Cost-effectiveness Model Research Articles

Personalized federated learning for cross-building energy knowledge sharing: Cost-effective strategies and model architectures

Sufficient building operational data serve as the key premise to enable the development of reliable data-driven technologies for building energy management. Considering that individual buildings may suffer from various data scarcity problems, it is highly promising to devise cross-building data and knowledge sharing methods as possible solutions. Assuming homogeneity among individual building datasets, federated learning can be readily implemented to break the data silos among buildings without violating privacy and security compliances. Nevertheless, individual buildings typically present different operation and energy patterns, making their data heterogeneous and challenging for standard federated learning. To solve such problems, this study explores the domain of personalized federated learning to effectively utilize multi-source building operational data for collaborative data analyses. Three customized personalization strategies have been proposed for building energy prediction tasks and a novel transformer layer has been devised to facilitate tabular data modeling with minimal computational costs. Comprehensive data experiments have been designed to quantify the value of personalized federated learning in 10 building communities with varying building types and scales. The research results show that federated learning can reduce building energy predictions errors by 5.0% to 22.0% over localized solutions, while personalization strategies can further boost the performance by 3.4% to 15.9% given different training data availabilities. The research outcomes are helpful in achieving efficient cross-building data integration and knowledge sharing for district-level building energy management.

Cost-effectiveness of low-dose colchicine in patients with chronic coronary disease in the netherlands.

Recent trials have shown that low-dose colchicine (0.5 mg once daily) reduces major cardiovascular events in patients with acute and chronic coronary syndromes. We aimed to estimate the cost-effectiveness of low-dose colchicine therapy in patients with chronic coronary disease when added to standard background therapy. This Markov cohort cost-effectiveness model used estimates of therapy effectiveness, transition probabilities, costs and quality of life obtained from the Low-dose Colchicine 2 (LoDoCo2) trial, as well as meta-analyses and public sources. In this trial, Low-dose colchicine was added to standard of care and compared to placebo. The main outcomes were cardiovascular events including myocardial infarction, stroke and coronary revascularisation, quality-adjusted life-year (QALY), the cost per QALY gained (incremental cost-effectiveness ratio), and net monetary benefit. In the model, low-dose colchicine therapy yielded 0.04 additional QALYs compared with standard of care at an incremental cost of €455 from a societal perspective and €729 from a healthcare perspective, resulting in a cost per QALY gained of €12,176/QALY from a societal perspective and €19,499/QALY from a healthcare perspective. Net monetary benefit was €1,414 from a societal perspective and €1,140 from a healthcare perspective. Low-dose colchicine has a 96% and 94% chance of being cost effective, from respectively a societal and healthcare perspective when using a willingness to pay of €50,000/QALY. Net monetary benefit would decrease below zero when annual low-dose colchicine costs would exceed an annual cost of €221 per patient. Adding low-dose colchicine to standard of care in patients with chronic coronary disease is cost-effective according to commonly accepted thresholds in Europe and Australia and compares favourably in cost-effectiveness to other drugs used in chronic coronary disease.

Regional Forest Structure Evaluation Model Based on Remote Sensing and Field Survey Data

The assessment of a forest’s structure is pivotal in guiding effective forest management, conservation efforts, and ensuring sustainable development. However, traditional evaluation methods often focus on isolated forest parameters and incur substantial data acquisition costs. To address these limitations, this study introduces a cost-effective and innovative evaluation model that incorporates remote sensing imagery and machine learning algorithms. This model holistically considers the forest composition, the tree age structure, and spatial configuration. Using a comprehensive approach, the forest structure in Longquan City was evaluated at the stand level and categorized into three distinct categories: good, moderate, and poor. The construction of this evaluation model drew upon multiple data sources, namely Sentinel-2 imagery, digital elevation models (DEMs), and forest resource planning and design survey data. The model employed the Recursive Feature Elimination with Cross-Validation (RFECV) method for feature selection, alongside various machine learning algorithms. The key findings from this research are summarized as follows: The application of the RFECV method proved effective in eliminating irrelevant factors, reducing data dimensionality and, subsequently, enhancing the model’s generalizability; among the tested machine learning algorithms, the CatBoost model emerged as the most accurate and stable across all the datasets; specifically, the CatBoost model achieved an impressive overall accuracy of 88.07%, a kappa coefficient of 0.6833, and a recall rate of 76.86%. These results significantly surpass the classification precision of previous methods. The forest structure assessment of Longquan City revealed notable variations in the forest quality distribution. Notably, forests classified as “good” quality comprised 11.18% of the total, while “medium” quality forests constituted the majority at 76.77%. In contrast, “poor” quality forests accounted for a relatively minor proportion of the total, at 12.05%. The distribution findings provide valuable insights for targeted forest management and conservation strategies.

A Cost-Effective Earthquake Disaster Assessment Model for Power Systems Based on Nighttime Light Information

The power system is one of the most important urban lifeline engineering systems. Identifying the damage to the power system is an important task in earthquake disaster assessments. Considering the importance of timeliness and accessibility, a hyperparameter optimization model is proposed to address the assessment of disaster losses in power systems on earthquakes. The power system vulnerability on earthquakes, PSVE, is assessed by the hyperparameter optimization model based on nighttime light information. Through the utilization of the computational resources provided by Google Earth Engine, the accuracy of the baseline model has been significantly improved to 87.9%; meanwhile, the cost-effectiveness in the evaluation process is maintained. The PSVE-based damage evaluation has the potential to aid in assessing earthquake damage to cities’ energy supply, power infrastructure, and lighting. Furthermore, the PSVE-based damage evaluation can provide valuable guidance for prioritizing and efficiently allocating resources for rapid repair and reconstruction efforts.

Reproducible isolation of bovine mammary macrophages for analysis of host pathogen interactions

BackgroundMacrophages residing in milk are vital during intramammary infections. This study sought to develop a method enabling the investigation of macrophage responses to pathogens. Streptococcus uberis is the predominant cause of bovine mastitis UK-wide and its pathogenesis is unusual compared to other intramammary pathogens. Previous studies utilise macrophage cell lines, isolated bovine blood derived monocytes, or macrophages from raw milk through complex or inconsistent strategies such as fluorescence activated cell sorting (FACS), centrifugation and selective adherence, and CD14 antibody-microbeads. The centrifuge steps required in the initial stages often damage cells. Thus, the aim of this study was to develop a reliable, reproducible, and cost-effective method for isolating mammary macrophages from milk in a way that allows their culture, challenge with bacteria, and measurement of their response ex-vivo.ResultsThis method achieves an average yield of 1.27 × 107 cells per litre of milk. Whole milk with somatic cell range of 45–65 cells/µL produced excellent yields, with efficient isolations accomplished with up to 150 cells/µL. This strategy uses milk diluted in PAE buffer to enable low-speed centrifugation steps followed by seeding on tissue-culture-treated plastic. Seeding 1,000,000 milk-extracted cells onto tissue culture plates was sufficient to obtain 50,000 macrophage. Isolated macrophage remained responsive to challenge, with the highest concentration of IL-1β measured by ELISA at 20 h after challenge with S. uberis. In this model, the optimal multiplicity of infection was found to be 50:1 bacteria:macrophage. No difference in IL-1β production was found between macrophages challenged with live or heat-killed S. uberis. Standardisation of the production of IL-1β to that obtained following macrophage stimulation with LPS allowed for comparisons between preparations.ConclusionsA cost-effective method, utilising low-speed centrifugation followed by adherence to plastic, was established to isolate bovine mammary macrophages from raw milk. This method was shown to be appropriate for bacterial challenge, therefore providing a cost-effective, ex-vivo, and non-invasive model of macrophage-pathogen interactions. The optimal multiplicity of infection for S. uberis challenge was demonstrated and a method for standardisation against LPS described which removes sample variation. This robust method enables, reproducible and reliable interrogation of critical pathogen-host interactions which occur in the mammary gland.

A cost-effectiveness evaluation of a dietitian-delivered telephone coaching program during pregnancy for preventing gestational diabetes mellitus

BackgroundThis study aimed to evaluate the cost-effectiveness of a telehealth coaching intervention to prevent gestational diabetes mellitus (GDM) and to calculate the breakeven point of preventing GDM.MethodsData to inform the economic evaluation model was sourced directly from the large quaternary hospital in Brisbane, where the Living Well during Pregnancy (LWdP) program was implemented, and further supplemented with literature-based estimates where data had not been directly collected in the trial. A cost-effectiveness model was developed using a decision tree framework to estimate the potential for cost savings and quality of life improvement. A total of 1,315 pregnant women (49% with a BMI 25-29.9, and 51% with a BMI ≥ 30) were included in the analyses.ResultsThe costs of providing routine care and routine care plus LWdP coaching intervention to pregnant women were calculated to be AUD 20,933 and AUD 20,828, respectively. The effectiveness of the LWdP coaching program (0.894 utility) was slightly higher compared to routine care (0.893). Therefore, the value of the incremental cost-effectiveness ratio (ICER) was negative, and it indicates that the LWdP coaching program is a dominant strategy to prevent GDM in pregnant women. We also performed a probabilistic sensitivity analysis using Monte Carlo simulation through 1,000 simulations. The ICE scatter plot showed that the LWdP coaching intervention was dominant over routine care in 93.60% of the trials using a willingness to pay threshold of AUD 50,000.ConclusionFindings support consideration by healthcare policy and decision makers of telehealth and broad-reach delivery of structured lifestyle interventions during pregnancy to lower short-term costs associated with GDM to the health system.

A Model to Study Wound Healing Over Exposed Avascular Structures in Rodents With a 3D-Printed Wound Frame.

Soft tissue defects with exposed avascular structures require reconstruction with well-vascularized tissues. Extensive research is ongoing to explore tissue engineered products that provide durable coverage. However, there is a lack of controlled and affordable testbeds in the preclinical setting to reflect this challenging clinical scenario. We aimed to address this gap in the literature and develop a feasible and easily reproducible model in rodents that reflects an avascular structure in the wound bed. We created 20 × 20 mm full thickness wounds on the dorsal skin of Lewis rats and secured 0.5-mm-thick silicone sheets of varying sizes to the wound bed. A 3D-printed wound frame was designed to isolate the wound environment. Skin graft and free flap survival along with exposure of the underlying silicone was assessed. Rats were followed for 4 weeks with weekly dressing changes and photography. Samples were retrieved at the endpoint for tissue viability and histologic analysis. The total wound surface area was constant throughout the duration of the experiment in all groups and the wound frames were well tolerated. The portion of the skin graft without underlying silicone demonstrated integration with the underlying fascia and a histologically intact epidermis. Gradual necrosis of the portion of the skin graft overlying the silicone sheet was observed with varying sizes of the silicone sheet. When the size of the silicone sheet was reduced from 50% of the wound surface area, the portion surviving over the silicone sheet increased at the 4-week timepoint. The free flap provided complete coverage over the silicone sheet. We developed a novel model of rodent wound healing to maintain the same wound size and isolate the wound environment for up to 4 weeks. This model is clinically relevant to a complex wound with an avascular structure in the wound bed. Skin grafts failed to completely cover increasing sizes of the avascular structure, whereas the free flap was able to provide viable coverage. This cost-effective model will establish an easily reproducible platform to evaluate more complex bioengineered wound coverage solutions.

Raw material management for rubber parts manufacturing supply chain using the LPSC model

The paper proposes a new raw material cost management concept in the rubber parts supply chain using the Linear Programming Sharing Cost (LPSC). Rubber parts are widely used in the auto parts industry. There are a variety of product models and material types as well as produced by several subcontractors in the 2nd Tier companies. However, the whole chain controls overall cost management as the 1st Tier company. Cost-effective models are more important in the competitive era.The research methodology combined linear programming (LP) with sharing cost (SC) and was applied to inventory management. The LPSC model is developed to deal with Tier 1 companies linked with Tier 2 companies of the subcontractors. The LPSC is combined with the EOQ inventory management model.A new approach has been developed to reduce the cost of raw material management in the rubber part supply chain.The limitation of the model development is that it does not yet have real-time control of the supply chain management system.The conceptual idea was introduced to Thailand's automotive rubber part supply chain and accepted for testing with the pilot test of actual orders.The paper presents the new conceptual raw material cost management model in the automotive rubber part supply chain. The LPSC combined with EOQ is effective, increases value to the supply chain, and simultaneously reduces waste and overproduction.

Design, Development, and Validation of a First, Low-cost, Three-dimensional (3D) Printed Optical Urethrotomy Simulation Model

ObjectivesTo develop and validate a low-cost, portable, and reusable simulation model for optical internal urethrotomy (OIU) training. MethodsA 3D-printed low-cost simulation model for OIU was designed locally and the final model was evaluated by trainees and trainers at the urology boot camps (UK, Belgium, Portugal, Poland). Participants were asked to complete a questionnaire, using a 6-item 5-point rating Likert Scale, to assess the model’s anatomic realism. ResultsA total of 27 trainees and 9 trainers evaluated the model. The model’s anatomy and color were rated as the most realistic features, with 88.9% and 11.1% of respondents rating them as good and excellent, respectively. There were no significant differences between consultants and trainees in their assessment of any of the simulation properties of the OIU model. ConclusionsOur study introduces an innovative, lifelike, and cost-effective simulation model for OIU training. Our model provides a realistic simulation of OIU. We feel that our low-cost and reusable model fills the gap in simulation-based training for young trainees in urology.

An Experimental and Analytical Approach to Evaluate Transponder-Based Aircraft Noise Monitoring Technology

Aviation is a vital modern transportation sector connecting millions of passengers globally. Sustainable aviation development holds substantial community benefits, necessitating effective management of its environmental impacts. This paper addresses the need for an accurate and cost-effective aircraft noise monitoring model tailored to non-towered general aviation airports with limited resources for official air traffic data collection. The existing literature highlights a heavy reliance on air traffic data from control facilities in prevailing aircraft noise modeling solutions, revealing a disparity between real-world constraints and optimal practices. Our study presents a validation of a three-stage framework centered on a low-cost transponder unit, employing an innovative experimental and analytical approach to assess the model’s accuracy. An economical Automatic Dependent Surveillance-broadcast (ADS-B) receiver is deployed at Purdue University Airport (ICAO Code: KLAF) to estimate aircraft noise levels using the developed approach. Simultaneously, a physical sound meter is positioned at KLAF to capture actual acoustic noise levels, facilitating a direct comparison with the modeled data. Results demonstrate that the developed noise model accurately identifies aircraft noise events with an average error of 4.50 dBA. This suggests the viability of our low-cost noise monitoring approach as an affordable solution for non-towered general aviation airports. In addition, this paper discusses the limitations and recommendations for future research.

Single Intravenous Dose Dalbavancin Pathway for the Treatment of Acute Bacterial Skin and Skin Structure Infections: Considerations for Emergency Department Implementation and Cost Savings

BackgroundA pathway for the treatment of acute bacterial skin and skin structure infections (ABSSSI) with a single intravenous (IV) dose of dalbavancin was previously shown to reduce hospital admissions and shorten inpatient length of stay (LOS). ObjectivesTo describe pathway implementation at the emergency department (ED) and evaluate cost-effectiveness of a single-dose dalbavancin administered to ED patients who would otherwise be hospitalized to receive usual care with multidose IV antibiotics. MethodsThe dalbavancin pathway was previously implemented at 11 U.S. EDs (doi:10.1111/acem.14258). Patients with ABSSSI, without an unstable comorbidity or infection complication requiring complex management, were treated with a single dose of dalbavancin. At the emergency physicians’ discretion, patients were either discharged and received outpatient follow-up or were hospitalized for continued management. A decision analytic cost-effectiveness model was developed from the U.S. healthcare's perspective to evaluate costs associated with the dalbavancin pathway compared with inpatient usual care. Costs (2021 USD) were modeled over a 14-day horizon and included ED visits, drug costs, inpatient stay, and physician visits. One-way and probabilistic sensitivity analyses examined input parameter uncertainty. ResultsDriven largely by the per diem inpatient cost and LOS for usual care, the dalbavancin pathway was associated with savings of $5133.20 per patient and $1211.57 per hospitalization day avoided, compared with inpatient usual care. The results remained robust in sensitivity and scenario analyses. ConclusionThe new single-dose dalbavancin ED pathway for ABSSSI treatment, which was previously implemented at 11 U.S. EDs, offers robust cost savings compared to inpatient usual care.

Universal Fetal Echocardiography for Pregestational Diabetes Mellitus: A Cost-Effectiveness Analysis.

To evaluate the cost effectiveness of universal fetal echocardiogram for patients with pregestational diabetes mellitus by first-trimester hemoglobin A 1c (Hb A 1c ) level. We developed a cost-effectiveness model comparing two strategies of screening for critical fetal congenital heart disease among patients with diabetes: universal fetal echocardiogram and fetal echocardiogram only after abnormal findings on detailed anatomy ultrasonogram. We excluded ventricular septal defect, atrial septal defects, and bicuspid aortic valve from the definition of critical fetal congenital heart disease. Probabilities and costs were derived from the literature. We used individual models to evaluate different scenarios: first-trimester Hb A 1c lower than 6.5%, Hb A 1c 6.5-9.0%, and Hb A 1c higher than 9.0%. Primary outcomes included fetal death, neonatal death, and false-positive and false-negative results. A cost-effectiveness threshold was set at $100,000 per quality-adjusted life-year. Univariable sensitivity analyses were performed to investigate the drivers of the model. Universal fetal echocardiogram is not cost effective except for when first-trimester Hb A 1c level is higher than 9.0% (incremental cost-effectiveness ratio $638,100, $223,693, and $67,697 for Hb A 1c lower than 6.5%, 6.5-9.0%, and higher than 9.0%, respectively). The models are sensitive to changes in the probability of congenital heart disease at a given Hb A 1c level, as well as the cost of neonatal transfer to a higher level of care. Universal fetal echocardiogram became both cost saving and more effective when the probability of congenital heart disease reached 14.48% (15.4 times the baseline risk). In the Monte Carlo simulation, universal fetal echocardiogram is cost effective in 22.7%, 48.6%, and 62.3% of scenarios for each of the three models, respectively. For pregnant patients with first-trimester Hb A 1c levels lower than 6.5%, universal fetal echocardiogram was not cost effective, whereas, for those with first-trimester Hb A 1c levels higher than 9.0%, universal fetal echocardiogram was cost effective. For those with intermediate Hb A 1c levels, universal fetal echocardiogram was cost effective in about 50% of cases; therefore, clinical judgment based on individual patient values, willingness to pay to detect congenital heart disease, and resource availability needs to be considered.

Enhancing Waste-to-Energy and Hydrogen Production through Urban–Industrial Symbiosis: A Multi-Objective Optimisation Model Incorporating a Bayesian Best-Worst Method

A surging demand for sustainable energy and the urgency to lower greenhouse gas emissions is driving industrial systems towards more eco-friendly and cost-effective models. Biogas from agricultural and municipal organic waste is gaining momentum as a renewable energy source. Concurrently, the European Hydrogen Strategy focuses on green hydrogen for decarbonising the industrial and transportation sectors. This paper presents a multi-objective network design model for urban–industrial symbiosis, incorporating anaerobic digestion, cogeneration, photovoltaic, and hydrogen production technologies. Additionally, a Bayesian best-worst method is used to evaluate the weights of the sustainability aspects by decision-makers, integrating these into the mathematical model. The model optimises industrial plant locations considering economic, environmental, and social parameters, including the net present value, energy consumption, and carbon footprint. The model’s functionalities are demonstrated through a real-world case study based in Emilia Romagna, Italy. It is subject to sensitivity analysis to evaluate how changes in the inputs affect the outcomes and highlights feasible trade-offs through the exploration of the ϵ-constraint. The findings demonstrate that the model substantially boosts energy and hydrogen production. It is not only economically viable but also reduces the carbon footprint associated with fossil fuels and landfilling. Additionally, it contributes to job creation. This research has significant implications, with potential future studies intended to focus on system resilience, plant location optimisation, and sustainability assessment.

Telemedicine's role in transforming healthcare delivery in the pharmaceutical industry: A systematic review

Telemedicine has emerged as a pivotal technology in transforming healthcare delivery, particularly within the pharmaceutical industry. This systematic review aims to evaluate the extent of telemedicine's impact, its applications, and the challenges it faces in this sector. By synthesizing data from peer-reviewed articles, reports, and case studies published up to 2023, this study adopts a comprehensive methodology to assess telemedicine's role in pharmaceutical care, patient management, and drug delivery systems. Key findings indicate that telemedicine significantly enhances patient access to pharmaceutical care, especially in remote and underserved areas. It facilitates real-time patient-pharmacist consultations, remote monitoring, and personalized medication management, thereby improving medication adherence and patient outcomes. Moreover, telemedicine platforms have been instrumental in streamlining drug development processes and clinical trials through virtual patient engagement and remote data collection. However, the review also identifies several challenges, including regulatory hurdles, privacy concerns, and the need for technological standardization across the industry. Despite these obstacles, the potential of telemedicine to revolutionize healthcare delivery in the pharmaceutical industry is undeniable, offering a more patient-centered, efficient, and cost-effective model of care. Telemedicine represents a transformative force in the pharmaceutical industry, promising to reshape the landscape of healthcare delivery. Its continued integration and adoption hinge on addressing existing challenges and leveraging technological advancements to fully realize its potential benefits for patients and healthcare providers alike.

Mathematical Modelling of Inventory Costs in Supply Chains with Horizontal Cooperation

Efficiency in the supply chain has become very important for companies in the era of increasingly fierce business competition. This has encouraged some companies to start implementing the concept of collaboration between companies or commonly called horizontal cooperation. Horizontal cooperation is important because companies need to work together to manage complex supply chains. To manage production in a collaborative supply chain requires designing a cost-effective model. The design of this model is expected to optimise inventory costs to achieve common goals in horizontal cooperation. This model is a form of linear programme consisting of objective function and constraint function. The objective function is to minimise the inventory cost in a four-tier supply chain with horizontal cooperation on post-production final storage at the production level only. The model was tested with 3 suppliers, 3 materials, 3 factories, 4 products, 3 distributors, and 3 consumers. The model with horizontal cooperation resulted in a total inventory cost of 1,349,830. This amount is 8% lower than the total inventory cost without horizontal cooperation. Thus, horizontal cooperation in the supply chain drastically reduces inventory costs throughout the supply chain.

Challenges, considerations, and approaches for developing a cost-effectiveness model for the adjuvant treatment of muscle-invasive urothelial carcinoma: with a spotlight on nivolumab versus placebo

Aims To present challenges and considerations for the development of a decision analytic model for evaluating the cost-effectiveness of adjuvant nivolumab compared with surveillance in patients with high-risk muscle-invasive urothelial carcinoma (MIUC) after radical resection. Methods and Results Alternative approaches related to both structural assumptions and data sources are presented to address challenges and data gaps, as well as discussion of strengths and limitations of each approach. Specifically, challenges and considerations related to the following are presented: (1) selection of a modeling approach (partitioned survival model or state transition model) given the available evidence, (2) choice of health state structure (three- or four-state) to model disease progression and subsequent therapy, (3) modeling of outcomes from subsequent therapy using tunnel states to account for time-dependent transition probabilities or absorbing health states with one-off costs and outcomes applied, and (4) methods for modeling health-state transitions in a setting where treatment has curative intent and available survival data are immature. Conclusions Multiple considerations must be taken into account when developing an economic model for new, emerging oncology treatments in early lines of therapy, all of which can affect the model’s overall ability to estimate (quality-adjusted) survival benefits over a lifetime horizon. This paper identifies a series of key structural and analytic considerations regarding modeling of nivolumab treatment in the adjuvant MIUC setting. Several alternative approaches with regard to structure and data have been included in a flexible cost-effectiveness model so the impact of the alternative approaches on model results can be explored. The impact of these alternative approaches on cost-effectiveness results and validation of outcomes predicted by the model will be presented in two companion articles. Our findings may also help inform the development of future models for other treatments and settings in early-stage cancer.

Cost-effective 3D lung tissue spheroid as a model for SARS-CoV-2 infection and drug screening.

The SARS-CoV-2 pandemic has spurred an unparalleled scientific endeavor to elucidate the virus' structure, infection mechanisms, and pathogenesis. Two-dimensional culture systems have been instrumental in shedding light on numerous aspects of COVID-19. However, these invitro systems lack the physiological complexity to comprehend the infection process and explore treatment options. Three-dimensional (3D) models have been proposed to fill the gap between 2D cultures and invivo studies. Specifically, spheroids, composed of lung cell types, have been suggested for studying SARS-CoV-2 infection and serving as a drug screening platform. 3D lung spheroids were prepared by coculturing human alveolar or bronchial epithelial cells with human lung stromal cells. The morphology, size, and ultrastructure of spheroids before and after SARS-CoV-2 infection were analyzed using optical and electron microscopy. Immunohistochemistry was used to detect spike protein and, thus, the virus presence in the spheroids. Multiplex analysis elucidated the cytokine release after virus infection. The spheroids were stable and kept their size and morphology after SARS-CoV-2 infection despite the presence of multivesicular bodies, endoplasmic reticulum rearrangement, tubular compartment-enclosed vesicles, and the accumulation of viral particles. The spheroid responded to the infection releasing IL-6 and IL-8 cytokines. This study demonstrates that coculture spheroids of epithelial and stromal cells can serve as a cost-effective infection model for the SARS-CoV-2 virus. We suggest using this 3D spheroid as a drug screening platform to explore new treatments related to the cytokines released during virus infection, especially for long COVID treatment.

Is the Comparator in Your Diagnostic Cost-Effectiveness Model “Standard of Care”? Recommendations from Literature Reviews and Expert Interviews on How to Identify and Operationalize It

ObjectivesThis research aimed to develop best-practice recommendations for identifying the “standard of care” (SoC) and integrate it when it is the comparator in diagnostic economic models (SoC comparator). MethodsA multi-methods approach comprising 2 pragmatic literature reviews and 9 expert interviews was used. Experts rated their agreement with draft recommendations based on the authors’ analysis of the reviews. These were refined iteratively to produce final recommendations. ResultsFourteen best-practice recommendations are provided. Care pathway mapping (using quantitative, qualitative, or mixed-methods approaches) should be used for identifying the SoC comparator. Guidelines analysis can be integrated with expert opinion to identify pathway variability and discrepancies from clinical practice. For integrating the SoC comparator into the model, recommendations around structure, input sourcing, data aggregation and reporting, input uncertainty, and model variability are presented. For example, modelers should consider that the reference standard is not synonymous with the SoC, and the SoC may not be the only comparator. The comparator limitations should be discussed with clinical experts, but elicitation of its diagnostic accuracy is not recommended. Probabilistic sensitivity analysis is recommended when evaluating the overall input uncertainty, and deterministic sensitivity analysis is useful when there is high model uncertainty or SoC variability. Consensus could not be reached for some topics (eg, the role of real-world data, model averaging, and alternative model structures), but the reported discussions provide points for consideration. ConclusionsTo our knowledge, this is the first guidance to support modelers when identifying and operationalizing the SoC comparator in diagnostic cost-effectiveness models.

Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case

Artificial intelligence (AI) models have shown great accuracy in health screening. However, for real-world implementation, high accuracy may not guarantee cost-effectiveness. Improving AI’s sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability. To evaluate the trade-off between AI model performance and the long-running cost-effectiveness, we conducted a cost-effectiveness analysis in a nationwide diabetic retinopathy (DR) screening program in China, comprising 251,535 participants with diabetes over 30 years. We tested a validated AI model in 1100 different diagnostic performances (presented as sensitivity/specificity pairs) and modeled annual screening scenarios. The status quo was defined as the scenario with the most accurate AI performance. The incremental cost-effectiveness ratio (ICER) was calculated for other scenarios against the status quo as cost-effectiveness metrics. Compared to the status quo (sensitivity/specificity: 93.3%/87.7%), six scenarios were cost-saving and seven were cost-effective. To achieve cost-saving or cost-effective, the AI model should reach a minimum sensitivity of 88.2% and specificity of 80.4%. The most cost-effective AI model exhibited higher sensitivity (96.3%) and lower specificity (80.4%) than the status quo. In settings with higher DR prevalence and willingness-to-pay levels, the AI needed higher sensitivity for optimal cost-effectiveness. Urban regions and younger patient groups also required higher sensitivity in AI-based screening. In real-world DR screening, the most accurate AI model may not be the most cost-effective. Cost-effectiveness should be independently evaluated, which is most likely to be affected by the AI’s sensitivity.

Risk-based lung cancer screening in heavy smokers: a benefit–harm and cost-effectiveness modeling study

BackgroundAnnual screening through low-dose computed tomography (LDCT) is recommended for heavy smokers. However, it is questionable whether all individuals require annual screening given the potential harms of LDCT screening. This study examines the benefit–harm and cost-effectiveness of risk-based screening in heavy smokers and determines the optimal risk threshold for screening and risk-stratified screening intervals.MethodsWe conducted a comparative cost-effectiveness analysis in China, using a cohort-based Markov model which simulated a lung cancer screening cohort of 19,146 heavy smokers aged 50 ~ 74 years old, who had a smoking history of at least 30 pack-years and were either current smokers or had quit for < 15 years. A total of 34 risk-based screening strategies, varying by different risk groups for screening eligibility and screening intervals (1-year, 2-year, 3-year, one-off, non-screening), were evaluated and were compared with annual screening for all heavy smokers (the status quo strategy). The analysis was undertaken from the health service perspective with a 30-year time horizon. The willingness-to-pay (WTP) threshold was adopted as three times the gross domestic product (GDP) of China in 2021 (CNY 242,928) per quality-adjusted life year (QALY) gained.ResultsCompared with the status quo strategy, nine risk-based screening strategies were found to be cost-effective, with two of them even resulting in cost-saving. The most cost-effective strategy was the risk-based approach of annual screening for individuals with a 5-year risk threshold of ≥ 1.70%, biennial screening for individuals with a 5-year risk threshold of 1.03 ~ 1.69%, and triennial screening for individuals with a 5-year risk threshold of < 1.03%. This strategy had the highest incremental net monetary benefit (iNMB) of CNY 1032. All risk-based screening strategies were more efficient than the status quo strategy, requiring 129 ~ 656 fewer screenings per lung cancer death avoided, and 0.5 ~ 28 fewer screenings per life-year gained. The cost-effectiveness of risk-based screening was further improved when individual adherence to screening improved and individuals quit smoking after being screened.ConclusionsRisk-based screening strategies are more efficient in reducing lung cancer deaths and gaining life years compared to the status quo strategy. Risk-stratified screening intervals can potentially balance long-term benefit–harm trade-offs and improve the cost-effectiveness of lung cancer screenings.