Search Results (23)

Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach that combines CMIP6 climate projections with localized air quality data. We analyzed daily concentrations of major pollutants (SO₂, NO₂) across 15 strategically selected monitoring stations representing diverse urban environments, including traffic corridors, residential areas, healthcare facilities, and semi-natural zones. Climate data from two Earth System Models (CNRM-ESM2-1 and MPI-ESM1.2) were bias-corrected and integrated with historical pollution measurements (2000–2015) using hierarchical Bayesian statistical modeling under SSP2-4.5 and SSP5-8.5 emission scenarios. Our results revealed substantial deterioration in air quality, with projected increases of 80–130% for SO₂ and 45–55% for NO₂ concentrations by 2070 under high-emission scenarios. Spatial analysis demonstrated pronounced pollution gradients, with traffic corridors (Eastern Ring Road, Northern Ring Road, Southern Ring Road) and densely urbanized areas (King Fahad Road, Makkah Road) experiencing the most severe increases, exceeding WHO guidelines by factors of 2–3. Even semi-natural areas showed significant increases in pollution due to regional transport effects. The hierarchical Bayesian framework effectively quantified uncertainties while revealing consistent degradation trends across both climate models, with the MPI-ESM1.2 model showing a greater sensitivity to anthropogenic forcing. Future concentrations are projected to reach up to 70 μg m⁻³ for SO₂ and exceed 100 μg m⁻³ for NO₂ in heavily trafficked areas by 2070, representing 2–3 times the Traffic corridors showed concentration increases of 21–24% compared to historical baselines, with some stations (R5, R13, and R14) recording projected levels above 4.0 ppb for SO₂ under the SSP5-8.5 scenario. These findings highlight the urgent need for comprehensive emission reduction strategies, accelerated renewable energy transition, and reformed urban planning approaches in rapidly developing arid cities. Full article

Background: Endometriosis is a chronic, estrogen-dependent inflammatory condition, that not only leads to significant physical symptoms but also exerts a profound psychological and social burden. This study aimed to asjsess the relationship between quality of life (QoL) in women with endometriosis and selected socio-demographic, diagnostic-therapeutic, and psychological factors, emphasizing self-efficacy and dispositional optimism as potential protective resources. Methods: A cross-sectional survey was conducted between 2020 and 2022 in healthcare facilities in eastern Poland. The study included 425 women diagnosed with endometriosis. The research tools were the Endometriosis Health Profile, the General Self-Efficacy Scale, and the Life Orientation Test-Revised, as well as an original socio-demographic and clinical questionnaire. Data were analyzed using descriptive statistics, linear regression, and hierarchical regression to assess the predictive role of psychological resources beyond sociodemographic and clinical variables. Results: A higher number of physicians from various specialties consulted before diagnosis was significantly associated with lower QoL in all EHP-30 domains except infertility (p < 0.05). The perceived economic burden of treatment was significantly related to lower QoL across all domains (p < 0.05). In contrast, higher levels of self-efficacy and dispositional optimism emerged as independent protective factors, positively associated with emotional well-being, social support, sexual functioning, and relationships with medical staff (p < 0.05). Psychological variables accounted for an additional 8.1% of the variance in QoL beyond socio-demographic and clinical predictors. Conclusions: The findings support the relevance of a biopsychosocial framework in managing endometriosis. Psychological resources play a critical role in coping with the disease and should be integrated into personalized care strategies. Full article

Ensuring secure communication for mobile patients in e-healthcare requires an efficient and robust key distribution mechanism. This study introduces a novel hierarchical key distribution architecture inspired by federated learning (FL), enabling seamless authentication for patients moving across different healthcare centers. Unlike existing approaches, the proposed system allows a central healthcare authority to share global security parameters with subordinate units, which then combine these with their own local parameters to generate and distribute symmetric keys to mobile patients. This FL-inspired method ensures that patients only need to store a single key, significantly reducing storage overhead while maintaining security. The architecture was rigorously evaluated using SPAN-AVISPA for formal security verification and BAN logic for authentication protocol analysis. Performance metrics—including storage, computation, and communication costs—were assessed, demonstrating that the system minimizes the computational load and reduces the number of exchanged messages during authentication compared to traditional methods. By leveraging FL principles, the solution enhances scalability and efficiency, particularly in dynamic healthcare environments where patients frequently switch between facilities. This work bridges a critical gap in e-healthcare security, offering a lightweight, scalable, and secure key distribution framework tailored for mobile patient authentication. Full article

The increasing complexity of urban building energy systems necessitates advanced prediction methods for efficient energy management. Urban buildings account for approximately 40% of global energy consumption, making accurate prediction crucial for sustainability goals. This research develops a novel transformer-based deep learning framework for multi-building energy consumption forecasting. Despite recent advances in energy prediction techniques, existing models struggle with multi-building scenarios due to limited ability to capture cross-building correlations, inadequate integration of diverse data streams, and poor scalability when deployed at urban scale—gaps this research specifically addresses. The study implemented a modified transformer architecture with hierarchical attention mechanisms, processing data from 100 commercial buildings across three climate zones over three years (2020–2023). The framework incorporated weather parameters, occupancy patterns, and historical energy consumption data through multi-head attention layers, employing a 4000-step warm-up period and adaptive regularization techniques. The evaluation included a comparison with the baseline models (ARIMA, LSTM, GRU) and robustness testing. The framework achieved a 23.7% improvement in prediction accuracy compared to traditional methods, with a mean absolute percentage error of 3.2%. Performance remained stable across building types, with office complexes showing the highest accuracy (MAPE = 2.8%) and healthcare facilities showing acceptable variance (MAPE = 3.5%). The model-maintained prediction stability under severe data perturbations while demonstrating near-linear computational scaling. The transformer-based approach significantly enhances building energy prediction capabilities, enabling more effective demand-side management strategies, though future research should address long-term adaptability. Full article

Background/Objectives: The Brazilian health system provides healthcare financed by the public and private sector, being the first designed to encompass universal healthcare coverage delivered to the population without charge to patients (Sistema Único de Saúde, SUS), whilst the second refers to healthcare coverage delivered for individuals with the capacity to pay for assistance through health insurance or out-of-pocket disbursements. Health insurance companies with beneficiaries receiving publicly financed healthcare from the SUS are required to provide the reimbursement of healthcare expenditures to the government, considering that the health insurance beneficiaries obtain deductions of income taxes designed to fund the SUS. Therefore, the study investigated patterns of healthcare utilization and public expenditure due to the use of public healthcare by beneficiaries of health insurance between 2003 and 2019. Methods: Datasets including annual information on healthcare utilization by beneficiaries of health insurance from the National Agency of Supplementary Health (Agência Nacional de Saúde Suplementar, ANS) were organized into a single database to allow for the identification of patterns of interest to inform public policies of health. The empirical strategy adopted included the estimation of regression models and agglomerative hierarchical cluster analysis to identify factors associated with public sector expenditure. Results: The regression results indicated lower expenditure with female patients, particularly children and adolescents under 20 years old, receiving treatment in public sector facilities linked to the federal government. The cluster analysis showed five types of health insurance beneficiaries with a higher level of healthcare utilization, being three clusters referring to medium complexity procedures with lower public expenditures, and two clusters with higher public expenditures, one cluster that refers to high complexity procedures, and one cluster referring to health insurance schemes without hospitalization. Conclusions: The findings of the study highlight the existence of patterns of healthcare utilization by health insurance beneficiaries that may compromise the sustainability of public funding within the Brazilian health system. Full article

In the context of healthcare technology management (HTM) in Saudi Arabia and the Gulf region, this study addresses a significant gap by exploring both external and internal risk factors affecting HTM performance. Previous studies have primarily focused on modeling or predicting failures in medical devices, mostly examining internal (endogenous) factors that impact device performance and the development of optimal service strategies. However, a comprehensive investigation of external (exogenous) factors has been notably absent. This research introduced a novel hierarchical risk management framework designed to accommodate a broad array of healthcare technologies, not limited to just medical devices. It significantly advanced the field by thoroughly investigating and validating a comprehensive set of 53 risk factors and assessed their influence on HTM. Additionally, this study embraced the perspective of enterprise risk management (ERM) and expanded it to identify and incorporate a wider range of risk factors, offering a more holistic and strategic approach to risk assessment in healthcare technology management. The findings revealed that several previously underexplored external and internal factors significantly impacted HTM performance. Notably, the Fuzzy AHP survey identified “design risk” under facility and environmental risks as the highest risk for HTM in this region. Furthermore, this study revealed that three out of the top ten risks were related to “facility and internal environmental” factors, six were related to technological endogenous factors, and only one was related to managerial factors. This distribution underscores the critical areas for intervention and the need for robust facility and technology management strategies. In conclusion, this research not only fills a critical void by providing a robust framework for healthcare technology risk assessment but also broadens the scope of risk analysis to include a wider array of technologies, thereby enhancing the efficacy and safety of healthcare interventions in the region. Additionally, the proposed hierarchy provides insights into the underlying risk factors for healthcare technology management, with potential applications extending beyond the regional context to a global scale. Moreover, the equation we proposed offers a novel perspective on the key risk factors involved in healthcare technology management, presenting insights with far-reaching implications applicable not only regionally but also on a global level. This framework also supports sustainability goals by encouraging the efficient and responsible utilization and management of healthcare technologies, essential for ensuring the long-term economic and environmental sustainability of medical technology use. This research is of an exploratory nature, with the findings from the Fuzzy AHP analysis being most applicable to the specific geographic regions examined. Additional research is required to validate these results and to confirm the trends observed in various other regions and contexts. Full article

Background: Preparing healthcare systems for emergencies is crucial to maintaining healthcare quality. Nursing homes (NHs) require tailored emergency plans. This article aims to develop a typology of French private NHs and study their early COVID-19 responses and mortality outcomes. Methods: We conducted a cross-sectional survey among NHs of a French network consisting of 290 facilities during the first wave of the COVID-19 pandemic. A Hierarchical Clustering on Principal Components (HCPC) was conducted to develop the typology of the NHs. Association tests were used to analyze the relationships between the typology, prevention and control measures, COVID-19 mortality, and the satisfaction of hospitalization requests. Results: The 290 NHs vary in size, services, and location characteristics. The HCPC identified three clusters: large urban NHs with low levels of primary care (Cluster 1), small rural NHs (Cluster 2), and medium urban NHs with high levels of primary care (Cluster 3). The COVID-19 outcomes and response measures differed by cluster, with Clusters 1 and 2 experiencing higher mortality rates. Nearly all the NHs implemented preventive measures, but the timing and extent varied. Conclusions: This typology could help in better preparing NHs for future health emergencies, allowing for targeted resource allocation and tailored adaptations. It underscores the importance of primary care territorial structuring in managing health crises. Full article

Introduction: The broader determinants of health including a wide range of community assets are extremely important in relation to public health outcomes. Multiple health conditions, multimorbidity, is a growing problem in many populations worldwide. Methods: This paper quantified the effect of community assets on major health conditions for the population of England over six years, at a fine spatial scale using a data analytic approach. Community assets, which included indices of the health system, green space, pollution, poverty, urban environment, safety, and sport and leisure facilities, were quantified in relation to major health conditions. The health conditions examined included high blood pressure, obesity, dementia, diabetes, mental health, cardiovascular conditions, musculoskeletal conditions, respiratory conditions, kidney and liver disease, and cancer. Cluster analysis and dendrograms were calculated for the community assets and major health conditions. For each health condition, a statistical model with all community assets was fitted, and model selection was performed. The number of significant community assets for each health condition was recorded. The unique variance, explained by each significant community asset per health condition, was quantified using hierarchical variance partitioning within an analysis of variance model. Results: The resulting data indicate major health conditions are often clustered, as are community assets. The results suggest that diversity and richness of community assets are key to major health condition outcomes. Primary care service waiting times and distance to public parks were significant predictors of all health conditions examined. Primary care waiting times explained the vast majority of the variances across health conditions, with the exception of obesity, which was better explained by absolute poverty. Conclusions: The implications of the combined findings of the health condition clusters and explanatory power of community assets are discussed. The vast majority of determinants of health could be accounted for by healthcare system performance and distance to public green space, with important covariate socioeconomic factors. Emphases on community approaches, significant relationships, and asset strengths and deficits are needed alongside targeted interventions. Whilst the performance of the public health system remains of key importance, community assets and local infrastructure remain paramount to the broader determinants of health. Full article

In developing countries, the interaction between rapid urban expansion and population growth brings forth a host of challenges, particularly concerning essential services like healthcare. While interest in site suitability analysis for identifying optimal hospital locations to ensure equitable and secure healthcare access is on the rise, the absence of a holistic study that encompasses social and environmental aspects in the assessment of hospital site suitability is evident. The objective of this research is to introduce a hybrid methodology that combines Geographic Information Systems (GIS) with Multi-Criteria Decision-Making (MCDM) weighting methods. This methodology aims to create hospital site suitability maps for districts 21 and 22 in Tehran, taking into account socio-environmental factors. In addition to the conventional Analytical Hierarchical Process (AHP) weighting method, this study employs two relatively less-explored methods, the Best-Worst Method (BWM) and Step-wise Weight Assessment Ratio Analysis (SWARA), to enhance the analysis of hospital site suitability. In the SWARA method, there are minimal variations in weights among criteria, indicating that all socio-environmental factors (e.g., distance from existing hospitals, distance from main roads, distance from green spaces) hold significant importance in the decision-making process. Additionally, the findings indicate that the western part of the study area is the most suitable location for the construction of a new hospital. To achieve the average hospital bed availability in Tehran, an additional 2206 beds are required in the studied area, in addition to the existing facilities. Considering the ongoing urban development, population growth, and the potential for natural disasters and epidemics, it becomes essential to enhance the healthcare system by increasing the number of hospitals and available hospital beds. The sensitivity analysis showed that GIS-based SWARA-WLC was the most suitable and stable model for determining hospital site suitability in the study area. This methodology can be adapted for use in other regions and further improved by incorporating additional criteria. In conclusion, the study recommended three specific alternative sites for establishing a new hospital in the study area. Full article

The development of reinforced polymer composite materials has had a significant influence on the challenging problem of shielding against high-energy photons, particularly X-rays and $\gamma$-rays in industrial and healthcare facilities. Heavy materials’ shielding characteristics hold a lot of potential for bolstering concrete chunks. The mass attenuation coefficient is the main physical factor that is utilized to measure the narrow beam $\gamma$-ray attenuation of various combinations of magnetite and mineral powders with concrete. Data-driven machine learning approaches can be investigated to assess the gamma-ray shielding behavior of composites as an alternative to theoretical calculations, which are often time- and resource-intensive during workbench testing. We developed a dataset using magnetite and seventeen mineral powder combinations at different densities and water/cement ratios, exposed to photon energy ranging from 1 to ${100}^6$ kiloelectronvolt (KeV). The National Institute of Standards and Technology (NIST) photon cross-section database and software methodology (XCOM) was used to compute the concrete’s $\gamma$-ray shielding characteristics (LAC). The XCOM-calculated LACs and seventeen mineral powders were exploited using a range of machine learning (ML) regressors. The goal was to investigate whether the available dataset and XCOM-simulated LAC can be replicated using ML techniques in a data-driven approach. The minimum absolute error (MAE), root mean square error (RMSE), and $R2score$ were employed to assess the performance of our proposed ML models, specifically a support vector machine (SVM), 1d-convolutional neural network (CNN), multi-Layer perceptrons (MLP), linear regressor, decision tree, hierarchical extreme machine learning (HELM), extreme learning machine (ELM), and random forest networks. Comparative results showed that our proposed HELM architecture outperformed state-of-the-art SVM, decision tree, polynomial regressor, random forest, MLP, CNN, and conventional ELM models. Stepwise regression and correlation analysis were further used to evaluate the forecasting capability of ML techniques compared to the benchmark XCOM approach. According to the statistical analysis, the HELM model showed strong consistency between XCOM and predicted LAC values. Additionally, the HELM model performed better in terms of accuracy than the other models used in this study, yielding the highest R2score and the lowest MAE and RMSE. Full article

Biomaterial research has led to revolutionary healthcare advances. Natural biological macromolecules can impact high-performance, multipurpose materials. This has prompted the quest for affordable healthcare solutions, with a focus on renewable biomaterials with a wide variety of applications and ecologically friendly techniques. Imitating their chemical compositions and hierarchical structures, bioinspired based materials have elevated rapidly over the past few decades. Bio-inspired strategies entail extracting fundamental components and reassembling them into programmable biomaterials. This method may improve its processability and modifiability, allowing it to meet the biological application criteria. Silk is a desirable biosourced raw material due to its high mechanical properties, flexibility, bioactive component sequestration, controlled biodegradability, remarkable biocompatibility, and inexpensiveness. Silk regulates temporo-spatial, biochemical and biophysical reactions. Extracellular biophysical factors regulate cellular destiny dynamically. This review examines the bioinspired structural and functional properties of silk material based scaffolds. We explored silk types, chemical composition, architecture, mechanical properties, topography, and 3D geometry to unlock the body’s innate regenerative potential, keeping in mind the novel biophysical properties of silk in film, fiber, and other potential forms, coupled with facile chemical changes, and its ability to match functional requirements for specific tissues. Full article

Introduction: Contemporary healthcare faces new challenges and expectations from society. The profession of a nurse, as well as a paramedic, is essential for the efficient functioning of healthcare. It has its importance not only in promoting and preserving health but also in prevention. With the increasing importance of providing medical care at the highest level, it is expected of these two professional groups to have more knowledge and skills than a few years earlier. The daily contact with patients and their families, the low level of control of the environment, the hierarchical system of professional dependence, and the dissatisfaction with remuneration are becoming extremely burdensome aspects of the nursing and paramedic professions. Long-term exposure to stressors associated with these medical professions may, in the long term, lead to the emergence of occupational burnout syndrome. The aim of this study is an attempt to answer the question of whether and how stress factors affect the occurrence of occupational burnout in the work of nurses and paramedics working in various medical entities. Material and Methods: The study covered a group of 434 respondents, including 220 nurses and 214 paramedics, working professionally in hospital departments and care and treatment facilities as well as in hospital emergency departments and ambulance services. The study was carried out using a diagnostic survey based on the questionnaire technique using the authors’ questionnaire and the standardized MBI Ch. Maslach. Two statistical values were used to statistically analyze the research results and verify the adopted hypotheses: the chi-square test and the Student’s t-test. Results and Conclusions: The current study showed that the phenomenon of occupational burnout among the studied group affects only nurses, while this problem does not apply to the studied paramedics. The main stressor among the nurses and paramedics is, above all, a very high level of responsibility. Nurses are overburdened by excessive demands and shift work, while paramedics are mostly burdened by an excess of duties. Both nurses and paramedics claim that their work is often stressful, which leads to physical and mental exhaustion. Full article

Good health and well-being are key to achieving the main goals of the UN Sustainable Development Goals (SDGs), especially after the outbreak of the COVID-19 epidemic. What is a concern for both government and society is how to understand the spatial match of hierarchical healthcare facilities and residential areas in terms of quantity and capacity, to meet the challenges of various diseases and build a healthy life. Using hierarchical healthcare data and cellphone signaling data in Beijing, China, we used the kernel density estimation, a bivariate spatial autocorrelation model, and a coupling index to explore the spatial relationships between hierarchical healthcare facilities and residential areas. We found large numbers of both healthcare facilities and residential areas in the urban center, and small numbers of both at the urban edge. The hospitals and designated retail pharmacies in the densely populated areas do not have enough capacity to meet the need of the population. In addition, the capacity of primary healthcare institutions can meet people’s needs. Our findings would serve as a reference for urban planning, optimization of hierarchical healthcare facilities, and research on similar themes. Full article

Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various “densities” were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the “densities” were actually an abstract reflection of the “contact” frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect “contact” frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional “densities”. Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R²) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling. Full article

Equal accessibility to healthcare services is essential to the achievement of health equality. Recent studies have made important progresses in leveraging GIS-based location–allocation models to optimize the equality of healthcare accessibility, but have overlooked the hierarchical nature of facilities. This study developed a hierarchical maximal accessibility equality model for optimizing hierarchical healthcare facilities. The model aims to maximize the equality of healthcare facilities, which is quantified as the variance of the accessibility to facilities at each level. It also accounts for different catchment area sizes of, and distance friction effects for hierarchical facilities. To make the optimization more realistic, it can also simultaneously consider both existing and new facilities that can be located anywhere. The model was operationalized in a case study of Shenzhen, China. Empirical results indicate that the optimal healthcare facility allocation based on the model provided more equal accessibility than the status quo. Compared to the current distribution, the accessibility equality of tertiary and secondary healthcare facilities in optimal solutions can be improved by 40% and 38%, respectively. Both newly added facilities and adjustments of existing facilities are needed to achieve equal healthcare accessibility. Furthermore, the optimization results are quite different for facilities at different levels, which highlights the feasibility and value of the proposed hierarchical maximal accessibility equality model. This study provides transferable methods for the equality-oriented optimization and planning of hierarchical facilities. Full article