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Public health systems in both industrialized and undeveloped countries continue to struggle with the worldwide problem of breast cancer. In sub-Saharan African countries, notably Ethiopia, it is the form of cancer that strikes women the most commonly. Despite the extreme difficulties, the causes of mortality in Ethiopia have not yet been identified. In addition, little study has been done in this area. Therefore, the major objective of this analysis was to pinpoint the factors that were most responsible for the decreased life expectancy of breast cancer patients at the University of Jimma Medical Center. 552 women who had been treated for breast cancer at Jimma University Medical Center between October 2018 and December 2022 were included in this study, which used a retrospective cohort study design and five-year follow-up data. The most frequent and widely used test for comparing the probability of survival curves between several categorical independent variables was the log-rank test. Next, semi-parametric methods for multivariable analysis using the Cox proportional hazards model were used. Furthermore, a parametric strategy that includes fully parametric survival models better achieves the goal of the analysis. Among covariate, age of patient (ϕ = 254.06; 95% CI (3.95, 7.13), P-value = 0.000), patient live in urban (ϕ = 0.84; 95% CI (-0.35,-0.00), P-value = 0.047), preexisting comorbidity (ϕ = 2.46; 95% CI (0.39, 1.41), P-value = 0.001), overweight women cancer patient (ϕ = 0.05; 95% CI(-4.41,-1.57), P-value = 0.000, positive Axillary Node status cancer patient (ϕ = 0.04; 95% CI(-4.45,-1.88), P-value = 0.000), both surgery and chemotropic baseline treatment patient (ϕ = 0.53; 95% CI(-1.12,-0.16), P-value = 0.009) significantly affected the survival of women breast cancer. Age of breast cancer patient, patient education level, place of residence, marital status, pre-existing comorbidity, axillary node status, estrogen receptor, tumor size, body mass index at diagnosis, stage of cancer, and baseline treatment were found to have a significant effect on time to survive for women with breast cancer at the University of Jimma Medical Center, Oromia region, Ethiopia. However, the covariate histologic grade, number of positive lymph nodes involved, and type of hormone used were insignificant to the survival of breast cancer patients.

Sesame is a major annual oil crop that is grown practically everywhere in tropical and subtropical Asia, as well as Africa, for its very nutritious and tasty seeds. Rising temperatures, droughts, floods, desertification, and weather all have a significant impact on agricultural production, particularly in developing countries like Ethiopia. Therefore, the main objective of this study is to examine the influence of climate change on the sesame yield in North Gondar, North Ethiopia, by using the autoregressive distributed Lag (ARDL) time series model. This study employed climate data from the Bahirdar Agrometeorological Center and secondary data on sesame production from the Ethiopian Statistical Service, spanning 36 years, from 1987 to 2023. Autoregressive Distributed LAG (ARDL) includes diagnostic tests for both short- and long-term autoregressive models. The results for the long-run and short-run elastic coefficients show a significant positive association between temperatures and sesame yield. Sesame yield and rainfall have a significant negative long-run and short-run relationship in North Gondar, North Ethiopia. ARDL results confirm that temperature and rainfall have significant effects on sesame productivity. Temperature had a considerable favorable effect on sesamen production, but rainfall had a negative effect in North Gondar, Ethiopia. Based on the evidence acquired from our study, we made several policy recommendations and suggestions to government officials, policymakers, new technologies, researchers, policy development planners, and other stakeholders in order to develop or implement new technology to halt its production and direct adaptation measures in light of the certainty of global warming and the characteristics of climate-dependent agricultural production.

Introduction Chronic and highly contagious, trachoma is a condition characterized by recurrent bacterial infection with ocular strains of Mycoplasma trachoma. It spreads through fingers, flies, and fomites, especially in situations where there is overcrowding. If untreated, the illness may result in blindness. Trachoma is an ancient disease and has previously been a significant public health problem in many areas of the world, including parts of Europe and North America. There are at least 400 million cases of active trachoma in the world, 8 million of which have resulted in blindness. Trachoma is a serious public health issue that is very common in Ethiopia. Therefore, the objective of this study is to identify the determinants of active trachoma among rural children aged 1–9 years old in Aw-bare woreda, Somali region of Ethiopia. Method A cross-sectional community-based study involving children aged 1–9 who lived in six selected rural kebeles in the Awbare woreda Somali region and carried out using an ordinal logistic regression model. The study comprised 377 children in total. Our sample youngsters were chosen through a two-stage cluster sampling procedure. Then also chose our sample kebeles by simple random sampling. The main environmental, personal, and demographic factors that influenced the outcomes of active trachoma status were modeled using partial proportional odds modeling and descriptive statistics. Result The study showed that the prevalence of active trachoma was found to be 47.7%. The covariate secondary level of education of mother OR = 1.357; 95% CI (1.051, 1.75), P-value = 0.0192, Inside house cooking place of children family OR = 0.789:95% CI (0.687, 0.927), P-value = 0.0031, children stay at home OR = 2.203:95%CI (1.526, 3.473), P-value = 0.0057,rich income family OR = 1.335:95%CI(1.166,1.528),P-value = 0.0001,Amount of water fetched per day OR = 2.129,95%CI(1.780,2.547),P-Vaue = 0.0001 were significant effect on active trachoma. PPOM represents the best fit as it has the smallest AIC and BIC. It is also more parsimonious. Conclusion The mother’s educational level, the location where the children spent the majority of their time indoors cooking, the fly density during the interview, the family’s income, the child’s age in years, the distance to the water source, the quantity of water fetched daily, and the number of people sharing a room have all been found to be significant predictors of the child’s active trachoma status. Thus, increasing maternal education, access to clean water, and socioeconomic position are all crucial measures in preventing trachoma. Preventing trachoma also involves reducing the number of kids in a room and enhancing activities linked to personal cleanliness, such as giving kids a thorough facial wash to remove debris and discharge from their eyes.

Background Neisseria meningitides, Streptococcus pneumonia, and hemophilic influenza type B are frequently linked to bacterial meningitis (BM) in children. It’s an infectious sickness that kills and severely mobilizes children. For a variety of reasons, bacterial meningitis remains a global public health concern; most cases and deaths are found in Sub-Saharan Africa, particularly in Ethiopia. Even though vaccination has made BM more preventable, children worldwide are still severely harmed by this serious illness. Age, sex, and co-morbidity are among the risk variables for BM that have been found. Therefore, the main objective of this study was to identify the variables influencing the time to recovery for children with bacterial meningitis at Jigjiga University referral hospital in the Somali regional state of Ethiopia. Method A retrospective cohort of 535 children with bacterial meningitis who received antibiotic treatment was the subject of this study. Parametric Shared Frailty ty and the AFT model were employed with log likelihood, BIC, and AIC methods of model selection. The frailty models all employed the patients' kebele as a clustering factor. Results The number of cases of BM declined in young children during the duration of the 2 year, 11 month study period, but not in the elderly. Streptococcus pneumonia (50%), hemophilic influenza (30.5%), and Neisseria meningitides (15%) were the most frequent causes of BM. The time to recovery of patients from bacteria was significantly influenced by the covariates male patients (ϕ = 0.927; 95% CI (0.866, 0.984); p-value = 0.014), patients without a vaccination history (ϕ = 0.898; 95% CI (0.834, 0.965); P value = 0.0037), and patients who were not breastfeeding (ϕ = 0.616; 95% CI (0.404, 0.039); P-value = 0.024). The recovery times for male, non-breastfed children with bacterial patients are 7.9 and 48.4% shorter, respectively. In contrast to children with comorbidity, the recovery time for children without comorbidity increased by 8.7%. Conclusion Age group, sex, vaccination status, co-morbidity, breastfeeding, and medication regimen were the main determinant factors for the time to recovery of patients with bacterial meningitis. Patients with co-morbidities require the doctor at Jigjiga University Referral Hospital to pay close attention to them.

Introduction Pneumonia is one of the most common and deadly infectious diseases affecting under-five children, responsible for about 15% of all deaths in this age group worldwide. In Ethiopia, the prevalence ranges from 16% to 21%, contributing substantially to under-five mortality. Despite national child survival efforts, pneumonia-related deaths remain a major public health concern. Understanding the burden and identifying key risk factors are essential for effective prevention and timely intervention. This study aimed to estimate the time to death and identify its predictors among under-five children with acute pneumonia using Bayesian parametric survival analysis. Methods A retrospective study was conducted with 451 under-five children diagnosed with acute pneumonia. Three survival analysis models were applied: the Cox proportional hazards model, the parametric accelerated failure time (AFT) model, and the Bayesian parametric survival model. In the Bayesian model, Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling and the Metropolis-Hastings algorithm were employed to obtain samples from the posterior distributions of the parameters. Each model was evaluated using appropriate model selection criteria to identify the best-fitting approach. Results The Bayesian Lognormal AFT model identified several significant predictors of time to death among under-five children with acute pneumonia. All model parameters showed good convergence, with Monte Carlo errors under 5% of their standard deviations. Female children had shorter survival times compared to males (AF = 0.46; 95% CI: 0.36–0.97). Children aged 1–11 months had better survival outcomes (AF = 0.10; 95% CI: 0.05–0.21) than those aged 48–59 months. Rural residence (AF = 1.48; 95% CI: 1.03–2.09), diagnosis during spring (AF = 0.73; 95% CI: 0.52–0.92) and summer (AF = 0.66; 95% CI: 0.49–0.84), comorbidities (AF = 1.26; 95% CI: 1.03–1.65), severe acute malnutrition (AF = 0.26; 95% CI: 0.13–0.43), anemia (AF = 0.88; 95% CI: 0.73–0.93), low weight (AF = 0.72; 95% CI: 0.55–0.90), and home delivery (AF = 0.75; 95% CI: 0.59–0.95) were all associated with reduced survival times. Conclusion This study identified key predictors of mortality among under-five children with acute pneumonia using a Bayesian parametric survival model. Female, rural residence, severe acute malnutrition, comorbidity, anemia, and low weight were significantly associated with reduced survival times. Seasonal variation and place of delivery also influenced mortality, highlighting the impact of environmental and health system factors. These findings emphasize the need for targeted interventions focusing on early diagnosis, nutritional support, and tailored care for high-risk groups. Furthermore, the Federal Ministry of Health could enhance community awareness of early pneumonia detection and effective home management, particularly in rural areas where mortality risk is higher.

Introduction Sanitation is a fundamental human right and a cornerstone of public health. In Ethiopia, access to improved sanitation facilities remains limited, especially in rural areas. Poor sanitation, coupled with inadequate water supply and hygiene, significantly contributes to illness and mortality worldwide, disproportionately affecting developing countries. This study aims to identify individual and community‐level factors associated with the use of improved sanitation facilities among Ethiopian households by applying a multilevel logistic regression model to data from the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS). Methods This study used data from the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS. A total of 6141 households were included in the analysis. A cross‐sectional design was used to estimate the use of improved sanitation services at national, regional, urban, and rural levels. To account for the hierarchical nature of the data households nested within community's multilevel logistic regression was employed. This approach allowed for the partitioning of variance into individual and community level components, thereby improving model accuracy and capturing contextual influences on sanitation use. Results The analysis revealed significant regional disparities in the use of improved sanitation services among Ethiopian households. Married households had higher odds of using improved sanitation (AOR = 1.81, 95% CI: 1.67–4.70), while unmarried (AOR = 0.35, 95% CI: 0.21–0.56) and divorced households (AOR = 0.24, 95% CI: 0.23–0.50) had lower odds compared to widowed households. Rural households were less likely to use improved sanitation than urban ones (AOR = 0.37, 95% CI: 0.140–0.99). Illiterate (AOR = 0.13, 95% CI: 0.08–0.20) and primary‐educated (AOR = 0.25, 95% CI: 0.16–0.39) household heads were less likely to access improved sanitation than those with higher education. Poor (AOR = 0.86, 95% CI: 0.44–0.98) and middle‐income (AOR = 0.84, 95% CI: 0.42–0.93) households had lower odds compared to rich households. Homeownership (AOR = 2.28, 95% CI: 1.13–2.46) and private latrine use (AOR = 1.43, 95% CI: 1.36–2.65) were significantly associated with higher sanitation use. Conclusion This study reveals substantial regional and sociodemographic disparities in the use of improved sanitation facilities in Ethiopia. Households headed by married, educated, and wealthier individuals living in urban areas with private latrines and home ownership are more likely to have improved sanitation. Targeted interventions focusing on rural, poorer, less educated, and female‐headed households are essential to enhance access to improved sanitation. Addressing these disparities is critical for Ethiopia to achieve its national sanitation goals and contribute toward the Sustainable Development Goals related to health and well‐being.

Background Child mortality remains a significant public health concern in Ethiopia, particularly among children under five. Despite recent improvements in healthcare and economic development, mortality rates remain high due to infectious diseases, malnutrition, and limited access to healthcare. This study aimed to identify key determinants of child mortality among ever-married women aged 15–49 in Ethiopia using advanced multi-level count regression models. Methods This study used data from the Ethiopia Demographic and Health Survey (EDHS), Data from 16,650 women were analyzed using single-level and multi-level count models, including Poisson, negative binomial, zero-inflated, and hurdle models. The multilevel hurdle negative binomial (HNB) model was selected for its ability to distinguish between factors influencing the occurrence and frequency of child deaths. Result The truncated negative binomial part of the selected multilevel HNB model demonstrated a significant reduction in the risk of child mortality for babies born to mothers with the following characteristics: older age at first birth (IRR = 0.9398; 95% CI: 0.9139–0.9655), primary education level (IRR = 0.9298; 95% CI: 0.6763–1.2783), secondary education or above (IRR = 0.5961; 95% CI: 0.4103–0.8658), breastfeeding (IRR = 0.7886; 95% CI: 0.6484–0.9592), and contraceptive use (IRR = 0.8038; 95% CI: 0.6593–0.9799). Additionally, the study revealed significant regional differences in child mortality ( p  = 0.0401 and 0.0418), indicating clustering effects. There were notable regional variations in how mother occupation, father education, family size, contraceptive use, and mother education level influenced child mortality in Ethiopia. The multilevel HNB model revealed strong regional variations, with statistically significant clustering effects. Conclusion The random coefficients model was the most accurate of the three multilevel HNB regression models for predicting newborn mortality per mother. Significant factors identified included family size, age at first birth, birth order, contraceptive use, maternal education level, maternal occupation, breastfeeding practices, residence, and multiple births. Regional disparities in child mortality rates highlight the need for localized interventions. Strategies should focus on improving maternal education, access to healthcare in rural and nomadic areas, promoting breastfeeding, and increasing contraceptive use. Policymakers should prioritize high-mortality regions like Afar and Gambela for targeted support.

IntroductionBacterial meningitis is a serious infection mainly affecting children, causing high illness and death rates, particularly in Sub-Saharan Africa. It is often seen in low-income countries, and children who survive may face long-term health issues. This study aimed to find out what factors affect the timing of death among children with bacterial meningitis, using parametric AFT, parametric shared frailty, multilevel parametric AFT, and multilevel parametric shared frailty models.MethodA retrospective cohort study was done with 510 children diagnosed with bacterial meningitis were admitted from February 22, 2021, to April 20, 2023. The researchers used several statistical models, including parametric Accelerated Failure Time (AFT) models, shared frailty models, and multilevel models to analyze the data. They applied log-likelihood, AIC, and BIC methods for model selection and considered Kebeles as a clustering effect in the study.ResultAmong the covariates, children patient with BM who lived in urban area (Φ = 0.56; 95% CI (− 0.98,-0.18); p-value = 0.004), male patient (ϕ = 0.41,95%CI ((− 1.38, − 0.39);p value = 0.000), vaccination group patient (ϕ = 1.82;95%CI (0.41, 0.79); p-value = 0.000), breastfeeding children patient (Φ = 0.46; 95% CI (− 1.24, − 0.17); p-value < 0.009), existence Altered consciousness (Φ = 0.36; 95% CI (− 1.22, − 0.79); p-value = 0.000), ≤  20 mg/dl CSF glucose of patient (Φ = 1.2; 95% CI (0.02, 0.45); p-value = 0.031) and overcrowding (Φ = 0.31; 95% CI (− 1.40, − 0.96); p-value = 0.000) significantly affected the time to death of bacterial meningitis patient. Clustering had a significant effect on the variable of interest.ConclusionThe study identified key factors that significantly influence the time to death among pediatric patients with bacterial meningitis. It was observed that male patients tend to have a shorter time to death compared to females, and a prolonged symptom duration before hospitalization is associated with increased mortality. Critical factors influencing outcomes included the child's sex, place of residence, breastfeeding habits, number of siblings, living conditions (crowding), altered consciousness, existing health conditions, vaccination status, and the specific pathogens causing the illness. These elements were correlated with a higher risk of death in affected children. This information can be instrumental for healthcare providers and policymakers in their efforts to reduce mortality rates and enhance health outcomes for children with bacterial meningitis.

Carbon dioxide (CO₂) emissions are a major contributor to global climate change. In Ethiopia, CO₂ emissions have increased steadily in recent decades due to industrialization, rising energy consumption, expanding transportation systems, continued dependence on biomass fuels, and rapid population growth. Although Ethiopia has historically contributed only a small share to global greenhouse gas emissions, the recent upward trend poses challenges for sustainable development and environmental management. Accurate forecasting of CO₂ emissions is essential for designing effective climate mitigation strategies and evidence-based policy decisions. Therefore, this study aimed to analyze and forecast CO₂ emissions in Ethiopia using Bayesian Autoregressive Integrated Moving Average (ARIMA) and Bayesian Structural Time Series (BSTS) models. This study analyzed and forecasted carbon dioxide (CO₂) emissions in Ethiopia using an 82-year time series dataset covering the period from 1941 to 2022. Two Bayesian time series models were employed: Bayesian ARIMA and Bayesian Structural Time Series (BSTS). The Bayesian ARIMA model captured temporal dependencies through autoregressive and moving average components, whereas the BSTS model decomposed the time series into trend, seasonality, and regression components, allowing the incorporation of external predictors. Model parameters were estimated using Markov Chain Monte Carlo (MCMC) simulation techniques. Model performance was assessed using the Watanabe-Akaike Information Criterion (WAIC) and Leave-One-Out Information Criterion (LOOIC), with the model having the lowest values selected as the optimal forecasting model. The findings revealed that Ethiopia's mean annual per capita CO₂ emissions from 1941 to 2022 were approximately 0.054 metric tons. Among the candidate models evaluated, the Bayesian ARIMA (0, 1, 1) model demonstrated the best fit and forecasting performance based on WAIC and LOOIC criteria. Forecast results from the selected model indicate that Ethiopia's per capita CO₂ emissions are projected to increase gradually, reaching approximately 0.167, 0.169, 0.171, 0.171, 0.175, 0.177, and 0.179 metric tons in 2024, 2025, 2026, 2027, 2028, 2029, and 2030, respectively. The study indicates a persistent upward trend in Ethiopia's annual per capita CO₂ emissions through 2030, which may intensify climate-related and environmental challenges. These findings underscore the need for strengthened environmental policies and sustainable energy interventions, including carbon taxation, cap-and-trade mechanisms, and the promotion of clean and energy-efficient technologies to reduce future emissions.

COVID-19, a severe respiratory illness, is caused by the SARS-CoV-2 virus. The pandemic has devastated public health, economies, and social structures worldwide. In Ethiopia, the government and health authorities have implemented various COVID-19 prevention strategies to contain the spread of the virus. This study aims to investigates the factors influencing the implementation and effectiveness of COVID-19 prevention strategies in Jigjiga Town, Ethiopia. A community-based cross-sectional study was conducted from April 2022 to December 2022, involving 593 participants in Jigjiga town. Multi-stage sampling techniques were used, and data was collected using a structured questionnaire covering demographic characteristics, socioeconomic status, attitude, knowledge, prevention practices, misconceptions, and COVID-19 prevention strategies. A multivariate model was developed to control for confounding, using variables suitable for multivariate logistic regression analysis with p-values less than 0.25. A variable is considered significant in multivariable logistic regression analysis if its p-value is less than 0.05. The study found that only 12.2% of participants used COVID-19 prevention strategies. Those with a bachelor's degree or higher had a strong association with prevention strategies (AOR: 20.08, 95% CI: 2.13-188.85). Participants informed about COVID-19 prevention were 6.886 times more likely to use strategies (95% CI: 2.975-15.938). People who received the COVID-19 vaccine were 1.14 times more likely to engage in reasonable preventive measures compared to those who did not get vaccinated. The study reveals low COVID-19 prevention practices among participants, with only 12.2% utilizing preventive strategies. The covariate, the kinds of information received on COVID-19 prevention mechanisms, participants with a favorable attitude toward COVID-19, educational level, mask-wearing, social distancing, vaccination, hand hygiene, public health communication, and household income were significantly associated with COVID-19 prevention strategies. The COVID-19 vaccination promotes preventive practices, reduces infection risk, protects against severe illness, and decreases community spread.