Antimicrobial resistance (AMR) is a major global public health concern, and antimicrobial use in animal agriculture represents an important component of broader One Health stewardship efforts. While veterinary antimicrobial policies have traditionally emphasized prescribing rules and professional behavior, less is known about how economic regulations governing pharmaceutical markets shape incentives linked to antimicrobial use. This study examines the effects of France's 2015 ban on commercial rebates and discounts on veterinary antimicrobial prices. Using listed product prices from purchase catalogues obtained from veterinary practices sourcing products through a leading French wholesaler from 2013 to 2021, we apply an interrupted time series design combined with a hedonic pricing framework to assess policy-related price dynamics. The hedonic results indicate substantial price segmentation by antimicrobial criticality, target species, administration route, marketing authorization status, and manufacturer size. The interrupted time series results show that the ban was associated with an immediate price decline of approximately 15% at implementation and a subsequent weakening of the pre-existing upward price trend. Price responses were largely uniform across critically important antimicrobials (CIAs) and non-CIAs, with only minor divergence in longer-run trends by antimicrobial criticality. Evidence of anticipatory price adjustments following the policy's announcement further suggests that market actors responded to regulatory signals before formal implementation. These findings indicate that regulatory interventions targeting financial arrangements in veterinary pharmaceutical markets can alter pricing incentives and complement traditional stewardship strategies by reshaping the economic context in which antimicrobial use decisions are made.
To evaluate the effectiveness and cost-effectiveness of pembrolizumab combined with chemotherapy and anti-angiogenesis treatment for PD-L1 CPS 1-10 cervical cancer (CC) patients from the perspective of US healthcare payers. KM curves for the CPS 1-10 subgroup were derived using the KMSubtraction workflow to reconstruct individual patient data, with analysis based on restricted mean survival time. A partitioned survival model (PFS, PD, death) was used for cost-effectiveness analysis, applying a $150,000 per QALY willingness-to-pay threshold. Pembrolizumab plus chemotherapy significantly improved PFS (RMST difference 5.06 months) and OS (RMST difference 5.84 months) versus placebo. The treatment increased QALYs by 1.17 at an additional cost of $297,505,yielding an ICER of 254,262 $/QALY. A threshold analysis indicated that an effective pembrolizumab price of $25.80 per mg would be required for the regimen to meet the $150,000/QALY threshold. Sensitivity analysis identified drug prices, health state utilities, and discount rates as key influencing factors. Pembrolizumab+chemotherapy significantly improved PFS and OS in PD-L1 CPS 1-10 CC patients, with higher mean RMST than the placebo group. However, it is not cost-effective at current prices, but reducing pembrolizumab's price could improve its cost-effectiveness.
Hot water bottles are widely used for warmth and therapeutic relief, but improper use can lead to burns, ranging from superficial to full-thickness skin injuries. Following the natural gas shortage caused by the complete halt of Russian gas supplies, European countries experienced a sharp increase in hot water bottle-related burns. However, data for Germany were not yet provided. This study examines the incidence of hot water bottle burns over a 10-year period, exploring potential correlations with natural gas prices, natural gas consumption, ambient temperature, and respiratory infection rates. A retrospective single-center analysis of 88 patients who sustained hot water bottle burns from 2014 to 2024 was conducted. Patient data, including burn severity and demographic information, were extracted from hospital records. Monthly counts of acute respiratory infections (ARIs), ambient temperature, natural gas price and gas consumption data were also analyzed. A Poisson regression model was applied to assess the association between hot water bottle burns and the mentioned independent variables. The majority of burns (81.8 %) were second-degree injuries, primarily affecting women (81.8 %). Burns were most common on the lower trunk, thighs, and forearms. The Poisson regression model revealed that for every 1 °C increase in ambient temperature, the incidence of burns decreased by 7 % (IRR=0.93, 95 % CI: 0.88-0.97). However, no significant association was found between ARI incidence, natural gas price and burn occurrence. There was no significant increase of water bottle burns during the recent European energy crisis in Germany. Hot water bottle burns are more frequent during colder months, particularly among women. Natural gas price or natural gas consumption seems like not playing an equivalent role in Germany as in other European countries. Public health efforts should focus on education and prevention strategies to reduce these preventable injuries. Further research should explore additional factors that may influence burn rates.
Rising concerns about climate change and growing consumer awareness of environmental sustainability have accelerated the adoption of cap-and-trade policies worldwide. This study investigates how different supply chain operation models influence manufacturers' carbon reduction decisions and retailers' green advertising strategies. Our analysis reveals that higher carbon trading prices generally stimulate greater emission reduction efforts. However, when both the carbon price and the cost of emission reduction are sufficiently high, further increases in carbon prices may instead weaken firms' incentives to reduce emissions. We further find that increasing cost-sharing ratios alone does not necessarily improve coordination outcomes. Instead, the effectiveness of coordination depends critically on the structure of cost-sharing. The effectiveness of supply chain coordination hinges on strategic allocation of cost-sharing ratios, specifically, the RC model performs better when carbon reduction cost-sharing is low and advertising cost-sharing is high. When carbon reduction cost-sharing is high, the MC model is preferred under low advertising cost-sharing, whereas the DC model becomes more effective when advertising cost-sharing is high.
Bidis are the most commonly used smoked tobacco product in India. Despite their significant health burden, bidi taxation remains low and there are tax exemptions for small producers. We used a multistate life table model to project the 50-year impact of bidi tax reform under two scenarios: 10% and 30% tax-induced price increases combined with removal of small-producer exemptions. Outcomes included years of life gained (YLG), changes in direct health expenditures, indirect morbidity costs, economic output from averted premature mortality, consumer spending and tax revenues. Total economic effects were defined as reductions in direct health expenditures and indirect morbidity costs plus gains in economic output. Long-run monetary outcomes were discounted at 3%. A 10% price increase yields 21.78 million YLG (95% uncertainty interval (UI) 13.25 to 32.42 million) and Indian rupees (INR) 560.1 billion (0.25% of total health expenditure (THE)) in discounted health savings over 50 years; a 30% increase yields 47.95 million YLG (95% UI 29.17 to 71.37 million) and INR 1232.3 billion (0.54% of THE). Total economic effects reach INR 2530.8 billion (1.12% of THE) and INR 5557.7 billion (2.45% of THE) under the 10% and 30% scenarios, respectively. Discounted tax revenues increase by INR 519.9 billion and INR 1390.0 billion. Absolute gains are largest in Uttar Pradesh and West Bengal, while Uttarakhand, Haryana and Tripura show the highest per capita and proportional benefits. Strengthening bidi taxation and removing exemptions would substantially reduce smoking, improve health and generate significant long-term economic and fiscal gains.
China's National Centralized Drug Procurement (NCDP) policy has substantially reduced the cost of essential medicines, yet concerns persist regarding the real-world therapeutic equivalence of NCDP-procured drugs compared to their non-NCDP counterparts. This study aimed to systematically evaluate, using real-world data, the comparative efficacy and safety of NCDP-procured versus non-NCDP oxaliplatin in patients with advanced pancreatic cancer. This single-center retrospective cohort study enrolled patients with unresectable or metastatic pancreatic cancer who received first-line mFOLFIRINOX between May 2022 and March 2025. Patients were stratified into NCDP and non-NCDP groups based on the procurement source of oxaliplatin. Primary endpoints were objective response rate (ORR) and progression-free survival (PFS). Safety was assessed by the incidence of adverse drug reactions (ADRs). Among 168 enrolled patients (NCDP, n=77; non-NCDP, n=91), no significant between-group differences were observed in ORR (9.1% vs. 13.2%, P=0.47), disease control rate (57.1% vs. 63.7%, P=0.43), or median PFS [6.5 vs. 6.0 months; adjusted hazard ratio (HR) =1.034, 95% confidence interval (CI): 0.691-1.547, P=0.87]. However, the NCDP group exhibited significantly lower incidences of elevated aspartate aminotransferase (AST) (28.6% vs. 45.1%, P=0.04) and grade ≥3 anemia (2.6% vs. 15.4%, P=0.007), which remained significant after multivariable adjustment. After propensity score matching, baseline characteristics were well-balanced between groups (all P>0.10), and the primary findings remained consistent. The unit price of NCDP oxaliplatin (¥ 76/vial) was 45% of the price of the non-NCDP agent (¥ 170/vial), representing substantial cost savings. NCDP-procured oxaliplatin demonstrated comparable efficacy to non-NCDP alternatives in advanced pancreatic cancer, with a more favorable safety profile for specific hepatic and hematologic toxicities. These real-world findings support the translation of the national procurement policy into clinical practice, offering significant cost savings without compromising patient outcomes.
Eggs originating from outdoor housing systems, such as organic and free-range production, are often sold at a higher price than conventional eggs. This price difference creates an incentive for potential fraud, highlighting the need for reliable and cost-effective authentication methods. In this study, we evaluated whether internal and external egg quality parameters could be used to classify eggs according to housing system (indoor vs. outdoor) using supervised machine learning. In 2019, a total of 33,216 eggs were collected from 76 commercial farms across Belgium. Egg quality parameters were measured, including whole egg weight, dynamic stiffness, shell deformation, breaking force, albumen height, Haugh unit, shell thickness, yolk color, and cuticle thickness. A classification model was developed using TPOT to optimize supervised machine learning pipelines. The best model trained with all features was an XGBClassifier, which achieved an overall testing accuracy of 76.6%. A second model trained using only yolk color as a feature, implemented with an ExtraTreesClassifier, reached an accuracy of 74.1%. Although the full model performed slightly better in terms of overall accuracy, the yolk-only model showed the lowest false positive rate for outdoor eggs (7% vs. 13%), an important parameter in the context of fraud detection. The overall accuracy of the models was moderate and the best predictor was yolk color. Its potential as screening tool has to be nuanced. Yolk color is highly influenced by the diet and a possible bias with housing system could be suggested as dietary recommendations vary according to management practices. Egg quality parameters seem to be robust and little affected by the housing system. The use of machine learning on quality traits needs to be reconsidered as a tool for distinguishing the origin of eggs. Since the model was trained exclusively on Belgian eggs and no white hens were included in the dataset, additional data such as diet, breed and origin, is needed to confirm potential other parameters.
Microfluidic devices enable high-throughput sample processing with remarkable parallelization and miniaturization. While fluorescence microscopy provides a convenient method for reading out signal from microfluidic assays, commercially-available microscopes impose a fundamental tradeoff between temporal resolution, spatial resolution, and numerical aperture (NA). Spatially tiled imaging enables high-resolution and high-NA imaging over a large area but reduces temporal resolution. Conversely, low magnification, low NA imaging captures large areas in one shot, but typically sacrifices spatial resolution and fluorescence sensitivity. To address this, we introduce an automated transfluorescence tandem-macro-lens optomechanical system (macroscope) capable of sensitive, multi-channel fluorescence imaging over a very large field of view (34 mm diameter, 740 mm2), with resolution determined by the sensor pixel size. We demonstrate bright-field resolution of low-micron features and detection of low- to mid-nanomolar concentrations of common fluorophores within microfluidic device channels. To demonstrate the utility of this macroscope, we image enzyme turnover within valved microfluidic devices (the HT-MEK system, for High-Throughput Microfluidic Enzyme Kinetics) and achieve >50-fold increased temporal resolution over common commercial instruments while maintaining high sensitivity. This macroscope imaging solution costs substantially less than the price of commercially available alternatives, providing a powerful new imaging approach for microfluidic applications requiring sensitive and rapid wide-field fluorescence imaging.
African swine fever (ASF) has reshaped meat demand by reducing pork supply, driving up pork prices, and triggering substitution in consumption, thereby creating a useful setting for examining changes in technical efficiency in the broiler sector. In recent years, China's broiler industry has become increasingly contract-based and larger in scale. However, under ASF shock, the effects of substitution demand on technical efficiency across different production arrangements and farm-size categories remain insufficiently understood. Using data from China's broiler industry for 2017-2024, this study applies a stochastic frontier analysis (SFA) framework to examine the impact of ASF-induced changes in substitution demand on broiler production technical efficiency and its heterogeneity. Three main findings emerge. First, technical efficiency in broiler production exhibited clear stage-specific variation, falling to a period low of 0.913 in 2018, then recovering and peaking at 0.959 in 2021, before declining again thereafter. Second, average technical efficiency was higher for contract growers (0.939) than for non-contract growers (0.924). However, during the adjustment process, efficiency gains among contract growers were smaller, and the subsequent decline was larger, than those among non-contract growers. Third, from 2018 to 2021, efficiency gains across farm-size groups followed an inverted U-shaped pattern, with the largest increase observed among medium-scale operations and the smallest among extra-large-scale operations. From 2021 to 2024, the decline followed a U-shaped pattern, with the smallest decrease among medium-scale operations and the largest among small-scale operations. These results indicate that, under ASF shock, substitution demand does not automatically translate into sustained improvements in broiler production efficiency; rather, its effects are jointly shaped by production organization, farm-size structure, and producers' adjustment capacity. This study reveals the dynamic patterns and heterogeneous responses of technical efficiency in China's broiler industry under ASF shock, and provides evidence relevant to optimizing industry organization and strengthening supply resilience.
The rapid growth of end-of-life power batteries creates an urgent need for economically efficient and spatially coordinated recycling networks in China. Taking the Yangtze River Delta urban agglomeration as a case study, this study develops a dynamic recycling network optimization framework by combining retired-battery flow projection with centralized and decentralized processing mode comparison. The results show that decentralized processing can reduce early-stage investment pressure by using existing qualified enterprises, whereas centralized processing gradually becomes more competitive as retired-battery volumes increase and economies of scale emerge. After accounting for the time value of money, the centralized mode still shows positive long-term economic advantages, but the magnitude and timing of this advantage vary substantially across regions. The transition from decentralized to centralized processing should therefore not be determined by a uniform year, but should depend on regional flow density, facility utilization, and investment recovery timing. The sensitivity analysis further indicates that cascade utilization price, recycling sales price, and government subsidy are the key drivers of recycling profitability. These findings provide practical guidance for region-specific recycling network planning and policy design in high-density urban agglomerations.
LJF (Lonicerae japonicae flos) is a high-value plant that is both edible and medicinal. It has multiple functions such as clearing heat, detoxifying and reducing inflammation, and is widely used in the field of food and traditional Chinese medicine. Due to the low yeiled and high value of LJF, LF (Lonicerae flos), which has a similar appearance and lower price, is often used as LJF. This not only affects the medicinal quality but also poses a risk to clinical medication. Therefore, establishing rapid and reliable detection methods is crucial for market supervision and ensuring the safe use of drugs. This paper collects the characteristic mass spectra of two medicinal materials based on miniaturized direct ionization mass spectrometry. Through the integration of chemometrics and systematic comparison of pretreatment methods and prediction models, the combination of original data and random forest was ultimately chosen, enabling accurate prediction of unknown samples. Meanwhile, based on variable importance analysis from the random forest model, swertimarin was identified as the characteristic discriminating component in LJF. Finally, the TLC-MS method verified that the feature component mined by machine learning was consistent with that recorded in the pharmacopoeia. Miniaturized direct ionization mass spectrometry is unrestricted by environment or location and offers a high degree of flexibility. The integration with machine learning methods offers a promising proof-of-concept for rapid and accurate quality assessment of traditional Chinese medicine.
Ultra-processed foods (UPFs) are increasingly recognized for their health harms, yet consumer selection of UPFs is strongly shaped by retail marketing strategies. Despite widespread acknowledgment of these tactics, detailed understanding of their implementation has been limited by restricted access to industry insider information. This study aimed to obtain direct, industry-based insights into the implementation of retail marketing strategies designed to influence consumer purchasing of UPFs. We conducted 49 interviews with 27 sales representatives and distributors of major UPF companies and 22 managers of chain and independent stores. Participants provided marketing agreements, planograms, and other business files (n = 46). Using reflexive thematic analysis, we coded and analyzed the 95 documents and identified key themes. Findings reveal UPF manufacturers and retailers deliberately orchestrate the 4Ps-product, placement, price, and promotion-to condition predictable UPF customer selection responses. Strategies included leveraging precise sales data, embedding scripted marketing tactics in contracts, monitoring retailer compliance, refreshing store layouts to create an illusion of choice and innovation, and integrating all 4Ps simultaneously while prioritizing placement. These strategies were organized into six sub-themes across two overarching themes: Guiding Principles of 4Ps Implementation (Simultaneous use of the 4Ps, Placement as a top priority, and Keeping things fresh) and Governance and Surveillance Mechanisms to Best Execute the 4Ps (Monitoring every move, Contracts dictate the 4Ps, and Maintaining store compliance). Overall, UPF companies exert extensive and deliberate control over retail spaces, challenging the narrative that food and beverage purchases reflect consumer choice. Enhanced regulation of the 4Ps and counter-marketing campaigns that reveal overlooked marketing practices may mitigate UPF companies' influence on customer purchases and thus reduce their related health harms.
To assess costs and cost-effectiveness of bare metal stent (BMS), drug-eluting stent (DES), and paclitaxel drug-coated balloon (DCB) relative to percutaneous transluminal angioplasty (PTA) for treatment of femoropopliteal peripheral artery disease (PAD) in the United States. An economic model was developed to assess the 3-year cost and cost-effectiveness from a U.S. payer perspective of PTA, BMS, DES, and paclitaxel DCB. Model inputs were derived from the literature. Costs were obtained from 2023 U.S. Medicare national-average rates and included hospital outpatient and physician reimbursement. We used Medicare national-average rates to reflect costs to the Medicare system. Sensitivity analyses evaluated the impact of bailout stent and target lesion revascularization rates (TLR) on results. The estimated average per-person cost for endovascular procedures over 3-years was $9,151 for PTA, $13,325 for BMS, $13,054 for DES, and $8,406 for DCB. FDA-approved DCBs showed comparable economic value, with costs between $8,027-$8,804 for IN.PACT Admiral, Stellarex, Lutonix, and Ranger devices. Incremental quality-adjusted life years (QALYs) gained were between 0.0035-0.0054 for strategies compared to PTA. DCBs were cost-saving, while BMS and DES were not cost-effective with incremental cost-effectiveness ratios >$150,000/QALY gained. The sensitivity analysis results showed PTA and DCB bailout stent rates had the largest impact on costs, however DCBs remained cost-saving in most cases. DCBs provided a moderate cost-savings in endovascular procedure costs over PTA due to lower TLR rates over 3 years. DES and BMS were not cost-effective from a U.S. payer perspective despite similar TLR rates as DCBs due to higher reimbursement levels and minimal gains in QALYs.
This eye-tracking study investigates the cognitive mechanisms underlying creative meaning decoding in Chinese logogriphs-character-based riddles whose solutions require a metalinguistic reinterpretation of the riddle surface. Unlike figurative language, where non-literal meanings operate via semantic-pragmatic extension, logogriphs demand that solvers inhibit the salient literal interpretation and instead access low-salience metalinguistic properties (e.g., the interpretation of orthographic decomposition, spatial manipulation of character components). Experiment 1 revealed a three-stage processing sequence: initial dominance of literal meanings (0-1000 ms), competition between literal and metalinguistic interpretations (1000-2000 ms), and eventual resolution toward the correct metalinguistic solution (2000-4500 ms). Experiment 2 manipulated contextual primes (literal vs. non-literal contexts), demonstrating that non-literal contexts enhanced accuracy and accelerated resolution by cueing the relevant metalinguistic operation, whereas literal contexts reinforced literal interference and delayed metalinguistic access. These findings support the Graded Salience Hypothesis, showing that salient literal meanings are activated automatically regardless of context, whereas non-salient metalinguistic meanings require controlled processing and contextual facilitation. By establishing metalinguistic meaning as a distinct category of non-literal language alongside figurative meaning, this study extends current models of non-literal comprehension and provides new insights into the cognitive architecture of creative meaning decoding.
Overweight and obesity represent significant public health challenges in Saudi Arabia with substantial economic implications. To estimate the economic burden of overweight and obesity in Saudi Arabia and identify associated factors. This cross-sectional study utilized a mixed-methods approach combining primary survey data with secondary national health data. A total of 667 participants were recruited through stratified random sampling using a validated structured questionnaire. Direct medical costs were estimated using healthcare utilization data, while indirect costs were calculated using the human capital approach. Logistic regression analysis identified factors associated with high economic burden. Statistical significance was set at P < 0.05. The total annual economic burden of overweight and obesity in Saudi Arabia was estimated at SAR 7.6 billion (US$2.0 billion), representing 1.2% of GDP. Direct medical costs accounted for SAR 5.2 billion, while indirect costs totaled SAR 2.4 billion. Among participants, 46.8% experienced a high individual economic burden. Younger age (18-30 years) was significantly associated with higher economic burden compared to older adults (adjusted odds ratio [AOR] = 2.9, 95% confidence interval [CI]: 2.2-4.0, P = 0.004). Primary education level was associated with higher economic burden compared to university education (AOR = 3.9, 95% CI: 2.0-9.2, P = 0.045). Body mass index (BMI) ≥ 30 kg/m2 was associated with higher economic burden (AOR= 3.0, 95% CI: 2.2-3.7, P = 0.001). The economic burden of overweight and obesity in Saudi Arabia is substantial and disproportionately affects younger adults, individuals with lower education, and those with higher BMI. These findings support targeted interventions and policy initiatives aligned with Saudi Vision 2030 healthcare transformation goals.
Domain shift, characterized by degraded model performance during the transfer from labeled source domains to unlabeled target domains, poses a persistent challenge for deploying deep learning systems. Current unsupervised domain adaptation (UDA) methods predominantly rely on fine-tuning feature extractors-an approach limited by high computational cost, reduced interpretability, and poor scalability to modern architectures. Our analysis reveals that models pre-trained on large-scale data exhibit domain-invariant geometric patterns in their feature space, characterized by intra-class clustering and inter-class separation, thereby preserving transferable discriminative structures. These findings suggest that cross-domain performance degradation is often associated with decision-boundary misalignment, and that correcting such misalignment can serve as an effective alternative to feature adaptation, particularly when pretrained representations are sufficiently strong. Unlike fine-tuning entire pre-trained models, which risks introducing unpredictable feature distortions, we propose the Feature-space Planes Searcher (FPS): a novel domain adaptation framework that optimizes decision boundaries by leveraging these geometric patterns while keeping the feature encoder frozen. This streamlined approach enables interpretable analysis of adaptation while substantially reducing memory and computational costs through offline feature extraction, permitting full-dataset optimization in a single training cycle. Moreover, we introduce an Intra-Class Distance Metric (ICDM) that enables fully unsupervised hyperparameter selection without requiring target-domain labels. Evaluations on public benchmarks show that FPS achieves competitive performance across standard benchmarks, with notable gains in several settings and tasks. FPS scales efficiently with large multimodal models and shows versatility across diverse domains including protein structure prediction, remote sensing classification, and earthquake detection. We anticipate FPS will provide a simple, effective, and generalizable framework for domain adaptation tasks.
Depression treatment outcomes remain difficult to predict for selective serotonin reuptake inhibitor (SSRI) and repetitive transcranial magnetic stimulation (rTMS) therapies, leading to trial-and-error treatment selection and delayed clinical benefit. This study develops an EEG-based prediction model using local Fibonacci pattern (LFP) features to classify treatment responders before therapy initiation. Pre-treatment EEG signals from the Mumtaz SSRI dataset and the Atieh Hospital rTMS datasets were processed using finite impulse response filtering and multi-scale principal component analysis. The LFP method extracts nonlinear temporal features directly from 19-channel EEG recordings through Fibonacci-indexed local differences and histogram encoding. Features were ranked using neighborhood component analysis and classified with a feedforward neural network under segment-level 10-fold cross-validation. The model achieved accuracies of 99.12% for SSRI, 100.00% for the small rTMS dataset, and 94.51% for the big rTMS dataset under this protocol. To address the risk of overfitting and to provide a stricter estimate of subject-level generalization, subject-wise leave-one-subject-out (LOSO) validation was also performed. Under LOSO validation, the corresponding accuracies were 61.83%, 77.93%, and 71.57%, with balanced accuracy/macro-F1 values of 59.07%/59.16%, 65.49%/66.68%, and 75.43%/71.51% for SSRI, small rTMS, and big rTMS datasets, respectively. Key discriminative channels corresponded to frontal, temporal, and parietal regions involved in emotion regulation and cognitive control. The revised findings support LFP as a computationally efficient EEG representation, but the lower LOSO results indicate that larger independent cohorts are required before any clinical-deployment claim can be made.
Psychological distress in Chronic obstructive pulmonary disease (COPD) patients often does not correspond to symptom severity. The clinical significance of psychological-symptom mismatch phenotypes is unclear. To identify psychological-symptom mismatch phenotypes in COPD and examine their associations with clinical outcomes. A cross-sectional study was conducted in 306 COPD patients from a tertiary hospital in Shandong, China. Latent profile analysis was applied to standardized measures of anxiety, depression, lung function, symptom severity, and dyspnea to identify phenotypes. Multivariable linear and negative binomial regressions assessed associations with clinical outcomes. Four phenotypes were identified: psychological-dominant mismatch (22%), low (47%), moderate (26%), and high psychological-symptom burden (5%). The mismatch phenotype was characterized by disproportionately elevated psychological burden relative to symptom burden and was associated with the poorest quality of life. In adjusted analyses, patients in the low, moderate, and high burden phenotypes had better quality of life than those in the mismatch phenotype (all P <0.001). Patients in the low and moderate burden phenotypes had fewer hospitalizations in the preceding year (incidence rate ratios, 0.592 [95% CI, 0.355-0.989] and 0.233 [95% CI, 0.141-0.386], respectively), whereas no significant difference was observed for the high burden phenotype. Differences in self-management and coping were limited, while patterns of health locus of control varied across phenotypes. Psychological-symptom mismatch appears to be a common and clinically relevant pattern in COPD. Patients with a psychological-dominant mismatch phenotype exhibited poorer quality of life and a higher likelihood of hospitalization. These findings highlight the potential importance of incorporating psychological assessment into routine COPD care and suggest that phenotype-informed approaches may help identify patients with differing clinical needs.
Atopic dermatitis (AD) presents a multidimensional burden, often beginning in infancy and accumulating over time. Beyond itch and pain, AD is associated with significant losses of health-related quality of life resulting from sleep disturbance, stigmatization, other atopic diseases and nonatopic comorbidities, among others. These associated conditions affect patients' physical and mental health, development, social interaction and productivity, resulting in what has been termed cumulative life course impairment (CLCI). To alter the trajectory of AD and improve patient outcomes, it is important to identify people at risk of CLCI, measure it in clinical practice and intervene with appropriate treatment. Although a digital, structured questionnaire for adults exists, a separate instrument to assess CLCI in children and adolescents would benefit our understanding of these issues in a patient population in need. This paper discusses the need for such a questionnaire, with the goal of increasing awareness of the lifelong impact of AD among healthcare professionals, caregivers and patients, potentially improving shared decision-making and facilitating conversations about treatment.
Escalating health-care costs and increasing patient complexity underscore the need for standardized, comparable measures of surgical value. Despite numerous proposed indices, methodological heterogeneity limits cross-specialty comparison. This scoping review aims to (1) characterize trends in value index utilization, (2) assess variability in index components, (3) identify specialty-specific and bibliometric patterns, and (4) propose a preliminary conceptual model to guide index selection. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews-guided scoping review of PubMed, Embase, and Google Scholar (July 2025) identified 2015-2025 studies evaluating surgical value. Eligible English-language, peer-reviewed studies applied quantitative value indices, while reviews, case reports, and nonoperative studies were excluded. Two reviewers screened independently, and data on specialty, index type, and components were extracted. Bibliometric analysis mapped keyword clusters and citation trends. Of 342 included studies, 11 distinct indices were identified. The most frequently reported indices were incremental cost-effectiveness ratio (n = 189, 55.3%), time-driven activity-based costing (n = 93, 27.2%), and net monetary benefit (n = 27, 7.9%), followed by return on investment (n = 23, 6.7%) and operative value index (n = 6, 1.8%); net health benefit, net present value, and procedure value index each accounted for under 1% of studies (n ≤ 6). General, orthopedic, and cardiothoracic surgery were the most represented specialties. Bibliometric analysis identified orthopedic-predominant keyword clusters and citation bursts in replacement and quality of life. Substantial heterogeneity in index selection across specialties motivated development of a preliminary conceptual model for index selection. This first scoping review of surgical value metrics reveals pervasive heterogeneity and introduces a preliminary conceptual model intended to organize index selection and motivate future validation efforts toward comparable, evidence-based value assessment.