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Obstetric fistula repair failure can result in increased depression, social isolation, financial burden for the woman, and fistula care programs. However, there is limited, comprehensive evidence on obstetric fistula repair failure in Sub-Saharan African countries. This systematic review and meta-analysis aimed to determine the pooled prevalence of obstetric fistula repair failure and associated factors among women who underwent surgical repair in Sub-Saharan African countries. To identify potential articles, a systematic search was done utilizing online databases (PubMed, Hinari, and Google Scholar). The Preferred Reporting Items for Systematic Review and Meta-Analysis Statement (PRISMA) guideline was used to report the review's findings. I2 test statistics were employed to examine study heterogeneity. A random-effects model was used to assess the pooled prevalence of obstetric fistula repair failure, and the association was determined using the log odds ratio. Publication bias was investigated using the funnel plot and Egger's statistical test at the 5% level of significance. Meta-regression and subgroup analysis were done to identify potential sources of heterogeneity. The data were analyzed using STATA version 17 statistical software. A total of 24 articles with 9866 study participants from 13 Sub-Saharan African countries were included in this meta-analysis. The pooled prevalence of obstetric fistula repair failure in sub-Saharan Africa was 24.92% [95% CI: 20.34-29.50%]. The sub-group analysis by country revealed that the highest prevalence was in Angola (58%, 95% CI: 53.20-62.80%) and the lowest in Rwanda (13.9, 95% CI: 9.79-18.01%). Total urethral damage [OR  =  3.50, 95% CI: 2.09, 4.91], large fistula [OR = 3.09, 95% CI: (2.00, 4.10)], duration of labor [OR = 0.45, 95% CI: 0.27, 0.76], and previous fistula repair [OR = 2.70, 95% CI: 1.94, 3.45] were factors associated with obstetric fistula repair failure. Women who received surgical treatment for obstetric fistulas in Sub-Saharan African countries experienced more repair failures than the WHO standards. Obstetric fistula repair failure was affected by urethral damage, fistula size, duration of labor, types of fistula, and history of previous repairs. Therefore, we suggest policy measures specific to each country to provide special attention to the prevention of all risk factors, including poor nutrition, multiparty, obstructed labor, and maternal age, which can result in conditions like large fistulas, urethral damage, and repeat repair, in order to reduce obstetric fistula repair failure.

Understanding women's fertility preferences is essential for addressing reproductive behaviors and family planning needs. In sub-Saharan Africa, fertility rates remain high, yet many women experience unrealized fertility, which is having fewer children than desired. However, the factors influencing unrealized fertility remain underexplored. This study assessed the determinants of unrealized fertility among women approaching the end of their reproductive years in sub-Saharan Africa. A secondary data analysis was conducted using phase eight Demographic and Health Surveys data from 19 sub-Saharan African countries. The weighted sample included 46,408 women aged 40-49 years. A multilevel Poisson regression model with robust variance was used to identify factors associated with unrealized fertility. Adjusted prevalence ratios with 95% confidence intervals (CI) were reported, and variables with a p value <0.05 were considered statistically significant. The pooled prevalence of unrealized fertility among women aged 40-49 years was 61.43% (95% CI: 57.63%, 65.24%). Rwanda (37.40; 95% CI: 27.92%, 46.88%) and Sierra Leone (69.34; 95% CI: 60.30%, 78.38%) had the lowest and highest prevalence, respectively. Older maternal age at first birth, being employed, having no children or only children of one sex, and experiencing child death were associated with higher prevalence of unrealized fertility. Conversely, higher maternal education, the use of contraceptives, having both male and female children, and residing in rural areas were associated with lower prevalence of unrealized fertility. A large proportion of women nearing the end of their reproductive careers in sub-Saharan Africa have experienced unrealized fertility. Therefore, addressing cultural norms surrounding sex preference and number of children, alongside empowering women through improved access to education, healthcare, and comprehensive sexual and reproductive health services, is critical.

Antenatal depression, ranging from mild to severe, is influenced by hormonal changes during pregnancy and childbearing years, making it a significant public health issue. Antenatal depression, with its far-reaching effects on mothers, infants, and children, continues to be a significant public health issue in developing countries such as Ethiopia. Research on antenatal depression in Ethiopia has produced varied results. Although previous systematic reviews and meta-analyses studies have addressed this topic, a comprehensive summary of existing reviews has not been available. Therefore, this umbrella review aims to consolidate the findings on antenatal depression and associated factors among pregnant women in Ethiopia. This review included five systematic reviews and meta-analyses from various databases, including PubMed, PsycINFO, Research4life, CINHALE and Science Direct. Only reviews published between January 1, 2010, and July 30, 2024, were considered. The search, conducted from August 5 to 15, 2024, used CoCoPop questions and included only English-language reviews. Study quality was assessed with the AMSTAR tool, and data extraction and analysis were performed using Microsoft Excel 2016 and STATA 14.0. The I2 and Cochran's Q tests were used to assess heterogeneity. Pooled effect sizes were calculated based on the pooled prevalence of antenatal depression and odds ratios for associated factors, with a 95% confidence interval indicating statistical significance. This umbrella review encompassed 50 primary studies from five systematic reviews and meta-analyses, involving a total of 25,233 pregnant women. The pooled prevalence of antenatal depression in Ethiopia was found to be 24.60% (95% CI: 22.46-26.73). Significant associations were identified between antenatal depression and several factors: unplanned pregnancy (POR = 2.29; 95% CI: 1.75, 2.82), poor social support (POR = 2.10; 95% CI: 1.37, 2.84), history of abortion (POR = 2.49; 95% CI: 1.64, 3.34), history of depression (POR = 3.57; 95% CI: 2.43, 4.71), and history of obstetric complications (POR = 2.94; 95% CI: 1.61, 4.28). The significant prevalence of antenatal depression (24.60%) among pregnant women in Ethiopia is closely linked to factors such as unplanned pregnancy, poor social support, history of abortion, previous depression, and obstetric complications. To tackle this issue, it is recommended to enhance social support networks, increase access to family planning services to minimize unplanned pregnancies, conduct regular mental health screenings, and incorporate mental health services into antenatal care.

This study aims to develop a hybridized deep learning model for generating semantically meaningful image captions in Amharic Language. Image captioning is a task that combines both computer vision and natural language processing (NLP) domains. However, existing studies in the English language primarily focus on visual features to generate captions, resulting in a gap between visual and textual features and inadequate semantic representation. To address this challenge, this study proposes a hybridized attention-based deep neural network (DNN) model. The model consists of an Inception-v3 convolutional neural network (CNN) encoder to extract image features, a visual attention mechanism to capture significant features, and a bidirectional gated recurrent unit (Bi-GRU) with attention decoder to generate the image captions. The model was trained on the Flickr8k and BNATURE datasets with English captions, which were translated into Amharic Language with the help of Google Translator and Amharic Language experts. The evaluation of the model showed improvement in its performance, with a 1G-BLEU score of 60.6, a 2G-BLEU score of 50.1, a 3G-BLEU score of 43.7, and a 4G-BLEU score of 38.8. Generally, this study highlights the effectiveness of the hybrid approach in generating Amharic Language image captions with better semantic meaning.

Early childhood development (ECD) plays a crucial role in shaping the future development of children and it influences their lifelong outcomes. The Early childhood development index 2030 (ECDI2030) serves as an effective tool for monitoring the overall development of children aged 24-59 months at the population level. This study employed machine learning algorithms to identify the predictors of ECD across three East African countries, using the ECDI2030. Data were derived from the Demographic and Health Surveys of Kenya, Mozambique, and Tanzania. Seven supervised machine learning algorithms and an ensemble of the best performing models were utilized to predict ECD. The dataset was randomly divided into 80% training and 20% testing sets. The predictive ability of each machine learning model was evaluated using area under the curve (AUC) and the classification metrics. We used SHapley Additive exPlanations (SHAP) to explain the predictions by interpreting feature importance. About 57.4% (95% CI = 56.5, 58.3) children were developmentally on track in health, learning, and psychosocial well-being. The ensemble model of extreme gradient boosting and random forest was the best algorithm with accuracy of 66% and AUC of 71%. The top three most important predictors of ECD were child age, media exposure, and maternal education level with a mean absolute SHAP value of +0.17, + 0.12, and +0.1, respectively. The beeswarm plot of SHAP revealed that children aged 24-35 months, those whose mothers were not exposed to media, or those whose mothers had completed at least secondary education were more likely to be developmentally on track. In East Africa, only the modest majority of children were developmentally on track. Policies should prioritize preprimary education, equitable access, and women's education to empower mothers and improve parenting practices. Promoting appropriate media use while limiting maternal screen time can enhance children's developmental outcomes in East Africa and other countries with similar socioeconomic contexts, including most sub-Saharan African countries.

Knowledge of medicinal plant species is necessary to preserve medicinal plants and safeguard biodiversity. The classification and identification of these plants by botanist experts are complex and time-consuming activities. This systematic review’s main objective is to systematically assess the prior research efforts on the applications and usage of deep learning approaches in classifying and recognizing medicinal plant species. Our objective was to pinpoint systematic reviews following the PRISMA guidelines related to the classification and recognition of medicinal plant species through the utilization of deep learning techniques. This review encompassed studies published between January 2018 and December 2022. Initially, we identified 1644 studies through title, keyword, and abstract screening. After applying our eligibility criteria, we selected 31 studies for a thorough and critical review. The main findings of this reviews are (1) the selected studies were carried out in 16 different countries, and India leads in paper contributions with 29%, followed by Indonesia and Sri Lanka. (2) A private dataset has been used in 67.7% of the studies subjected to image augmentation and preprocessing techniques. (3) In 96.7% of the studies, researchers have employed plant leaf organs, with 74% of them utilizing leaf shapes for the classification and recognition of medicinal plant species. (4) Transfer learning with the pre-trained model was used in 83.8% of the studies as a future extraction technique. (5) Convolutional Neural Network (CNN) is used by 64.5% of the paper as a deep learning classifier. (6) The lack of a globally available and public dataset need for medicinal plants indigenous to a specific country and the trustworthiness of the deep learning approach for the classification and recognition of medicinal plants is an observable research gap in this literature review. Therefore, further investigations and collaboration between different stakeholders are required to fulfilling the aforementioned research gaps.

Background Work-related stress and anxiety are emerging global public health problems causing serious social and economic consequences. Working women bear a heavy burden due to high social disparity, gender inequality, and an important responsibility to balance work and family life in undeveloped society. Objective To assess the prevalence and associated factors of work related stress and anxiety among female employees of Hawassa industrial park in Sidama Region, Ethiopia, 2021. Methods Institution-based cross-sectional study design was conducted among 417 female employees using structured interviewer-administered questionnaires and depression, Anxiety, and Stress scale (DASS) 21 items. A simple random sampling technique was used through the computer-generated random method. The outcome variables were work related stress and anxiety. Work related stress and anxiety were ascertained using the DASS 21( stress ≥ 15 &anxiety8 – 14). The associated factors assessed included sociodemographic, behavioral factor, job and organization related factors, past illness and social support related factors. Bivariate and multivariable logistic regression analyses were done. The strength of association was declared by using an adjusted odds ratio (AOR) with a 95% confidence interval and, the statistical significance of P- value < 0.05. Result The prevalence of work-related stress and anxiety were 59.3% [95% CI: (54.7, 63.9)] and 79.8% [95% CI: 75.5, 83.6)] respectively. Respondents with single marital status [AOR = 5.31, 95% CI: (1.68, 16.86)], having chronic illness [AOR = 4:00, 95% CI: (1.24, 12.9)], and current alcohol drinking [AOR = 12.5, 95% CI: (4.56, 34.2)] were significantly associated with stress. Likewise, being single in marital status [AOR = 1.99, 95% CI: (1.15, 3.46)], poor social support [AOR = 3.78, 95% CI: (1.53, 9.35)], overtime work [AOR = 2.31, 95% CI: (1.12, 4.74)], having work experience (3–4 years) [AOR = 4.71, 95% CI: (1.49, 14.84)], and fear of losing job [AOR = 1.72, 95% CI: (1.01, 2.93)] were significantly associated with anxiety. Conclusion The prevalence of work-related stress and anxiety was high in the study area. Marital status, alcohol drinking, and chronic illnesses were factors associated with work-related stress. In contrast the fear of losing a job, work experience, overtime work, and having poor social support were factors associated with anxiety.. The significant factors identified in this study can be targeted to reduce the occurrence of work related stress and anxiety among women through designing preventive programs and strategies which includes acknowledging the importance of mental health services for the welfare of the public, screening for work related stress and anxiety, counselling, and the provision of support for women as well as lifestyle modification.

People diagnosed with advanced HIV disease (AHD) should be provided with the World Health Organization's (WHO) package of care to address their specific healthcare needs. Although the WHO-recommended package of care is considered feasible and effective, its implementation remains sub-optimal across many sub-Saharan African (SSA) countries. This study aimed to explore challenges in implementing the WHO-recommended package of care for advanced HIV disease in resource-constrained settings. A sequential explanatory mixed-methods study was conducted between March 1 and April 30, 2024, in the Gedeo Zone of Southern Ethiopia. The quantitative data involved extraction from medical records of 145 individuals newly diagnosed with AHD. For the qualitative inquiry, healthcare providers engaged in the HIV care continuum were purposively selected for in-depth key informant interviews. An inductive thematic analysis was conducted to identify and interpret recurrent patterns within the qualitative data. Quantitative data were analyzed using R version 4.3.3, while qualitative data were organized and managed using NVivo version 14. Only about half (47.6%) of the newly diagnosed AHD cases underwent baseline CD4 count testing. All 145 individuals were screened for TB using the WHO four-symptom algorithm, and 78.6% underwent confirmatory GeneXpert® MTB/RIF testing. Among individuals with AHD, 92.4% received co-trimoxazole prophylaxis, and 14.5% received tuberculosis preventive therapy. Rapid ART initiation was implemented for 20.0% of individuals with AHD. All newly diagnosed individuals with AHD received tailored counseling to ensure optimal adherence. Qualitative data analysis identified three principal challenges to the implementation of the WHO-recommended package of care: structural and organizational obstacles, service delivery constraints, and patient-related concerns as expressed by healthcare workers. The implementation of the WHO-recommended package of care for individuals with AHD remains inconsistent. Although adherence support is routinely offered to all newly diagnosed individuals with AHD, the delivery of other key components is frequently hindered by a range of systemic challenges. These include the unavailability or frequent stockouts of essential medications and services for managing opportunistic infections, weak referral and linkage systems, and the absence of dedicated AHD care clinics. Such challenges underscore significant gaps in the continuum of AHD care and highlight the pressing need for targeted, system-level interventions to ensure comprehensive service delivery.

Medicinal plants are crucial for traditional healers in preparing remedies and also hold significant importance for the modern pharmaceutical industry, facilitating drug discovery processes. Accurate and effective identification and classification of Ethiopian indigenous medicinal plants are vital for their conservation and preservation. However, the existing identification and classification process is time-consuming, and tedious, and demands the expertise of specialists. Botanists traditionally rely on traditional and experience-based methods for identifying various medicinal plant species. This research aims to develop an efficient deep learning model through transfer learning for the identification and classification of Ethiopian indigenous medicinal plant species. A custom dataset of 1853 leaf images from 35 species was prepared and labeled by botanist experts. Experiments have been done with the use of pretrained deep learning models, specifically VGG16, VGG19, Inception-V3, and Xception. The results demonstrate that fine-tuning the models significantly improves training and test accuracy, indicating the potential of deep learning in this domain. VGG19 outperforms other models with a test accuracy of 94%, followed by VGG16, Inception-V3, and Xception with test accuracies of 92%, 91%, and 87%, respectively. The study successfully addresses the challenges in the identification and classification of Ethiopian indigenous medicinal plant species. With an inspiring accuracy performance of 95%, it can be concluded that fine-tuning emerged as a highly effective strategy for boosting the performance of deep learning models.

Teenage pregnancy remains one of the major reproductive health problems, especially in sub-Saharan African countries. It can lead to maternal and neonatal complications and social consequences. The proportion of teenage pregnancy differs across regions of Ethiopia. Thus, this study aimed to determine the spatial variation in determinants of teenage pregnancy among adolescents aged 15-19 years in Ethiopia using the 2019 Demographic and Health Survey (DHS). This study included a total weighted sample of 2165 teenage girls aged 15 to 19 years. A mixed-effect binary logistic regression model was employed to consider the hierarchical nature of the DHS data using STATA version 17. Adjusted odds ratios with 95% confidence intervals are reported, and a p-value less than 0.05 was used to identify significant predictors. The spatial analysis was conducted with ArcGIS version 10.7 and Python 3. To identify factors associated with the hotspots of teenage pregnancy, a multiscale geographically weighted regression (MGWR) was performed. Spatial regression models were compared using adjusted R2, the corrected Akaike information criterion (AICc), and the residual sum of squares (RSS). The prevalence of teenage pregnancy among adolescents aged 15 to 19 years was 12.98% (95% CI: 11.6%, 14.5%). It was spatially clustered throughout the country with a significant Moran's I value. Significant hotspot areas were detected in central and southern Afar; northern, central, and western Gambela; northeastern and southern central Oromia; and the eastern Somali region. The MGWR analysis revealed that the significant predictors of spatial variations in teenage pregnancy were being illiterate and being married. Based on the multivariable multilevel analysis, age 17 (AOR = 3.54; 95% CI: 1.60, 7.81), 18 (AOR = 8.21; 95% CI: 3.96, 17.0), 19 (AOR = 15.0; 95% CI: 6.84, 32.9), being literate (AOR = 0.57; 95% CI: 0.35, 0.92), being married (AOR = 22.8; 95% CI: 14.1, 37.0), age of household head (AOR = 0.98; 95% CI: 0.98, 0.99) and residing in the Gambela region (AOR = 3.27; 95% CI: 1.21, 8.86) were significantly associated with teenage pregnancy among adolescents aged 15 to 19. Teenage pregnancy is a public health problem in Ethiopia. Policymakers should prioritize addressing early marriage and improving teenage literacy rates, with a focus on the Gambela region and other hotspot areas. It is crucial to implement policies aimed at transforming the traditional practice of early marriage and to take measures to enhance literacy levels and promote awareness about sexual and reproductive health at the family and school levels. This will help ensure that young people have the opportunity to pursue education and make informed decisions about their reproductive health.