To describe exploratory findings and lessons learned from the discontinued WAVE trial, which sought to determine the effectiveness and costs of adding an early vocational advice intervention to usual primary care on number of days of sickness absence over 6 months. Pragmatic, multicentre, two-parallel arm, superiority, randomised controlled trial with health economic analysis in 10 general practices in England, with nested qualitative interviews. Population: Adults with fit notes for any health condition, absent from work ≥ 2 weeks and ≤ 6 months were invited to participate. Intervention and comparator: Participants were randomised (1 : 1) to usual primary care with/without vocational advice delivered by trained Vocational Support Workers. The planned sample size was 720, the first 4 months of recruitment served as an internal pilot phase and the primary outcome was self-reported days of work absence over 6 months. One hundred and thirty participants were recruited from 7955 invitations (May 2022-May 2023) before trial closure (64 usual care, 66 usual care plus vocational advice). Exploratory analysis of 125 participants (with outcome data) indicated small additional benefits of the vocational advice intervention over usual care [mean days absence = 37.86 (standard deviation = 48.76) vs. usual care = 42.66 (standard deviation = 57.67), incidence rate ratio = 0.913, 80% confidence interval (0.653 to 1.276)]. The vocational advice intervention was delivered remotely [mean = 4.8 contacts (range 1-12)]. Partial health economic evaluation found lower work productivity losses at 6 months after vocational advice intervention (£5513.84, standard deviation = £7101.43) compared to usual care (£6146.21, standard deviation = £8431.88). Exploratory analysis indicated a signal of effect, with differences in the number of days absent from work, costs and secondary outcomes. Key lessons learned included the need for closer working with primary care teams and more flexible recruitment methods. A future fully powered randomised controlled trial of vocational advice intervention added to usual primary care is needed to determine the effectiveness and cost-effectiveness. This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number 17/94/49.

IntroductionPatients with a penicillin allergy label (PAL) use more and broader-spectrum antibiotics, have worse health outcomes and cost more to treat than patients without a PAL. A significant proportion of penicillin allergy labels are incorrect; here we review the published evidence on the costs, health-related quality of life, and cost-effectiveness of penicillin allergy testing.MethodsWe conducted a systematic review of published economic evaluations of penicillin allergy testing in accordance with Cochrane guidelines. We searched Medline, Embase, Scopus, Web of Science, EconPapers (RePeC) and the International HTA Database (INAHTA) and included reports of full or partial economic evaluations of costs and/or health benefits of penicillin allergy testing strategies. The outcomes of interest were healthcare resource use, medical costs, and health-related quality of life for patients with a penicillin allergy label and for patients with the label removed, and cost-effectiveness. We evaluated the methodological quality of the studies using a published checklist designed for systematic reviews. The review followed a narrative synthesis.ResultsThirty-six studies met the inclusion criteria. Most studies analysed the effect of testing on the costs of antibiotic use among patients admitted to hospital with a PAL. Studies measured costs of testing (n = 19); antibiotic medication use (n = 23); adverse reactions with penicillin use (n = 4), alternative antibiotic drugs (n = 3); length of hospital stay (n = 5); subsequent health care use episodes (n = 4); and antibiotic medication use in subsequent care episodes (n = 3). The median cost of skin testing plus oral challenge across six primary costing studies was USD 246 (range: 164, 514), which contrasts with the USD 42–258 range of antibiotic cost savings during the initial hospital admission. Two studies presented evidence that penicillin allergy testing is cost-saving in an outpatient setting over 3.5–4.5 years. One model-based study reported that testing in inpatient settings is cost-saving. No reports on the effect of penicillin allergy testing on health-related quality of life were found and the two cost-effectiveness studies that accounted for this outcome employed the opinion of healthcare professional or an assumption of a common generic value for adverse reactions.ConclusionsWhile penicillin allergy testing shows promise in reducing antibiotic costs, the evidence remains insufficient to definitively establish whether these savings consistently outweigh testing costs across various healthcare settings.

Patients with a penicillin allergy label (PAL) use more and broader-spectrum antibiotics, have worse health outcomes and cost more to treat than patients without a PAL. A significant proportion of penicillin allergy labels are incorrect; here we review the published evidence on the costs, health-related quality of life, and cost-effectiveness of penicillin allergy testing. We conducted a systematic review of published economic evaluations of penicillin allergy testing in accordance with Cochrane guidelines. We searched Medline, Embase, Scopus, Web of Science, EconPapers (RePeC) and the International HTA Database (INAHTA) and included reports of full or partial economic evaluations of costs and/or health benefits of penicillin allergy testing strategies. The outcomes of interest were healthcare resource use, medical costs, and health-related quality of life for patients with a penicillin allergy label and for patients with the label removed, and cost-effectiveness. We evaluated the methodological quality of the studies using a published checklist designed for systematic reviews. The review followed a narrative synthesis. Thirty-six studies met the inclusion criteria. Most studies analysed the effect of testing on the costs of antibiotic use among patients admitted to hospital with a PAL. Studies measured costs of testing (n = 19); antibiotic medication use (n = 23); adverse reactions with penicillin use (n = 4), alternative antibiotic drugs (n = 3); length of hospital stay (n = 5); subsequent health care use episodes (n = 4); and antibiotic medication use in subsequent care episodes (n = 3). The median cost of skin testing plus oral challenge across six primary costing studies was USD 246 (range: 164, 514), which contrasts with the USD 42-258 range of antibiotic cost savings during the initial hospital admission. Two studies presented evidence that penicillin allergy testing is cost-saving in an outpatient setting over 3.5-4.5 years. One model-based study reported that testing in inpatient settings is cost-saving. No reports on the effect of penicillin allergy testing on health-related quality of life were found and the two cost-effectiveness studies that accounted for this outcome employed the opinion of healthcare professional or an assumption of a common generic value for adverse reactions. While penicillin allergy testing shows promise in reducing antibiotic costs, the evidence remains insufficient to definitively establish whether these savings consistently outweigh testing costs across various healthcare settings.

IntroductionHealthcare systems are substantial greenhouse gas emitters, responsible for five percent of emissions worldwide. Inhalers are particularly problematic, and while all inhalers have a negative impact, pressurized metered-dose inhalers (pMDIs) are the worst offenders. With carbon minimal pMDIs in development, this research evaluated the economic value that selected carbon minimal pMDIs could offer and explored how this value can be represented in economic evaluations.MethodsThe analysis employed a partial economic evaluation framework. Given the carbon minimal and current standard pMDIs are expected to be clinically equivalent, clinical outcomes and downstream resources offset each other in an incremental analysis. Thus, the evaluation centered around reduction in carbon and incremental costs. Two approaches were undertaken: parallel and integrated evaluation. Environment impact (carbon footprint) was sourced from published life cycle assessments, dosage was based on international guidelines, and costs were taken from the British National Formulary. The cost of carbon used was the UK Treasury’s carbon value of GBP269 (USD370) per ton of carbon dioxide equivalent (CO2e).ResultsCurrent selected pMDIs produce 103 kg to 150 kg of CO2e per person per year depending on which product is used, whereas the carbon minimal pMDIs produce 13 kg to 18 kg of CO2e per person per year, a reduction of around 88 percent. The undiscounted additional value of the selected carbon minimal pMDIs ranges from GBP776 (USD1,068) to GBP1,134 (USD1,561) per person with asthma over their lifetime. Based on these results, at a population level, if all people currently being prescribed pMDIs moved to a low carbon pMDIs, this could save carbon emissions to the value of GBP112 to 166 million (USD154 to 229 million) annually in the UK alone.ConclusionsThe added value demonstrates the environmental and economic benefits available from switching from existing pMDIs to low carbon pMDIs where clinical outcomes are equivalent. This additional value can be reflected in economic evaluations as part of an HTA process, although challenges remain related to data availability and in the interpretation of environmental impact data in cases of non-dominance.

Integrating physics-based twins to enhance partial discharge evaluation

Abstract In the context of an increase in the share of distance learning the educational institutions that train specialists in the field of power generation, use and supply are faced with certain difficulties in conducting practical training for students. This article discusses the features and stages of creating virtual enterprises as a tool for the practical training of future power engineers. The authors propose a scalable and adaptive solution: the implementation of a “Virtual Electrical Enterprise” using virtual reality (VR) technologies and specialized software for immersive and interactive practical training. The system simulates various departments of a real production enterprise and allows students to perform professional tasks in a controlled virtual environment. A partial experimental evaluation was conducted to evaluate the effectiveness of this approach, demonstrating improved learning outcomes, heightened student interest, and the potential to compensate for limited access to real industrial equipment. The proposed system not only enhances student preparedness for future employment but also offers prospects for lifelong learning and closer collaboration between universities and industrial enterprises. Future work involves the progressive implementation of this concept into the educational process for electrical engineering students

Balancing Risk and Robustness in Dynamic Decision Making Many real systems, such as networks, finance, and safety-critical autonomy, must hedge against rare but costly events. Risk-sensitive control formalizes this idea by optimizing an exponential cost objective that prioritizes reliability over just average performance. Classical dynamic programming methods such as value iteration and policy iteration are well-understood in this risk-sensitive setting. However, modified policy iteration (MPI), which combines the strengths of both through partial policy evaluation, has lacked any theoretical understanding. This paper addresses this gap. It analyzes MPI for risk-sensitive Markov decision processes governed by a multiplicative Bellman equation, develops normalization and contraction tools suited to this setting, and proves both convergence and finite-time guarantees. The results provide a principled foundation for algorithms that combine computational efficiency with robustness, supporting the development of reinforcement learning methods that emphasize long-term reliability.

ObjectiveThis study aims to develop and validate of a span-based annotation framework for clinical named entity recognition (NER) using large language models (LLMs) based on Korean emergency department clinical notes.Materials and MethodsTwo datasets with the same entity types but different annotation spans (word- vs phrase-level) were constructed, with the phrase-level dataset further was expanded into a doubled version. A Korean language-specific LLM was fine-tuned on each dataset, producing three variants that were compared with two baseline models, few-shot LLM and fine-tuned small language model (SLM). The final variant fine-tuned on the doubled phrase-level dataset was further evaluated against a human annotator.ResultsIn all experimental settings, three variants outperformed the baselines by achieving the highest F1 scores across all metrics. The final variant achieved F1 scores exceeding 0.80 across all averaging strategies and evaluation metrics, including token-based, span-based exact, and span-based partial evaluations demonstrating its robustness applicable in a practical setting.DiscussionWhile prompt engineering with few-shot is widely adopted for LLM-based clinical NER, our results proved that supervised fine-tuning (SFT) is consistently superior. The final variant outperformed the human annotator, emphasizing its potential as an automatic labeling tool.ConclusionThis study introduced a novel span-based annotation framework for LLM-based clinical NER verified by three independent experiments. In multilingual and real-world clinical settings, LLMs have proven in handling complex entity spans that include word-level and phrase-level annotations, particularly for long and attribute-rich entities.

<i>Background</i>: Timely and reliable remuneration of frontline health workers is critical for vaccination campaign performance, yet in many low- and middle-income countries cash-based disbursement remains vulnerable to delays, leakage, and inefficiencies. These weaknesses undermine accountability and create recurrent financial burdens, particularly in large-scale operations such as polio immunization campaigns. <i>Objective</i>: This study aimed to evaluate the cost implications of shifting from traditional cash payments to digital disbursement through mobile money during Cote d’Ivoire’s October 2023 national polio immunization campaign. <i>Method:</i> A partial economic evaluation was conducted using a top-down costing approach from the payer’s perspective. Data from all 113 districts and 33 regions were analyzed to compare cash-based and mobile money systems. The analysis focused on direct financial costs, including transaction fees (digital) and transport allowances or ghost worker disbursements (cash). A cost-minimization framework was applied under the assumption of equivalent immunization outputs, and deterministic sensitivity analysis was performed to test robustness. <i>Result</i>: Both systems disbursed the same total amount to vaccinators (397,582,460 FCFA). However, cash payments incurred additional costs of 7,820,967 FCFA in transport and 3,938,930 FCFA in ghost payments, while digital payments generated a 1% transaction fee (3,975,825 FCFA). The digital modality produced a net saving of 7,784,073 FCFA (12,435 USD), or 257 FCFA (0.41 USD) per vaccinator. Sensitivity analysis confirmed the stability of these savings across plausible parameter variations. <i>Conclusion</i>: Digital disbursement via mobile money offers a cost-saving and governance-enhancing alternative to cash payments in vaccination campaigns. Although the absolute savings represent less than 2% of total outlays, the benefits in efficiency, fraud reduction, and transparency are significant for health systems facing budget constraints. Cote d’Ivoire’s experience provides evidence for policymakers to consider institutionalizing digital payments to strengthen financial sustainability and support universal health coverage.

A data lake maintains a large amounts of heterogeneous data with different data schemas and query interfaces. Efficiently querying and analyzing the heterogeneous data enables users to gain more complete insights. In this paper, we study a novel problem of distributed keyword search across heterogeneous data sources. Traditional distributed search algorithms generally require the predefined crossing edges connecting relevant data instances for communication between different sources, which is unpractical for the data lake due to the schema heterogeneity. To effectively perform keyword search over the data lake, we first introduce canonical graphs and then develop a best-first search algorithm called UnifySea, which explores the answers across different sources based on the unified identification of related instances. To further improve the query efficiency, we propose a novel incremental keyword search algorithm called DistSea, which just need to identify the promising relevant data between different sources. DistSea incrementally calculates the optimal answers based on locally partial evaluation. Equipped with several efficient pruning rules, DistSea reduces unpromising tree calculation across different sources. Experimental evaluations on six real-world benchmarks demonstrate the effectiveness, efficiency and scalability of the proposed algorithms.

Vegetation phenology (VP) is a crucial biological indicator for monitoring terrestrial ecosystems and global climate change. However, VP monitoring using traditional remote sensing vegetation indices has significant limitations in precise analysis. Furthermore, most studies have overlooked the distinction between stable and short-term VP in relation to climate change and have failed to clearly identify the seasonal variation in the impact of climatic factors on stable VP (SVP). This study compared the accuracy of solar-induced chlorophyll fluorescence (SIF) and three traditional vegetation indices (e.g., Normalized Difference Vegetation Index) for estimating SVP in China, using ground-based data for validation. Additionally, this study employs Sen’s slope, the Mann–Kendall (MK) test, and the Hurst index to reveal the spatiotemporal evolution of the Start of Season (SOS), End of Season (EOS), and Length of Growing Season (LOS) over the past two decades. Partial correlation analysis and random forest importance evaluation are used to accurately identify the key climatic drivers of SVP across different climate zones and to assess the seasonal contributions of climate to SVP. The results indicate that (1) phenological metrics derived from SIF data showed the strongest correlation coefficients with ground-based observations, with all correlation coefficients (R) exceeding 0.69 and an average of 0.75. (2) The spatial distribution of SVP in China has revealed three primary spatial patterns: the Tibetan Plateau, and regions north and south of the Qinling–Huaihe Line. From arid, cold-to-warm, and humid regions, the rate of SOS advancement gradually increases; EOS transitions from earlier to nearly unchanged; and the rate of LOS delay increases accordingly. (3) The spring climate primarily drives the advancement of SOS across China, contributing up to 70%, with temperatures generally having a negative effect on SOS (r = −0.53, p < 0.05). In contrast, EOS is regulated and more complex, with the vapor pressure deficit exerting a dual ‘limitation–promotion’ effect in autumn (r = −0.39, p < 0.05) and summer (r = 0.77, p < 0.05). This study contributes to a deeper scientific understanding of the interannual variability in SVP under seasonal climate change.

The tourism and hospitality industry are one of the leading sectors in Malaysia, making a significant contribution to the country’s GDP growth. Malaysia is widely recognized as a prominent tourist destination, and this sector plays a vital role in national economic development. To remain competitive and sustainable, organizations in this sector must develop a deeper understanding of the factors that influence customer satisfaction. Therefore, this study aims to examine the relationship between hospitality service quality and tourist satisfaction in Kuala Lumpur, Malaysia. The research is grounded in Expectancy-Disconfirmation Theory (EDT), which posits that customer satisfaction is shaped by the comparison between expected and actual service performance. A quantitative research design was employed, and data were collected from a sample of 404 tourists who had stayed in various hotels across Kuala Lumpur. Stratified sampling was utilized to ensure a representative distribution of the tourist population. The statistical analysis using SPSS version 27, SmartPLS 4 was used to perform Partial Least Squares Structural Equation Modelling (PLS-SEM), encompassing both measurement and structural model evaluations. Surprisingly, the findings revealed no statistically significant direct relationship between hospitality service quality and tourist satisfaction. This suggests that superior service quality alone may not directly influence tourist satisfaction levels, highlighting the importance of managing customer expectations and considering other psychological or contextual factors that contribute to satisfaction.

Mapping Methodologies for Economic Evaluation of Digital Health Technologies: A Scoping Review.

Economic burden of skin cancer in Southern Iran: A cost-of-illness study.

ABSTRACT Digital Learning Models for Teachers is an important educational development to improve teaching effectiveness through digital technology. It creates learning experiences with personalization and customization by providing teachers with more accurate feedback on their teaching. However, the current problem is that the learning question types must be recommended according to the students’ needs, leading to inadequate learning effects. To solve the problem, this paper proposed a personalized recommendation algorithm based on a graph neural network for teachers’ digital learning models (PRAGNN). In particular, firstly, integrating DINA cognitive diagnosis and gray partial correlation evaluation is used to construct a student model by modeling students’ mastery of knowledge points and cognitive ability level. Secondly, a graph convolutional neural network is introduced and combined with the sequential relationship between subject knowledge points to automatically capture the semantic information of higher-order structures in the knowledge points to achieve personalized recommendations. Ultimately, the algorithm achieved 85.60% accuracy through comparative experiments. This research provides a new idea for constructing a personalized recommendation system for the digital learning model of teachers, it has significant value for the sustainable development of lifelong learning that promotes mutual growth between teachers and students.

Between 1959 and 1969, Colombia implemented the Campaña de Erradicación de la Malaria (Malaria Eradication Campaign) as part of a global initiative. To estimate the institutional costs of the Campaña de Erradicación de la Malaria in Colombia. A partial economic evaluation was conducted to describe costs and outcomes based on microdata from reports by the Servicio de Erradicación de la Malaria, the División de Campañas Directas, and the Pan American Health Organization, from 1958 to 1969. Costs were classified according to the operational phases of the Campaña de Erradicación de la Malaria –attack, surveillance, and maintenance– and expenditures were categorized into personnel, benefits, equipment, and other associated costs. The total cost of the Campaña de Erradicación de la Malaria was USD $41’788,924; 74.1% funded by national sources. The attack phase accounted for 80.4% of the total expenditure (USD $33’603,645), while the remaining USD $8’185,279 corresponded to the preparatory, consolidation, and maintenance phases. Forty percent of the attack phase expenditure was allocated to personnel responsible for spraying activities, while 19% percent was spent on insecticide acquisition. The average cost per household spraying was USD $2.70. The campaign prevented an estimated 568,439 malaria cases. The institutional cost of the Campaña de Erradicación de la Malaria in Colombia amounted to USD $41’788,924, with 74.1% corresponding to national resources. Although the campaign prevented an average of 41,965 malaria cases per year, it ultimately failed to achieve malaria eradication.

IntroductionMany patients with tuberculosis (TB) suffer from a huge economic burden, even though TB services are often provided free of charge at the point of care. Costs can create significant barriers, hindering patients’ access to TB treatment. These costs include direct medical costs (such as consultation fees), direct non-medical costs (such as transportation costs) and indirect costs (such as wages foregone). This systematic review aims to synthesise the best available evidence on economic evaluations of patient-cost studies on self-administered treatment (SAT) for drug-sensitive TB compared with facility-based directly observed treatment, short-course (FB DOTS), globally.Methods and analysisWe will conduct a systematic review following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines and search PubMed, Academic Search Complete, Scopus, CINAHL Plus (EBSCO) and Google Scholar for articles published up to 2025, without date restrictions. Eligible studies must be full or partial (cost analyses without effectiveness data) economic evaluations conducted globally, comparing SAT to FB DOTS regarding TB patient costs. Grey literature will be included. Exclusion criteria include studies not reporting patient costs between SAT and FB DOTS, and non-economic evaluations (non-original research). Two independent reviewers will conduct the screening, data extraction and quality assessment. A quality assessment will be performed using the Consolidated Health Economic Evaluation Reporting Standards statement, the Consensus on Health Economic Criteria checklist and the ROBINS-I tool.Ethics and disseminationEthics approval is not required for this systematic review because it does not use individual patient data. Instead, we will use publicly available economic evaluation research studies. Findings will be presented at international and national conferences and published in open-access, peer-reviewed journals. PROSPERO registration number CRD42024591221.

Community-based allied health (AH) services have previously demonstrated a potential positive impact on acute care utilization, with wide acceptance among consumers. However, little is known about their economic impact. This systematic review aimed to address this gap. The primary outcomes of interest included: (a) costs of at least one type of acute care utilization; and (b) cost-effectiveness regarding acute care. The secondary outcomes of interest included total healthcare and/or non-healthcare costs. An a priori protocol was registered with PROSPERO [CRD42023437013]. Inclusion criteria were: (a) stand-alone interventions led by practitioners/graduates from one or more target AH professions; (b) reported acute care utilization costs as a primary or secondary outcome; (c) full or partial economic evaluations; and (d) studies published in English from 2010 onward. Eligible studies were identified from relevant bibliographic databases and gray literature search (September and October 2023). Modified McMaster Critical Appraisal Tool for quantitative studies, McGill Mixed Methods Appraisal Tool, and Consensus on Health Economic Criteria List were used to assess methodological quality. Narrative synthesis and cost-effectiveness planes were used for synthesizing and presenting the findings. Twelve studies, comprising eight cost analyses and four full economic evaluations, were included. Both single disciplinary (led by physiotherapists, dietitians, social workers, or exercise physiologists) and multidisciplinary (involved two to five AH professions) services were identified. Collectively, ten studies showed cost savings in acute care, while seven indicated varying degrees of cost-effectiveness and cost savings in total healthcare and non-healthcare, from pre-post and between-group comparisons. The findings demonstrated trends towards economic benefits of AH, highlighting their potential to alleviate the pressures on the acute sector and even the wider health system. However, the evidence is limited and of lower quality, emphasizing cautious interpretation. This review underscores the value of AH services and highlights key areas requiring action to strengthen the evidence base.

Characterization of the evaluation method for hepatobiliary phase image by liver magnetic resonance imaging using Gd-EOB-DTPA.

Community health workers (CHWs) play a vital role in delivering primary health care in low- and middle-income countries (LMICs), addressing multiple diseases through horizontal programmes. Despite their effectiveness, there is a US$4.4 billion annual funding gap for professional CHW programmes. Some countries have adopted these programmes, while others require stronger economic evidence to justify investments. This study updates a 2015 review, critically examining the costs and cost-effectiveness of horizontal CHW programmes in LMICs. A scoping review was conducted using 10 databases and grey literature, covering studies published between August 2015 and July 2024. Search terms related to 'Community Health Workers' and 'Economic Evaluations' were used. Studies were screened via Covidence software based on inclusion and exclusion criteria. Data on study methodology, cost and outcomes were extracted, tabulated in Microsoft Excel and analysed. A total of 18 studies, covering 42 scenarios, were included. Most studies focused on partial economic evaluations, with cost analyses being the most common method. CHW compensation varied widely, with a median monthly salary of US$265 (range US$3033 ($148 (Ethiopia)-$3181 (Malawi)); IQR US$346 (US$203-US$549)). The most commonly reported cost metric was the annual cost per capita, with a median of $6.02 (range: $0.29-$67.95). Sensitivity analyses were conducted in 29% of the scenarios, with six scenarios concluding CHW programmes were cost-effective. However, most did not conclude on cost-effectiveness or affordability, highlighting gaps in the evidence base. Service provision was the most frequently reported outcome, while cost per outcome and affordability were under-reported. This review highlights gaps in the economic evaluation of horizontal CHW programmes, particularly in cost-effectiveness and affordability. More large-scale evaluations are needed to inform national health policies and support sustained investment in CHW programmes to strengthen health systems and address workforce shortages.