Predictors of time to first symptomatic recovery of major depressive disordered patients: a case study at Jimma University Medical Center.

BACKGROUND: Major Depressive Disorder is one of the most common mental disorders, and it is the main cause of disability worldwide with a prevalence ranging from 7 to 21%. OBJECTIVE: The goal of this study was to predict the time it took for patients with severe depressive disorders at Jimma University Medical Center to experience their initial symptomatic recovery. STUDY DESIGN: The researchers utilized a prospective study design. METHODS: Patients with major depressive disorder were followed up on at Jimma University Medical Center from September 2018 to August 2020 for this study. The Gamma and Inverse Gaussian frailty distributions were employed with Weibull, Log-logistic, and Log-normal as baseline hazard functions. Akaike Information Criteria were used to choose the best model for describing the data. RESULTS: This study comprised 366 patients, with 54.1% of them experiencing their first symptomatic recovery from a severe depressive disorder. The median time from the onset of symptoms to symptomatic recovery was 7 months. In the study area, there was a clustering effect in terms of time to first symptomatic recovery from major depressive disorder. According to the Log-normal Inverse-Gaussian frailty model, marital status, chewing khat, educational status, work status, substance addiction, and other co-variables were significant predictors of major depressive disorder (p-value < 0.05). CONCLUSION: The best model for describing the time to the first symptomatic recovery of major depressive disorder is the log-normal Inverse-Gaussian frailty model. Being educated and working considerably were the variables that reduces the time to first symptomatic recovery from major depressive disorder; whereas being divorced, chewing khat, substance abused and other co-factors were the variables that significantly extends the time to first symptomatic recovery.

Modeling Time to Death of Patients with Multidrug-Resistant Tuberculosis at Saint Peter's Specialized Hospital.

BACKGROUND: Currently, the worldwide prevalence and incidence of multidrug-resistant tuberculosis (MDR-TB) is drastically increasing. The main objective of this study was modeling the time-to-death of patients with MDR-TB at St. Peter's Specialized Hospital, Addis Ababa, Ethiopia, by using various parametric shared frailty models. STUDY DESIGN: A retrospective study design was used. METHODS: The study population was TB patients with MDR at St. Peter's Specialized Hospital from January 2016 through December 2019. Exponential, Weibull, and log-normal were used as baseline hazard functions with the gamma and inverse Gaussian frailty distributions. All the models were compared based on Akaike's Information Criteria. RESULTS: The overall median time to death was 11 months and 123 (33.5%) patients died. Patients who lived in rural areas had shorter survival time than those who lived in urban areas with an accelerated factor of 0.135 (P=0.002). Patients with a history of anti-TB drug consumption had a short survival time than those without such a history with an accelerated factor of 0.02 (P=0.001). The variability (heterogeneity) of time to death of patients in the region for the selected model (Weibull-inverse Gaussian shared frailty model) was =0.144 (P=0.027). CONCLUSION: The MDR-TB patients with weight gain, khat and alcohol consumption, clinical complication of pneumothorax and pneumonia, extrapulmonary TB, and history of anti-TB drug consumption as well as those who lived in rural areas had a shorter survival time, compared to others. There was a significant heterogeneity effect in the St. Peter's Specialized Hospital. The best model for predicting the time to death of MDR-TB patients was Weibull-inverse Gaussian shared frailty model.