ArticleFrontiers in public health2026
Risk factors and prediction modeling for drug-resistant tuberculosis in Shenzhen: a 9-year retrospective study.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Antimicrobial resistance in Methods: 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. Results: 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 Conclusion: 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. Clinical trial registration: https://www.chictr.org.cn/showproj.html?proj=188950, Identifier [ChiCTR2300067585].
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.