Search Results (6)

Background/Objectives: Respiratory infections impact older adults due to immunosenescence and comorbidities, resulting in increased healthcare costs and mortality. While vaccination is a critical preventive measure, research on respiratory vaccines in older adults in Latin America and the Caribbean (LAC) remains underexplored. This study aims to map the research landscape and identify emerging themes to guide future studies. Methods: A bibliometric analysis was conducted using the Web of Science database, focusing on publications up to 2023 related to respiratory vaccines in LAC’s older adult population. PRISMA-ScR guidelines were followed for data extraction and analysis, with performance metrics and scientometric mapping conducted using Biblioshiny 4.1 and VOSviewer. Results: Ninety-nine studies spanning forty-one journals and 575 authors were included. Brazil contributed 70% of publications, followed by Mexico and Argentina. Influenza and pneumococcal vaccines were the most studied, focusing on coverage, acceptance, and cost-effectiveness. Emerging themes included COVID-19 vaccine effectiveness and vaccination-associated factors. Brazil was identified as the primary hub for collaboration across the region, while other countries made limited contributions. Conclusions: The findings highlight disparities in research output, with Brazil dominating and significant gaps in other LAC countries. Future research should prioritize genomic studies, vaccine efficacy in comorbid populations, and adaptive immunization strategies. Building research capacity and fostering international collaborations are essential for improving vaccination outcomes in older adults across LAC. Full article

Background and Objectives: During the COVID-19, the demand for non-invasive ventilatory support equipment significantly increased. In response, a novel non-invasive ventilatory support model called CPAP-AirFlife™ was developed utilizing existing technologies. This model offers technological advantages, including an aerosol-controlled helmet suitable for high-risk environments such as ambulances. Additionally, it is cost-effective and does not require medical air, making it accessible for implementation in low-level hospitals, particularly in rural areas. This study aimed to assess the efficacy of CPAP-AirFlife™ by conducting a non-inferiority comparison with conventional ventilation equipment used in the Intensive Care Unit. Materials and Methods: A clinical study was conducted on normal subjects in a randomized and sequential manner. Parameters such as hemoglobin oxygen saturation by pulse oximetry, exhaled PCO₂ levels, vital signs, and individual tolerance were compared between the CPAP-AirFlife™ and conventional equipment. The study population was described in terms of demographic characteristics and included in the analysis. Results: It was shown that the CPAP-AirFlife™ was not inferior to conventional equipment in terms of efficacy or tolerability. Hemoglobin oxygen saturation levels, exhaled PCO₂ levels, vital signs, and individual tolerance did not significantly differ between the two models. Conclusions: The findings suggest that CPAP-AirFlife™ is a practical and cost-effective alternative for non-invasive ventilatory support. Its technological advantages, including the aerosol-controlled helmet, make it suitable for high-risk environments. The device’s accessibility and affordability make it a promising solution for implementation in low-level hospitals, particularly in rural areas. This study supports using CPAP-AirFlife™ as a practical option for non-invasive ventilatory support, providing a valuable contribution to respiratory care during the COVID-19 pandemic and beyond. Full article

The determination of susceptibility to rainfall-induced landslides is crucial in developing a robust Landslide Early Warning System (LEWS). With the potential uncertainty of susceptibility changes in mountain environments due to different precipitation thresholds related to climate change, it becomes important to evaluate these changes. In this study, we employed a machine learning approach (logistic models) to assess susceptibility changes to landslides in the Central Andes. We integrated geomorphological features such as slope and slope curvature, and precipitation data on different days before the landslide. We then split the data into a calibration and validation database in a 50/50% ratio, respectively. The results showed an area under the curve (AUC) performance of over 0.790, indicating the model’s capacity to represent prone-landslide changes based on geomorphological and precipitation antecedents. We further evaluated susceptibility changes using different precipitation scenarios by integrating Intensity/Duration/Frequency (IDF) products based on CHIRPS data. We concluded that this methodology could be implemented as a Rainfall-Induced Landslides Early Warning System (RILEWS) to forecast RIL occurrence zones and constrain precipitation thresholds. Our study estimates that half of the basin area in the study zone showed a 59% landslide probability for a return of two years at four hours. Given the extent and high population in the area, authorities must increase monitoring over unstable slopes or generate landslide early warning at an operational scale to improve risk management. We encourage decision-makers to focus on better understanding and analysing short-duration extreme events, and future urbanization and public infrastructure designs must consider RIL impact. Full article

(1) Aims: To assess the incidence of inflammatory bowel disease (IBD) in Spain, to describe the main epidemiological and clinical characteristics at diagnosis and the evolution of the disease, and to explore the use of drug treatments. (2) Methods: Prospective, population-based nationwide registry. Adult patients diagnosed with IBD—Crohn’s disease (CD), ulcerative colitis (UC) or IBD unclassified (IBD-U)—during 2017 in Spain were included and were followed-up for 1 year. (3) Results: We identified 3611 incident cases of IBD diagnosed during 2017 in 108 hospitals covering over 22 million inhabitants. The overall incidence (cases/100,000 person-years) was 16 for IBD, 7.5 for CD, 8 for UC, and 0.5 for IBD-U; 53% of patients were male and median age was 43 years (interquartile range = 31–56 years). During a median 12-month follow-up, 34% of patients were treated with systemic steroids, 25% with immunomodulators, 15% with biologics and 5.6% underwent surgery. The percentage of patients under these treatments was significantly higher in CD than UC and IBD-U. Use of systemic steroids and biologics was significantly higher in hospitals with high resources. In total, 28% of patients were hospitalized (35% CD and 22% UC patients, p < 0.01). (4) Conclusion: The incidence of IBD in Spain is rather high and similar to that reported in Northern Europe. IBD patients require substantial therapeutic resources, which are greater in CD and in hospitals with high resources, and much higher than previously reported. One third of patients are hospitalized in the first year after diagnosis and a relevant proportion undergo surgery. Full article

Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility. Full article