Survival analysis of time to cure among tuberculosis patients in Jigjiga Ethiopia a retrospective follow up study

Tuberculosis is a significant global health concern and remains a major public health challenge, particularly in developing countries like Ethiopia. Understanding the time to cure and its determinants is essential for improving treatment outcomes and guiding interventions. This study aimed to assess time to cure and associated factors among TB patients in Jigjiga, Ethiopia. A retrospective cohort study was conducted among 309 TB patients registered between September 2022 and December 2023. Survival analysis techniques were used to analyze time to cure. Kaplan–Meier methods and Cox proportional hazards models were used to identify predictors of time to cure. Additionally, parametric accelerated failure time (AFT) models were compared using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to determine the best-fitting model. A total of 309 tuberculosis patients were included in the study, of whom 226 (73.1%) achieved cure during follow-up. The median time to cure was 184 days. Among the candidate parametric survival models, the log-logistic accelerated failure time (AFT) model provided the best fit to the data. The extra-pulmonary tuberculosis (β = 0.161, 95% CI 0.123–0.200; p < 0.001) and relapse cases (β = 0.072, 95% CI 0.021–0.123; p = 0.006) were associated with longer time-to-cure, whereas female sex (β = − 0.037, 95% CI −0.072 to − 0.001; p = 0.042) and higher daily pill burden (2 tablets: β = − 0.099, p = 0.039; 3 tablets: β = −0.101, p = 0.021; 4 tablets: β = − 0.123, p = 0.004) were associated with shorter time to cure. The log-logistic AFT model was the most appropriate model for analyzing time to cure among tuberculosis patients. Extra-pulmonary tuberculosis and relapse cases were associated with delayed recovery, highlighting the need for closer clinical monitoring and targeted interventions for these patient groups to improve treatment outcomes. These findings highlight the importance of strengthening TB control strategies with particular attention to high-risk clinical categories.