Background: Glaucoma is a worldwide problem that causes vision loss and even blindness, with a prevalence rate ranging from 1.9% to 15%. In Ethiopia, glaucoma is the fifth cause of blindness. This study aimed to explore the dependence between blindness of the right and the left eyes of glaucoma patients and assess the effects of the covariates under the dependence structure. Study Design: A retrospective cohort study. Methods: The study population included the glaucoma patients at Alert hospital from January 1, 2018, to December 30, 2021. The copula model was used to estimate the time to the blindness of the right and the left eyes of the glaucoma patients by specifying the dependence between the event times. Results: Out of 537 glaucoma patients, 224 (41.71%) became blind at least in one eye during the follow-up period. The results of the Clayton copula model revealed that factors, such as age, residence, diabetes mellitus, stage of glaucoma, and hypertension are considered the most prognostic factors for blindness in glaucoma patients. The findings also revealed that there was a strong dependence between the time to the blindness of the right and the left eyes in the glaucoma patients (τ=0.43). Conclusion: Based on the obtained results, high age, urban residence, hypertension, diabetes mellitus, and higher stage of glaucoma were factors associated with time to the blindness in the glaucoma patients. There was also a dependence between the right and the left eyes of the glaucoma patients. The results revealed that the Clayton Archimedean copula model was the best statistical model for accurate description of glaucoma patients’ datasets.

Background: Worldwide, there were 12.7 million new cervical cancer cases, of which 5.6 million took place in industrialized nations and 7.1 million in underdeveloped nations. In eastern, western, middle, and southern Africa, it is the main cancer-related cause of death in female patients. In Ethiopia, cancer was responsible for roughly 5.8% of all fatalities. This study makes use of sophisticated statistical models that take into account population heterogeneity in terms of frailty and dependence between two endpoints in terms of copulas. Methods: Based on hospital registry data, this retrospective study intends to examine the time to relapse and time to death of cervical cancer. This study analyzes 907 cervical cancer-positive women from various parts of Ethiopia. The copula model was used to link time to relapse and time to death of women with cervical cancer. Shared frailty model was used to incorporate unexplained heterogeneity for women with cervical cancer patients. Results: Of the 907 cervical cancer patients, 275 (30.32%) experienced a relapse, 353 (38.92%) died, and 554 (61.08%) were censored. Age, smoking status, family planning, HIV status, family history, abortion, and stage are the most reliable predictors of both time to relapse and time to death of cervical cancer patients. The estimate of the copula parameter (θ = 1.476, 95% CI: 1.082, 1.870) shows moderate amount of dependence between time to relapse and time to death (Kendall's rank correlation (τ) = 0.425). The estimate of the variability (heterogeneity) parameter in the population of clusters (region) is η = 0.495, 95% CI: 0.101, 0.889. Conclusion: Age, smoking status, family planning, HIV status, family history, abortion, and more advanced stage significantly increase the risk of relapse and death of female cervical patients. There was a significant association between the time to relapse and the time to die for women with cervical cancer. There was a significant heterogeneity effect in the Tikur Anbessa Specialized Hospital.

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.

Background: Diabetic retinopathy is a complication of diabetes, caused by high blood sugar levels damaging the eye. Globally, diabetic retinopathy  affects more than 103.12 million people. Diabetic retinopathy is among the leading causes of vision loss at the global level, including in Ethiopia.  Therefore, the study aimed to assess the time to develop diabetic retinopathy and identify factors associated with diabetic retinopathy among  diabetes patients. Methods: A retrospective study was conducted from September 1, 2021 to January 30, 2022. Data was collected using semistructured questionnaire.  The Cox proportional hazard model were used to determine the median time to develop diabetic retinopathy and identify predictors  of diabetic retinopathy. Data was analyzed using R software. Results: A total of 373 diabetes patients were included in this study. The prevalence of  diabetic retinopathy was 41.3%. The median time was 41 months, ranging from 39 to 73 months. Elder age (HR=3.17, 95%CI: 1.53, 6.58), being male  (HR=2.34, 95%CI: 1.35, 6.15), previous family history of diabetes (HR=4.16, 95%CI: 2.19, 8.37), longer duration of diabetes (HR=2.86, 95%CI: 1.41,  5.31) received only insulin therapy (HR=3.91, 95%CI: 1.36, 7.94), and high systolic blood pressure(HR=2.32; 95%CI: 1.12, 4.39) were statistically  significant factors related to development of diabetes retinopathy. Conclusions: More than half of diabetic patinets in this study were developed  retinopathy diabetes within a few months of being diagnosed. As a result, we advocate that the best way to preserve our vision from diabetic  retinopathy is to maintain our diabetes under control, and the high-risk population receive early screening for diabetes.