Frontiers in Public Health

Ambulatory Care Sensitive Conditions (ACSCs) are conditions for which effective and timely primary health care (PHC) can prevent hospitalizations. They are widely used as a proxy indicator of access to and quality of PHC. Despite their relevance, evidence from Central America remains scarce. This study aimed to quantify the burden, describe the epidemiological profile, and assess temporal trends of ACSCs hospitalizations in Honduras from 2014 to 2024. We conducted a retrospective observational study using national administrative hospital discharge data from all Ministry of Health hospitals. ACSCs were defined using a standardized list of 20 diagnostic groups based on ICD-10 codes. We estimated percentages and sex-age-standardized hospitalization rates per 10,000 inhabitants. Clinical indicators included length of stay (LOS) and in-hospital fatality rates. Temporal trends were evaluated using joinpoint regression models to estimate annual percent changes (APC). Analyses included stratification by age, sex, and disease category. A total of 4,023,944 hospitalizations were analyzed, of which 547,486 (13.6%) were classified as ACSCs. The overall sex-age-standardized rate was 54.1 per 10,000 inhabitants. ACSCs' standardized rates increased between 2014 and 2018 (APC: 2.7%; 95% CI: -2.4; 15.2), declined sharply between 2018 and 2021 (APC: -17.8%; 95% CI: -30.6; -10.3), and increased again between 2021 and 2024 (APC: 15.9%; 95% CI: 4.6; 37.6). Despite this rebound, rates remained below pre-pandemic levels. ACSCs were concentrated among children under 5 years (27.7%) and adults aged 60 years and older (29.9%). Noncommunicable diseases accounted for 56.8% of cases, with diabetes mellitus as the leading cause. Compared with non-ACSCs hospitalizations, ACSCs were associated with longer LOS (4.9 vs. 3.9 days; p <0.001) and higher in-hospital fatality rates (2.4% vs. 1.7%; p <0.001). ACSCs hospitalizations constitute a substantial burden in Honduras and reflect persistent gaps in PHC performance. Strengthening PHC resilience and capacity, particularly for chronic disease management and vulnerable populations, is essential to reduce avoidable hospitalizations and improve health system efficiency and equity.

Background: Nursing education in conflict-affected settings faces significant disruptions that compromise the preparation of a competent and resilient workforce. In regions such as Iraq, prolonged instability, resource constraints, and fragmented health systems challenge traditional educational models, necessitating innovative and context-responsive approaches to ensure continuity, quality, and equity in nursing training. Purpose: This study aimed to explore innovative strategies in nursing education within conflict-affected settings and to examine their implications for leadership development, health policy reform, and the advancement of health equity. Methods: A cross-sectional descriptive study was conducted among undergraduate nursing students across selected universities in the Nineveh Governorate, Iraq, during the 2025-2026 academic year. Data were collected using a structured, self-administered questionnaire designed to assess students educational experiences, engagement with digital learning approaches, perceived barriers, and attitudes toward innovation in nursing education. The instrument captured multiple dimensions of the learning environment, including access to educational resources, institutional support, and exposure to blended and technology-enhanced learning. Descriptive and inferential statistical analyses were performed using SPSS (version 28), including frequency distributions, chi-square tests, and binary logistic regression modeling to identify key predictors of positive educational outcomes, such as engagement, satisfaction, and perceived clinical readiness. Results: The findings indicate that, although students demonstrated a high level of motivation to engage with innovative learning approaches, notable gaps remained in access to digital resources, faculty preparedness, and institutional support. A majority of participants reported engagement with blended and technology-enhanced learning, which was significantly associated with higher levels of engagement, improved critical thinking, and greater perceived clinical readiness (p < 0.001). Multivariable analysis identified institutional support, digital learning access, and learner-centered teaching strategies as significant predictors of positive educational outcomes. Students with access to digital learning resources and supportive educational environments were more likely to report higher levels of satisfaction and competence. Conclusions: Innovating nursing education in conflict-affected settings is essential to building a resilient and future-ready nursing workforce. Integrating digital technologies, flexible learning models, and competency-based approaches can enhance educational outcomes despite contextual constraints. Implications for Nursing Practice and Policy: Strategic investment in nursing education infrastructure, faculty development, and digital transformation is critical to strengthening health systems in fragile contexts. Policymakers and academic leaders must prioritize inclusive, scalable, and sustainable educational reforms to promote health equity and empower nurses as key agents of system-level change.

Comprehensive and responsive interventions are increasingly prioritized to address the diverse and evolving health challenges faced by mothers and children during the first 1,000 days of life. However, evidence remains limited on how such interventions can be operationalized in low-resource settings without overstretching frontline health workers. We developed a comprehensive yet flexible community-based intervention, the Healthy Future program, which integrates a stage-based maternal and child health curriculum with mHealth-enabled infrastructure to deliver targeted, stage-based support through home visits in low-resource settings. We evaluated its impact through a cluster-randomized controlled trial across 119 rural townships in China. The program demonstrated improvements across multiple health, behavioral, and intermediate outcomes, including young child feeding practices, caregiving knowledge, maternal mental health, and perceived social support. Overall, this study illustrates a move beyond stand-alone interventions toward a scalable, multidimensional delivery model capable of providing comprehensive, flexible, and timely support to mothers and children in low-resource communities while remaining feasible for large-scale implementation.

Background: China has implemented policies to strengthen its pharmacist workforce since the 2009 healthcare reform, yet a comprehensive evaluation of their long-term systemic effects is lacking. Objective: To systematically analyze the evolution of Chinas pharmacist workforce in healthcare institutions from 2007 to 2023 across four dimensions: quantity, quality, structure, and distribution, providing an empirical foundation for policy optimization. Methods: A retrospective analysis was conducted using longitudinal data from the China Health Statistics Yearbooks. Trends were delineated via descriptive statistics. Equity and spatial evolution were assessed using the Gini coefficient, Theil index decomposition, and spatial autocorrelation analyses (Global Morans I and hotspot analysis). Results: From 2007 to 2023, the total number of pharmacists increased from 357,700 to 569,500 (average annual growth: 2.2%). This growth lagged behind physicians (4.6%) and nurses (7.4%), causing the pharmacist-to-physician ratio to decline from 1:5.15 to 1:8.39. The workforce showed trends of feminization (female proportion rose from 59.7% to 70.8%) and aging. While quality improved, 51.1% still held an associate degree or below, and only 6.6% held senior titles. Equity analysis revealed the provincial Gini coefficient improved from 0.145 to 0.093. Theil index decomposition confirmed intra-provincial disparities as the primary inequality driver. Spatial analysis showed a non-significant global Morans I by 2023 (0.154, P*>0.05), down from 0.254 (P<0.01) in 2007. Hotspot analysis confirmed this transition, revealing a contraction of high-confidence clusters and a trend toward balanced distribution. Conclusions: China has made measurable progress in expanding pharmacist workforce size and improving inter-provincial equity since 2007. However, persistent structural challenges remain: relative workforce contraction compared to other health professions, an aging demographic, a shortage of senior talent, and significant intra-provincial inequity. Future policies must prioritize optimizing workforce structure and enhancing clinical service capabilities to catalyze a shift toward patient-centered pharmaceutical care.

Background: Despite the growing global burden of multimorbidity, the patterns of disease combinations, have not been extensively categorized. We aimed to explore the predictors, health consequences, and patterns of discordant and concordant multimorbidity. Methods: We used the 2018 China Health and Retirement Longitudinal Study (CHARLS), a representative database of adults aged >45 years from China. We conducted logistic regression analyses to assess the likelihood of having discordant (conditions from different disease systems) versus concordant (only cardiometabolic, or only respiratory diseases) multimorbidity, and to compare the health status and healthcare utilization between patients with discordant and concordant multimorbidity. Latent class analysis (LCA) was applied to both the entire sample and to patients with discordant multimorbidity to identify clusters of disease combinations. Results: The sample included 1668 patients with concordant (mainly cardiometabolic), and 7306 patients with discordant, multimorbidity. Female patients, patients living in rural settings, former and current smokers, and patients engaging in high-intensity physical activity, were more likely to have discordant instead of concordant multimorbidity. Depression, limitations in daily activities, poor self-reported health, and frequent healthcare use were more common in patients with discordant than concordant multimorbidity. The LCA identified five clusters when all multimorbid patients were included (cardiometabolic, arthritis-digestive, respiratory, multisystem, and arthritis-hypertension classes), and four clusters when restricted to discordant multimorbidity (digestive, arthritis-cardiometabolic, respiratory, and multisystem classes). Conclusion: Discordant multimorbidity is associated with poorer health and increased use of healthcare. Cardiometabolic diseases, arthritis, and digestive diseases have a central role in defining disease patterns.

Care workers are central to health systems and the broader care economy, but they often lack the legal protections afforded to other workers. Furthermore, there currently exists no single legal definition of "care worker" under any binding or non-binding international legal instrument. Drawing on the WHO Global Health and Care Worker Compact, we analyzed whether national laws and policies in 10 countries protect care workers. Using comparative legal methods and primary source legal and policy documents, we evaluated both care worker coverage and alignment with four indicators: guaranteed access to protective equipment, protection against discrimination on internationally recognized grounds, unemployment insurance, and the right to join independent unions. We reviewed 43 laws and policies and found that 56% fully or partially met the relevant indicator criteria. The United Kingdom was the only country meeting all four indicators. Overall, we found while many countries recognize these protections in law, care workers are often left outside their coverage, underscoring the need for clearer legal recognition and more inclusive worker protections.

Safety net healthcare delivery organizations (SNOs) serve vulnerable populations but face persistent challenges in adopting new technologies, including AI. While systematic barriers to technology adoption in SNOs are well documented, little is known about how AI is implemented in these settings. This study explored real-world AI adoption in SNOs, focusing on identifying barriers encountered across the AI lifecycle and strategies used to overcome them. Five SNOs in the U.S. participated in a 12-month technical assistance program, the Practice Network, to implement AI tools of their choosing. Observed barriers and mitigation strategies were documented throughout program activities and, at the conclusion of the program, reviewed and refined with participants using a participatory research approach to ensure findings reflected lived experiences and organizational contexts. Key barriers emerged during the Integration and Lifecycle Management phases and included gaps in AI performance evaluation and impact assessments, communication with patients about AI use, foundational AI education, financial resources for purchasing and maintaining AI tools, and AI governance structures. Effective strategies for addressing these barriers were primarily supported through centralized expertise, structured guidance, and peer learning. These findings provide granular, actionable insights for SNO leaders, offering guidance for anticipating barriers and proactively planning mitigation strategies. By including SNO perspectives, the study also contributes to the broader health AI ecosystem and underscores the importance of participatory, collaborative approaches to support safe, effective, and ethical AI adoption in resource-constrained settings. Author SummarySafety net organizations (SNOs) are healthcare systems that primarily serve low-income and underinsured patients. While interest in artificial intelligence (AI) in healthcare has grown rapidly, little is known about how these organizations experience AI adoption in practice. In this study, we partnered with five SNOs over a 12-month program to document the challenges they encountered when implementing AI tools and the strategies they used to address them. We worked closely with SNO staff throughout the process to ensure our findings reflected their lived experiences with AI implementation. We found that the most common challenges arose when organizations tried to integrate AI into daily operations and monitor and maintain those tools over time. Specific barriers included difficulty evaluating whether AI was performing as expected, limited guidance on communicating with patients about AI use, a lack of resources for staff training, limited financial resources, and the absence of formal governance structures. Successful strategies for overcoming these challenges drew on shared knowledge and structured support provided by the program, as well as learning from peer organizations. These findings offer practical guidance for SNO leaders planning or managing AI adoption, and contribute to a broader conversation about what is required to implement AI safely and effectively in healthcare settings that serve the most medically and socially vulnerable patients.

In the context of global health, the ability of frontline primary health providers to identify potential Drug-Drug Interactions (DDIs) is a critical component of patient safety. This is particularly true in settings like Tanzania, where drug dispensers often serve as the primary point of contact for healthcare. In this study, we establish a baseline for drug decision-making capabilities across multiple cadres of healthcare providers in Kibaha, Tanzania. We specifically distinguish between the ability to recognize safe drug combinations versus harmful ones. The findings reveal a critical asymmetry in provider performance: while professional training improves the recognition of safe combinations, it provides no advantage over lay intuition (and in some cases, a significant disadvantage) in detecting potentially harmful interactions.

With Japans rapidly aging population, demand for home healthcare is projected to increase by 62% by 2040. This study quantitatively evaluated accessibility to 24-hour home healthcare and regional disparities across all 335 secondary medical areas (SMAs) in Japan using the Enhanced Two-Step Floating Catchment Area (E2SFCA) method. We conducted a nationwide cross-sectional study analyzing approximately 430,000 population points at 500-meter mesh resolution. The E2SFCA integrated demand (age-adjusted population), supply (24-hour home care support clinics and hospitals), and transportation (road networks). Accessibility scores (ASs) and Gini coefficients were calculated for each SMA. Wards hierarchical cluster analysis classified regional types, and multiple regression based on the Penchansky and Thomas five-dimensional access framework identified factors associated with the median AS (ASM) and Gini coefficient. The median ASM was 45.71 (0.00-153.49), and the median Gini coefficient was 0.33 (0.06-0.93). Cluster analysis identified six types ranked by descending ASM, from C1 (high access, equitable; n = 48) to C6 (access desert; n = 23). C6 had a median ASM of 0.00 and Gini coefficient of 0.74, indicating virtually no access within a 30-minute catchment. Home-visit standardized claim ratios, used as external validation, declined monotonically from C1 (125.6) to C6 (17.6). For ASM, 24-hour visiting nursing stations ({beta} = +0.369) and clinic physicians ({beta} = +0.342) showed the strongest positive associations, with non-residential area negatively associated ({beta} = -0.273). For the Gini coefficient, non-residential area showed the strongest positive association ({beta} = +0.523). Taxable income per taxpayer was not significantly associated with either outcome. Non-residential area was associated with both lower accessibility and greater intra-regional inequality, suggesting that geographic constraints may limit the effectiveness of resource investment alone. Uniform nationwide implementation of policies shifting care from long-term care beds to home healthcare may not be feasible; region-specific approaches considering geographic characteristics are necessary.

BackgroundThe COVID-19 pandemic exposed critical shortages of mechanical ventilators, particularly in low-resource settings. Disruptions in global supply chains and dependence on specialized components highlighted the need for scalable, locally manufacturing alternatives for emergency respiratory support. AimTo describe and evaluate a simplified, supply-chain-independent mechanical ventilator assembled from widely available automotive and simple hardware components, and intended as a last-resort solution. MethodsThe ventilator is based on a reciprocating air pump driven by an automotive windshield wiper motor coupled to parallel shaft bellows and readily assembled passive membrane valves, only requiring materials available from standard hardware retailers, minimal tools, and basic manual skills. Ventilator performance was assessed through bench testing using a patient model simulating severe lung disease in an adult (R=20 cmH2O{middle dot}s/L, C=15 mL/cmH2O) and pediatric (R=50 cmH2O{middle dot}s/L, C=10 mL/cmH2O) patients. Realistic proof of concept was performed in four mechanically ventilated 50-kg pigs. ResultsThe device delivered tidal volumes up to 600 mL and respiratory rates up to 45 breaths/min with PEEP up to 10 cmH2O, covering pediatric and adult ventilation ranges. In vivo testing showed that the ventilator maintained arterial blood gases within the targeted range. Technical details for ventilator construction are provided in an open-source video tutorial. DiscussionThis low-cost ventilator demonstrated adequate performance under demanding conditions. Although not a substitute for commercial intensive care ventilators, its simplicity, autonomy, and independence from fragile supply chains provide a potentially life-saving option in resource-constrained emergency scenarios.

Social stigma surrounding chronic skin Ulcer leads patients to hide their wounds or delay seeking medical care. The aim of this study was to explore the types and causes of chronic skin ulcers among patients seen in the dermatology departments of two university hospitals in Burkina Faso. This was a cross-sectional, retrospective study covering an 11-year period, from 2013 to 2023. A review of consultation records allowed for the collection of sociodemographic and clinical data from 104 patients who were seen for chronic skin ulcers over the 11-year period, averaging 9 patients per year. The patients were primarily adults (n=60) and older adults (n=21). Leg ulcers were the condition observed in most patients (n=59). Eight cases of Buruli ulcer (7.69%) were identified among the 104 patients. Five of the eight cases, or 62.50%, were aged between 0 and 19 years. Half of the eight patients resided in Ouagadougou. These results highlight low utilization of dermatology services for chronic skin ulcers. Furthermore, indigenous cases of Buruli ulcer have been identified in Burkina Faso. Consequently, our findings call for the implementation of strategies focused on addressing social perceptions of these ulcers and on the screening and management of this disease.

Background: Oral health remains inadequately integrated within the Ayushman Bharat Digital Mission (ABDM), particularly in terms of structured risk assessment and its linkage to insurance-based decision-making. There is a growing need for scalable models that can connect clinical oral health data with digital health systems and support future artificial intelligence (AI)-driven applications. Aim: To develop and pilot test the ABHA-O-SHINE framework for oral health risk prediction and insurance prioritization, with a future scope for AI integration within the Ayushman Bharat Health Account (ABHA) ecosystem. Materials and Methods: A cross-sectional pilot study was conducted among 126 participants attending the outpatient department of Swargiya Dadasaheb Kalmegh Smruti Dental College and Hospital, Nagpur. Participants were selected based on predefined inclusion and exclusion criteria. Data collection included a structured questionnaire and clinical examination using the WHO Oral Health Assessment Form (2013). A composite risk score (0 to 14) was developed incorporating behavioral and clinical parameters. Participants were categorized into low, moderate, and high-risk groups, and corresponding insurance priority levels were assigned. Statistical analysis included descriptive statistics, Chi-square test, Spearman correlation, and binary logistic regression. Results: The majority of participants were categorized under moderate to high-risk groups. Tobacco use showed a statistically significant association with higher risk levels (p less than 0.05). Positive correlations were observed between total risk score and clinical indicators such as DMFT and CPI. Logistic regression analysis identified tobacco use and clinical scores as significant predictors of high-risk categorization. Conclusion: The ABHA-O-SHINE framework demonstrates feasibility in integrating oral health risk assessment with an insurance prioritization model. The framework is designed to be AI-compatible, enabling future automation through machine learning and image-based analysis within the ABDM ecosystem. Keywords: ABHA, ABDM, Oral Health, Risk Assessment, Insurance, Artificial Intelligence.

Background: Digital health platforms can improve clinical efficiency and patient outcomes, but adoption in routine care remains limited due to workflow and integration challenges. Rheumatoid arthritis (RA) management relies on consistent capture of patient-reported and clinical data, which is often time-intensive and inconsistently documented. Objective: To assess the impact of the cliexa-RA digital platform on patient experience, physician workflow, and cost-related outcomes using the Quadruple Aim framework. Methods: A six-month pilot study was conducted at the Colorado Arthritis Center involving 300 RA patients. Patients completed a 16-question intake (RAPID3-based), followed by clinician-entered joint assessments. The platform generated five disease activity scores (DAS28-ESR, DAS28-CRP, SDAI, CDAI, RAPID3) and produced EMR-compatible outputs. Time metrics, patient satisfaction, and workflow efficiency were evaluated. Results: Mean patient intake time was 2.4 minutes, a 52% reduction compared to paper-based processes. Clinician time for calculation and documentation decreased by 77%, with near real-time EMR integration. Overall patient satisfaction was high (3.55/4), with 85% recommending the platform. Physicians reported improved documentation efficiency and workflow integration. Administrative cost reductions were observed through decreased reporting burden and improved compliance with quality reporting requirements. Conclusions: The cliexa-RA platform significantly improved efficiency and user experience in RA management. These findings support the role of integrated digital tools in reducing administrative burden and enabling scalable, data-driven care, with potential downstream benefits for cost and population health.

Methods: Using Global Burden of Disease (GBD) data, we analyzed prevalence, incidence, mortality, and disability-adjusted life years (DALYs) rates across global and 21 GBD regions from 1990-2023. Joinpoint regression identified temporal trends, age-period-cohort models analyzed effect contributions, Das Gupta decomposition quantified demographic and epidemiological impacts, inequality indices assessed health equity, and Bayesian models projected 2024-2038 trends. Results: In 2023, the global number of children and adolescents with asthma reached 131 million, with an age-standardized prevalence rate (ASPR) of 1,789.9 per 100,000. From 1990 to 2023, the global ASPR and age-standardized incidence rate (ASIR) of asthma in children and adolescents showed an upward trend, while the age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years (DALYs) rate (ASDR) exhibited a downward trend. Among the 0-14 age group, the disease burden was greater in males than in females, whereas in the 15-19 age group, males had a lower disease burden than females. Projections indicate that over the next 15 years, the overall disease burden will continue to decline; however, female mortality rates and DALYs rates are projected to show an upward trend. Conclusions: The increasing prevalence and incidence rates, coupled with declining mortality and DALYs rates of asthma among children and adolescents globally, underscore the necessity for targeted public health interventions. These findings provide crucial insights for early diagnosis, treatment optimization, and global health policy formulation.

Background Non communicable diseases (NCDs) are significant public health concerns in Bangladesh, placing a heavy burden on the healthcare system. While the situation before COVID-19 was well-documented, it is unclear how the pandemic has impacted the prevalence and risk factors of these diseases. This study provides the first comparative assessment of the prevalence and determinants of diabetes mellitus (DM) and hypertension (HTN) before and after the pandemic, utilizing comprehensive multilevel data source and mixed-effects modeling to capture the shifting epidemiological burden. Methods We analyzed biomarker data from two nationally representative Bangladesh Demographic and Health Surveys (BDHS) 2017-18 and 2022. Diagnosis followed WHO guidelines for fasting blood glucose and blood pressure. Mixed-effect logistic regression models were employed to identify risk factors while accounting for the hierarchical survey design. The Intra-class Correlation Coefficient (ICC) was calculated to quantify the proportion of variance attributable to unobserved community-level heterogeneity. Results The study indicates a profound shift in the national burden of NCDs. Diabetes prevalence more than doubled, from 23% in 2017-18 to 49% in 2022, while hypertension prevalence declined from 22% to 15%, a pattern that may reflect survival bias among individuals with severe comorbidities. The previously strong bidirectional association between DM and HTN weakened in the post pandemic period, hypertension continued to predict diabetes (AOR = 1.17), but diabetes was no longer a significant predictor of hypertension. Community-level determinants became substantially more influential, with local environmental factors playing a much larger role in shaping diabetes prevalence compared to the pre-pandemic period. Urban residence emerged as a significant new risk factor for diabetes in 2022 (AOR = 1.62; 95% CI: 1.34-1.96). Furthermore, the socioeconomic gap in diabetes risk narrowed as the disease affected more wealth groups, while higher educational attainment continued to serve as a protective factor against hypertension (AOR = 0.64; 95% CI: 0.54-0.75). Conclusion The post pandemic landscape of NCDs in Bangladesh shows a clear divergence, marked by a rapid increase in diabetes contrasted with a stabilization in hypertension prevalence. Through comparative mixed effects modeling, this study advances beyond simple prevalence comparisons to demonstrate the growing impact of urban environments and community level factors on metabolic health. These evolving patterns underscore the need for integrated public health strategies that address emerging environmental risks and geographically specific vulnerabilities to support progress toward Sustainable Development Goal Target3.4. Keywords: Bangladesh, BDHS, Community-level variability, COVID 19, Diabetes mellitus, Hypertension, Mixed-effects modeling, Non-communicable diseases, Public health

This study evaluates the feasibility of implementing artificial intelligence (AI)-driven disease surveillance systems at Julius Nyerere International Airport (JNIA) in Tanzania, a key hub for regional and international travel. Through a mixed-methods approach combining qualitative interviews and quantitative surveys, the research assesses the infrastructure, human resource capacity, and regulatory frameworks necessary for AI integration. Findings indicate that while Port Health Officers are strongly optimistic about AIs potential to enhance disease detection, the airport faces significant barriers, including outdated infrastructure, insufficient technical resources, and a lack of trained personnel. Ethical and privacy concerns, particularly surrounding data security, also emerged as key challenges, compounded by limited public awareness and the socio-cultural acceptability of AI systems. Furthermore, the study identifies gaps in national policies and inter-agency coordination that hinder the effective implementation of AI technologies. The research concludes that while current conditions render AI adoption infeasible, strategic investments in infrastructure, workforce training, and policy development could pave the way for future integration, enhancing public health surveillance at JNIA and potentially other airports in low- and middle-income countries. This study contributes critical insights into the barriers and opportunities for AI-driven disease surveillance in low-resource settings, specifically focusing on a high-priority transit point, international airports. It emphasizes the importance of region-specific solutions to enhance health security in East Africa and supports the broader global health agenda by advocating for international collaboration and the development of scalable disease surveillance systems. Future research should explore pilot AI implementations at other airports to evaluate real-world challenges and refine AI systems for broader applicability, including cost-effectiveness analyses and integration of public perspectives on AI.

Abstract The ISBAR framework is used to standardize clinical handovers and enhance patient safety. Observational tools based on ISBAR have been developed to assess the completeness of information transfer. However, these instruments have primarily been developed in non-Chinese contexts, and validated Chinese-language observational tools suitable for clinical practice remain limited. In this study, a cross-cultural adaptation and psychometric validation of the ISBAR Structured Handover Observation Tool was conducted, examining its reliability and discriminant validity in Chinese clinical settings. The study was conducted in two phases: cross-cultural adaptation and psychometric evaluation in real-world clinical settings. Content validity was assessed using the Content Validity Index (CVI), and inter-rater reliability was evaluated using the Intraclass Correlation Coefficient (ICC) based on a two-way mixed-effects model with absolute agreement. Discriminant validity was examined using the Mann-Whitney U test to compare scores across nurses with varying levels of clinical experience. A total of 233 handover cases involving patient transfers from the intensive care unit (ICU) to general wards were collected, involving 84 nurses. The scale demonstrated good content validity, with item-level content validity indices (CVI) ranging from 0.88 to 1.00 and a scale-level CVI/Ave of 0.98. The inter-rater reliability, assessed using fifty randomly selected cases, was high, with an intraclass correlation coefficient (ICC) of 0.885 for single-rater assessments and 0.939 for average-rater assessments. Discriminant validity analysis showed that nurses with more clinical experience had significantly higher total scores than those with less experience (Z = -4.772, p < 0.001). The Chinese version of the ISBAR Structured Handover Observation Tool demonstrates good content validity, high inter-rater reliability, and acceptable discriminant validity. This tool provides a standardized and practical method for assessing the completeness of information transfer and is expected to support quality improvement in patient handover from the ICU to general wards in Chinese clinical settings.

This study develops a methodological framework that combines conventional antimicrobial susceptibility testing with Particle Swarm Optimisation (PSO) to enhance toothpaste formulations, employing Escherichia coli isolated from the oral cavity as a model organism. We used the agar well diffusion method to see if two fluoride toothpastes (Oral B and My-my) could kill oral E. coli isolates at 6.25%, 12.5%, 25%, 50%, and 100% concentrations. A surrogate Random Forest model was created using these experimental data to link formulation parameters to antimicrobial activity. Then, PSO was used to find the best formulation traits. Multi-objective optimisation that looks at the trade-offs between antimicrobial effectiveness and cytotoxicity was shown as a conceptual framework. Both toothpastes showed antimicrobial activity that depended on the concentration, with Oral B being more effective (23.0 mm at 100% concentration) than My-my (20.0 mm). The PSO framework, utilised as a methodological illustration while explicitly recognising data constraints, determined hypothetical formulation parameters (sodium fluoride 1100 ppm, hydrated silica abrasive, 2.5% SLS) with an anticipated zone of inhibition of 26.3 mm. These predictions are mathematically optimal for a surrogate model that was trained on very little data (n=10 formulation points). They need a lot of experimental testing before any claims about the formulation can be made. This work is presented as a proof-of-concept methodological framework, not as validated formulation guidance.

Misinformation circulating through digital platforms and community networks increasingly challenges public health communication, particularly in low- and middle-income countries. Frontline health educators play a critical role in addressing misinformation and promoting accurate health information within primary health care systems; however, empirical evidence on their preparedness to manage infodemics remains limited. This study assessed the training needs and response capacity of primary health care health educators in Lagos State, Nigeria. A convergent mixed-methods design was employed across three districts. Quantitative data were collected from 95 health educators using the 30-item Health Educators Infodemic Management Training Needs Assessment Questionnaire (HEIM-TNAQ). Qualitative data were obtained through six focus group discussions involving 56 educators and 25 key informant interviews with supervisors and programme managers. Quantitative data were analysed using descriptive statistics and t-tests, while qualitative data were analysed thematically. Participants demonstrated relatively strong knowledge of health misinformation (mean = 71.5), but only moderate decision-response skills (48.6) and low confidence in addressing misinformation (42.5). Integration of misinformation response into routine practice was also limited (46.3), and no significant differences were observed between respondents with or without prior training. Qualitative findings revealed frequent exposure to vaccine rumours, spiritual explanations for illness, and misinformation circulating through social media and community networks. Strengthening infodemic management within primary health care requires practical training, behavioural communication skills, and institutional mechanisms for systematic rumour monitoring and response.

Background Type 2 diabetes mellitus (T2DM) is a chronic disease that imposes a significant burden on healthcare systems and society. In Vietnam, the prevalence of T2DM is rapidly increasing; however, evidence on treatment expenditure derived from large administrative databases remains limited. This study was carried out provides an overview of total treatment expenditures for T2DM across hospital tiers between 2018 and 2022. Methods This cross-sectional descriptive study utilized retrospective health insurance (HI) data from 2018-2022. Data was collected and analyzed based on cost components (medications, diagnostic tests, hospital beds, etc.) across healthcare facilities classified by hospital level. Costs were converted to 2024 USD using the CCEMG-EPPI-Centre cost converter. Results Total expenditure increased from 227.17 million USD in 2018 to 425.53 million USD in 2022 with spending concentrated in Class I and Class II healthcare facilities, although their shares declined over time, while the proportions attributed to unclassified and special-class facilities increased. Drugs accounted for the largest share of expenditure (49.65%-78.95%), followed by laboratory tests (7.31%-19.89%) across all hospital classifications. Other components, including hospital beds, diagnostic imaging, procedures/surgeries, and medical supplies, contributed smaller proportions but increased over time in several facility groups. Conclusion The study indicates that medication costs constitute the largest share of treatment expenditure for type 2 diabetes mellitus at healthcare facilities, reflecting the long-term treatment requirements of this chronic disease. In addition, health expenditure remained concentrated in Class I and Class II healthcare facilities, although their shares declined over the study period, while the proportions attributed to unclassified and special-class facilities increased. These findings suggest the need to strengthen diabetes screening, treatment, and follow-up at lower-level healthcare facilities in order to reduce the burden on higher-level hospitals and improve the efficiency of healthcare resource allocation.