| 王琦,王晓萌,陈文明,周琳,孟琼,陈松华,柳正卫,王伟炳.以广义估计方程研究浙江省肺结核耐药预测方程[J].中华流行病学杂志,2018,39(3):368-373 |
| 以广义估计方程研究浙江省肺结核耐药预测方程 |
| Application of generalized estimation equations to establish prediction equation for tuberculosis drug resistance in Zhejiang province |
| 收稿日期:2017-07-06 出版日期:2018-03-21 |
| DOI：10.3760/cma.j.issn.0254-6450.2018.03.023 |
| 中文关键词: 广义估计方程 耐药肺结核 预测方程 影响因素 |
| 英文关键词: Generalized estimation equations Drug-resistant tuberculosis Prediction equation Influencing factor |
| 基金项目:浙江省科技厅重大专项（2014C03034） |
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| 中文摘要: |
| 目的 耐药肺结核患者可能对一种或多种抗结核药物耐药。对这类因变量为多结局非独立的数据，本文探讨应用广义估计方程分析耐药危险因素，构建预测方程，探索预警模型建立方向。方法 对浙江省30个耐药监测点的涂阳患者进行药敏检测和问卷调查，以对13种抗结核药物的耐药情况为因变量，可能危险因素为自变量，用SAS的GENMOD模块构建广义估计模型。结果 本研究中基线水平下发生耐药的概率为20.26%，有统计学意义的耐药影响因素包括年龄、保险、是否合并乙型肝炎、治疗史及停药情况。根据各因素对耐药发生的影响程度得到预测方程。结论 广义估计方程解决了耐药数据因变量相关性的问题，有效利用非独立数据提供的信息，且参数估计稳健，为耐药危险因素评价和预警模型构建提供更全面的信息。 |
| 英文摘要: |
| Objective Drug-resistant tuberculosis (TB) may be resistant to one or multiple anti-TB drugs. We used generalized estimation equations to analysis the risk factors of drug-resistant TB and provide information for the establishment of a warning model for these non-independent data. Methods The drug susceptibility test and questionnaire survey were performed in sputum positive TB patients from 30 anti TB drug-resistance surveillance sites in Zhejiang province. The generalized estimation model was established by the GENMOD module of SAS, with resistance to 13 kinds of anti-TB drugs as dependent variables and possible influencing factors, such as age, having insurance, HBV infection status, and history of anti-TB drug intake, as independent variables. Results In this study, the probability of drug resistance at baseline level was 20.26%. Age, insurance, whether being co-infected with HBV, and treatment history or treatment withdrawal were statistically significantly correlated with anti-TB drug resistance. The prediction equation was established according to the influence degree of the factors mentioned above on drug resistance. Conclusion The generalized estimation equations can effectively and robustly analyze the correlated binary outcomes, and thus provide more comprehensive information for drug resistance risk factor evaluation and warning model establishment. |
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