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山东大学学报 (医学版) ›› 2025, Vol. 63 ›› Issue (7): 92-101.doi: 10.6040/j.issn.1671-7554.0.2024.1389

• 公共卫生与预防医学 • 上一篇    下一篇

基于三种统计模型的长期空气污染物混合暴露与耐药性结核病发病风险关联

王莹1,李怀臣2,龙飞3,刘铱1   

  1. 1.山东大学齐鲁医学院公共卫生学院生物统计学系, 山东 济南 250012;2.山东第一医科大学附属山东省立医院呼吸与危重症医学科, 山东 济南 250021;3.山东第一医科大学第三附属医院呼吸与危重症医学科, 山东 济南 250031
  • 发布日期:2025-07-08
  • 通讯作者: 刘铱. E-mail:liuyi238@sdu.edu.cn龙飞. E-mail:earlf792002@163.com
  • 基金资助:
    国家自然科学基金(82103948)

Association between long-term mixed air pollution exposure and the risk of drug-resistant tuberculosis based on three statistical models

WANG Ying1, LI Huaichen2, LONG Fei3, LIU Yi1   

  1. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Department of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China;
    3. Department of Respiratory and Critical Care Medicine, The Third Affiliated Hospital of Shandong First Medical University, Jinan 250031, Shandong, China
  • Published:2025-07-08

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摘要： 目的 探讨五种常见空气污染物(PM10、PM2.5、SO2、NO2、O3)的长期混合暴露对耐药性结核病(drug-resistant tuberculosis, DR-TB)发病风险的影响,为DR-TB的防治提供科学依据。 方法 收集2015—2019年山东省首次被诊断为DR-TB的3 369例患者的实验室检测结果、临床指标和社会人口学信息。监测患者在4个暴露窗口期(确诊前90、180、270、360 d)内的空气污染物暴露情况。采用Logistic回归(Logistic regression, LR)模型评估单一污染物的影响;采用加权分位数和(weighted quantile sum, WQS)模型与贝叶斯核机器回归(Bayesian kernel machine regression, BKMR)模型检验污染物的联合效应;采用BKMR模型研究污染物与DR-TB发病风险之间的浓度-反应(concentration-response, C-R)关系以及污染物间的交互作用。 结果 LR模型结果显示,在90 d暴露窗口期内,O3浓度的增加与异烟肼耐药性结核病(isoniazid-resistant tuberculosis, IR-TB)的发病风险上升相关(OR=1.008, P=0.02)。WQS和BKMR模型结果显示,空气污染物的混合暴露可降低IR-TB与多耐药性结核病(multi-drug resistant tuberculosis, MDR-TB)的发病风险(β2=0.75, P=0.01)。BKMR模型结果显示,NO2在90 d和360 d暴露窗口期内降低了IR-TB(90 d: β=-0.12,95%CI:-0.22~-0.02;360 d: β=-0.10,95%CI:-0.19~-0.01)和MDR-TB(90 d: β=-0.10,95%CI:-0.19~-0.01;360 d: β=-0.13,95%CI:-0.22~-0.04)的发病风险,关联具有统计学意义(P<0.05);NO2与DR-TB发病风险之间存在非线性关系以及与其他污染物在混合暴露条件下存在交互作用。 结论 高浓度O3暴露可以增加IR-TB的发病风险,长期混合空气污染物暴露与DR-TB发病风险无关联。

关键词: 贝叶斯核机器回归, 耐药性结核病, 空气污染物混合暴露, 健康风险评估

Abstract: Objective To study the effects of long-term mixed exposure to five common air pollutants(PM10, PM2.5, SO2, NO2, and O3)on the incidence of drug-resistant tuberculosis(DR-TB), and to provide a basis for the prevention and treatment of DR-TB. Methods Laboratory test results, clinical indicators and sociodemographic information of 3,369 patients who were first diagnosed DR-TB patients in Shandong Province from 2015 to 2019 were collected. Patients were monitored for exposure to air pollutants during four exposure windows(90, 180, 270 and 360 days before diagnosis). The Logistic regression(LR)model was used to evaluate the impact of a single pollutant. Weighted quantile sum(WQS)and Bayesian kernel machine regression(BKMR)models were applied to examine the joint effects of pollutants, with the BKMR model was used to study the concentration-response(C-R)relationship between pollutants and the risk of DR-TB, as well as the interactions between pollutants. Results The results of LR model showed that the increase of O3 concentration was associated with the increased risk of IR-TB during the 90 days exposure window(OR=1.008,P=0.02). The results of WQS and BKMR models showed that the mixed exposure to air pollutants reduced the risk of IR-TB and MDR-TB(β2=0.75, P=0.01). The results of the BKMR model showed that NO2 reduced the risk of IR-TB(90 days: β=-0.12, 95%CI: -0.22~-0.02; 360 days: β=-0.10, 95%CI: -0.19~-0.01)and MDR-TB(90 days: β=-0.10, 95%CI: -0.19~-0.01; 360 days: β=-0.13, 95%CI: -0.22~-0.04), and the association was statistically significant(P<0.05). In addition, the model also showed a non-linear relationship between NO2 and the risk of DR-TB, as well as interactions with other pollutants under mixed exposure conditions. Conclusion High levels of O3 exposure can increase the risk of IR-TB; long-term exposure to mixed air pollutants was not associated with the risk of DR-TB. Long-term exposure to mixed air pollutants is not associated with the risk of DR-TB.

Key words: Bayesian kernel machine regression, Drug-resistant tuberculosis, Mixed exposure to air pollutants, Health risk assessment

中图分类号: 

  • R122.7
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