Background: Surgical site infection is an infection occurring within 30 days after surgery. It is recently reported that evidence-based information on the specific time when the majority of surgical site infections would develop is a key to early detect the infection as well as to preventing and early intervene against their pressing and fatal complications. Therefore, the current study aimed to determine the incidence, predictors, and time to development of surgical site infection among general surgery patients at specialized hospitals in the Amhara region. Method: An institution-based prospective follow-up study was conducted. The two-stage cluster sampling procedure was used. A systematic sampling technique with a K interval of 2 was applied to prospectively recruit 454 surgical patients. Patients were followed up for 30 days. Data were collected using Epicollect5 v 3.0.5 software. Post-discharge follow-up and diagnosis were done by telephone call follow-up. Data were analyzed using STATA™ version 14.0. Kaplan–Meier curve was used to estimate survival time. Cox proportional regression model was used to determine significant predictors. Variables with a P-value less than 0.05 in the multiple Cox regression models were independent predictors. Result: The incidence density was 17.59 per 1000 person-day-observation. The incidence of post-discharge Surgical site infection was 70.3%. The majority of surgical site infections were discovered after discharge between postoperative days 9 to 16. Being male (AHR: 1.98, 95% CI: 1.201 – 3.277, diabetes Mellitus (AHR: 1.819, 95% CI: 1.097 – 3.016), surgical history (AHR: 2.078, 95% CI: 1.345, 3.211), early antimicrobial prophylaxis (AHR: 2.60, 95% CI: 1.676, 4.039), American Society of Anesthesiologists score ≥ III AHR: 6.710, 95% CI: 4.108, 10.960), duration of the surgery (AHR: 1.035 95% CI: 1.001, 1.070), Age (AHR: 1.022 95% CI: 1.000, 1.043), and the number of professionals in the Operation Room (AHR: 1.085 95% CI: 1.037, 1.134) were found to be the predictors of time to development of Surgical site infection. Conclusion: The incidence of surgical site infection was higher than the acceptable international range. The majority of infections were detected after hospital discharge between 9 to 16 postoperative days. The main predictors of Surgical site infection were Age, Sex, Diabetes Mellitus, previous surgical history, the timing of Antimicrobial prophylaxis, American Society of Anesthesiologists score, pre-operative hospital stay, duration of surgery, and the number of professionals in the operation room. Hence, hospitals should give great emphasis on pre-operative preparation, post-discharge surveillance, modifiable predictors, and high-risk patients, as they found in this study.

Background: Female sex workers (FSWs) are marginalized groups of the population who have limited access to healthcare and a higher risk of HIV infection due to biobehavioral and structural risk factors. Although it is known that the Eastern and Southern African region is affected by the highest HIV incidence and prevalence, understanding the burden among FSWs in the region remains limited. We aimed to assess the pooled prevalence and associated factors of HIV in this vulnerable population. Methods: Relevant studies were searched on PubMed, Embase, Scopus, ScienceDirect, Hinari, African Journals Online (AJOL), Google, and Google Scholar. The searching mechanism was constructed using keywords identified by CoCoPop (Condition, Context, and Population) framework and medical subject heading terms to recruit studies published between January 1, 2015 and March 30, 2024. Observational studies that estimate the prevalence or incidence or associated factors of HIV among FSWs, even if FSWs were not the main focus of the study, were included. The quality of included studies was assessed using Joana Brigg’s Institute checklist. Data were extracted and analyzed using STATA 17 software. To estimate the pooled effect sizes with their 95% confidence intervals, a random effect model was fitted. The I² statistic was used to evaluate heterogeneity. Funnel plot and Egger’s regression test were utilized to assess the small study effect. Publication bias was managed using trim-and-fill analysis. Subgroup and sensitivity analysis were considered to handle heterogeneity among studies. Results: A total of 54 articles with 78,747 FSWs who had successful HIV biological test were included in the analysis. The pooled prevalence of HIV among FSWs in Eastern and Southern Africa was 36.0% (95% CI: 31.0%–41.0%). Regionally, the pooled prevalence was 59.0% (95% CI: 53.0%–64.0%) and 29.0% (95% CI: 25.0%–34.0%) in Southern and Eastern Africa, respectively. Age above 35 (POR = 7.35; 95% CI: 5.26, 10.28) and 25–34 years (POR = 2.91; 95% CI: 2.02, 4.21), being married (POR = 1.33; 95% CI: 1.07, 1.66), divorced (POR = 1.72; 95% CI: 1.39, 2.13), and widowed (POR = 2.70; 95% CI: 2.09, 3.49), primary education (POR = 1.29; 95% CI:1.08, 1.55), history of sexually transmitted infection (POR = 1.51; 95% CI: 1.20, 1.90), syphilis (POR = 2.57; 95% CI: 1.66, 3.98), Hepatitis B infection (POR = 2.60; 95% CI: 1.07,6.32), drinking alcohol (POR = 1.21; 95% CI: 1.01, 1.45) and injectable drug use before sex (POR = 1.75; 95% CI: 1.24, 2.47) were found significantly associated with the increased risk of HIV infection among FSWs. Conclusion: These data suggest an urgent need to improve access to HIV prevention programs for FSWs. Providing behavioral interventions including reduction of alcohol and injectable drug use before sex, initiating tailored healthcare services, strengthening the psychosocial and legal support network, and fostering partnership might reduce the burden in the region. Clinicians could facilitate early detection and treatment of STIs, and Hepatitis B virus infection. Trial registration: Protocol registration: The protocol for this systematic review and meta-analysis was registered in the PROSPERO with registration number CRD42024509200.

Introduction Despite the availability of a safe and effective measles vaccine in Ethiopia, the country has experienced recurrent and significant measles outbreaks, with a nearly fivefold increase in confirmed cases from 2021 to 2023. The WHO has identified being unvaccinated against measles as a major factor driving this resurgence of cases and deaths. Consequently, this study aimed to apply robust machine learning algorithms to predict the key factors contributing to measles vaccination dropout. Methods This study utilised data from the 2016 Ethiopian Demographic and Health Survey to evaluate measles vaccination dropout. Eight supervised machine learning algorithms were implemented: eXtreme Gradient Boosting (XGBoost), Random Forest, Gradient Boosting, Support Vector Machine, Decision Tree, Naïve Bayes, K-Nearest Neighbours and Logistic Regression. Data preprocessing and model development were performed using R language V.4.2.1. The predictive models were evaluated using accuracy, precision, recall, F1-score and area under the curve (AUC). Unlike previous studies, this research utilised Shapley values to interpret individual predictions made by the top-performing machine learning model. Results The XGBoost algorithm surpassed all classifiers in predicting measles vaccination dropout (Accuracy and AUC values of 73.9% and 0.813, respectively). The Shapley Beeswarm plot displayed how each feature influenced the best model’s predictions. The model predicted that the younger mother’s age, religion-Jehovah/Adventist, husband with no and mother with primary education, unemployment of the mother, residence in the Oromia and Somali regions, large family size and older paternal age have a strong positive impact on the measles vaccination dropout. Conclusion The measles dropout rate in the country exceeded the recommended threshold of <10%. To tackle this issue, targeted interventions are crucial. Public awareness campaigns, regular health education and partnerships with religious institutions and health extension workers should be implemented, particularly in the identified underprivileged regions. These measures can help reduce measles vaccination dropout rates and enhance overall coverage.

Background: Malaria continues to be the most prevalent life-threatening parasitic illness in Ethiopia. Its clinical spectrum ranges from mild to severe, with a propensity for death. In Ethiopia, it accounts for 10% of hospital admission. Identifying predictors of malaria-related mortality is crucial for aiding high-risk patient identification and enabling timely intervention. Objective: Our study aimed to assess treatment outcomes and factors associated with mortality among severe malaria patients at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia. Methods: A retrospective cross-sectional study examined 383 randomly chosen patients with severe malaria, spanning a four-year period leading up to the data collection date, encompassing July 2023 back to June 2020. Data were collected from the hospital records. A structured questionnaire was used to collect the data. EpiData version 3.1 and SPSS version 20 were used to clean and analyze the data, respectively. Logistic regression analysis was conducted to determine associations and reported by the odds ratio at p < 0.05 with 95% confidence intervals. Results: Among the 383 eligible patients, the majorities were males (56.66%) and resided in rural areas (66.32%). Over 84% of them were referred from health facilities. Plasmodium falciparum was the major parasite identified in 78% of cases. The magnitude of death among severe malaria patients was 10.97%. Impaired consciousness, convulsions, jaundice, parasitemia level >2, and creatinine level ≥3 were significantly associated with death, with adjusted odds ratios (AOR) of 3.4 (95% CI: 1.3–8.3), 2.7 (95% CI: 1.004–7.492), 3.2 (95% CI: 1.173–9.182), 3.7 (95% CI: 1.516–9.113), and 11.7 (95% CI: 4.756–29.239), respectively. Conclusion: Our study revealed a significant number of malaria-related deaths, with predictors such as age, impaired consciousness, convulsions, jaundice, parasitemia level, and creatinine level identified. Hence, it is imperative to implement intense and timely interventions for patients exhibiting these clinical manifestations to prevent malaria-related fatalities.

Background: Age-appropriate vaccination or vaccine timeliness is the administering of vaccines on the specified schedule of immunization. One of the qualities of the immunization program is an age-appropriate vaccine, it has become an ignored indicator of program performance. Even though age-appropriate vaccination is critical for child health, there are no national-level studies to generate conclusive and tangible evidence about the determination of timely vaccination in Ethiopia. Objective: To assess the spatial pattern and associated factors of timely vaccination in Ethiopia using EDHS-2016 data: A multilevel and spatial analysis. Method: Community based cross-sectional study design was employed from 18 January to 27 June 2016. To select the participants, two-stage cluster sampling was employedin the Ethiopian Demographic Health Survey 2016 data. Permission was obtained via online request by explaining the aim of this particular study from DHS international. A statistical package for social science-21 software was used for data cleaning, recoding, and analysis. Arc GIS 10.3 software was used to show the spatial variation of age-appropriate vaccination practices. A generalized linear mixed-effect model was used. For all models, intra-class correlation, a proportional change in variance, the log-likelihood test, and the Akaike information criterion were calculated. The best model was selected by the lowest value Akaike information criterion. Variables with a p-value less than 0.05 and a 95% confidence level were considered for the statistically significant association. Result: The spatial distribution of age-appropriate vaccination practice in Ethiopia was non-randomly distributed with the global Moran’s I value of 0.22 (p-value <0.001). The prevalence of age-appropriate vaccination practice in Ethiopia was 33.8%. Women who receive ≥ 2 TT vaccines (AOR = 1.48; 1.22, 1.79), women who lived in rural residences (AOR = 0.77; 0.62, 0.96), gave birth at a health facility (AOR = 1.2; 1.12, 1.37), ANC follow up (AOR = 174; 1.45, 2.1), richest (AOR = 1.65; 1.15, 2.37), richer (AOR = 1.72; 1.3, 2.22), middle-level income (AOR = 1.65; 1.16, 2.36), poorer (AOR = 1.47; 1.11, 1.96) were the factors of age-appropriate vaccination practice. Conclusion: The spatial distribution of age-appropriate vaccination practice in Ethiopia was non-randomly distributed across the regions. Age-appropriate vaccination practice was low in Ethiopia. Wealth index, usual caretaker of the child, ANC utilization, history of TT vaccination, place of delivery, and residence were associated with age-appropriate vaccination practice.

Background Exclusive breastfeeding (EBF) is a major public health problem in Ethiopia. However, the spatial variation of EBF and the associated factors have not been studied as much as we have searched. This study aimed at assessing geospatial variation and the predictors of EBF using geographically weighted regression. Methods A cross-sectional study was conducted using the 2019 Mini-Ethiopian Demographic and Health Survey data set. The study used a total weighted sample of 548 infants. Hotspot spatial analysis showed the hotspot and cold spot areas of EBF. The spatial distribution of EBF was interpolated for the target population using spatial interpolation analysis. SaTScan V.9.6 software was used to detect significant clusters. Ordinary least squares regression analysis identified significant spatial predictors. In geographically weighted regression analysis, the effect of predictor variables on the spatial variation of EBF was detected using local coefficients. Results The weighted prevalence of EBF in Ethiopia was 58.97% (95% CI 52.67% to 64.99%), and its spatial distribution was found to be clustered (global Moran’s I=0.56, p<0.001). Significant hotspot areas were located in Amhara, Tigray, Southern Nations, Nationalities, and Peoples’ Region, and Somali regions, while significant cold spots were located in Dire Dawa, Addis Ababa and Oromia regions. Kulldorff’s SaTScan V.9.6 was used to detect significant clusters of EBF using a 50% maximum cluster size per population. The geographically weighted regression model explained 35.75% of the spatial variation in EBF. The proportions of households with middle wealth index and married women were significant spatial predictors of EBF. Conclusion Middle wealth index and married women were significant spatial predictors of EBF. Our detailed map of EBF hotspot areas will help policymakers and health programmers encourage the practice of EBF in hotspot areas and set national and regional programmes focused on improving EBF in cold spots by considering significant predictor variables.