Search Results (10,040)

(1) Background: Geospatial associations for COVID-19 mortality were estimated using a cohort of 28,128 hospitalized Medicaid patients identified from the 2020–2021 Kentucky Health Facility and Services administrative claims data. (2) Methods: County-level patient information (age, sex, chronic obstructive pulmonary disease [COPD], and mechanical ventilation use [96 hrs. plus]); social deprivation index (SDI) scores; physician and nurse rates per 100,000; and annual average particulate matter 2.5 (PM_2.5) were used as the predictors. Ordinary least-squares (OLS) regression and multiscale geographically weighted regression (MGWR) with the dependent variable, COVID-19 mortality per 100,000, were performed to compute global and local effects, respectively. (3) Results: MGWR (adjusted R²: 0.52; corrected Akaike information criterion [AICc]: 292.51) performed better at explaining the association between the dependent variable and predictors than the OLS regression (adjusted R²: 0.36; AICc: 301.20). The percentages of patients with COPD and who were mechanically ventilated (96 hrs. plus) were significantly associated with COVID-19 mortality, respectively (OLS standardized βCOPD: 0.22; βventilation: 0.53; MGWR mean βCOPD: 0.38; βventilation: 0.57). Other predictors were not statistically significant in both models. (4) Conclusions: A risk of COVID-19 mortality was observed among patients with COPD and prolonged mechanical ventilation use, after controlling for social determinants, the healthcare workforce, and PM_2.5 in rural and Appalachian counties of Kentucky. These counties are characterized by persistent poverty, healthcare workforce shortages, economic distress, and poor population health outcomes. Improving population health protection through multisector collaborations in rural and Appalachian counties may help reduce future health burdens. Full article

Background: High in-domain accuracy in healthcare machine learning (ML) models does not guarantee reliable clinical performance, especially when training and validation protocols are insufficiently robust. This paper presents a standardized framework for training and validating ML models intended for classifying medical conditions, emphasizing the need for clinically relevant evaluation metrics and external validation. Methods: We apply this framework to a case study in knee osteoarthritis grading, demonstrating how overfitting, data leakage, and inadequate validation can lead to deceptively high accuracy that fails to translate into clinical reliability. In addition to conventional metrics, we introduce composite clinical measures that better capture real-world utility. Results: Our findings show that models with strong in-domain performance may underperform on external datasets, and that composite metrics provide a more nuanced assessment of clinical applicability. Conclusions: Standardized training and validation protocols, together with clinically oriented evaluation, are essential for developing ML models that are both statistically robust and clinically reliable across a range of medical classification tasks. Full article

Wastewater-based epidemiology (WBE) offers a promising tool for sexually transmitted infection (STI) surveillance, especially in settings where underdiagnosis or social stigma complicates conventional reporting. To assess its utility, we conducted a year-long study examining six STIs, Chlamydia trachomatis, Treponema pallidum, Neisseria gonorrhoeae, human immunodeficiency virus (HIV), hepatitis C virus (HCV), and herpes simplex virus (HSV), in weekly composite samples from the primary influent of a small-sized Midwestern wastewater treatment plant. Pathogen detection and quantification were performed via digital PCR. Among the tested targets, Gonorrhea, HIV, HCV, and HSV were detected at the highest frequencies, often in 40–50% of the samples, while Chlamydia and Syphilis appeared less frequently. Despite the variability in detection patterns, this study demonstrates that even infrequent signals can reveal community-level shedding of poorly reported or asymptomatic infections. Although month-to-month wastewater data were not strongly correlated with corresponding clinical records, which could potentially reflect delayed healthcare seeking and pathogen-specific shedding dynamics, the overall findings underscore WBE’s ability to complement existing surveillance by capturing infections outside traditional healthcare channels. These results not only advance our understanding of STI prevalence and population shedding but also highlight the practical benefits of WBE as an early warning and targeted intervention tool. Full article

Background: Strategies are needed to address the elevated prevalence of chronic kidney disease (CKD) in socioeconomically disadvantaged regions where obesity, smoking, and type 2 diabetes rates are high. Methods: Recognising the inadequacy of generic health approaches in complex contexts, this study employed a participatory action research (PAR) framework to design and deliver five co-design community workshops in two stages over one year. Stage one workshops identified key matters of concern and stage two focussed on problem solving and co-creating solutions. The goal was to inform health service delivery in a region with high CKD prevalence and explore strategies to overcome barriers to individualised, collaborative care, and promote self-management. Results: The workshops identified three themes: 1. achieving person/family-centred care; 2. multimorbidity and siloed care (stage one); and 3. a kidney wellness framework (stage two). Conclusions: The findings reinforce the need for enhanced care coordination, and highlight the importance of consistent information sources, clear referral pathways, and centralised data sharing among health professionals. The proposed kidney healthcare framework aims to support various professionals, fostering linkages between primary and tertiary care, with an emphasis on professional development, especially in communicating complex information to individuals with multimorbidities. While co-designed healthcare models show promise, challenges persist in effective self-management amidst complex disease information and multimorbidity. Full article

Body Mass Index (BMI) is a crucial indicator for assessing human obesity and overall health, providing valuable insights for applications such as health monitoring, patient re-identification, and personalized healthcare. Recently, several data-driven methods have been developed to estimate BMI using 2D and 3D features extracted from facial and body images or RGB-D data. However, current research faces challenges such as the incomplete consideration of anthropometric features, the neglect of multiplex networks, and low-BMI-estimation performance. To address these issues, this paper proposes three 3D anthropometric features, one 2D anthropometric feature, and a deep feature extraction method to comprehensively consider anthropometric features. Additionally, a BMI estimation method based on a multiplex network is introduced. In this method, three types of features are extracted by constructing a multichannel network, and BMI estimation is performed using Kernel Ridge Regression (KRR). The experimental results demonstrate that the proposed method significantly outperforms state-of-the-art methods. By incorporating symmetry into our analysis, we can uncover deeper patterns and relationships within complex systems, leading to a more comprehensive understanding of the phenomena under investigation. Full article

Healthcare institutions are under increasing pressure to deliver high-quality, patient-centered care while reducing their environmental footprint. Integrating quality and environmental management systems (ISO 9001 and ISO 14001) into a unified integrated management system (IMS) offers a potential pathway to meet these dual imperatives. This study investigates the effects of IMS implementation in three European hospitals through a comparative qualitative analysis of institutional reports, audit documentation, and performance indicators. The methodology combines a literature-informed conceptual framework with a multi-case analysis guided by four domains: environmental impact, care quality, process efficiency, and stakeholder engagement. The data were collected from institutional documentation over a six-year period (three years before and after IMS implementation), covering key indicators such as energy and water consumption, medical waste recycling, audit compliance, and patient satisfaction. The findings show that IMS adoption was associated with a 20–28% improvement in resource efficiency, increased recycling rates, and consistent gains in compliance and satisfaction metrics. These results were supported by strategic leadership, cross-functional training, and digital monitoring tools. The study concludes that IMS enhances institutional performance and sustainability while aligning healthcare operations with broader governance and policy goals. Further research is recommended to explore the long-term impacts and generalize the findings across healthcare systems. Full article

This paper introduces a pioneering hybrid framework that integrates Q-learning/deep Q-network (DQN) with a locally deployed large language model (LLM) to enhance obstacle avoidance in embedded robotic systems. The STM32WB55RG microcontroller handles real-time decision-making using sensor data, while a Raspberry Pi 5 computer runs a quantized TinyLlama LLM to dynamically refine navigation strategies. The LLM addresses traditional Q-learning limitations, such as slow convergence and poor adaptability, by analyzing action histories and optimizing decision-making policies in complex, dynamic environments. A selective triggering mechanism ensures efficient LLM intervention, minimizing computational overhead. Experimental results demonstrate significant improvements, including up to 41% higher deadlock recovery (81% vs. 40% for Q-learning + LLM), up to 34% faster time to goal (38 s vs. 58 s for Q-learning + LLM), and up to 14% lower collision rates (11% vs. 25% for Q-learning + LLM) compared to standalone Q-learning/DQN. This novel approach presents a solution for scalable, adaptive navigation in resource-constrained embedded robotics, with potential applications in logistics and healthcare. Full article

Objectives: We aimed to investigate how receiving integrated healthcare services from a case manager via instant messaging affected patients with disabilities. Methods: This database-matched case–control study was conducted at one medical center. Patients with officially certified disabilities were recruited and assigned to either the LINE-based group or the control group, which accessed services in the traditional manner. Their baseline characteristics were collected through chart reviews. Medical service utilization data—including their number of outpatient visits, prescribed medications, and hospitalizations—were obtained at baseline and 3, 6, and 12 months into the intervention. In the LINE group, quality of life, caregiver burden, and perceived social support were also assessed. A repeated-measures ANOVA was used to analyze within- and between-group differences over time. Results: Both the LINE group and the control group contained 66 patients. The number of outpatient visits (p < 0.001) and quantity of medication taken (p = 0.026) were significantly lower in the LINE group than in the control group. Furthermore, the caregiver burden in the LINE group (p = 0.024) was significantly lower 12 months after receiving integrated healthcare services. Conclusions: Providing integrated healthcare services via instant messaging enabled patients with disabilities to access medical services promptly and efficiently, thus enhancing the accessibility of healthcare and improving care for the disabled population. Full article

Background: Ensuring the safety of patients in the operating room, through the monitoring and prevention of adverse events is a central priority of healthcare delivery. In the professionalization of operating room nurses, the processes of identifying, assessing, developing, monitoring, and certifying transversal competencies are crucial. While national and international frameworks have attempted to define such competencies, they often vary in scope and remain inconsistently integrated into education and clinical practice. There is, therefore, a need for a comprehensive and structured identification of transversal competencies relevant to both perioperative and perianesthesiological nursing roles. Objectives: To formulate a validated and structured repertoire of transversal competencies demonstrated by operating room nurses in both perioperative and perianesthesiological contexts. Methods: A qualitative descriptive design was adopted, combining shadowed observation with Hierarchical Task Analysis (HTA). A convenience sample of 46 participants was recruited from a university and a public hospital in Italy. Data were collected between September 2021 and June 2023 and analyzed using content analysis and data triangulation. Results: Through a qualitative, inductive and iterative approach the study identified 15 transversal competencies, 50 sub-competencies, and 153 specific tasks and activities. Specifically, operating room nurses working in perioperative and perianesthesiological roles presented the following transversal competencies: communication and interpersonal relationships, situation awareness, teamwork, problem solving and decision-making, self-awareness, coping with stressors, resilience and fatigue management, leadership, coping with emotions, task and time management, ethical and sustainable thinking, adaptation to the context, critical thinking, learning through experiences, and data, information and digital content management. Each competency was associated with specific tasks observed. Conclusions: This framework complements the existing repertoire of technical-specialist competencies by integrating essential transversal competencies. It serves as a valuable tool for the assessment, validation, and certification of competencies related to patient and professional safety, emotional well-being, relational dynamics, and social competencies. The findings underscore the need for academic institutions to revise traditional training models and embed transversal competencies in both undergraduate and postgraduate nursing education. Full article

Allergic rhinitis (AR) is a global health problem on the rise. More and more people are affected, and climate change is exacerbating this health problem in the long term. The quality of life of those affected is often severely compromised, and the financial burden on healthcare systems cannot be disregarded. Therefore, effective and safe medicines are needed to counteract this trend. P. hybridus (butterbur) leaf extract (Ze 339) displays a promising alternative to antihistamines in the treatment of AR symptoms. More than two decades after the first market launch it is now possible to draw a meaningful conclusion on its safety and efficacy. This review summarizes the available preclinical and clinical data, real-world data (RWD) as well as data from post-marketing pharmacovigilance monitoring about the herbal medicinal drug Ze 339. It focusses on the current knowledge about the mode of action as well as the evaluation of its efficacy and safety in the treatment of AR. Given its favourable safety profile and lack of sedative side effects, Ze 339 offers a valuable alternative to antihistamines and should therefore continue to be considered by medical practitioners for the treatment of allergic rhinitis symptoms. Full article

Male infertility is a prevalent condition affecting approximately 15% of couples worldwide. Recent evidence indicates that, beyond its immediate reproductive implications, male infertility may reflect broader health concerns. Large-scale cohort studies consistently show that men with poorer semen parameters have elevated all-cause mortality compared to fertile counterparts, with a dose-dependent pattern whereby more severe abnormalities correlate with a higher risk of early death. Proposed mechanisms linking infertility to reduced life expectancy encompass genetic, hormonal, and lifestyle factors. For instance, Klinefelter syndrome exemplifies a genetic cause of azoospermia that also predisposes to metabolic syndrome, diabetes, and certain malignancies. Low testosterone, a frequent finding in testicular dysfunction, is implicated in obesity, insulin resistance, and cardiovascular disease, all of which can shorten lifespan. Additionally, psychosocial stress and depression—commonly reported among infertile men—may contribute to health-compromising behaviors. Environmental exposures and socioeconomic factors further compound these risks. Collectively, these data underscore the importance of recognizing male infertility as an early indicator of potentially modifiable health vulnerabilities. A comprehensive evaluation of infertile men should therefore extend beyond fertility assessments to include screening for chronic diseases, hormonal imbalances, and mental health issues. Targeted surveillance for specific cancers (e.g., testicular and prostate) and early interventions—such as lifestyle modifications, appropriate hormonal therapies, and psychosocial support—can improve both reproductive outcomes and long-term well-being. Given these insights, male fertility assessment may serve as a valuable gateway to broader men’s healthcare, prompting proactive strategies that mitigate associated risks and potentially enhance longevity. Full article

Transit deserts, defined by insufficient public transit provision relative to demand, aggravate socio-economic inequalities by restricting access to employment, education, and healthcare. With increasing urbanization and growing disparities in public transport accessibility, identifying transit deserts is critical for equitable mobility planning. As urban populations expand, addressing transit accessibility requires advanced data-driven approaches. This study applies machine learning (ML) models, decision trees (DTs), logistic regression (LR), and random forest (RF), within an Intelligent Transport System (ITS) framework to detect transit deserts in Lucknow, India. Employing a 100 × 100 m spatial grid data, the models classify transit accessibility based on economic status, trip frequency, population density, and service access. The results indicate that RF achieves superior classification accuracy, while DT offers interpretability with slightly lower recall. LR underperforms due to its linear assumptions. The findings reveal the spatial clustering of transit deserts in socio-economically disadvantaged areas, highlighting the need for targeted interventions. This study advances ML-driven ITS analytics, offering a novel approach for classifying transit accessibility patterns at a granular level, thereby aiding policy interventions for improved urban mobility. Full article

Background/Objectives: The COVID-19 pandemic presented unprecedented challenges to healthcare systems worldwide, significantly impacting individuals with chronic conditions who depend on continuous medical care. In Romania, the pandemic revealed systemic vulnerabilities, particularly in ensuring access to services for older adults and rural populations. This study aimed to assess perceived barriers to healthcare access and service quality among Romanian patients with chronic diseases and a confirmed history of COVID-19, within the framework of the country’s multi-tiered healthcare system. Methods: A cross-sectional study was conducted between January and March 2025, involving 16 adult participants diagnosed with at least one chronic illness. Data were collected using a 30-item questionnaire administered by the principal investigator after obtaining informed consent. The instrument explored access to services, challenges related to remote consultations, and satisfaction with nursing care. Descriptive and comparative analyses were carried out based on age group and area of residence. Due to the small sample size, the results are considered exploratory and context-specific. Results: Most participants reported disrupted access to healthcare services, especially within public sector facilities. Rural residents experienced longer delays in receiving care than those in urban areas. Digital health tools were perceived as barriers by 75% of respondents aged 60 and above, while younger participants adapted more easily. Overall satisfaction with nursing care was moderate to high (mean score: 3.56/5), with the highest ratings observed among patients aged 30–60 years. Conclusions: This study highlights significant barriers to healthcare access among Romanian patients with chronic illnesses and a confirmed COVID-19 diagnosis during the pandemic. The key challenges included digital exclusion and rural–urban disparities. The findings underscore the need for targeted strategies to enhance digital health literacy, adapt care delivery models, and strengthen healthcare system resilience in future public health emergencies. Full article

Objectives: This study aimed to evaluate changes in the rate and spectrum of healthcare-associated infections (HCAIs) and to analyse the rate and spectrum of antimicrobial prescriptions in four long-term care facilities (LTCFs) in Graz, Austria, from 2018 to 2022 in a prospective cohort study. Methods: Nursing staff prospectively collected data on HCAIs and antimicrobial prescriptions once a week. Log-linear Poisson models for counts were applied mostly to evaluate the difference effects of the various calendar years compared to the reference year of 2018. Results: A total of 1684 infections were recorded in 720 residents during the study period. The overall annual incidence rate of HCAIs varied over time with a significant increase to 2.86/1000 resident days in 2019 and to 4.09/1000 resident days in 2022, both compared to 2018, p < 0.001. A large peak in respiratory tract infections (RTIs) occurred in winter 2021/2022 due to a large number of SARS-CoV-2 infections in all four LTCFs. Urinary tract infections (UTIs) were the most commonly recorded infections. Beta-lactams were the most frequently prescribed systemic anti-infectives. A statistically significant increase in the rate of beta-lactam prescriptions/1000 resident days occurred between 2018 and 2022 (p = 0.016), whereas a statistically significant decrease in quinolone prescriptions/1000 resident days occurred in the same time period (p < 0.001). Conclusions: The incidence rates of HCAIs varied over time with a significant increase during the COVID-19 pandemic in 2022 compared to 2018. Continued surveillance efforts are necessary to assess the effect of infection control efforts after the pandemic. Full article

Background: Self-medication is the use of medicinal products to treat self-diagnosed disorders or symptoms without the prescription or supervision of a healthcare professional. There is a lack of data about self-medication knowledge, attitudes, and practices among pharmacy undergraduates in Malaysia. This study assessed the knowledge, attitudes, and practices among undergraduate pharmacy students in Penang regarding self-medication. Method: A descriptive cross-sectional study was conducted using a self-administered, web-based survey (Google Forms), which was completed and responded to by 203 undergraduate pharmacy students from Penang, Malaysia, between October and December 2023. Descriptive statistics were used to summarize the socio-demographic characteristics of the participants. Associations between the socio-demographic characteristics of the participants and the knowledge, attitudes, and practices regarding self-medication were assessed using a chi-square test. Regression analyses were carried out to determine whether the socio-demographic characteristics of the participants were associated with practices of self-medication. Results: A total of 203 of the undergraduate pharmacy students completed the questionnaire. More than half of the participants’ age ranged between 19 and 21 years old, the majority were females (77.3%), and 31.5% of the participants had family members employed in the healthcare sector. Most respondents showed good knowledge in a variety of domains: 97.5% acknowledged the potential for drug interaction with other medications, indicating a high awareness of proper self-medication practices. A positive attitude was found regarding participants’ attitudes toward self-medication, and 65.5% practiced self-medication, primarily for treating minor illnesses (75.9%). Common conditions included fever (83.3%), cough/cold/flu (76.8%), and headache (71.4%). Reasons for not self-medicating included the absence of illness (20.2%), lack of knowledge/prior experience (19.2%), and fear of using the wrong medication (18.7%). Only academic year level was the predictor of practicing self-medication within the last six months among the participants. Conclusions: Generally, the participants possessed good knowledge and positive attitudes toward self-medication. The study revealed no significant associations between demographic characteristics and knowledge or attitudes. Insights from this research contribute to understanding self-medication practices among pharmacy students in Penang, informing potential interventions to promote responsible self-medication practices. Full article