Africa faces escalating ecological challenges driven by rapid urbanization, demographic changes, and the intensifying exploitation of its natural capital. In line with this motivation, analysis of ecological footprint (efp) drivers is revisited using robust and diverse machine learning techniques on a panel dataset of 52 African countries. By implementing in-sample and out-of-sample analyses in predicting key efp drivers, the study evaluates the predictive performance of the outperforming model among six machine learning models—Random Forest (RF), XGBoost, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), Adaptive LASSO, and Histogram-Based Gradient Boosting (HGB). According to the results, the RF model demonstrates the highest predictive power compared to all other models. By incorporating the cumulative importance-based feature selection technique into the RF prediction, our analysis identifies agricultural production, GDP per capita, population density, renewable energy consumption, trade openness, and income inequality as the most influential factors, collectively accounting for 70
Forests offer numerous ecosystem services that enhance human livelihoods. However, without proper regulation, human activities can deteriorate forest ecosystems. This study aims to evaluate the land use and cover dynamics in Mumosho municipality, South Kivu, Democratic Republic of Congo (DRC), and assess the diversity and carbon sequestration capacity of its woody species. We conducted a supervised classification of Landsat images from 1994, 2004, 2013, and 2023 and calculated spatial structure indices to analyze anthropization and deforestation in the area. From 1994 to 2013, the Mumosho landscape experienced significant deforestation, with rates of 3.69
Background Since 2000, the world has achieved notable progress in reducing maternal mortality, though regional disparities remain pronounced. These uneven outcomes raise critical questions about whether countries are collectively converging toward the Sustainable Development Goal (SDG) 3.1 target of fewer than 70 deaths per 100,000 live births by 2030, or diverging into distinct clusters marked by persistent inequality.Method This study employs Gini coefficient decomposition and kernel density estimation (KDE) to quantify regional differences and trace the evolving distribution of maternal mortality ratios (MMRs) across 163 countries from 1990 to 2023. A convergence model is then applied to analyze long-run dynamics. Specifically, recognizing that the Phillips-Sul framework overlooks spatial dependence and mobility between clubs, the Local Directional Moran Scatterplot (LDMS) methodology is adopted, situating each country in Moran space and modeling trajectories as random vector fields (RVF).Results The findings show that: (1) Global maternal mortality fell consistently from 1990 to 2023, with distributions shifting lower. COVID-19 briefly widened disparities, but recovery by 2023 confirmed continued progress. (2) The global MMR Gini coefficient remained persistently high, averaging about 0.66, with inter-regional disparities as the primary source of inequality. (3) Countries are not converging along a single universal path but instead into three distinct clubs. By 2030, forecasts indicate that out of 163 countries, 65 (40%) will achieve the SDG 3.1 target, while 98 (60%) will remain above the threshold.Conclusions This divergence highlights the urgent need for stronger health systems, socio-economic empowerment, and global partnerships to help lagging countries overcome entrenched high MMR trajectories.
Background: Extended-spectrum cephalosporin-resistant Enterobacterales (ESCR-E) and carbapenem-resistant Enterobacteriaceae (CRE) pose a growing threat to maternal and neonatal health, particularly in low-resource settings. In eastern Democratic Republic of the Congo (DRC), data on antimicrobial resistance in these populations are scarce. Objective: To determine resistance profiles of ESCR-E and CRE among pregnant women, postpartum mothers, and their newborns in South Kivu, eastern DRC. Methods: A cross-sectional multicentre study was conducted from April 2023 to October 2024 in urban and rural health facilities. Rectal swabs or stool samples were collected. Bacterial identification was performed using conventional biochemical galleries, and antimicrobial susceptibility testing was performed by the standard disk diffusion (Kirby–Bauer) method. Results: High rates of ESCR-E and CRE colonisation were found across all groups. Over 90% of ESCR-E isolates were resistant to third-generation cephalosporins and showed multidrug resistance, including to aminoglycosides and fluoroquinolones. Carbapenem-resistant Enterobacteriaceae isolates were resistant to penicillin and cephalosporins but remained susceptible to ceftazidime–tazobactam. These resistance profiles severely limit treatment options in maternal and neonatal care. Conclusion: The high prevalence of multidrug-resistant ESCR-E and CRE in mothers and newborns highlights an urgent need for improved antimicrobial stewardship, resistance surveillance, and infection prevention strategies. The potential use of probiotics to restore gut microbiota and reduce colonisation should also be explored. What this study adds: This study provides the first comprehensive data on ESCR-E and CRE colonisation in mothers and newborns in eastern DRC, revealing alarming multidrug resistance patterns and highlighting the urgent need for targeted stewardship, surveillance, and Infection Prevention and Control strategies in maternal and neonatal care settings.
ABSTRACT Background and Aim Since its establishment by the World Health Organization (WHO), the surgical safety checklist (SSC) is variably adopted worldwide despite evidence of advantages related to its use. To our knowledge, no data exists about the use of the SSC in DRC. We aimed to report the frequency of its use and reasons for its non‐use. Methods Using Google Forms, a survey was conducted in the Democratic Republic of the Congo public hospitals for 20 days (25 April to 15 May 2024). Results Fifty‐nine hospitals were included in the survey, and 32 (54.2%) are using the SSC for a mean time of 4 (± 2) years. Of these, 16 (50%) use it regularly, with a surgeon coordinating the checklist process in 14 hospitals (43.8%). Twenty‐seven hospitals do not use the SSC due to the absence of training for its use in 16 hospitals (59.3%) or due to the unavailability of the SSC in 15 hospitals (55.6%). Conclusion Half of the public hospitals in DRC have used the SSC for an average of 4 years. Lack of previous training and unavailability of the SCC are the main reasons of its non‐use. Seminars and training on the use of SSC would allow its use in these health facilities.
