Background: Cancer, a major global health challenge, not only threatens lives but also imposes severe financial burdens, known as ‘financial toxicity’. This strain extends beyond treatment costs to include indirect expenses like lost income and out-of-pocket payments. Despite its urgency, global insights remain fragmented, often limited to specific regions with inconsistent findings. This systematic review and meta-analysis aims to provide a comprehensive overview of cancer-related financial toxicity and identify key risk factors contributing to this burden. Methods: We conducted a systematic review and meta-analysis, searching databases like Medline, Web of Science, PubMed, Scopus, International Scientific Indexing and Google Scholar for peer-reviewed observational studies. Data were extracted into Microsoft Excel 2021, and study quality was assessed using the Joanna Briggs Institute critical appraisal tool. Statistical analysis was performed using STATA V.17, with catastrophic health expenditures (CHEs) pooled via a random-effects model. Heterogeneity was explored to understand variations in study outcomes. Subgroup and sensitivity analyses were conducted to assess individual study impacts, while publication bias was evaluated using Begger and Egger’s tests. Univariate meta-regression analysis determined the impact of study-level covariates on CHE estimates. Results: This analysis included 35 observational studies, covering 428 373 patients with cancer. The pooled prevalence of CHE was 56.1% (95% CI 48.3% to 63.8%). Key risk factors included large family size, low income, lack of health insurance, longer disease duration, older age and multiple treatments. Higher education levels were associated with reduced CHE risk. Conclusion: Over half of patients with cancer (56.1%) face catastrophic health expenditures, highlighting a severe financial burden. Addressing this issue requires expanding health insurance, providing financial support and ensuring affordable cancer care. Improving education access can also reduce CHE risk among patients with cancer.

In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of anemia among youth girls in Ethiopia. A total of 5642 weighted samples of young girls from the 2016 Ethiopian Demographic and Health Survey dataset were utilized. The data underwent preprocessing, with 80% of the observations used for training the model and 20% for testing. Eight machine learning algorithms were employed to build and compare models. The model performance was assessed using evaluation metrics in Python software. Various data balancing techniques were applied, and the Boruta algorithm was used to select the most relevant features. Besides, association rule mining was conducted using the Apriori algorithm in R software. The random forest classifier with an AUC value of 82% outperformed in predicting anemia among all the tested classifiers. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having more than 5 family size were the top attributes to predict anemia. Association rule mining was identified the top seven best rules that most frequently associated with anemia. The random forest classifier is the best for predicting anemia. Therefore, making it potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt anemia among youth girls.

Background: Immunization estimated to prevent 2 to 3 million children deaths every year from vaccine preventable disease. In Ethiopia, limited and inconclusive studies have been conducted on immunization coverage so far. Therefore, this umbrella review was intended to estimate the pooled national immunization coverage and its associated factors among children age 12–23 months in Ethiopia. Methods: This umbrella review included five systematic reviews and meta-analyses through literature search from PubMed, Science direct, and web of science, CINHALE, and data bases specific to systematic reviews such as the Cochrane Database of Systematic Reviews and Prospero, the International Prospective Register of Systematic Reviews from May 1 to 30/ 2023. Only systematic reviews and meta-analyses published in English from inception to May 1, 2023, were included. The quality of each study was assessed using Assessment of Multiple Systematic Reviews. Data were extracted using Microsoft excel 2016 and analyzed using STATA 17.0 statistical software. Heterogeneity among studies was assessed using the Cochran Q statistics and I² test. The pooled effect sizes were determined using pooled proportion for the full vaccination coverage and odds ratios for the associated factors with the corresponding 95% confidence interval were used to declare statically significance. Results: Five studies with 77,161 children aged 12–23 months were included. The overall pooled full vaccination coverage was 57.72% (95% CI 50.17, 65.28). Institutional delivery (OR: 2.12, 95% CI: 1.78–2.52), travel to vaccination site for <2 hours (OR: 2.43, 95%CI: 1.97–3.00), received at least one antenatal (ANC) visit (OR: 3.2, 95%CI: 2.46–4.1), good maternal knowledge of immunization (OR: 3.63, 95%CI: 2.82–4.67), being informed on immunization schedule (OR: 2.54, 95%CI: 2.02–3.2), living in urban areas (OR: 2, 95% CI: 1.54–2.6), and a household visit by health-care providers (HCP) during the postnatal period (OR: 2.23, 95%CI: 1.22–4.09) were the independent predictors of immunization coverage. Conclusion: This study showed the full immunization coverage in Ethiopia was lower compared to the WHO-recommended level. Besides, the current umbrella review identifies several factors that contribute to higher immunization coverage. These includes; institutional delivery, near to vaccination site, having ANC visit, being urban residence, household visited by HCP, having good knowledge and informed on immunization schedule. Thus, the government should intensify the growth of immunization services by emphasizing outreach initiatives to reach remote areas and professionals must combine child immunization service with other medical services offered by health institutions.

Background: Intestinal parasites (IP) are a major public health problem in several developing countries. It accounts for 1.5 billion infections with one or more intestinal parasitic agents. The prevalence of helminthiases in Ethiopia is 29.8% with variable degree of prevalence among the different regions. Young children have a high infestation rate and suffer a substantial burden of Ascaris lumbricoides, Trichuris trichiura, and Schistosomes. Intestinal parasitic infections have serious consequences for human health; such as hepatomegaly, splenomegaly, esophageal varices, and delay in physical development. Therefore, this study was intended to assess mothers’ knowledge, attitude, and practice on prevention and control of intestinal parasitic infestations. Methods: A cross-sectional study was carried out on 378 mothers who had under six and over two year old children. Although 384 mothers were selected using sample size calculation, the data were collected from 378 mother-child pairs using a face to face interview-based questionnaire. The level of maternal knowledge, attitude, and practice are presented in the form of percentage, frequency, and tables. In order to ensure the quality of data, in all of the data collection, data analysis, and write up, a standard operational procedure was followed. Results: The overall level of good maternal knowledge, positive attitude, and good practice in preventing and controlling intestinal parasitic infection concerning pre-school children in Sekota town was 45.2%, 55.3%, and 51.1% respectively. Seventy-seven (20.4%) respondents reported that they knew about A. lumbricoides, and 62 (16.4%) participants washed vegetables as a means of intestinal prevention. Conclusion: The overall level of optimum knowledge, attitude, and practice of mothers on prevention and control of intestinal parasites is significantly low. Therefore, community awareness about intestinal parasitic infestation prevention and control should be created through campaigns or structured training.

Introduction Respiratory distress syndrome is a catastrophic respiratory problem among low birth weight neonates. It increases the suffering of neonates and the economic expenditure of the countries. Notably, it is a major public health issue in low-income and middle-income countries such as Ethiopia. Despite this, studies regarding respiratory distress syndrome among low birth weight neonates were limited in Ethiopia. Objective To assess the incidence and predictors of respiratory distress syndrome among low birth weight neonates in the first 7 days in Northwest Ethiopia Comprehensive Specialized Hospitals. Method Multicentred institution-based retrospective follow-up study was conducted from 19 September 2021 to 1 January 2023, among 423 low birthweight neonates. A simple random sampling technique was used. The data were collected using a data extraction checklist from the medical registry of neonates. The collected data were entered into EPI-DATA V.4.6.0.6. and analysed using STATA V.14. The Kaplan-Meier failure curve and log-rank test were employed. Bivariable and multivariable Weibull regression was carried out to identify predictors of respiratory distress syndrome. Statistical significance was declared at a p≤0.05. Result The incidence rate of respiratory distress syndrome was found to be 10.78 (95% CI 9.35 to 12.42) per 100 neonate days. Fifth minute Appearance, Pulse, Grimace, Activity, Respiration (APGAR score) <7 (AHR 1.86; 95% CI 1.18 to 2.92), multiple pregnancy (AHR 1.43; 95% CI 1.04 to 1.96), caesarean section delivery (AHR 0.62; 95% CI 0.41 to 0.93), prematurity (AHR 1.56; 95% CI 1.06 to 2.30) and birth weight <1000 g (AHR 3.14; 95% CI 1.81 to 5.40) and 1000–1499 g (AHR 2.06; 95% CI 1.42 to 2.83) were significant predictors. Conclusion The incidence of respiratory distress syndrome was higher than other studies conducted on other groups of neonates. Multiple pregnancy, fifth minute APGAR score, caesarean section, prematurity, extremely low birth weight and very low birth weight were predictors of respiratory distress syndrome. However, it needs further prospective study. Therefore, the concerned stakeholders should give due attention and appropriate intervention for these predictors.

Background: Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the available studies in Ethiopia have been usually focused in early childhood and they used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia. Methods: A total of 3156 weighted samples of adolescent girls aged 15–19 years were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 80% and 20% of the observations were used for training, and testing the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. The performance of the predictive model was evaluated using evaluation metrics value through Python software. The synthetic minority oversampling technique was used for data balancing and Boruta algorithm was used to identify best features. Association rule mining using an Apriori algorithm was employed to generate the best rule for the association between the independent feature and the targeted feature using R software. Results: The random forest classifier (sensitivity = 81%, accuracy = 77%, precision = 75%, f1-score = 78%, AUC = 85%) outperformed in predicting stunting compared to other ML algorithms considered in this study. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having one or more children were the top attributes to predict stunting. Association rule mining was identified the top seven best rules that most frequently associated with stunting among adolescent girls in Ethiopia. Conclusion: The random forest classifier outperformed in predicting and identifying the relevant predictors of stunting. Results have shown that machine learning algorithms can accurately predict stunting, making them potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt stunting among adolescent girls.