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中国防痨杂志 ›› 2026, Vol. 48 ›› Issue (9): 1364-1374.doi: 10.19982/j.issn.1000-6621.20260223

• 论著 • 上一篇    下一篇

重庆市利福平敏感肺结核患者家庭灾难性支出的风险预测研究

周然1, 邓晶1(), 陈海怡1, 张婷2, 郑登虎3   

  1. 1 重庆医科大学公共卫生学院, 重庆 400016
    2 重庆市结核病防治所区县防治科, 重庆 400050
    3 重庆市结核病防治所行政办公室, 重庆 400050
  • 收稿日期:2026-04-20 出版日期:2026-09-10 发布日期:2026-09-08
  • 通信作者: 邓晶 E-mail:cqmudj@cqmu.edu.cn
  • 基金资助:
    重庆市卫生健康委科研项目(2025WSJK107)

Risk prediction of catastrophic household cost among patients with rifampicin-susceptible pulmonary tuberculosis in Chongqing Municipality

Zhou Ran1, Deng Jing1(), Chen Haiyi1, Zhang Ting2, Zheng Denghu3   

  1. 1 School of Public Health, Chongqing Medical University, Chongqing 400016, China
    2 Department of District and County Control, Chongqing Tuberculosis Control Institute, Chongqing 400050, China
    3 Administrative Office, Chongqing Tuberculosis Control Institute, Chongqing 400050, China
  • Received:2026-04-20 Online:2026-09-10 Published:2026-09-08
  • Contact: Deng Jing E-mail:cqmudj@cqmu.edu.cn
  • Supported by:
    Scientific Research Project of Chongqing Health Commission(2025WSJK107)

摘要:

目的: 分析重庆市利福平敏感肺结核患者家庭灾难性支出的发生情况并构建风险预测模型,为治疗管理过程中高风险家庭的识别和救助提供依据。 方法: 采用分层多阶段抽样的方法,选取2023年1月至2024年12月重庆市5个县(区)定点医院管理的355例治疗时间超过2周的利福平敏感肺结核患者进行问卷调查。将研究对象按7∶3比例随机分为训练集249例和验证集106例。采用LASSO回归筛选潜在因素后进行多因素logistic回归分析,根据结果构建列线图预测模型(Nomogram),采用受试者工作特征曲线、校正曲线和决策曲线评估预测模型的区分度、校准度和临床实用价值。 结果: 355例利福平敏感肺结核患者中,家庭灾难性支出发生率为58.59%(208/355)。多因素logistic回归分析结果显示,住院(OR=5.906,95%CI:2.752~13.279)、职业变动(OR=12.074,95%CI:4.844~33.425)、家庭主要收入来源者(OR=2.388,95%CI:1.174~4.985)及合并慢性病(OR=3.304,95%CI:1.485~7.686)是利福平敏感肺结核患者发生家庭灾难性支出的相关因素。基于上述变量构建的风险预测模型曲线下面积为0.883(95%CI: 0.843~0.924);验证集曲线下面积为0.867(95%CI:0.800~0.935)。训练集和验证集的校准曲线与理想曲线基本拟合,当阈值概率为0.20~0.96时,模型预测净收益高。 结论: 重庆市利福平敏感肺结核患者家庭灾难性支出发生率较高;本研究构建的Nomogram风险预测模型效能良好,可作为肺结核治疗管理中早期筛查高风险家庭并实施精准救助的科学依据。

关键词: 结核,肺, 利福平, 费用,医疗, 模型,经济学, 列线图

Abstract:

Objective: To analyze the occurrence of catastrophic household cost among patients with rifampicin-susceptible pulmonary tuberculosis in Chongqing and to develop a risk prediction model, so as to provide evidence for identifying and supporting high-risk households during treatment management. Methods: A stratified multistage sampling method was used to select 355 patients with rifampicin-susceptible pulmonary tuberculosis who had received treatment for more than two weeks and were managed by tuberculosis designated hospitals in five counties/districts of Chongqing from January 2023 to December 2024. The participants were randomly divided into a training set and a validation set at a ratio of 7∶3, with 249 cases in the training set and 106 cases in the validation set. LASSO regression was used to screen potential predictors, followed by multivariable logistic regression analysis. A nomogram prediction model was constructed based on the results. The discrimination, calibration, and clinical utility of the model were evaluated using ROC curve, calibration curve, and decision curve analysis. Results: Among the 355 patients with rifampicin-susceptible pulmonary tuberculosis, the incidence of catastrophic household cost was 58.59% (208/355). Multivariable logistic regression analysis showed that hospitalization (OR=5.906, 95%CI: 2.752-13.279), occupational change (OR=12.074, 95%CI: 4.844-33.425), being the main household income earner (OR=2.388, 95%CI: 1.174-4.985), and comorbidity with chronic diseases (OR=3.304, 95%CI: 1.485-7.686) were predictors of catastrophic household cost. AUC of the risk prediction model based on these variables was 0.883 (95%CI: 0.843-0.924) in the training set and 0.867 (95%CI: 0.800-0.935) in the validation set. The calibration curves of both the training and validation sets were generally consistent with the ideal curve. Decision curve analysis showed that the model had a high net benefit when the threshold probability ranged from 0.20 to 0.96. Conclusion: The incidence of catastrophic household cost among patients with rifampicin-susceptible pulmonary tuberculosis in Chongqing was relatively high. The nomogram-based risk prediction model developed in this study showed good predictive performance and may provide a scientific basis for early identification of high-risk households and targeted assistance during tuberculosis treatment management.

Key words: Tuberculosis, pulmonary, Rifampin, Fees, medical, Models, economic, Nomograms

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