Risk factors and prediction modeling for drug-resistant tuberculosis in Shenzhen: a 9-year retrospective study.

Authors

Jin Wang, Moru Chen, Xiaoliang Zha, Feng Ding, He Zhang, Shui-Hua Lu, Xiangxiang Liu

Published in

Frontiers in public health. Volume 14. Pages 1875521. Epub Sep 01, 2026.

Abstract

Antimicrobial resistance in Mycobacterium tuberculosis (M.tb) poses a major threat to global tuberculosis (TB) control, limiting treatment options and increasing mortality. The prevalence and risk factors for drug-resistant TB (DR-TB) vary by socioeconomic and demographic context. Shenzhen, a rapidly developing megacity in China with a large migrant population, provides a critical setting for investigating this issue. This study aimed to analyze the DR-TB trends and risk factors in Shenzhen using a nine-year retrospective analysis.
We retrospectively analyzed 16,466 TB patients managed at Shenzhen Third People's Hospital from January 2017 and December 2025. Risk factors for DR-TB were identified using univariate and multivariate logistic regression. Based on these factors, we constructed four prediction models (demographic, demographic+clinical symptom, demographic+clinical symptom+diagnostic, and demographic+clinical symptom+diagnostic+treatment delay), and evaluated their performance using Receiver Operating Characteristic (ROC) curves and calibration curves.
The prevalence of DR-TB decreased from 11.36% in 2017 to 10.42% in 2019, dropped sharply to 7.9% in 2020-2021 (likely due to COVID-19 disruptions), rebounded to 14.63% in 2022 (above pre-pandemic levels), peaked in 2023, and then gradually declined after 2024. Multivariate logistic regression identified multiple factors significantly associated with drug resistance: gender (female), age (25-44, 45-64, ≥65 years), occupation (unemployed, family workers, business/service providers, office workers, students), HIV/AIDS comorbidity, clinical symptoms (cough, sputum production), ≥1 negative etiological test result, re-treatment status, concurrent extra pulmonary TB, time from symptom onset to first medical visit (28-42 days), and time from first visit to diagnosis (≥42 days). The AUC values for the four models were 0.571, 0.599, 0.760, and 0.787, respectively (all p < 0.001). Hosmer-Lemeshow χ ² values were 14.014, 13.402, 9.618 and 8.612, respectively (p > 0.05).
We successfully developed a DR-TB risk prediction model with good discrimination and calibration. Early identification of risk factors is crucial for improving clinical awareness and management of DR-TB. Enhancing early diagnosis and treatment is essential for controlling the disease.
https://www.chictr.org.cn/showproj.html?proj=188950, Identifier [ChiCTR2300067585].

PMID:
42745786
Bibliographic data and abstract were imported from PubMed on 16 Sep 2026.

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