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Background: Non-adherence to anti-tuberculosis treatment is one of the crucial challenges to improving TB treatment outcomes and reducing healthcare costs. The prevalence of non-adherence to anti-tuberculosis treatment is not well documented in the study context. Therefore, this study was aimed at estimating the prevalence of non-adherence to anti-tuberculosis treatment and associated factors among TB patients attending TB clinics in Hosanna town, Southern Ethiopia, in 2022. Methods: An institution-based cross-sectional study was conducted from April to May 2022. A systematic random sampling technique was employed to select a sample of 233 study subjects from all four public health facilities. According to the order of arrival, every second person was interviewed. Data were collected using a structured questionnaire that was created using several works of literature. A multivariable binary logistic regression analysis was used to identify factors associated with non-adherence to anti-TB drugs. The adjusted odds ratio (AOR) with a 95% confidence interval (CI) was estimated. Results: The study included 233 tuberculosis (TB) patients with a response rate of 100%. The prevalence of non-adherence was 18% (95% CI: 15.39, 21.82). Being in the continuation phase (AOR = 3.09, 95% CI: 1.16, 8.23), not attending formal education (AOR = 2.47, 95% CI: 1.12, 5.42), not disclosing TB status to their family (AOR = 2.36, 95% CI: 0.11, 5.04) and having poor TB knowledge (AOR = 3.09, 95% CI: 1.48, 6.48) were significantly associated with non-adherence to TB treatment. Conclusion: Among TB patients, there was a significant prevalence of non-adherence to anti-TB medications. Interventions that target patients with low education status, are in the continuation phase, and do not disclose their TB status to their families are required to improve TB treatment adherence. In addition, improving health education is important to enhance TB knowledge, which has an impact on TB treatment adherence. The need for good drug adherence should be emphasized while counseling TB patients.
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Background: Unsuppressed viral load count in patients on anti-retroviral therapy is linked to poorer survival and increased transmission of the virus. Despite efforts made in Ethiopia, the viral load suppression rate is still low. Objective: To estimate time to viral load suppression and predictors of viral load suppression among adults on anti-retroviral therapy in Nigist Elen Mohamed Memorial Comprehensive Specialized Hospital, 2022. Materials and Methods: A retrospective follow-up study was conducted among 297 adults on anti-retroviral therapy from January 1, 2016, to December 31, 2021. A simple random sampling technique was used to select study participants. The data were analyzed using STATA 14. Cox regression model was used. The adjusted hazard ratio with 95% CI was estimated. Results: A total of 296 records of patients on anti-retroviral therapy were included in this study. The incidence of viral load suppression was 9.68 per 100-person months. The median time for viral load suppression was 9 months. Patients with baseline CD4 ≥200 cell/mm3 (AHR: 1.87; 95% CI = 1.34, 2.63), who had no opportunistic infections (AHR = 1.84; 95% CI = 1.34, 2.52), who were on WHO clinical stage-I or II (AHR = 2.12; 95% CI = 1.18, 3.79) and who have taken tuberculosis preventive therapy (AHR = 2.24; 95% CI = 1.66, 3.02) had higher hazards of viral load suppression. Conclusion: The median time for viral load suppression was 9 months. Patients who had no opportunistic infection, with higher CD4 count, on WHO clinical stage-I or II, who have taken tuberculosis preventive therapy had higher hazards of viral load suppression. Careful monitoring and counseling of patients with CD4 levels lower than 200 cells/mm3 are necessary. Careful monitoring and counseling of patients in advanced WHO clinical stages, with lower CD4 count levels and with opportunistic infections is crucial. Strengthening the provision of tuberculosis preventive therapy is warranted.
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Background: The world is currently facing a pandemic of Coronavirus Disease 2019 (Covid-19). It has caused significant morbidity and mortality. So far little is known about recovery time (prolonged hospital stay) from Covid-19 and its determinants in Ethiopia as well as in the study area. Therefore, the aim of this study was to determine time to recovery from Covid-19, and identify predictors of time to recovery among patients admitted to treatment centers of Southern Nations Nationalities and Peoples Region (SNNPR). Methods and Materials: A facility-based retrospective cohort study was conducted among Covid-19 patients admitted to care centers of SNNPR from May 30, 2020 to October 15, 2021. A sample of 845 patients was included in the study. Summarization of the data was done using mean (standard deviation) and median (inter quartile range). Kaplan-Meier Survival Curve was used to estimate recovery time from Covid-19 and the independent effects of covariates on recovery time was analyzed using multivariable Cox-proportional hazard model. Results: The incidence density of recovery was 8.24 per 100 person-days (95% CI: 7.67, 8.85). The overall median recovery time was 10 days (IQR: 8-16 days). Critical stage of Covid-19 (aHR = 0.19, 95% CI: 0.12, 0.29), severe stage of Covid-19 (aHR = 0.40, 95% CI: 0.29, 0.56), mechanical ventilation (aHR = 0.20, 95% CI: 0.073, 0.56) and treatment center (aHR = 0.68, 95% CI: 0.51, 0.90) were significant predictors of recovery rate among Covid-19 patients. Conclusion: The median time to recovery from Covid-19 was relatively short. The incidence density of recovery was 8.24 per 100 person-days. The hazard of recovery was lower for patients at higher levels of Covid-19 severity and for patients in need of mechanical ventilation. Early identification of severity levels of the patients is required at the time of admission. Special attention, critical follow-up and management is warranted for patients at higher levels of Covid-19 severity.
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BACKGROUND: Anemia is disproportionately borne among children in the African regions including Ethiopia. In Ethiopia, there is limited information on the prevalence and factors associated with anemia among children aged 6-23 months. Therefore, the aim of this study was to identify individual- and community-level factors associated with anemia among children 6-23 months of age. METHODS: The data were obtained from the 2016 Ethiopia Demographic and Health Survey, conducted from January to June 2016. A sample of 2554 children aged 6-23 months was included. Data were analyzed using STATA version 14. A multilevel ordinal logistic regression model was fitted and an adjusted odds ratio with a 95% confidence interval was obtained. RESULTS: The prevalence of anemia among children 6-23 months of age was 72.3%; 27.5% mild, 41% moderate, and 3.8% severe anemia. child age 18-23 months (AOR: 0.76; 95%CI: 0.61-0.93), female sex (AOR: 0.84; 95%CI: 0.72-0.98), maternal anemia (AOR: 1.53; 95%CI: 1.28-1.82), exclusive breastfeeding (AOR: 0.73; 95%CI: 0.54-0.98), child fever (AOR: 1.41; 95%CI: 1.03-1.93), underweight children (AOR: 1.42; 95%CI: 1.17-1.73) and exposure to either newspaper, radio or television (AOR: 0.78; 95%CI: 0.61-0.99) were the individual-level factors associated with anemia. High community-poverty (AOR: 1.30; 95%CI: 1.01-1.67), living in the regions of Somali (AOR: 2.08; 95%CI: 1.31-3.29), Amhara (AOR: 0.65; 95%CI: 0.45-0.94), Benishangul (AOR: 0.39; 95%CI: 0.25- 0.61) and Harari (AOR: 1.97; 95%CI: 1.18-3.31) were the community-level factors associated with anemia. CONCLUSION: This study showed that childhood anemia is affected both by the individual- and community-level factors. The strategies of promoting exclusive breastfeeding, addressing maternal anemia, child fever, giving special attention for underweight children, and targeting regions identified to have a high risk of anemia should be strengthened to reduce childhood anemia.
