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.

Prevalence and Antimicrobial Susceptibility Profile of Salmonella Serovars Isolated from Slaughtered Cattle in Addis Ababa, Ethiopia.

Salmonella is one of the top causes of foodborne bacterial illnesses in humans. The primary sources of human Salmonella infection are food producing animals such as cattle, poultry, and swine. A cross-sectional study was undertaken to estimate the prevalence and to determine the serovar distribution and antimicrobial susceptibility profiles of Salmonella spp. isolated from fecal (n=567) and carcass swab (n=159) samples of slaughtered cattle at Addis Ababa Abattoir Enterprise and Kara'alo PLC, Abattoirs, in Addis Ababa, Ethiopia between January 2014 and April 2015. Salmonella isolation was conducted according to Global Foodborne Infections Network Laboratory Protocol and isolates were confirmed by genus specific PCR and serotyped by slide agglutination test. Susceptibility of the isolates to 17 antimicrobials was testedusing the Kirby-Bauer disk diffusion method according to the guidelines of the Clinical and Laboratory Standards Institute. Out of the total 726 samples examined, 27 (3.7%) were positive for Salmonella. Salmonella was detected in 4.1% (23/567) fecal and 2.5% (4/159) carcass swab samples. Twelve different serovars were identified and the most predominant serovars were S. Dublin (n=10, 35.7%) and S. Virchow (n=5, 17.9%), followed by S. Braendrerup, S. Haifa, and S. Saintpaul which were isolated from 2 samples each (7.1%). All of the Salmonella isolates investigated were resistant or intermediately resistant to four or more of the 17 drugs tested. High resistance rate was recorded to streptomycin 25 (89.3%), cephalothin 20 (71.4%), ampicillin 19 (67.9%), and amoxicillin+clavulanic acid 19 (67.9%). Resistance to five or more antimicrobials was detected in 20 (71.5%) of the isolates. Multidrug resistance to more than 7 antimicrobials was detected in 5 (17.9%) of the isolates. Isolation of such multidrug resistant strains of Salmonella from slaughtered cattle poses a major public health concern. These findings imply the need for a strict biosecurity and regulation of antimicrobial use across the country.