Climate change has adverse effects on ecosystems and several socio-economic sectors including health. Indeed, infrastructure, continuity of medical services, and the hospital environment are all directly affected by the effects of climate-related risks. This study aims to describe the observations of the effects of climate change risks on health systems in the Greater Lomé health region of Togo. We used an interview guide and a questionnaire to collect information. The observations allowed us to assess the effects caused by climate risks. According to the results, 84.62% of respondents attest that health centers experience flooding during rainy periods and damage caused by strong winds is noticeable among 76.92% of respondents. More than 25.40% and 61.86% respectively of respondents mention that droughts and floods have effects on health systems. The results of this study will allow health system managers to become aware of how to plan useful actions to facilitate the management of climate-related risks in health facilities in the Greater Lomé health region. In view of all these results, it is necessary that measures be taken to strengthen the resilience of health systems through awareness campaigns and training of actors throughout the health pyramid.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

The hospital is a complex system, which evolving practices, knowledge, tools, and risks. This study aims to assess the level of knowledge about risks at Hassan II Hospital among healthcare workers (HCWs) working in three COVID-19 units. The action-research method was adopted to address occupational risks associated with the pandemic. The study involved 82 healthcare professionals in the three COVID-19 units mentioned above. All participants stated they were familiar with hospital risks. Seventy-four HCPs reported no knowledge of how to calculate risk criticality, while eight mentioned the Occurrence rating, Severity rating, and Detection rating (OSD) method, considering Occurrence rating, Severity rating, and Detection rating as key elements for risk classification. Staff indicated that managing COVID-19 patients differs from other pathologies due to the pandemic’s evolving protocols. There is a significant lack of information among healthcare professionals about risks associated with COVID-19, highlighting the need for a hospital risk management plan at a subsequent stage.

This study aims to identify key strategies and tactics necessary to effectively implement national social security in a democratic Indonesia. Indonesia established the Law on the National Social Security System in 2004. However, the national social security programs did not commence until 2014. The national social security implementation has faced significant obstacles. These challenges include recurring delays, legal disputes, appeals, judicial reviews, and deviations from the original policy objectives, all threatening the long-term viability of the national social security programs. This article applies a qualitative approach by critically analyzing regulations, government reports, and publicly available data and observing open public meetings and hearings concerning implementing national social security programs. Our findings indicate that implementing national social security policies in a democratic Indonesia depends on effectively managing the dynamic processes involved in policy formulation and adoption. We propose a risk-based decision-making model to assist policymakers in mitigating policy-related risks and enhance the effectiveness of future policy agendas in social security.

This study introduces an innovative approach to assessing seismic risks and urban vulnerabilities in Nador, a coastal city in northeastern Morocco at the convergence of the African and Eurasian tectonic plates. By integrating advanced spatial datasets, including Landsat 8–9 OLI imagery, Digital Elevation Models (DEM), and seismic intensity metrics, the research develops a robust urban vulnerability index model. This model incorporates urban land cover dynamics, topography, and seismic activity to identify high-risk zones. The application of Landsat 8–9 OLI data enables precise monitoring of urban expansion and environmental changes, while DEM analysis reveals critical topographical factors, such as slope instability, contributing to landslide susceptibility. Seismic intensity metrics further enhance the model by quantifying earthquake risk based on historical event frequency and magnitude. The calculation based on higher density in urban areas, allowing for a more accurate representation of seismic vulnerability in densely populated areas. The modeling of seismic intensity reveals that the most susceptible impact area is located in the southern part of Nador, where approximately 50% of the urban surface covering 1780.5 hectares is at significant risk of earthquake disaster due to vulnerable geological formations, such as unconsolidated sediments. While the findings provide valuable insights into urban vulnerabilities, some uncertainties remain, particularly due to the reliance on historical seismic data and the resolution of spatial datasets, which may limit the precision of risk estimations in less densely populated areas. Additionally, future urban expansion and environmental changes could alter vulnerability patterns, underscoring the need for continuous monitoring and model refinement. Nonetheless, this research offers actionable recommendations for local policymakers to enhance urban planning, enforce earthquake-resistant building codes, and establish early warning systems. The methodology also contributes to the global discourse on urban resilience in seismically active regions, offering a transferable framework for assessing vulnerability in other coastal cities with similar tectonic risks.