ArticleBMC infectious diseases2024
Determinants of drug-resistant tuberculosis in Hunan province, China: a case-control study.
Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Trends and Characteristics of Drug-Resistant Tuberculosis: A Five-Year Study at a Major Specialized Hospital in Guizhou, China (2018-2023).Infection and drug resistance · 2026Article
- Epidemiology of drug-resistant tuberculosis in Hunan China over a 10-year period.Frontiers in public health · 2026Article
- Prediction of risk factors associated with the development of multidrug-resistant tuberculosis in patients with tuberculosis.Frontiers in public health · 2025Article
- Risk factors for multidrug-resistant tuberculosis: a predictive model study.Frontiers in medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug-resistant tuberculosis (DR-TB) is a major public health threat in Hunan Province, with an increasing clinical burden in recent years. This study aimed to identify socio-demographic and clinical factors associated with DR-TB in Hunan province, China.
methodsA case-control study was conducted in Hunan province. Cases were all DR-TB patients who were confirmed by culture and Drug susceptibility testing (DST) and enrolled at the DR-TB treatment center of Hunan Chest Hospital from 2013 to 2018. Controls were all Drug Susceptible TB (DS-TB) patients confirmed by DST and enrolled at the same hospital during the same period. A multivariable logistic regression model was fitted to identify factors significantly associated with DR-TB.
resultsA total of 17,808 patients (15,534 DS-TB controls and 2274 DR-TB cases) were included in the study, with a mean age of 42.5 years (standard deviation (SD) ± 17.5 years) for cases and 46.1 years (SD ± 19.1 years) for controls. Age 15-64 years (Adjusted odds ratio (AOR = 1.5, 95% CI; 1.4, 1.8)), ethnic minorities (AOR = 1.5; 95% CI; 1.4, 1.8), and a history of previous TB treatment (AOR) = 1.84; 95% CI: 1.57, 2.15) was significantly associated with DR-TB. Being resident in a province outside Hunan was also a significant risk factor (AOR = 1.67; 1.27, 2.21) for DR-TB. CONCLUSION AND RECOMMENDATIONS: To prevent the occurrence of DR-TB in Hunan Province, interventions should be targeted at high-risk demographic groups such as ethnic minorities, individuals of productive age, and residents living outside the province. Interventions must also be targeted to previously treated cases, suggesting the appropriateness of diagnosis, treatment, and follow-up. Understanding the risk factors at the province level helps design strategies for controlling DR-TB due to variations by socioeconomic differences, quality of health care, and healthcare access.
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.