Abstract:
Background: Major Depressive Disorder is one of the most prevalent mental disorders and the
leading cause of disability worldwide with lifetime prevalence estimates ranging from 7% to 21%.
Symptoms of major depressive disorder are depressed mood, loss of interest, decreased energy,
sadness, feelings of guilt or low selfworth, disturbed sleep or appetite, etc. Symptomatic recovery
is a dimensional measure that refers to improvement in the magnitude of symptoms.
Objective This study aimed to model time to first symptomatic recovery of major depressive dis order in Jimma University Medical Center.
Methods: The data for this study was major depressive disorder patients under follow up at Jimma
University Medical Center from Semptember 1, 2018 through August 31, 2020. Weibull, Log logistic and Lognormal as baseline hazard functions with the Gamma and Inverse Gaussian frailty
distributions were used. To select best model Akaike Information Criteria was used. Data analysis
is done using R statistical software.
Results: The median first symptomatic recovery time of patients was 7 months of which about
54.1% were experienced first symptomatic recovery from major depressive disorder. The clus tering effect is significant on modeling time to first symptomatic recovery from major depressive
disorder. According to the result from lognormal inverse-gaussian frailty model, marital status,
khat chewing, educational level, employment status, substance abuse and other cofactors were the
significant factors at 5% level of significance.
Conclusion: Lognormal-inverse- Gaussian frailty model is the model that best describes time-to first symptomatic recovery of the major depressive disorder dataset. Being educated and employed
significantly shortens the time-to-first symptomatic recovery from major depressive disorder while
being divorced, khat chewers, substance abuse and with other cofactors prolongs the time- to-first
recovery from major depressive disorder. For those groups whose recovery time was prolonged,
health professionals (physicians) should give good treatment for patients for identified stakehold ers on identified risk factors.
