Search Results (328)

Background: Rural communities face persistent healthcare barriers related to workforce shortages, geographic isolation, transportation limitations and constrained emergency response capacity. Nurse-led mobile clinics may support healthcare access, continuity of care, and disaster preparedness in underserved settings. This study examined the Texas A&M University (TAMU) nurse-led mobile clinic model with respect to rural service delivery, health equity, operational considerations, and disaster preparedness. Methods: A mixed-methods descriptive program evaluation was conducted using programmatic operational data, survey responses, and preparedness-planning records. The TAMU mobile clinic serves six rural counties through primary, preventive, and behavioral healthcare delivery using in-person care, telehealth, and home visits. Disaster preparedness activities were integrated through the annual Disaster Day interprofessional simulation involving approximately 600–700 learners. A 2025 Central Texas flooding event served as a case study to evaluate operational preparedness and system readiness. Results: Mobile clinic operations supported healthcare access, continuity of care, and community engagement in rural settings. Interprofessional education simulation findings demonstrated perceived gains in teamwork, triage, communication, and rapid decision-making. During the 2025 flooding event, activation protocols were initiated; however, deployment was not authorized, highlighting system-level constraints related to administrative approval pathways despite operational readiness and workforce preparedness. Conclusions: Nurse-led mobile clinics may serve as an adaptable infrastructure for improving rural healthcare access, supporting continuity of care, and strengthening disaster preparedness. Findings further emphasize that clinical preparedness alone is insufficient without coordinated administrative processes, interoperable systems, and governance structures capable of supporting rapid emergency deployment. Full article

Environmental health services play a critical role in communicable disease outbreaks by addressing environmental determinants of disease transmission. However, the scope, impact, and challenges of Environmental Health Practitioner (EHP)-led interventions remain insufficiently documented. Aim and objectives: This systematic review objectively assessed the role, impacts, and challenges of municipal environmental health services in outbreak response, with a focus on South Africa, to inform the standardisation and strengthening of disease surveillance and prevention. Methods: The PICO framework guided the development of search terms and research questions. PubMed, Scopus, Google Scholar, and Web of Science were searched for English-language, full-text studies published between 2010 and 2024. Studies not meeting these inclusion criteria were excluded. Screening and reporting followed PRISMA guidelines, and data were synthesised using a standardised extraction tool. Results: A total of 58 studies were included. The key EHP functions identified were water quality monitoring, vector control, food safety, waste management, and outbreak response. While South Africa demonstrated comparatively advanced systems, persistent implementation challenges remain, including the integration of environmental monitoring with disease surveillance. The findings emphasised the need for integrating environmental monitoring with disease surveillance systems and integrating WASH and climate-responsive strategies. Conclusions and recommendation: The review recommends strengthening guidelines and advancing evidence-based practice. Enhancing EHP roles within surveillance frameworks is essential for improving outbreak preparedness and public health resilience. Full article

Background/Objectives: Vaccine development pipelines are forward-looking indicators of public health preparedness, reflecting the capacity to address unmet medical needs and emerging threats. This descriptive analysis aims to characterise the 2025 clinical-stage pipeline of infectious disease vaccines and prophylactic monoclonal antibody candidates developed by Vaccines Europe member companies, and to describe how pipeline characteristics address evolving public health priorities. Methods: A descriptive analysis was conducted using publicly available data compiled in the Vaccines Europe Pipeline Review 2025, with validation by participating companies. Candidates in clinical development or regulatory review were classified using a standardised framework by pathogen/disease, target population, public health priority, and technologies. Results: The Vaccines Europe member company pipeline comprises 91 candidates across clinical development phases, 19% of which are in Phase III and 7% undergoing regulatory review. This pipeline is predominantly targeting respiratory pathogens (75%), with a strong life-course focus (85% evaluated in adults and/or older adults), and sustained activity in bacterial pathogens relevant to antimicrobial resistance. Notably, 41% of candidates were classified as addressing diseases, disease combinations, or indications for which no licenced preventive product exists. This category includes candidates targeting diseases without a preventive solution, as well as novel combination vaccines and therapeutic approaches in areas where individual components or preventive vaccines are already available. This captures vaccines candidates in different stages of development, not necessarily first-in-disease innovation. The pipeline shows broad technological diversity (12 technologies), dominated by RNA approaches and multivalent candidates, with growing focus on climate-sensitive, zoonotic, and pandemic-prone pathogens. Conclusions: Within the pipeline of Vaccines Europe member companies, this analysis describes development activity oriented toward broader prevention, platform-based approaches, and preparedness-relevant targets. As a structured and recurring annual assessment, the Vaccines Europe Pipeline Review supports horizon scanning and evidence-based dialogue between industry and vaccine ecosystem stakeholders. In order to maximise the impact of vaccine development pipelines to public health, predictable investment, streamlined trial and regulatory pathways, strong surveillance, and real-world data systems, coordinated decision-making is required to enable timely and equitable access, and complementary incentive and procurement reforms. Full article

Bangladesh is an extremely climate-exposed country, with erosion, accretion, tidal surges, and cyclones continuously modifying coastal districts. Shoreline change in Bangladesh is crucial for sustainable coastal management and disaster resilience. Therefore, the objectives of this research are as follows: (i) to assess accretion- and erosion-based shoreline changes of the Bangladesh delta adjacent to the Bay of Bengal for 2021–2025 using a fixed 2021 reference shoreline and a 2025 shoreline proxy extracted from Landsat 8/9 imagery, and (ii) to explore onshore change dynamics from satellite-derived NDVI, NDBI, and NDWI for 2022–2025. The study covers 14 coastal districts and integrates the 2021 baseline shoreline, Survey of Bangladesh geospatial datasets, and 17,055 Ground Reference Points (GRPs) to support geometric consistency and spatially explicit reporting at the delta scale. Three spectral indices—Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI)—were applied to assess vegetation health, surface water distribution, and built-up/exposed land characteristics. Results indicate spatial variability in coastal change, with 383.49 km² of land gained through accretion and 124.12 km² lost to erosion, resulting in a neat accretion of 259.37 km² between 2021 and 2025; 8747.91 km² remained geomorphologically stable. Spectral index trends show minimal inter-annual NDVI and NDWI variability, suggesting stable vegetation cover and no long-term expansion of surface water. In contrast, a slight increase in NDBI indicates localized exposure of new sediments or small-scale land-use transitions along emerging coastal zones. Spearman correlation analysis highlights consistent negative relationships between NDVI and NDWI and moderate contrasts between NDVI and NDBI, reinforcing the coexistence of vegetation recovery, water withdrawal, and sediment-driven land emergence. The novelty of this study lies in the provision of consistent, near-real-time coastal change inventory for the full ~710 km Bangladesh delta coastline by combining a common 2021 baseline shoreline with harmonized Landsat 8/9 OLI surface reflectance (2022–2025) and linked onshore spectral-index dynamics over the same period. Overall, this short-term assessment reveals a sedimentary system that is active but balanced, with accretion surpassing erosion despite cyclone-affected disturbances, underscoring the value of operational satellite monitoring for coastal management, hazard preparedness, and climate-adaptive planning. Full article

Background: Diarrheal disease remains a major and persistent cause of morbidity and mortality in Rwanda, with substantial seasonal surges that strain routine services; however, transparent and operationally interpretable national forecasting has been underused for age-stratified burden. Methods: We analyzed the Rwanda Health Management Information System (HMIS) monthly diarrhea case counts (January 2015–December 2025), stratified by age group (under-five and five-and-above), and developed validated Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasts for January 2026–December 2027. Stationarity was assessed using the Augmented Dickey–Fuller test and addressed through differencing. Candidate models were selected via rolling 5-fold cross-validation: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Mean Absolute Percentage Error (MAPE) and confirmed via Ljung–Box residual diagnostics, and benchmarked against seasonal naïve, Exponential Smoothing State-Space (ETS), and Seasonal-Trend decomposition using Loess (STL) + drift reference models. Results: Rwanda recorded 6,309,098 diarrhea cases during 2015–2025, with 49.2% among under-fives; while absolute counts were higher in those aged ≥5 years, risk remained consistently higher in under-fives (91.7–229.5 per 1000) than in those ≥5 years (17.9–34.3 per 1000). Both series showed strong annual seasonality with recurrent peaks in August–November, and forecasts suggest this pattern will persist through 2026–2027. Conclusions: These findings suggest a provisional seasonal (pre-peak, peak, and post-peak) preparedness framework and age-differentiated planning signals, underscoring that burden and risk are not inter changeable across age groups. Full article

Background/Objectives: Individuals with limitations in their daily activities use assistive technology (AT), which helps them restore body structures and functions. During the pandemic, to prevent the spread of infection, health policies have disrupted the traditional delivery mode of AT service, and the lack of preparedness for contingency measures has further caused AT service disruptions, making the continuity of AT services a major challenge. This study explores the critical success factors (CSFs) for preventing AT service interruptions in the post-pandemic era and supporting decision-makers in responding rapidly to similar infectious disease pandemics in the future, while ensuring delivery of high-quality AT services. Methods: A systematic literature review was conducted, and then, the multicriteria decision-making (MCDM) model, combined with a decision-making trial and evaluation laboratory (DEMATEL) and an analytic network process (ANP), was applied to stratify complex problems in a structured manner, thereby constructing a multicriteria decision analysis structure for identifying the CSFs for avoiding AT service interruptions in the post-pandemic era. Results: The study results revealed that the three most influential direct factors are improving the providers’ telemedicine capabilities, enhancing access to digital AT service support, and establishing a digital AT ecosystem. Indirect factors include addressing resource shortages. Conclusions: To avoid repeating past mistakes during future pandemics involving similar infectious diseases, strengthening the telemedicine capabilities of medical staff and ensuring a complete AT service delivery system are the most essential priorities. Full article

Healthcare artificial intelligence (AI) is moving from the laboratory into the infrastructure of care. As these systems become embedded in imaging, electronic health records, triage, and clinical decision support, their failures can affect not only individual encounters but also institutions and patient populations. Yet governance still centers on model development, local validation, and one-time compliance, with limited attention to cross-site failure after deployment. This article examines how public health preparedness can help close that gap. It presents a conceptual analysis grounded in two cases: a pneumonia-screening convolutional neural network that learned institutional confounders rather than portable clinical signals, and a widely deployed sepsis prediction model whose external performance and alert burden fell short of developer claims. Together, these cases reveal five governance features of systemic healthcare AI risk: population-level exposure, cascade effects across shared infrastructures, unequal vulnerability, delayed recognition, and coordination needs beyond any single institution. In response, we propose a tripartite framework combining stronger pre-deployment assurance, post-deployment surveillance with escalation thresholds, and tertiary response through investigation, rollback, remediation, and cross-site learning. The argument is not that AI failures are epidemics, but that high-impact clinical AI systems now function as critical digital health infrastructure requiring preparedness alongside lifecycle oversight. Full article

Thailand is facing a rapidly aging society, raising concerns about how retiring workers will maintain their quality of life. Insured persons in the social security system—especially voluntary members under Section 40 of the Social Security Act B.E. 2533 (1990), who are often informal workers—frequently lack formal retirement plans, underscoring the need for interventions that address financial security and subjective well-being (SWB) in later life. This study aimed to develop and evaluate a retirement planning program designed to enhance subjective well-being and improve the quality of life for pre-retirees in Songkhla Province. A Research and Development (R&D) design was employed in four phases. Phase 1 (R1) involved a needs assessment: survey data from 500 insured individuals (ages 40–60) were collected to identify gaps between current and desired retirement preparedness. Phase 2 (D1) utilized the needs assessment results and theoretical frameworks to design a Subjective Well-being Retirement Planning Program, encompassing financial, health, and psychosocial components. Content-relevance experts validated the draft program. Phase 3 (R2) involved implementing the program with 15 volunteer participants over four weekly workshops (each 3 h long) and evaluating its short-term pilot outcomes using pretest-posttest measures of subjective well-being. Phase 4 (D2) refined the program based on evaluation findings and expert feedback. Results indicated that following participation in the program, participants’ overall subjective well-being and all sub-dimensions (life satisfaction, positive and negative affect balance, sense of meaning, social connectedness, security, and health) were significantly higher than before (p < 0.001). Additionally, the proportion of participants classified as inadequately prepared for retirement (high-risk due to low planning) decreased markedly, suggesting increased readiness within the pilot group. Expert evaluations of the program design reflected a high content validity index and strong agreement on the program’s accuracy, appropriateness, and usefulness for the target group. In conclusion, the developed retirement planning program was associated with short-term improvements in subjective well-being and quality-of-life indicators among insured pre-retirees. This theory-informed program, developed through an R&D process, offers a model for supporting aging workers in the social security system, with implications for policymakers and practitioners seeking to promote healthy, happy, and secure retirements in an aging society. Full article

Background/Objectives: The COVID-19 pandemic imposed logistical challenges on health systems, particularly for mass vaccination campaigns under emergency conditions. In decentralized health systems, the absence of a structured preparedness phase may compromise coordination, allocation, and operational performance. This study analyzes the vaccination campaign in a municipality in southern Brazil, examining how the overlap of the preparedness and response phases affected outcomes and how alternative logistical scenarios could have altered campaign performance. Methods: An empirical analysis was conducted using scenario-based simulation with stock and flow structures. The model represents vaccine procurement, distribution across national, state, regional, and municipal levels, and municipal vaccination capacity. Real data from the 2021 vaccination campaign in the municipality were used to build a Business-as-Usual scenario, compared with alternative scenarios involving changes in procurement predictability, allocation rules, and operational capacity. Results: Vaccination outcomes were strongly conditioned by upstream allocation decisions, particularly at the national state level. Isolated adjustments at intermediate supply chain levels produced limited improvements when upstream constraints persisted. Scenarios combining improved alignment between forecasted and acquired doses with operational capacity showed higher vaccination potential, revealing a gap between observed performance and system capacity. Conclusions: The findings reinforce that preparedness is a critical determinant of vaccination performance and must precede response in emergency contexts. Supply predictability alone is insufficient without coordinated allocation mechanisms and operational readiness across governance levels. This study provides empirical evidence on how preparation-related decisions shape vaccination outcomes in decentralized health systems and inform logistical coordination in future emergencies. Full article

Background: Although pre-exposure prophylaxis (PrEP) has demonstrated strong clinical efficacy in preventing HIV infection among men who have sex with men (MSM), real-world utilization remains suboptimal. In South Korea, MSM constitute a major population within the domestic HIV epidemic; however, PrEP uptake has not increased pro-portionally to awareness. This discrepancy has been conceptualized as the “awareness–uptake gap,” reflecting multi-level barriers beyond individual knowledge. Purpose: This integrative review aimed to compare PrEP awareness, acceptance, and utilization among MSM populations in South Korea and international settings, and to identify structural, institutional, and psychosocial determinants contributing to the awaness, uptake gap. The study further sought to derive practical implications for nursing practice and health policy. Methods: An integrative literature review was conducted following Whittemore and Knafl’s five-step methodology and reported in line with PRISMA guidance. Electronic searches were performed in PubMed, Google Scholar, RISS, ScienceON, and DBpia for peer-reviewed studies published between 2015 and 2025 in English or Korean. The final search was completed on 31 January 2026. A total of 5952 records were identified, and 187 studies met the inclusion criteria after screening and duplicate removal. Quality appraisal was conducted using AXIS, Newcastle-Ottawa Scale, RoB 2.0, CASP, and MMAT according to study design, and the findings were synthesized within an environmental–structural–individual framework. Results: The included studies consistently showed that awareness of PrEP exceeded actual uptake. Across settings, the awareness–uptake gap was shaped by policy environment, service accessibility, stigma, privacy concerns, economic burden, institutional complexity, and provider preparedness. Comparative evidence from China, Thailand, Belgium and France, Brazil, and West Africa further suggested that awareness alone did not ensure uptake when service pathways were fragmented, culturally unsafe, or poorly understood. Conclusions: Closing the awareness–uptake gap requires integrated policy and practice strategies that extend beyond cost reduction. Strengthening confidentiality systems, simplifying service pathways, and enhancing provider competency—particularly through nurse-centered PrEP navigation and counseling models—may support more sustainable PrEP expansion among MSM populations in global settings. Full article

Translating diverse stakeholders’ needs for tourism into precise technical requirements for mobile resilience applications is a significant challenge, especially for at-risk coastal communities. Therefore, we developed a structured decision-support framework that uses the Analytic Hierarchy Process (AHP) combined with Multi-Criteria Decision Analysis (MCDA) to systematically identify and prioritize functional features for a disaster-resilient tourism application called AuTour. The framework was validated through a case study in Aurora Province, Philippines, involving 152 diverse stakeholders, including government officials, tourism operators, and technology students. The AHP analysis results revealed that safety infrastructure (a mean weight of 0.5256) was the dominant design criterion, far outweighing environmental sustainability (0.2480) and community preparedness (0.1241). The MCDA ranked key functional modules using these criteria to determine an optimal system architecture. The highest-priority features identified were a real-time Disaster Preparedness Alert module, a geospatial Smart Tourism Guide, and a participatory Health Surveillance module. The analysis results confirmed high utility for features incorporating AI-powered chatbots (a mean score of 4.1921) and multi-dialect communication capabilities (4.1513). The developed scalable, data-driven framework can be used for user-centered design in the critical domain of disaster-resilient technology. By translating stakeholder priorities into a ranked set of technical specifications, the framework contributes to the development of resilient mobile systems, supporting the achievement of Sustainable Development Goals for innovation (SDG 9) and resilient infrastructure (SDG 11). Full article

Nipah virus (NiV) is a highly pathogenic zoonotic paramyxovirus responsible for sporadic outbreaks of severe disease with high case fatality rates in South and Southeast Asia. Human infection occurs through spillover from natural reservoirs, primarily fruit bats, or via human-to-human transmission, and is characterized by a broad clinical spectrum ranging from asymptomatic infection to acute respiratory disease and fatal encephalitis. Following entry via ephrin-B2 and ephrin-B3 receptors, NiV exhibits marked endothelial and neuronal tropism, leading to systemic vasculitis, disruption of the blood–brain barrier, and direct infection of the central nervous system. Disease progression is driven by a complex interplay between viral replication strategies and host immune responses. NiV effectively counteracts innate immunity through multiple viral proteins that inhibit interferon signaling, while simultaneously inducing dysregulated inflammatory responses that contribute to tissue damage and multi-organ failure. Neurological involvement represents the most severe manifestation, often resulting in acute or relapsing encephalitis with long-term sequelae among survivors. Despite the severity of the disease, no licensed antiviral therapies or human vaccines are currently available. Therapeutic development has focused on neutralizing monoclonal antibodies targeting viral glycoproteins and small-molecule antivirals that inhibit viral RNA synthesis, both of which show promising results in preclinical models, but remain limited by timing and translational challenges. In parallel, several vaccine platforms—including viral vectors, mRNA-based constructs, and recombinant protein subunits—have advanced to early-phase clinical trials, demonstrating encouraging immunogenicity. Beyond biomedical interventions, effective outbreak containment relies on integrated public health strategies. The “Kerala model” highlights the importance of rapid case identification, isolation, contact tracing, and community engagement within a One Health framework to mitigate transmission and reduce mortality. This review synthesizes the current knowledge on NiV pathogenesis, immune evasion, clinical manifestations, and emerging therapeutic and vaccine strategies, while highlighting critical gaps and future directions for improving the preparedness and response to this high-consequence emerging pathogen. Full article

Acute infectious diseases represent a persistent public health burden that exerts sustained pressure on hospital bed capacity, treatment resources, and the allocation of the healthcare workforce. Strengthening hospital-level preparedness and resource planning requires reliable early-risk stratification tools that remain robust to real-world temporal shifts. However, many existing clinical prediction studies simplify inherently ordered outcomes into binary categories and rely on random data splits, limiting their relevance for real-world health system decision-making. In this study, we developed and evaluated an ordinal machine learning framework using clinical data from 5066 patients hospitalized with acute infectious diseases between 2022 and 2024. Recovery trajectories were modeled as an ordinal outcome, reflecting changes in status between admission and discharge. Models were trained on 2022–2023 data and externally evaluated on a fully isolated 2024 cohort to assess temporal generalizability under realistic deployment conditions. Performance was evaluated using order-aware metrics, including Quadratic Weighted Kappa, Macro-F1, Balanced Accuracy, and ordinal mean absolute error, with explicit analysis of clinically meaningful error structures. Although predictive performance under future holdout validation was modest, misclassifications were predominantly concentrated between adjacent recovery levels, and no clinically critical extreme errors were observed. Model reliability was further assessed through calibration analysis, bootstrap-based uncertainty estimation, and temporal stability of explanatory patterns. Finally, ordinal predictions were translated into structured risk stratification categories aligned with hospital bed management, treatment prioritization, and workforce allocation logic. These findings demonstrate the methodological potential of temporally validated ordinal modeling as a proof-of-concept framework. Given the modest predictive performance and the absence of key clinical variables, the current model should not be regarded as a ready-made clinical decision-support tool, but rather as a foundation for further development with richer data in future research. monitoring prioritization. In practical terms, this framework demonstrates how ordinal predictions could, in principle, be structured for use at admission points. However, given the modest predictive performance observed, further development with richer clinical data is required before deployment. Full article

Background/Objectives: Integrating heterogeneous One Health time series into transparent and usable surveillance workflows remains difficult because data preparation, modeling, and interpretation are often separated across tools. In this paper, we introduce OH-MEMA (One Health Mixed-Effects Modeling and Analytics), an interactive visual analytics framework that integrates heterogeneous One Health data streams, including human clinical outcomes, environmental factors, and wastewater surveillance data, to support syndromic surveillance and pandemic preparedness. Methods: The system enables users to upload and analyze multi-source datasets through an interactive web-based interface. The modeling component supports fixed effects for multi-source predictors, random effects for spatial, temporal, and demographic grouping variables, optional random slopes, and rolling time-series validation. Model results are visualized as time series comparing observed and predicted outcomes, with evaluation metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and correlation. To support iterative exploration, the system incorporates analytic provenance through a visual model tree that records prior configurations. Results: OH-MEMA was validated through both quantitative and qualitative evaluations. Quantitatively, mixed-effects models were assessed across multiple counties and outcomes using RMSE, MAE, and correlation, demonstrating robust predictive performance. Qualitatively, expert users, including epidemiologists and disease surveillance analysts, evaluated the system using the NASA Task Load Index and open-ended interviews, indicating improved interpretability, manageable cognitive workload, and effective workflow integration. Conclusions: OH-MEMA provides an interpretable, human-in-the-loop platform for exploratory forecasting and comparative model analysis in syndromic surveillance. The framework effectively bridges data integration, modeling, and interpretation, supporting user-centered analytical reasoning and decision-making in One Health applications. Full article

Background: Artificial intelligence (AI) is increasingly integrated into healthcare systems, with important implications for nursing practice and clinical workflows. However, evidence regarding nurses’ digital skills, perceptions, and readiness to adopt AI-enabled technologies remains limited, particularly in national healthcare contexts such as Greece. Objectives: This study examined nurses’ digital skills, perceptions of AI, and readiness for AI adoption in clinical practice, and explored demographic and professional factors associated with these outcomes. Methods: A cross-sectional survey was conducted among 166 nurses working in two public hospitals in Greece. Results: Nurses reported moderate digital skills, with 59.1% indicating competence in email/video communication and 27.2% reporting adequate use of digital security tools, while exposure to AI remained limited (18.0% reported using AI products/services in daily life). Perceived professional impact of AI was moderate, whereas readiness for AI adoption was comparatively lower, with only 7.8% considering health professionals adequately prepared and 7.2% reporting adequate AI training. Statistical analyses indicated that educational level and computer literacy certification were positively associated with digital skills, whereas longer professional experience was negatively associated with readiness for AI adoption. Conclusions: These findings highlight a gap between general digital competence and preparedness for AI-driven healthcare applications and underline the need for targeted education and implementation strategies to support effective and ethical integration of AI in nursing practice. From a nursing workforce perspective, the results underscore the importance of integrating AI literacy into continuing professional education and aligning digital health implementation strategies with clinical nursing practice. Full article