Search Results (2,564)

Background/Objectives: High-quality pediatric emergency care requires timely access, effective communication, privacy, pain management, comfort, and child- and family-centered practices; however, implementation may be constrained by several barriers. The aim of the study was to evaluate the quality of pediatric emergency care as perceived by healthcare professionals, with emphasis on child-centered care and variations based on workplace and professional characteristics. Methods: A cross-sectional survey was performed in the emergency departments in two tertiary public pediatric hospitals in Athens, Greece. A study-developed 14-item Quality of Care Assessment Scale with paired ratings of agreement with quality principles and implementation in practice was completed by 162 professionals (122 doctors, 24 nurses, 16 assistant nurses). Independent items evaluated perceived barriers, overall assessments (0–100), and information provided to parents/children (5-point Likert scale). Inferential tests and descriptive statistics were also used (p < 0.05). Results: There was a significant degree of agreement with quality principles, but there was a constant lack of implementation (principle–practice gap). The primary perceived weakness was waiting times; child-friendly settings and privacy during examinations and information-giving were also lacking. Internal consistency ranged from good to acceptable (implementation α = 0.800; agreement α = 0.711). Children were most frequently rated as “moderately informed” (48.1%), while parents were most frequently rated as “quite informed” (50.0%). Compared to the organization of care (mean 60.85), perceived safety was higher (mean 73.27). Perceptions varied by age, educational level, profession, department, shift rotations, and hospital. The main barriers were workload (30.2%), poor coordination (34.0%), and lack of resources (46.9%). Conclusions: Health professionals seem to perceive that consistent delivery of child-centered care is impaired by organizational and structural limitations. Reducing the standards-to-practice gap requires targeted system-level interventions that focus on staffing, care organization, environment, and professional support. Full article

In recent years, the mortality due to diabetes has increased around the world. In particular, diabetes is the second leading cause of mortality in Mexico, with a heterogeneous distribution of mortality rates at the municipality level. The objective of this study is the analysis of clusters of municipalities with similar values for sociodemographic indices and diabetes mortality. In this sense, an application is presented that was developed using a data science methodology and a machine learning algorithm called fuzzy c-means. For this research, 4,604,360 death certificates from 2019 to 2023 were assessed, among other official data. As a result of the analysis, two key indicators related to diabetes mortality were found, i.e., one is the percentage of population in poverty and the other is population density. The main results of this research are as follows: a direct correlation was found between population density and mortality, and an inverse correlation was found between population in poverty and mortality. In the study interval, it was observed that the cluster with less mortality showed an increase in mortality rate year after year. Finally, we consider that the tendencies found can be useful to public health authorities for optimizing the distribution of resources for treating diabetes and reducing diabetes-related mortality. Full article

Background/Objectives: Software is now core infrastructure in biomedical science, yet fragmented workflows across subfields hinder reproducibility and delay the translation of data into actionable decisions. There is a critical need for a cross-disciplinary synthesis to bridge these silos and establish a unified framework for software maturity. This narrative review addresses this gap by synthesizing representative software ecosystems across three major pillars: bioinformatics, molecular modeling/simulations, and epidemiology/public health. Methods: A narrative review of articles indexed in PubMed/NCBI, Web of Science, and Scopus between 2000 and 2025 was conducted. Domain-specific terms related to bioinformatics, molecular modeling, docking, molecular dynamics, epidemiology, public health, and workflow management were combined with software- and algorithm-focused keywords. Studies describing, validating, or applying documented tools with biomedical relevance were included. Results: Across domains, mature data standards and reference resources (e.g., FASTQ, BAM/CRAM, VCF, mzML), widely adopted platforms (e.g., BLAST+ (v2.16.0, NCBI, Bethesda, USA), Bioconductor (v3.20, Bioconductor Foundation, Seattle, USA), AutoDock Vina (v1.2.5, Scripps Research, La Jolla, USA), GROMACS (v2024.3, GROMACS Team, Stockholm, Sweden), Epi Info (v7.2.6, CDC, Atlanta, USA), QGIS (v3.40, QGIS.org, Gossau, Switzerland), and increasing use of workflow engines were identified. Software pipelines routinely transform molecular and surveillance data into interpretable features supporting hypothesis generation. Conclusions: Integrated, standards-based, and validated software pipelines can shorten the path from measurement to decision in biomedicine and public health. Future progress depends on reproducibility practices, benchmarking, user-centered design, portable implementations, and responsible deployment of machine learning. Full article

Weather modification has gained significant and growing interest in the United States (US) in recent years. The trend can be largely attributed to the changing climate, persistent droughts, and other extreme weather events that have been experienced across various regions of the US. This paper provides a critical review of weather modification program costs, benefits, policy, and governance to help shed light on policymaking and program management associated with the growing interest in adopting weather modification as a local climate management strategy in the US. Additionally, to deepen our understanding of the widely concerning issues, such as the financial burden on taxpayers and potential environmental risks, the paper explored the local implementation challenges and common environmental and public health concerns related to weather modification activities. A synthesis of the literature and policy debates reached four general conclusions: (1) The need for weather modification programs is expected to keep growing, though regional variations may exist due to regulatory and other local factors; (2) weather modification can bring significant local benefits, ranging from enhanced agricultural yield and recreational economy to extreme weather management and public environmental health benefits; (3) state-level and local support, including financial resources, will be essential for program development in the foreseeable future; and (4) technological advancements will be critical for addressing many of the project operation efficiency challenges and environmental and public health concerns related to weather modification programs. More specifically for program governance and local implementation, aspects such as project planning (including resource pooling), risk and liability management, communication and reporting, outcome measurability, and stakeholder engagement are indispensable for addressing issues related to program legality and oversight, public acceptance, and sustainability. Full article

Maintaining sustainable mental health is an increasing societal challenge in Japan, where psychological distress and sleep problems have become major public health concerns. This study examined how three culturally grounded dimensions of self-construal—Subjective Self (SS), Objective Self (OS), and Autonomous Self (AS)—relate to both positive and negative indicators of psychological adjustment among Japanese adults. This study aimed to examine whether internally guided forms of self-regulation (SS and AS) function as psychological resources, whereas externally guided self-regulation (OS) operates as a potential vulnerability factor in a culturally tight social context. By simultaneously examining multiple indicators of adjustment, this research clarifies how culturally shared self-regulatory patterns are linked to distress and sleep difficulties that affect large segments of the population. From a public health perspective, the findings highlight socially reinforced risk and protective patterns that can inform population-level prevention and mental health promotion in settings such as schools, workplaces, and communities, rather than relying solely on individual clinical intervention. These results underscore the importance of integrating cultural psychology into public health frameworks aimed at promoting sustainable mental health in contemporary and increasingly diverse social environments. Full article

Background: The emergence of artificial intelligence (AI) has triggered a global transformation, with the healthcare sector experiencing significant disruption and innovation. In current public health practice, AI is being deployed to power various aspects of public functions, including the assessment and monitoring of health, surveillance and disease control, health promotion and education, policy development and planning, health protection and regulation, prevention services, workforce development, community engagement and partnerships, emergency preparedness and response, and evaluation and research. Nevertheless, its use in leadership and management, such as in change management, process development and integration, problem solving, and decision-making, is still evolving. Aim: This study proposes the adoption of the Public Health AI Framework to ensure that inclusive data are used in AI development, the right policies are deployed, and appropriate partnerships are developed, with human-relevant resources trained to maximize AI potential. Implications: AI holds immense potential to reshape public health by enabling personalized interventions, democratizing access to actionable data, supporting rapid and effective crisis response, advancing equity in health outcomes, promoting ethical and participatory public health practices, and strengthening environmental health and climate resilience. Achieving this goal will require a deliberate and proactive leadership vision, where public health leaders move beyond passive adoption to collaborate with AI specialists to co-create, co-design, co-develop, and co-deploy tools and resources tailored to the unique needs of public health practice. Call to action: Public health professionals can co-innovate in shaping AI evolution to ensure equitable, ethical, and value-based public health. Full article

Global health faces unprecedented challenges driven by communicable diseases, which are increasingly amplified by persistent health inequities, the impact of climate change, and the speed of emerging crises. Prevention is not merely a component but the foundational strategy for an effective, sustainable, and fiscally responsible public health response. This paper delves into the pivotal role of core prevention levers: robust vaccination programs, stringent hygiene standards, advanced epidemiological surveillance, and targeted health education. We detail how contemporary technological advancements, including Artificial Intelligence (AI), big data analytics, and genomics, are fundamentally reshaping infectious disease management, enabling superior predictive capabilities, faster early warning systems, and personalized prevention models. Furthermore, we thoroughly examine the imperative of integrating the One Health approach, which formally recognizes the close, interdependent links between human, animal, and environmental health as critical for combating complex threats like zoonoses and Antimicrobial Resistance (AMR). Despite significant scientific progress, persistent socio-economic disparities, the pervasive influence of health-related misinformation (infodemics), and structural weaknesses in global preparedness underscore the urgent need for decisive international cooperation and equitable financing models. We conclude that only through integrated, multidisciplinary, and resource-equitable strategies can the global community ensure effective prevention, mitigate severe socio-economic disruption, and successfully build resilient healthcare systems capable of withstanding future global health threats. Full article

Livestock contributes to economic stability and food security by providing income, employment, and nutrient-dense animal-source foods, particularly in low- and middle-income regions. However, the sector is also a major source of anthropogenic greenhouse gas emissions, primarily methane, nitrous oxide, and carbon dioxide, raising growing environmental and public health concerns. This review synthesizes current evidence on strategies to mitigate greenhouse gas emissions from livestock systems while safeguarding productivity, food security, and human health. Emphasis is placed on the need to balance supply-side mitigation measures with demand-side interventions to avoid unintended nutritional and socio-economic consequences. Key supply-side approaches discussed include genetic improvement, optimized feeding strategies, manure and land resource management, and system-level efficiency gains. Demand-side strategies include food loss and waste reduction, shifts toward sustainable dietary patterns, and the development of alternative protein sources. Central to this review is the integration of these approaches within a planetary health framework, highlighting the interconnectedness of environmental sustainability, human and animal health, and socio-economic resilience. The review underscores that mitigation policies should be context-specific, equity-focused, and health-centered to ensure that climate goals are met without compromising access to affordable, nutritious foods. Collectively, the evidence indicates that coordinated policy action across production, consumption, and health systems is essential for achieving sustainable animal-source food production with reduced climate impact. Full article

Active and healthy ageing has become a strategic objective in European and national policy agendas, grounded in grounded in internationally recognised definitions and policy frameworks such as the Madrid International Plan of Action on Ageing (MIPAA) and the European Innovation Partnership on Active and Healthy Ageing (EIPAHA). In Italy, the translation of this paradigm has taken place within a fragmented welfare system characterised by strong regional autonomy and persistent social and territorial inequalities, particularly along regional and gender lines. This scoping review has a twofold aim: (1) to map the Italian scientific and grey literature on active and healthy ageing, identifying dominant dimensions, priorities and gaps, and (2) to examine how policies and interventions frame, address or overlook social, territorial and gender inequalities across the life course Following established scoping review methodological frameworks and PRISMA-ScR guidelines, the review systematically identified, selected and synthesised Italian scientific studies and institutional documents published between 2012 and 2024. An inductive thematic analysis was conducted across four main areas—health and wellbeing; social inclusion and participation; indicators and measurement tools; and governance and public policies—with specific attention to the explicit and implicit treatment of inequalities. The analysis reveals a heterogeneous and regionally unbalanced policy landscape. While some territories have developed more integrated approaches linking prevention, participation and social inclusion, others remain largely confined to sectoral and fragmented interventions. Gendered patterns of unpaid care, differential access to programmes and services, and uneven territorial distribution of resources emerge as key dimensions of inequality shaping opportunities for active ageing. A partial discontinuity can be observed after 2019, with the introduction of national coordination mechanisms, although substantial differences in regional implementation capacity persist. The findings highlight the need for more coherent and equity-oriented strategies capable of integrating health, social and educational dimensions through a life-course and intersectional perspective. Strengthening multi-level governance and explicitly addressing social, territorial and gender inequalities as structural determinants—rather than residual variables—appears crucial to enhancing both the effectiveness and the fairness of active and healthy ageing policies in Italy. Full article

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) and Obesity Hypoventilation Syndrome (OHS) are core components of the obesity-related respiratory disease spectrum, and their comorbidity has become a major challenge in the global public health field. This review systematically summarizes the epidemiological characteristics, pathophysiological mechanisms, diagnostic criteria, diagnostic technologies and treatment strategies of OSAHS-OHS comorbidity, with a focus on the cutting-edge progress of digital therapeutics and metabolic intervention, as well as the historical evolution and current status of clinical management. We also conduct an in-depth analysis of the unresolved controversies and practical challenges in the current clinical management of this comorbidity. OSAHS-OHS comorbid patients have a significantly higher risk of cardiovascular complications than those with a single disease, and chronic intermittent hypoxia (CIH) forms a vicious cycle with obesity through multiple pathophysiological pathways. The combination of multi-dimensional assessment tools and portable monitoring devices has improved the screening efficiency of OSAHS-OHS comorbidity, and the selection of respiratory support therapies such as continuous positive airway pressure (CPAP) and non-invasive ventilation (NIV) depends on patient phenotypes. Digital therapeutics and novel metabolic intervention drugs have shown promising clinical value in the management of this comorbidity. The multidisciplinary collaboration model is the key to improving the prognosis of comorbid patients, while current clinical management is still faced with challenges such as policy lag, ethical controversies and uneven resource allocation. Future research should focus on individualized therapeutic targets, the integration of digital technologies and the optimization of health policies to achieve precise and efficient management of OSAHS-OHS comorbidity. Full article

Dengue virus (DENV) infection is a significant public health concern in Indonesia, with increasing cases and severity posing challenges to the country’s healthcare systems. This study aims to develop and validate a machine learning-based prediction model for assessing dengue infection cases and their severity. The model incorporates epidemiological, clinical, and environmental factors to enhance early detection and resource allocation. Additionally, the model can be utilized to support logistics planning, such as the distribution of diagnostic kits and the preparation of health facilities in each region across Indonesia, ensuring timely and targeted responses to potential outbreaks. We applied various machine learning algorithms, including logistic regression, random forest, XGBoost, and SVM models, and evaluated them to determine the most effective predictive model. The results demonstrate the model’s efficacy in predicting dengue cases and severity, which can support public health interventions and clinical decision-making. Geospatial clustering and correlation matrices were generated to visualize risk patterns and support predictions. The XGBoost model demonstrated the highest performance, achieving an accuracy of 85%. Our findings suggest that integrating clinical and environmental data through machine learning (ML) techniques can significantly improve early detection and inform resource allocation strategies. The model offers a promising approach for public health surveillance and targeted interventions in dengue-endemic regions. Full article

Tuberculosis remains a major public health concern in Uganda, one among the thirty high TB burden countries globally. Despite national progress, gaps persist due to asymptomatic disease, diagnostic limitations, and uneven access to healthcare within the country. This study implemented the Epi-control platform, an AI-driven predictive modelling tool, to predict community-level hotspots and support data-driven active case-finding (ACF). Using retrospective chest X-ray screening data, we integrated demographic, environmental, and human development indicators from open-source databases to model TB risk at sub-parish level. A proprietary Bayesian modelling framework was deployed and validated by comparing TB yields between predicted hotspots and non-hotspot locations. Across Uganda, the model identified significantly higher TB yields in hotspot areas (risk ratio = 1.69, 95% CI 1.41–2.02; p < 0.001). The Central and Western regions showed the highest concentrations of hotspots, consistent with their population density and urbanization patterns. The results show that the model prioritized areas with higher observed ACF yield in this retrospective dataset, supporting its potential operational use for screening prioritization under similar implementation conditions. The results demonstrate that AI-based predictive modelling can enhance the efficiency of ACF by targeting high-risk areas for screening. Integrating such predictive tools within national TB programmes may support screening planning and resource prioritization; prospective evaluation and external validation are needed to assess generalisability and incremental impact. Full article

Background/Objectives: Acute leukemia, the most common childhood cancer, poses a significant public health challenge in low- and middle-income countries (LMICs) due to its high incidence and mortality rates. Survival rates in these regions are often lower, primarily due to delayed and inaccurate diagnoses, limited access to treatment, therapy abandonment, therapy-related toxicity, and inadequate healthcare infrastructure. In Mexico, a new initiative called OncoCREAN has been developed to address this urgent need by establishing local treatment centers near pediatric patients’ home cities, ensuring timely cancer detection and comprehensive disease treatment. Methods: A retrospective observational study was conducted on pediatric patients treated at the Mexican Social Security Institute (IMSS) between 18 May 2022 and 30 June 2025. Patients presenting clinical suspicion of acute leukemia were referred to OncoCREAN centers for sample collection and subsequent shipment to the Oncoimmunology and Cytomics Laboratory (OCL), where immunophenotyping confirmed the diagnoses. Results: The implementation of the OncoCREAN model significantly reduced diagnostic turnaround times, facilitating timely therapeutic decisions, minimized uncertainty, and optimized clinical management. The decentralized framework demonstrated feasibility across diverse geographic regions, ensuring access to advanced diagnostic technology for vulnerable populations and generating valuable data on disease incidence and molecular profiles. Conclusions: The OncoCREAN model highlights the critical importance of decentralizing high-technology diagnostic resources in modern pediatric oncology. This new approach to translational research that is accessible, inclusive, and relevant to society creates a paradigm shift in the management of childhood cancer and other diseases. Full article

Digital transformation poses significant challenges to employee well-being, particularly in public administration, where hierarchical structures, increasing digitalization pressures, and high mental health-related absenteeism underscore the need to understand individual and job demands and resources. This study explores these aspects from the perspectives of employees and supervisors in public administration. Between September 2023 and February 2024, semi-structured interviews were conducted with eight employees and eleven supervisors from public administration organizations in Northern Germany and analyzed using deductive–inductive qualitative content analysis based on the Job Demands-Resources model. Identified individual resources included technical affinity, error tolerance, and willingness to learn, while key job resources involved early and transparent communication, attentive leadership, technical support, and counseling services, with most job resources linked to leadership behavior and work organization. Reported job demands comprised insufficient participation, inadequate planning, and lengthy procedures, whereas personal demands included fears and concerns about upcoming changes and negative attitudes toward transformation. The variation in perceived demands and resources highlights the individuality of the employees’ experiences. The findings provide initial insights into factors influencing psychological well-being at work during digital transformation, emphasizing the importance of participatory communication, employee involvement, leadership awareness of stressors, and competence development. Future research should employ longitudinal and interventional designs to improve causal understanding and generalizability. Full article

Milk-borne outbreaks remain a significant issue in low-income countries due to unhygienic practices. Currently, there are no known easily accessible and low-cost diagnostic tests that can detect Salmonella enterica subsp. enterica serovar Typhi, the causative agent of typhoid fever, in raw milk samples at high specificity, sensitivity, and speed without preprocessing. Early detection of Salmonella enterica subsp. enterica serovar Typhi in food matrices is critical for preventing infection prior to consumption and reducing disease burden. Using colorimetric loop-mediated isothermal amplification, we screened 15 novel and two previously published primer sets. We identified one novel primer set capable of detecting the STY2879 gene in as few as 2000 genomes in 2% v/v raw milk reactions and 1000 genomes in 1% v/v raw milk reactions of Salmonella enterica subsp. enterica serovar Typhi after 30 minutes of incubation in a 65° C water bath. The colorimetric readout offers promising potential applications for on-site detection in remote and low-resource settings where infection with Salmonella enterica subsp. enterica serovar Typhi remains a public health concern. Full article