Application of generalized estimation equations to establish prediction equation for tuberculosis drug resistance in Zhejiang province.
10.3760/cma.j.issn.0254-6450.2018.03.023
- Author:
Q WANG
1
;
X M WANG
2
;
W M CHEN
1
;
L ZHOU
2
;
Q MENG
2
;
S H CHEN
2
;
Z W LIU
2
;
W B WANG
1
Author Information
1. Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Collaborative Innovation Center of Social Risk Governance in Health, Shanghai 200032, China.
2. Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China.
- Publication Type:Journal Article
- Keywords:
Drug-resistant tuberculosis;
Generalized estimation equations;
Influencing factor;
Prediction equation
- MeSH:
Antitubercular Agents/therapeutic use*;
Drug Resistance, Multiple, Bacterial;
Humans;
Models, Statistical;
Mycobacterium tuberculosis/drug effects*;
Risk Factors;
Sputum/microbiology*;
Surveys and Questionnaires;
Tuberculosis/epidemiology*;
Tuberculosis, Multidrug-Resistant
- From:
Chinese Journal of Epidemiology
2018;39(3):368-373
- CountryChina
- Language:Chinese
-
Abstract:
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.
