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Abstract 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.

Background. Stroke is a life-threatening condition that occurs due to impaired blood flow to brain tissues. Every year, about 15 million people worldwide suffer from a stroke, with five million of them suffering from some form of permanent physical disability. Globally, stroke is the second-leading cause of death following ischemic heart disease. It is a public health burden for both developed and developing nations, including Ethiopia. Objectives. This study is aimed at estimating the time to death among stroke patients at Jimma University Medical Center, Southwest Ethiopia. Methods. A facility-based retrospective cohort study was conducted among 432 patients. The data were collected from stroke patients under follow-up at Jimma University Medical Center from January 1, 2016, to January 30, 2019. A log-rank test was used to compare the survival experiences of different categories of patients. The Cox proportional hazard model and the accelerated failure time model were used to analyze the survival analysis of stroke patients using R software. An Akaike’s information criterion was used to compare the fitted models. Results. Of the 432 stroke patients followed, 223 (51.6%) experienced the event of death. The median time to death among the patients was 15 days. According to the results of the Weibull accelerated failure time model, the age of patients, atrial fibrillation, alcohol consumption, types of stroke diagnosed, hypertension, and diabetes mellitus were found to be the significant prognostic factors that contribute to shorter survival times among stroke patients. Conclusion. The Weibull accelerated failure time model better described the time to death of the stroke patients’ data set than other distributions used in this study. Patients’ age, atrial fibrillation, alcohol consumption, being diagnosed with hemorrhagic types of stroke, having hypertension, and having diabetes mellitus were found to be factors shortening survival time to death for stroke patients. Hence, healthcare professionals need to thoroughly follow the patients who pass risk factors. Moreover, patients need to be educated about lifestyle modifications.