Debre Tabor University

Background Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia. Methods Our study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC). Results The study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18–79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features. Conclusion Our study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management. Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills. This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.

Metabolic syndrome (MetS) poses a significant public health challenge globally, including in Ethiopia, with risks for both mothers and children. Unfortunately, there is limited data on MetS in pregnant Ethiopian women. This study aims to evaluate the prevalence and factors associated with MetS in this population. A cross-sectional study was conducted using a systematic random sampling technique. Data were collected through face-to-face interviews using a structured questionnaire adapted from the World Health Organization Steps Survey Tool for Non-communicable Diseases. About five ml of fasting peripheral blood samples were collected from each participant. The Beckman Coulter DXC 700 AU clinical chemistry analyzer was employed for lipid profile and glucose analysis. Subsequently, data were inputted into Epi Data and later exported to SPSS Version 20 for further analysis. Bivariable and multivariable binary logistic regression analyses were carried out, with a predefined level of statistical significance at p < 0.05. A total of 318 pregnant women were included in this study. The prevalence of MetS was 13.2% (95% CI: 9.7, 17.0) based on the American Heart Association/National Heart Lung and Blood Institute definition. The most prevalent components of MetS were elevated triglyceride levels, reduced high-density lipoprotein levels, and elevated blood pressure. Unhealthy sleep duration (AOR = 5.6, 95% CI (2.4, 13.1), p < 0.001), high daily salt intake (AOR = 4.2, 95% CI (1.8, 9.5), p = 0.001), and alcohol consumption [AOR = 4.2, 95% CI (1.6, 10.9), p = 0.003] were significantly associated with MetS. The study reported a high prevalence of MetS in pregnant Ethiopian women. Factors including alcohol, high salt intake, and sleep disturbances were associated with MetS. Policymakers might utilize this data to create targeted interventions and public health policies for MetS among pregnant women, focusing on nutrition, sleep, and alcohol consumption during pregnancy to safeguard maternal and fetal health.

Background Children living with HIV/AIDS are particularly vulnerable to under-nutrition. Under-nutrition associated with HIV/AIDS infection increases the rate of morbidity and mortality in children. To reaffirm a future objective, there needs to be evidence regarding the current national burden of under-nutrition and related factors among children infected with HIV. Hence, the objective of this systematic review and meta-analysis was to estimate the pooled prevalence of under-nutrition, and the pooled effect sizes of associated factors among HIV-infected children in Ethiopia. Methods We searched Ethiopian universities’ online libraries, Google, Google Scholar, PubMed, CINAHL, Cochrane Library, and Scopus to find the primary studies for this review. Publication bias was checked through Egger’s regression test. Heterogeneity among the included studies was assessed using the I² test. The data were extracted using Microsoft Excel and exported to STATA Version 14 statistical software. A random effect meta-analysis model was performed to estimate the pooled prevalence of Under-nutrition. Results After reviewing 1449 primary studies, 16 articles met the inclusion criteria and were included in the final meta-analysis. The estimated pooled prevalence of stunting, underweight, and wasting among children living with HIV/AIDS was 32.98% (95% CI: 22.47, 43.50), 29.76% (95% CI: 21.87, 37.66), and 21.16% (95% CI: 14.96, 27.35) respectively. Conclusions This study showed that under-nutrition among HIV-infected children in Ethiopia was significantly high. Under-nutrition is more common among HIV-infected children with opportunistic infections, child feeding problems, do not adhere to dietary recommendations, and have diarrhea. The national policies and strategies for ART service- provider centers should maximize their emphasis on reducing under-nutrition among HIV-infected children. Based on this finding, we recommend HIV intervention programs to address nutritional assessment and interventions for HIV-infected children. Protocol registration The protocol has been registered in the PROSPERO database with a registration number of CRD-394170.

Background Depression commonly coexists with diabetes leads to complications and worsens the outcome. Even though the problem affects low‐ and middle‐income countries including Ethiopia, only a few studies have been done to show the magnitude of the problem and factors associated with it. So, the study was conducted to fill those gaps Objective The main objective of this study was to assess psychosocial and clinical factors associated with depression among diabetic patients in Amhara region comprehensive specialized hospitals, Ethiopia, 2022. Methods A hospital‐based cross‐sectional study was conducted in randomly selected hospitals of Amhara region from January 7 to February 10, 2022. A total of 426 diabetic patients who were on outpatient follow‐up were selected using a multistage sampling technique. A p‐value of ≤0.25 in the bivariable analysis was used to select variables for the multivariable analysis. A p‐value < 0.05 within a 95% confidence interval was considered to be significantly associated factors. Result Out of 426 interviewed diabetes patients 203 (47.7%) had depression. Moderate physical activity (AOR = 0.50, 95% CI (0.29, 0.86)). low medication adherence (AOR = 2.10, 95% CI (1.22, 3.62)), medium medication adherence (AOR = 1.78, 95% CI (1.04, 3.06)), and high social support (AOR = 0.54, 95% CI (0.33, 0.91)) were significantly associated with depression among diabetic patients. Conclusion The overall prevalence of depression among diabetic patients was higher than in other developing countries. Hence, special attention to preventing depression and maintaining mental illness among patients with chronic illnesses, especially diabetes should be given.

Objective The present study was aimed at investigating the antinociceptive and anti-inflammatory activities of the solvent fractions of the roots of Echinops kebericho Mesfin in rodent models of pain and inflammation. Methods Successive maceration was used as a method of extraction using solvents of increasing polarity: methanol and water. Ethyl acetate, chloroform and distilled water were used as solvents of the fraction process. Swiss albino mice models were used in acetic acid induced writhing, hot plate, carrageenan induced paw edema and cotton pellet granuloma to assess the analgesic and anti-inflammatory activities. The test groups received different doses (100 mg/kg, 200 mg/kg and 400 mg/kg) of the three fractions (chloroform, ethyl acetate and aqueous). The positive control groups received ASA (150 mg/kg) for the writing test, morphine (10 mg/kg) for the hot plate method, diclofenac Na for carrageenan-induced paw edema, and dexamethasone (10 mg/kg) for granuloma, while the negative control group received distilled water. Results EA fraction at all test doses employed (100 mg/kg, 200 mg/kg, and 400 mg/kg) showed statistically significant (p<0.05, p<0.01, p<0.001 respectively) analgesic and anti-inflammatory activities in a dose-dependent manner. The AQ fraction on the other hand produced statistically significant (p<0.05, p<0.012) analgesic and anti-inflammatory activities at the doses of 200 mg/kg and 400 mg/kg, while the CH fraction exhibited statistically significant (p<0.05) analgesic and anti-inflammatory activity at the dose of 400 mg/kg. Conclusions In general, the data obtained from the present study elucidated that the solvent fractions of the study plant possessed significant analgesic and anti-inflammatory activities and were recommended for further investigations.

Livestock plays a significant role in the livelihood enhancement of the people, especially in rural areas. The requirement for dietary protein for people from the animal sources is expected to increase. However, due to problems of feed shortages and high costs of feed the livestock sector and its contribution to the country’s economy are threatened in many countries. To address this problem, exploration of alternative sources of cheap feed resources is imperative. Azolla is one of the cheapest feed resources grown in water bodies, mainly in tropical and sub-tropical countries. There are various species of Azolla used as a feed source for livestock. Azolla filiculoides, Azolla pinnata and Azolla microphylla are the main species of Azolla that contribute to livestock feed. Nutritionally, Azolla is a good source of amino acids, minerals, and vitamins. It is commonly used for feeding poultry, sheep, and goats, and dairy cattle. Azolla is used as a supplementary feed with other feed resources to increase feed intake, growth, egg production, and milk yield, as proven by many researches. Besides animal feeding, Azolla is also used for soil fertility improvement, bioremediation, compost making, and biogas production. Therefore, the production and utilization of Azolla in an appropriate, way particularly in wetland areas will contribute to improving livestock nutrition and productivity.

Background and Aims Food‐borne illness is a public health concern in developing countries because of improper food handling and sanitation practices, irregular medical checkups, lack of clean water supplies, and inadequate education among food handlers. This study investigated the burden of bacterial food‐borne illness, antibiotic resistance patterns, and associated factors among food handlers in prison and nonprison food establishment settings. Methods A cross‐sectional study was conducted from August 2022 to January 2023 among asymptomatic food handlers in Shewa Robit town. A total of 384 food handlers participated. Data were collected using structured questionnaires. Stool and hand swab samples were collected and cultivated onto MacConkey agar, xylose‐lysine‐deoxycholate, Mannitol salt agar, and blood agar, and incubated at 37°C. Bacterial species were identified using biochemical tests and gram staining. Mueller–Hinton agar was used in Kirby Bauer's disk diffusion method. Data were entered and analyzed using SPSS. Descriptive and logistic regression analysis were performed. Results Fecal and hand carriage rate of bacterial isolates were 106 (27.6%), and 214 (55.7%), respectively. Out of the 102 bacterial isolates, the most common ones from stool samples were Escherichia coli 71 (18.5%), Klebsiella aerogenes 12 (3.1%), and Salmonella spp. 7 (1.8%). Among 214 bacterial isolates, coagulase‐negative Staphylococci 115 (29.9%) and Staphylococci aureus 66 (17.3%) were identified from hand swab samples. Hand washing practice after restroom with water (adjusted odds ratio [AOR] = 2; 95% confidence interval [CI]: 1.16–3.45), irregular medical checkups (AOR = 2.49; 95% CI: 1.35–4.59), and did not receive food safety and hygiene training (AOR = 2.33; 95% CI: 1.34–4.05) were statistically significant association with food‐borne illness. Conclusions Foodborne pathogens pose a serious health risk in the study areas. The level of antimicrobial resistance are also concerning. Food handlers should therefore get strict regular health education, medical checkups, and training programs to prevent the spread of infections to the customers.

In this manuscript, a numerical model based on the electric field, threshold voltage, sub-threshold current, and electrostatic potential in cylindrical coordinates using Poisson’s equation for triple hybrid metal (THM) gate dielectric modulated junctionless silicon-nanowire gate all around FET based uricase and ChOX biosensor was developed at 40 nm technology (20 nm gate length) to study different gate engineering optimization effects on the performance of the proposed device. The results of the ATLAS-3D TCAD" device simulator agreed with a derived analytical model. Three types of gate optimization (gate engineering) are denoted by Mϕ (4.86, 4.96 and 4.50 eV), Oϕ (4.96, 4.86 and 4.50 eV), and Qϕ (4.86, 4.50 and 4.96 eV) each have three different metal work-function, including uricase and cholesterol oxidase (ChOX) biomolecules have been coated in the nanocavity to determine their impact on the device performance and also, the effect of nanogap cavity length on the proposed device was examined taking numerous simulations. Our findings conclude that nanocavity coated with ChOX dielectric and having tunable work-function optimized at “O” signifies better output results in the device sensitivity, shifting threshold voltage, switching ratio, transconductance, intrinsic voltage gain, and device efficiency. For instance, the switching ratio in the case of ChOX biomolecule for M, O, and Q gate optimizations are 5.22 × 10⁵, 1.36 × 10⁶, and 2.18 × 10⁴, respectively. We conclude that the proposed devices with optimizing gate work function at “O” suggest new opportunities for future ultra-large-scale integration (ULSI) development to achieve highly efficient device performance.

Background Malaria is a critical public health concern in Ethiopia, with significant socioeconomic consequences. Malaria data trend analysis is essential for understanding transmission patterns and adopting evidence-based malaria control measures. The purpose of this study was to determine the 5 year distribution of malaria in North Shewa zone, Amhara region, Ethiopia, in 2023. Methods A descriptive cross-sectional study design was employed to analyse the 5 year trend of malaria surveillance data in the North Shewa zone of the Amhara regional, Ethiopia, spanning from July 2018 to June 2023. The malaria indicator data were gathered from the zone’s public health emergency management database. Malaria data from the previous 5 years was collected, compiled, processed, and analysed using Microsoft Excel 2019. Results Among a total of 434,110 suspected cases 47,889 (11.03%) cases were confirmed as malaria, with an average annual malaria incidence rate of 4.4 per 1000 population in the Zone. Malaria cases exhibited an increase from Epidemiological Week (Epi week) 37 to Epi week 49 (September to November) and again from Epi week 22 to week 30 (May to July). Individuals aged 15 and above, and all districts in the Zone except Angolela were notably affected by malaria. Conclusion Despite implementing various measures to reduce malaria incidence, the disease continues to persist in the zone. Therefore, the Zone Health Department should intensify its preventive and control efforts.

This article introduces a new fitted operator method for singularly perturbed parabolic convection-diffusion having right boundary layer. We approximate the time derivative using the implicit Euler’s approach and the spatial derivative using polynomial cubic spline method. Using the idea of a singular perturbation, a fitting factor is added to the scheme. The resulting tridiagonal system of equations is solved using Thomas algorithm. We demonstrate the order of convergence and the proposed method have order two in space variable and order one in time variable. Three numerical examples are considered to illustrate the applicability of the present methods and compared with the methods produced by different authors. In general, the presented method is \(\epsilon \)-uniformly convergent for any values of mesh size and more accurate than some methods existing in the literature.

Background: Neurocognitive impairment, characterized by reduced performance in various cognitive domains, has been significantly linked with glycemic control in type 2 diabetes mellitus (T2DM) patients. Poorly controlled diabetes often results in decreased cognitive abilities, and a longer duration of the disease is associated with lower cognitive levels. Objective: This study aimed to evaluate the prevalence of cognitive impairment in adults with T2DM and identify related factors. Methods: An institution-based cross-sectional study was conducted among 421 adults with T2DM. A systematic random sampling was used to select study participants in two referral hospitals in Bahir Dar, Ethiopia. Standardized Mini-Mental State Examination tool was used. Binary logistic regression was used. Significance was declared at p value≤0.05 with 95% confidence interval. Results: Over a quarter (27.6%) of participants were identified as cognitively impaired. Factors associated with lower cognitive status included older age, being single, lower education level, farming occupation, presence of comorbidity, and engagement in moderate physical activity. Conclusions: In conclusion, the prevalence of cognitive impairment among T2DM patients is a growing concern. Several risk factors have been identified like age group, marital status, education level, occupation, presence of comorbidity, and moderate physical activities. The impact of cognitive impairment on the quality of life and functional abilities of T2DM patients should not be underestimated.

Introduction Hypertension is a major global public health problem. It currently affects more than 1.4 billion people worldwide, projected to increase to 1.6 billion by 2025. Despite numerous primary studies have been conducted to determine the prevalence of uncontrolled hypertension and identify its associated factors among hypertensive patients in Sub-Saharan Africa, these studies presented inconsistent findings. Therefore, this review aimed to determine the pooled prevalence of uncontrolled hypertension and identify its associated factors. Methods We have searched PubMed, Google Scholar, and Web of Science databases extensively for all relevant studies. A manual search of the reference lists of included studies was performed. A weighted inverse-variance random-effects model was used to compute the overall pooled prevalence of uncontrolled hypertension and the effect size of its associated factors. Variations across the included studies were checked using forest plot, funnel plot, I² statistics, and Egger’s test. Results A total of twenty-six primary studies with a sample size of 11,600 participants were included in the final meta-analysis. The pooled prevalence of uncontrolled hypertension was 50.29% (95% CI: 41.88, 58.69; I² = 98.98%; P<0.001). Age of the patient [AOR = 1.57: 95% CI: 1.004, 2.44], duration of diagnosis [AOR = 2.57: 95% CI: 1.18, 5.57], non-adherence to physical activity [AOR = 2.13: 95% CI: 1.15, 3.95], khat chewing [AOR = 3.83: 95% CI: 1.59, 9.24] and habitual coffee consumption [AOR = 10.79: 95% CI: 1.84, 63.24] were significantly associated with uncontrolled hypertension among hypertensive patients. Conclusions The pooled prevalence of uncontrolled hypertension was considerably high. Older age, duration of diagnosis, non-adherence to physical activity, khat chewing and habitual coffee consumption were independent predictors of uncontrolled hypertension. Therefore, health professionals and other responsible stakeholders should encourage hypertensive patients to adhere to regular physical activity, and abstain from khat chewing and habitual coffee consumption. Early identification of hypertension and management of comorbidities is crucial, and it should be emphasized to control hypertension easily.

Background Acute myeloid leukemia (AML) is aggressive type of hematological malignancy. Its poses challenges in early diagnosis, necessitating the identification of an effective biomarker. This study aims to assess the diagnostic accuracy of long noncoding RNAs (lncRNA) in the diagnosis of AML through a meta‐analysis. The study is registered on the PROSPERO website with the number 493518. Method A literature search was conducted in the PubMed, Embase, Hinari, and the Scopus databases to identify relevant studies. We pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristics (ROC) using Stata 14.1 software. Heterogeneity between studies was determined through the I² statistic and Cochran‐Q test. A random effect model was chosen due to significant heterogeneity among included studies. Meta‐regression and subgroup analysis were performed to assess the potential source of heterogeneity. Furthermore, potential publication bias was estimated using Deek's funnel plot asymmetry test. Results A total of 14 articles covering 19 studies were included in this meta‐analysis comprising 1588 AML patients and 529 healthy participants. The overall pooled sensitivity, specificity, PLR, NLR, DOR, and the area under the summary ROC curve were 0.85 (95% CI = 0.78–0.91), 0.82 (95% CI = 0.72–0.89), 4.7 (95% CI = 2.9–7.4), 0.18 (95% CI = 0.12–0.28), 26 (95% CI = 12–53), and 0.90 (95% CI = 0.87–0.93), respectively. Moreover, lncRNAs from non‐bone marrow mononuclear cells (BMMC) had superior diagnostic value with pooled sensitivity, specificity, and AUC were 0.93, 0.82, and 0.95, respectively. Conclusion This meta‐analysis demonstrated that circulating lncRNAs can serve as potential diagnostic markers for AML. High accuracy of diagnosis was observed in non‐BMMC lncRNAs, given cutoff value, and the GADPH internal reference gene used. However, further studies with large sample size are required to confirm our results.

The emergence and spread of antibiotic resistance (ABR) have been a public health challenge globally. The burden is even higher in low-income countries where there is a lack of appropriate healthcare systems, and inappropriate antibiotic disposal practices and utilization. Due to poor solid waste disposal practices in developing nations, municipal solid waste dumpsite (MSWDS) can be a reservoir for ABR bacteria. However, only a few studies demonstrated the prevalence of ABR in non-clinical environments such as MSWDS. This study assessed the prevalence of ABR bacteria at Bahir Dar City MSWDS, to understand the public health risks related to poor solid waste disposal systems. Nine soil samples were collected from the dumpsite. Bacteria were isolated, identified and tested for ABR. Seventy-one distinct colonies were isolated from all samples and identified into 10 bacterial genera based on morphological features and biochemical tests. For ABR tests, gentamicin (GN, 10 μg), streptomycin (ST, 30 μg), tetracycline (TE, 30 μg), ciprofloxacin (CIP, 5 μg), nalidixic acid (NAA, 30 μg), sulfonamide (SA, 250 μg), chloramphenicol (C, 30 μg), erythromycin (E, 15 μg), vancomycin (V, 30 μg), and amoxicillin (AMX, 25 μg) were used. The most frequently isolated bacteria were Staphylococcus (23%) followed by Escherichia species (17%). Ten isolates related to Bacillus spp. were excluded from the antibiotic sensitivity test as there is no standard regarding this genus in the Clinical and Laboratory Standards Institute. The overall antibiotic résistance rate was 95.08%, and most isolates were found to be resistant to amoxicillin (100%), nalidixic acid (75.5%), and vancomycin (75%). Substantial proportions of the isolates were also resistant to tetracycline (55.35%), streptomycin (54.5%), and sulfonamide (50%). The overall multidrug resistance (MDR) rate was 36.06%. This high level of ABR calls for urgent intervention in waste management systems and regular surveillance programs.

In this paper, we propose a scheme to boost a macroscopic entanglement between two mechanical modes assisted by an optical parametric amplifier (OPA) with a coherent feedback loop. Thus, the hybrid quantum system consists of an optical cavity, an OPA is placed inside, and two charged mechanical oscillators are coupled through a Coulomb force interaction, where the coherent feedback loop is implemented. Following the dynamics of quantum Langevin equations, we employed the quantum Lyapunov continuous variable solution numerically to quantify the macroscopic entanglement through logarithmic negativity. The results show that the presence of OPA, coherent feedback, and robust Coulomb interaction improve the macroscopic entanglement between the two mechanical modes. This phenomenon occurs due to the enhancement of the non-linear gain in the optical parametric amplifier, which leads to an increased number of photons in the cavity. Furthermore, we observe that the state transfer between the spatially separated mechanical oscillators is enhanced with an increase in the intensity of the driving laser. We believe that the present scheme enhances phonon-phonon entanglement transfer through the presence of an OPA and coherent feedback loop and has potential applications in continuous-variable quantum information processing.

Background: Immunization is one of the most cost-effective interventions, averting 3.5–5 million deaths every year worldwide. However, incomplete immunization remains a major public health concern, particularly in Ethiopia. The objective of this study is to investigate the geographical inequalities and determinants of incomplete immunization in Ethiopia. Methods: A secondary analysis of the mini-Ethiopian Demographic Health Survey (EDHS 2019) was performed, utilizing a weighted sample of 3,865 children aged 12–23 months. A spatial auto-correlation (Global Moran's I) statistic was computed using ArcGIS version 10.7.1 to assess the geographical distribution of incomplete immunization. Hot-spot (areas with a high proportion of incomplete immunization), and cold spot areas were identified through Getis-Ord Gi* hot spot analysis. Additionally, a Bernoulli probability-based spatial scan statistics was conducted in SaTScan version 9.6 software to determine purely statistically significant clusters of incomplete immunization. Finally, a multilevel fixed-effects logistic regression model was employed to identify factors determining the status of incomplete immunization. Results: Overall, in Ethiopia, more than half (54%, 95% CI: 48–58%) of children aged 12–23 months were not fully immunized. The spatial analysis revealed that the distribution of incomplete immunization was highly clustered in certain areas of Ethiopia (Z-score value = 8.379419, p-value < 0.001). Hotspot areas of incomplete immunization were observed in the Afar, Somali, and southwestern parts of Ethiopia. The SaTScan spatial analysis detected a total of 55 statistically significant clusters of incomplete immunization, with the primary SaTScan cluster found in the Afar region (zones 1, 3, and 4), and the most likely secondary clusters detected in Jarar, Doola, Korahe, Shabelle, Nogob, and Afdar administrative zones of the Somali region of Ethiopia. Indeed, in the multilevel mixed-effect logistic regression analysis, the respondent's age (AOR: 0.92; 95% CI: 0.86–0.98), residence (AOR: 3.11, 95% CI: 1.36–7.14), living in a pastoralist region (AOR: 3.41; 95% CI: 1.29–9.00), educational status (AOR: 0.26; 95% CI: 0.08–0.88), place of delivery (AOR: 2.44; 95% CI: 1.15–5.16), and having PNC utilization status (AOR: 2.70; 95% CI: 1.4–5.29) were identified as significant predictors of incomplete immunization. Conclusion and recommendation: In Ethiopia, incomplete immunization is not randomly distributed. Various factors at both individual and community levels significantly influence childhood immunization status in the country. It is crucial to reduce disparities in socio-demographic status through enhanced collaboration across multiple sectors and by bolstering the utilization of maternal health care services. This requires concerted efforts from stakeholders.

Background Despite various interventions to combat child malnutrition in sub-Saharan Africa, wasting remains a critical public health concern for children aged 6–59 months. Wasting is a significant predictor of child survival and development, with a heightened risk of mortality among children. However, there is a lack of recent comprehensive data on the prevalence, severity level, and factors contributing to wasting in this age group. Objective To identify the severity levels of wasting and its individual and community-level factors contributing to wasting among children aged 6–59 months in Sub-Saharan African countries. Methods This research utilized Demographic and Health Survey data from 34 Sub-Saharan African countries, spanning the period from 2007 to 2022. The study included a weighted sample of 180,317 6–59-month-old children. We employed a multilevel proportional odds model to identify factors predicting the severity of wasting. Adjusted odds ratios and 95% confidence intervals were reported to demonstrate significant relationships (p < 0.05) in the final model. Results In Sub-Saharan Africa, 7.09% of children aged 6–59 months experience wasting (95% CI: 6.97, 7.20%). Among these children, the prevalence of moderate wasting is 4.97% (95% CI: 4.90, 5.10%), while severe wasting affects 2.12% (95% CI: 2.0, 2.20%). Factors such as term/post-term babies, wealth, frequency of feeding, improved toilet facilities, water sources, employed and educated mothers, rural residence, high community maternal education, and community media exposure are strongly associated with a lower chance of experiencing severe form of wasting. Conversely, birth order, family size, breastfeeding, diarrhea, cough, and fever, high community poverty, female household heads, and all Sub-Saharan Africa regions are linked to higher levels of wasting. Conclusion The study findings underscore the persistent challenge of wasting among Sub-Saharan Africa’s children, with 7.09% affected, of which 4.97% experience moderate wasting and 2.12% severe wasting. The identified predictors of wasting highlight the complex interplay of socio-economic, environmental, and health-related determinants. To address this issue improve access to healthcare and nutrition services, enhance sanitation infrastructure, promote women’s empowerment, and implement community-based education programs. Additionally, prioritize early detection through routine screening and strengthen health systems’ capacity to provide timely interventions.

Introduction The largest risk of child mortality occurs within the first week after birth. Early neonatal mortality remains a global public health concern, especially in sub-Saharan African countries. More than 75% of neonatal death occurs within the first seven days of birth, but there are limited prospective follow- up studies to determine time to death, incidence and predictors of death in Ethiopia particularly in the study area. The study aimed to determine incidence and predictors of early neonatal mortality among neonates admitted to the neonatal intensive care unit of Addis Ababa public hospitals, Ethiopia 2021. Methods Institutional prospective cohort study was conducted in four public hospitals found in Addis Ababa City, Ethiopia from June 7th, 2021 to July 13th, 2021. All early neonates consecutively admitted to the corresponding neonatal intensive care unit of selected hospitals were included in the study and followed until 7 days-old. Data were coded, cleaned, edited, and entered into Epi data version 3.1 and then exported to STATA software version 14.0 for analysis. The Kaplan Meier survival curve with log- rank test was used to compare survival time between groups. Moreover, both bi-variable and multivariable Cox proportional hazard regression model was used to identify the predictors of early neonatal mortality. All variables having P-value ≤0.2 in the bi-variable analysis model were further fitted to the multivariable model. The assumption of the model was checked graphically and using a global test. The goodness of fit of the model was performed using the Cox-Snell residual test and it was adequate. Results A total of 391 early neonates with their mothers were involved in this study. The incidence rate among admitted early neonates was 33.25 per 1000 neonate day’s observation [95% confidence interval (CI): 26.22, 42.17]. Being preterm birth [adjusted hazard ratio (AHR): 6.0 (95% CI 2.02, 17.50)], having low fifth minute Apgar score [AHR: 3.93 (95% CI; 1.5, 6.77)], low temperatures [AHR: 2.67 (95%CI; 1.41, 5.02)] and, resuscitating of early neonate [AHR: 2.80 (95% CI; 1.51,5.10)] were associated with increased hazard of early neonatal death. However, early neonatal crying at birth [AHR: 0.48 (95%CI; 0.26, 0.87)] was associated with reduced hazard of death. Conclusions Early neonatal mortality is high in Addis Ababa public Hospitals. Preterm birth, low five-minute Apgar score, hypothermia and crying at birth were found to be independent predictors of early neonatal death. Good care and attention to neonate with low Apgar scores, premature, and hypothermic neonates.

The study was conducted in Endiras Forest, northwest Ethiopia, to evaluate the effects of environmental variables on the patterns of plant community formation. A systematic random sampling technique was used to collect vegetation data from 56 (20 m × 20 m) plots laid at 100 m intervals on ten transects. In each plot, the species encountered and its percent cover abundance were recorded, which was later transformed into a modified Braun-Blanquet scale. The composite soil samples collected from 15 cm × 15 cm subplots were examined for 13 soil parameters. Communities were determined using cluster analysis. The Shannon-Wiener index was employed to quantify species diversity. The relationships between species and environmental variables were evaluated using canonical correspondence analysis (CCA). Seventy-three woody plant species, distributed in 40 families, were documented. Fabaceae was found to be the most species-rich family (20.55%). Five communities were generated from the cluster analyses that vary in diversity. Nine environmental variables were found to be significant in determining patterns of community formation (P < 0.05). Organic matter, pH, and altitude, highly correlated with CCA axis 1, largely shaped the community formation patterns. Various patterns of community formation demonstrate the need to design different conservation measures.