Background. The average duration of recovery from COVID-19 and influencing factors, which would help inform optimal control strategies, remain unclear. Moreover, studies regarding this issue are limited in Ethiopia, and no region-wise studies were conducted. Hence, this study aimed to investigate the median recovery time from COVID-19, and its predictors among patients admitted to Amhara regional state COVID-19 treatment centers, Ethiopia. Methods. A facility-based retrospective follow-up study was conducted at Amhara regional state COVID-19 treatment centers from 13 March 2020 through 30 March 2021. Data were entered using EpiData version 3.1, and STATA version 14 was used for analysis. A Kaplan–Meier curve was used to estimate survival time, and the Cox regression model was fitted to identify independent predictors. P value with 95% CI for the hazard ratio was used for testing the significance at alpha 0.05. Results. Six hundred twenty-two cases followed, and 540 observations developed an event at the end of the follow-up. The median time to recovery was 11 days with an interquartile range of 9–14 days. Most of the patients were recovered from COVID-19 between days seven and fourteen. In the first six days of admission, only 4.2% of cases had recovered, but by day 14, 73.8% had recovered. Patients without comorbid illness/s were faster to recover than their counterparts (AHR = 1.44 : 95% CI: 1.10, 1.91) and those who have signs and symptoms on admission (AHR = 0.42 : 95% CI: 0.30, 0.60) and old-aged (AHR = 0.988; 95% CI: 0.982, 0.994) took longer to recover. Conclusion. In conclusion, a relatively short median recovery time was found in this study. Significant predictors for delayed recovery from COVID-19 were older age, presence of symptoms at admission, and having at least one comorbid condition. These factors should be placed under consideration while developing a strategy for quarantining and treating COVID-19 patients.

Background Antenatal care (ANC) is one of the four pillars of the initiative for safe motherhood. ANC helps to improve the health of pregnant women and reduce the risk of adverse pregnancy outcome. First ANC is used to know the health status of the mothers and the fetus, to estimate the gestational age and expected date of delivery. Our research aims to investigate the Spatio-temporal distribution of delayed first ANC visit and its predictors using multilevel binary logistic regression analysis. Method A total of 10,184 women (2061 in 2005, 3366 in 2011, and 4757 in 2016) were included for this study. The data were cleaned and weighted using STATA version 14. A multilevel binary logistic regression model was fitted to identify significant predictors of delayed first ANC visit. ArcGIS software was used to explore the spatial distribution of delayed first ANC visits and a Bernoulli model was fitted using SaTScan software to identify significant clusters of delayed first ANC visits. Results Overall, 77.69, 73.95, and 67.61% of women had delayed their first ANC visit in 2005, 2011, and 2016 EDHSs respectively. Women education [AOR = 0.71; 95%CI; 0.60, 0.84], unwanted pregnancy [AOR = 1.41;95%CI; 1.04, 1.89], and rural residence [AOR = 1.68;95%CI; 1.19, 2.38] have significantly associated with delayed first ANC visit. The spatial analysis revealed that delayed first ANC visit varies in each EDHS period. The SaTScan analysis result of EDHS 2005 data identified 122 primary clusters located between the border of Oromia and Eastern SNNPR regions (RR = 1.30, LLR = 32.31, P-value< 0.001), whereas in 2011 EDHS, 145 primary clusters were identified in entire Tigray, B/Gumuz, Amhara western part of Afar and northwest Oromia regions (RR = 1.30, LLR = 40.79, P-value< 0.001). Besides in 2016 EDHS,198 primary clusters were located in the entire SNNPR, Gambella, Northen B/Gumuz, and western Oromia regions. (RR = 1.35, LLR = 83.21, P-value< 0.001). Conclusion In Ethiopia delayed first ANC visit was significantly varied across the country over time Women’s education, wanted the last child, and residence were significantly associated with delayed first ANC booking. The effect of each predictor was found to be different across regions of Ethiopia. Therefore, a targeted intervention program is required in highly affected areas of Ethiopia.

Background Multidrug-resistant tuberculosis (MDR-TB) is a global problem and a health security threat, which makes “Ending the global TB epidemic in 2035” unachievable. Globally, the unfavourable treatment outcome remains unacceptably high. Therefore, this study aimed to develop a risk prediction model for unfavorable treatment outcomes in MDR-TB patients, which can be used by clinicians as a simple clinical tool in their decision-making. Objective The objective of this study was to develop and validate a risk prediction model for the prediction of unfavorable treatment outcomes among MDR-TB patients in North-West Ethiopia. Methods We used MDR-TB data collected from the University of Gondar and Debre Markos referral hospitals. A retrospective follow-up study was conducted and a total of 517 patients were included in the study. STATA version 16 statistical software and R version 4.0.5 were used for the analysis. Descriptive statistics were carried out. A multivariable model was fitted using all potent predictors selected by the lasso regression method. A simplified risk prediction model (nomogram) was developed based on the binomial logit-based model, and its performance was described by assessing its discriminatory power and calibration. Finally, decision curve analysis (DCA) was done to evaluate the clinical and public health impact of the developed model. Results The developed nomogram comprised six predictors: baseline anemia, major adverse event, comorbidity, age, marital status, and treatment supporter. The model has a discriminatory power of 0.753 (95% CI: 0.708, 0.798) and calibration test of (P-value = 0.695). It was internally validated by bootstrapping method, and it has a relatively corrected discrimination performance (AUC = 0.744, 95CI: 0.699, 0.788). The optimism coefficient was found to be 0.009. The decision curve analysis showed the net benefit of the model as threshold probabilities varied. Conclusion The developed nomogram can be used for individualized prediction of unfavorable treatment outcomes in MDR-TB patients for it has a satisfactory level of accuracy and good calibration. The model is clinically interpretable and was found to have added benefits in clinical practice.

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BackgroundThe Corona virus disease 19 (COVID-19) pandemic is a human tragedy that occurred in this era. It poses an unprecedented psychological, social, economic, and health crisis. The mental health and well-being of entire societies are suffering as a result of this crisis, but the suffering is greater in students at all levels of education and must be addressed immediately. Thus, this study was aimed to estimate the pooled prevalence and associated factors of the psychological impact of COVID-19 among higher education students.MethodsThe potential studies were searched via PubMed, HINARI, the Cochrane Library, and Google Scholar. Studies were appraised using the Joanna Briggs Institute appraisal checklist. Micro Soft Excel was used to extract the data, which was then exported to Stata version 14 for analysis. Heterogeneity between studies was tested using Cochrane statistics and the I2 test, and small-study effects were checked using Egger’s statistical test. A random-effects model was employed to estimate the pooled prevalence of the psychological impact of COVID-19 and its associated factor.ResultsAfter reviewing 227 studies, eight fulfilled the inclusion criteria and were included in the meta-analysis. The pooled prevalence of the psychological impact of Corona virus disease 19 among higher education students in Ethiopia, including depression, anxiety, and stress was 43.49% (95% CI: 29.59, 57.40%), 46.27% (95% CI: 32.77, 59.78%), and 31.43% (95% CI: 22.71, 40.15), respectively. Having a medical illness, being an urban resident, living with parents, having relative death due to pandemics, and having a non-health field of study were identified as significant associated factors for the impact of the pandemic in higher education students.ConclusionThe COVID-19 pandemic had a significant psychological impact on college and university students. Depression, anxiety, and stress were the most commonly reported psychological impacts across studies among higher education students. Hence, applying tele-psychotherapy using, smartphones, and social media platforms has an effect on reducing the impact. Programs for preventing and controlling epidemics should be developed by the government and higher education institutions that incorporate mental health interventions and build resilience.