotic resistance in mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach［J］.Nat Commun，2024，15（1）：488-490.
［4］ Zhou R， Zheng T， Luo D， et al. Drug resistance characteristics of mycobacterium tuberculosis isolates obtained between 2018 and 2020 in Sichuan，China［J］.Epidemiol Infect，2022，150（2）：e27.
［5］ Hosseinali Z， Mohammadshahi J， Teimourpour A， et al. Molecular identification of multiple drug resistance （MDR） strain of mycobacterium tuberculosis［J］.Mol Biol Rep，2023，50（12）：10271-10275.
［6］ Puyana Ortiz JD， Garcés Rodríguez AC， Aznar ML， et al. Adherence and toxicity during the treatment of latent tuberculous infection in a referral center in spain［J］. Trop Med Infect Dis， 2023，8（7）：373-375.
［7］ Chin KL， Anibarro L， Sarmiento ME， et al. Challenges and the way forward in diagnosis and treatment of tuberculosis infection［J］.Trop Med Infect Dis，2023，8（2）：89-92.
［8］ Overbeck V， Malatesta S， Carney T， et al. Understanding the impact of pandemics on long-term medication adherence： directly observed therapy in a tuberculosis treatment cohort pre-and post-COVID-19 lockdowns［J］.BMC Infect Dis，2024，24（1）：1154-1156.
［9］ Davis RA， Leavitt HB， Singh A， et al. Examining interventions that aim to enhance TB treatment adherence in Southeast Asia： a systematic review and meta-analysis［J］.Indian J Tuberc，2024，71（1）：48-63.
［10］ 郑珍慧，江巧敏，张尊敬.基于倾向性评分法评估耐药肺结核患者疾病感知与服药依从性的相关性［J］.中华全科医学，2024，21（11）：1872-1875.
［11］ Yagi K， Asakura T， Namkoong H， et al. Association between six-minute walk test parameters and the health-related quality of life in patients with pulmonary mycobacterium avium complex disease［J］.BMC Pulm Med，2018，18（1）：114-116.
［12］ Moon SJ， Lee WY， Hwang JS， et al. Accuracy of a screening tool for medication adherence： a systematic review and meta-analysis of the morisky medication adherence scale-8［J］.PLoS One，2017，12（11）：e0187139.
［13］ Cao WJ， Chen CS， Hua Y， et al. Factor analysis of a health-promoting lifestyle profile（HPLP）：application to older adults in China's mainland［J］.Arch Gerontol Geriatr，2012，55（3）：632-638.
［14］ Pei S， Song Z， Yang W， et al. The catalogue of mycobacterium tuberculosis mutations associated with drug resistance to 12 drugs in China from a nationwide survey： a genomic analysis［J］.Lancet Microbe，2024，5（11）：e100899.
［15］ Leong KWC， Gautam SS， Pradhan M， et al. Comparative genomic analyses of multi-drug resistant mycobacterium tuberculosis from nepal and other geographical locations［J］.Genomics，2022，114（2）：110278.
［16］ Davis RA， Leavitt HB， Singh A， et al. Examining interventions that aim to enhance TB treatment adherence in Southeast Asia： a systematic review and meta-analysis［J］.Indian J Tuberc，2024，71（1）：48-63.
［17］ Loiseau C， Windels EM， Gygli SM， et al. The relative transmission fitness of multidrug-resistant mycobacterium tuberculosis in a drug resistance hotspot［J］.Nat Commun，2023，14（1）：1988-1990.
［18］ Datta D， Jamwal S， Jyoti N， et al. Actionable mechanisms of drug tolerance and resistance in mycobacterium tuberculosis［J］.FEBS J，2024，291（20）：4433-4452.
［19］ Schami A， Islam MN， Belisle JT， et al. Drug-resistant strains of mycobacterium tuberculosis： cell envelope profiles and interactions with the host［J］.Front Cell Infect Microbiol，2023，13（8）：e1274175.
［20］ Ku JH， Henkle E， Carlson KF， et al. Evaluation of mycobacterium avium complex pulmonary disease treatment completion and adherence to ATS/IDSA guidelines［J］.Clin Infect Dis，2023，76（3）：e1408-e1415.
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