Identifying risk factors for recurrent multidrug resistant tuberculosis based on patient's record data from 2016 to 2021: retrospective study.

Autor: Wotale TW; Department of Statistics, College of Natural and Computational Sciences, Dilla University, Dilla, Ethiopia. teramaj24@gmail.com.; Department of Statistics, College of Natural and Computational Sciences, Mattu University, Mattu, Ethiopia. teramaj24@gmail.com., Lelisho ME; Department of Statistics, College of Natural and Computational Sciences, Mizan-Tepi University, Tepi, Ethiopia. mesfinstat27@gmail.com., Negasa BW; Department of Statistics, College of Natural and Computational Sciences, Mattu University, Mattu, Ethiopia., Tareke SA; Department of Statistics, College of Natural and Computational Sciences, Mizan-Tepi University, Tepi, Ethiopia., Gobena WE; Department of Statistics, College of Natural and Computational Sciences, Mattu University, Mattu, Ethiopia., Amesa EG; Department of Statistics, College of Natural and Computational Sciences, Mattu University, Mattu, Ethiopia.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Oct 13; Vol. 14 (1), pp. 23912. Date of Electronic Publication: 2024 Oct 13.
DOI: 10.1038/s41598-024-73209-x
Abstrakt: Globally, the prevalence of multidrug-resistant tuberculosis (MDR-TB) has been increasing recently. This is a major public health concern, as MDR-TB is more difficult to treat and has poorer outcomes compared to drug-sensitive tuberculosis. The main objective of the study was to identify risk factors for recurrent multidrug-resistant tuberculosis, at Alert Specialized Hospital, Addis Ababa, by using different parametric shared frailty models. From January 2016 to December 2021, a retrospective study was conducted on MDR-TB patients at Alert Specialized Hospital in Addis Ababa. The data for the study were collected from the medical records of MDR-TB patients at the hospital during this time period. Gamma and inverse-Gaussian shared frailty models were used to analyze the dataset, with the exponential, Weibull, and lognormal distributions included as baseline hazard functions. The data were analyzed using R statistical software. The median recurrence time of the patients was 12 months, and 149 (34.3%) had recurrences. The clustering effect was statistically significant for multiple drug-resistant tuberculosis patients' recurrence. According to the Weibull-Inverse-Gaussian model, factors that reduced time to MDR-TB recurrence included lower weight (ɸ = 0.944), smoking (ɸ = 0.045), alcohol use (ɸ = 0.631), hemoptysis (ɸ = 0.041), pneumonia (ɸ = 0.564), previous anti-TB treatment (ɸ = 0.106), rural residence (ɸ = 0.163), and chronic diseases like diabetes (ɸ = 0.442) were associated with faster recurrence. While, higher education (ɸ = 3.525) and age (ɸ = 1.021) extended time to recurrence. For weight increment, smokers and alcohol users, clinical complications of hemoptysis and pneumonia, patients with pulmonary disease who had a history of previous anti-TB treatment, and being rural residents are prognostic factors. There was a significant clustering effect at the Alert Specialized Hospital in Addis Ababa, Ethiopia. The Weibull-Inverse Gaussian Shared Frailty Model was chosen as the best model for predicting the time to recurrence of MDR-TB.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje