Impact Analysis Model Research Articles

Budget impact analysis of insulin glargine vs. isophane protamine insulin treatment for patients with type-2 diabetes and severe hypoglycemia in Thailand.

This study aimed to assess the financial impact of different adoption rates of insulin glargine (IGlar) treatment compared to isophane protamine (neutral protamine hagedorn [NPH]) insulin treatment for patients with type-2 diabetes (T2D) and severe hypoglycemia in Thailand from the payer's perspective. The budget impact analysis (BIA) model over a period of 5 years was used to estimate the net budget impact (NBI) of IGlar treatment by comparing the total budget expenditures under two scenarios: scenario 1 involved only NPH insulin and scenario 2 included the introduction of IGlar. The total budget included either the cost of insulin or a combination of the costs of insulin and the expense related to severe hypoglycemia. Scenario 2 started at 20% uptake of IGlar and a yearly increase of 20%. NBI was calculated as the difference between the total budgets of scenarios 1 and 2. NBI and one-way sensitivity analyses were conducted for evaluation. Considering only the cost of insulin, the use of IGlar for patients with T2D and severe hypoglycemia resulted in a yearly average NBI of 174.9 million Thai baht (THB) (5.1 million USD). However, when the cost related to severe hypoglycemia was included, the total budget incurred from scenario 2 was less than that of scenario 1, leading to a negative NBI or cost savings. The NBI of IGlar adoption would be substantial when considering only the cost of insulin; however, the significant benefit of IGlar in terms of a lower rate of severe hypoglycemia compared with NPH insulin would clearly offset the additional cost of IGlar.

Estimating Budget Impact and Joint Replacement Avoidance by Implementing a Standardized Education and Exercise Therapy Program for Hip and Knee Osteoarthritis in a Publicly Insured Health Care System.

The study objective was to estimate the budget impact of funding a standardized education and exercise therapy program, Good Life with osteoArthritis in Denmark (GLA:D) for people with hip and knee osteoarthritis (OA) waiting for total joint replacement (TJR) consultation in a universal publicly insured health care system in Canada. We built a budget impact analysis model to estimate the annual cost (Canadian dollars)of providing the GLA:D program to people waiting for a TJR consultation and then forecasted a three-year budget cycle. The base case assumes that 40% attend GLA:D sessions, that 11% avoid surgery, uniform care delivery, that training costs are incurred separately, and that the health care system has enough trained staff to meet demand. The population of people with hip and knee OA waiting for a TJR consultation was estimated with government statistics, peer-reviewed evidence, and routinely collected data from five orthopedic centralized intake clinics (serving 80% of people seeking TJR). Patient-level costs were collected prospectively. International published evidence informed the TJR avoidance estimates. A one-way sensitivity analysis of key parameters evaluated model robustness. Four scenarios were analyzed: public funding for everyone (base case), low-income, rural, and uninsured persons. Funding GLA:D would cost $4.3 million, serve 12,500 people, and save $8.5 million by avoiding 1,300 TJRs in year one. Savings grow to $8.8 and $8.7 million in years two and three. The number of TJRs performed annually produced the most uncertainty in budget impact (-$15.3 million, -$1.8 million). The most cautious parameter estimates still produce cost savings. Publicly funding standardized education and exercise therapy programs for everyone waiting for a TJR consultation would avoid surgeries, improve access to evidence-based treatments, and save more than the program costs.

Expanding access to sodium-glucose cotransporter 2 inhibitors (SGLT2i) in the Ministry of Health Malaysia - a multiple HTA approach.

Ministry of Health (MOH) Malaysia stakeholders seek primary care access to sodium-glucose cotransporter 2 inhibitor (SGLT2i). Addressing this required a complex decision, selecting among three SGLT2i for two different indications and two practice settings. The options include expanding the existing SGLT2i (empagliflozin) in the MOH Medicines Formulary to primary care and/or having dapagliflozin and/or luseogliflozin as alternatives. This study aimed to conduct a multiple health technology assessment (HTA) to determine the SGLT2i of choice for the MOH setting. The clinical benefits of SGLT2i were assessed through a systematic literature review and affordability was assessed through the development of three budget impact analysis models simulating seventy scenarios. Each model varied by prescribing indications, restrictions, and SGLT2i involved (M1: glycemic control, HbA1c between 6.5 percent and 10 percent, empagliflozin-dapagliflozin-luseogliflozin; M2: cardiovascular benefits, HbA1c less than 10 percent, empagliflozin-dapagliflozin; M3: a composite of M1 and M2). The outcome of the HTA was presented to the MOH decision-makers. Although there was no significant difference in glycemic control between the SGLT2i, differences exist in cardiovascular benefits conferred. Despite having scenarios with lower net budget impact (NBI) in the M1, M2, and M3 models, decision-makers decided to expand empagliflozin use to primary care setting and add dapagliflozin for hospital-only setting for both indications [NBI of $4.38 mil] due to empagliflozin's advantage in reducing risk for cardiovascular death and prior experience of its use in MOH. The multiple HTA approach guided the complex decision-making process by providing a holistic understanding of the decision's impact.

A comprehensive quantitative lifecycle cost and environmental impact analysis model for computing infrastructure

This paper presents a comprehensive quantitative model for quantitative assessment of the lifecycle costs and environmental impacts of computing infrastructure, with a focus on internet data centers (IDCs) and high-performance computing (HPC) facilities. The key innovation lies in the integration of interdisciplinary cost evaluation and carbon emission methods for the establishment of this quantitative model. This framework, which outlines key cost components and carbon emission factors, enables the calculation of total costs, electricity expenses, and greenhouse gas emissions throughout the lifecycle of infrastructure. With IDCs as a case study, the research clarifies the intricate cost structure associated with equipment procurement, energy usage, land acquisition, and operational expenses. This paper provides an in-depth understanding of the cost structure and environmental impact of computing infrastructure in support of sustainable decision-making in its development.•Based on established cost estimation methods, such as Lifecycle Cost Analysis (LCCA) and the Analogous Estimating Method, this study examines costs across construction and operation phases.•The Emission Factor Method is used to quantify environmental impact, emphasizing the significance of regional energy mix and power usage effectiveness (PUE).

A Budget Impact Analysis of Adalimumab Biosimilars; The Albanian Context

Adalimumab is used to treat various autoimmune conditions, such as rheumatoid arthritis, ankylosing spondylitis, psoriasis, psoriatic arthritis, Crohn’s disease, and ulcerative colitis. Adalimumab is a tumor necrosis factor (TNF) inhibitor. Adalimumab biosimilars have the potential to reduce costs for the healthcare system. The aim of the study is to develop a drug budget impact analysis to assess the potential financial impact of the availability of the biosimilar adalimumab-aacf in terms of cost savings due to its lower price compared to adalimumab-adaz. Methods: This is a budget impact analysis and cost calculator model in Microsoft Excel based on FDSKSH’s data for 2023. Results: Adalimumab biosimilars accounted for approximately 25% of biologic drug prescriptions in 2023. The reimbursement cost of biologic drugs for autoimmune diseases was ALL 186,523,409 in 2023 (1 ALL = 0.01 USD). Of that cost, 15.21% accounted for treatment with adalimumab biosimilars. Adalimumab-aacf was available at a 15.82% lower price than adalimumab–adaz. The analysis indicated that the replacement of the biosimilar adalimumab-adaz with the biosimilar adalimumab-aacf could have resulted in cost savings of ALL 968,138, and the reimbursement cost of biologic drugs for autoimmune diseases could have been 3% lower. With the potential cost savings, two more patients could have had access to biologic drugs in 2023. Conclusions: The Drug Budget Impact Analysis estimated that the availability of adalimumab-aacf would be more profitable in terms of cost savings compared to adalimumab-adaz. The scenario could result in increased treatment access for patients.

Budget impact of upadacitinib in patients with moderate to severe rheumatoid arthritis in Argentina.

To analyze the budget impact of upadacitinib (UPA) 15 mg + methotrexate (MTX) for the treatment of moderate-to-severe rheumatoid arthritis (RA) in patients with an inadequate response to conventional disease-modifying antirheumatic drugs (cDMARD-IR) from the perspective of social security and the private health sector in Argentina. A budget impact analysis model was developed for a hypothetical cohort of 100,000 adults with health insurance coverage who were diagnosed with RA over a 5-year time horizon. The model parameters were obtained through literature review and validated by local experts. The costs are expressed in 2024 US dollars (USD). The introduction of UPA 15 mg + MTX for the treatment of moderate-to-severe RA and cDMARD-IR resulted in minimal increase, with a five-year total cumulative incremental cost of USD 1,855 for social security and USD 1,812 for the private health sector, representing 2% of the total budget. The acquisition cost of UPA was the most influential variable in the sensitivity analysis. The introduction of UPA 15 mg + MTX for the treatment of moderate-to-severe RA and cDMARD-IR can provide an effective treatment option with a minimal increase in costs for the healthcare system in Argentina, which is especially important in developing countries where health system budgets are more limited. Providing evidence-based estimates is a valuable tool for informing healthcare policies and can help policymakers make informed decisions about the allocation of healthcare resources to improve patient outcomes while also managing costs.

Assessing tourism impact on cultural landscapes using mobile phone data: the case of Las Médulas (Spain).

Cultural landscapes often pose problems for planning and management because of insufficient information regarding visitor movements, interests and infrastructure burden. Traditionally visitor surveys have helped to fill this gap, albeit limited to their incomplete and occasional nature. This paper presents a methodology for using cellular antenna metadata for understanding tourism flows. This information is collected systematically by the mobile phone network but only recently has its big data potential begun to be exploited for understanding socio-economic phenomena. The present case study of the Roman gold mine of Las Médulas (León, Spain) provides a model for tourism impact analysis using anonymized data from cell phone towers.

Cost-Effectiveness and Budget Impact Analyses of Selective Internal Radiation Therapy versus Atezolizumab Plus Bevacizumab from a German Statutory Health Insurance Perspective.

To compare personalized dosimetry with yttrium-90 (90Y)-loaded glass microspheres (SIRT) vs atezolizumab and bevacizumab (A+B) in hepatocellular carcinoma (HCC) treatment in terms of cost-effectiveness and budget impact from a German statutory health insurance (SHI) perspective. Cost-effectiveness analysis (CEA) and budget impact analysis (BIA) models were developed in MS Excel. The available key studies (IMbrave150 and DOSISPHERE-01) suggest that both strategies are comparable in terms of progression-free survival and overall survival in HCC, but a difference in severe adverse events (SAE) in favor of SIRT was observed. Accordingly, the CEA model investigates the endpoints "cost per SAE avoided" and "cost per quality-adjusted life year (QALY) gained", whereas the BIA simulates the impact of a stepwise re-allocation of current market share to the option which emerges as more cost-effective from the CEA. The model suite estimated a mean annual total per-patient costs of € 29,984 for SIRT, compared to € 75,725 for A+B. SIRT was associated with a lower number of SAE and a higher number of QALYs compared to A+B. Switching additionally 25% of the eligible patients (≈500) from systemic therapy to SIRT could generate annual savings of approximately € 22.6 million Euros to the SHI. SIRT was identified as dominant treatment strategy. SIRT use not only saves SHI expenditure compared to systemic immunotherapy but also yields extra QALYs. This positions SIRT as the dominant and more cost-effective treatment strategy for patients with HCC. The savings to the SHI system, derived from the BIA conducted, become increasingly significant with rising adoption rates of SIRT.

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. These quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Renewable Energy Sourcing to Enhance Sustainable Manufacturing by Using Madhab’s EEE Impact Analysis Model

The world's increasing energy demand, coupled with the depletion of finite energy resources, necessitates a shift towards sustainable energy solutions. This research explores the multifaceted benefits of substituting conventional fossil fuel-based energy sources with renewable energy in industrial settings, with a focus on sustainable manufacturing. A case study was conducted at a printing and packaging factory in Bhubaneswar, Odisha, to analyze economic, environmental, health, safety, and efficiency factors associated with various energy options based on the Madhab’s EEE (Environmental, Efficiency, Economic) impact analysis method. The study identified solar power generation as the optimal energy source, boasting the lowest EEE impact index of 1.90. Wind energy ranked second, followed by conventional GRID power and DG (Diesel Generator) power sources, which were found to be less favorable due to their higher EEE impact indices. Feasibility assessments revealed that the factory had ample rooftop and vacant land space for solar power plant installation, making it self-sufficient in power generation. The return on investment (ROI) for the solar power project was calculated to be 5.54 years, making it a viable option from a sustainability perspective. Moreover, the solar system could be integrated with the GRID through a reverse metering system, enabling excess energy to be sold back to the GRID. This research underscores the significance of transitioning to renewable energy sources in industries for environmental sustainability, energy security, and economic benefits. It emphasizes the need for similar studies in diverse industrial settings to identify the most suitable energy sources, considering all relevant factors.

Dynamic behavior investigation of reinforced concrete bridge under multi-hazard effect of rockfall impact and material corrosion

Rockfall disasters pose a significant threat in mountainous areas, leading to severe damage and potential collapse of transportation infrastructure. On the other hand, the concrete and steel materials used in these structures deteriorate over time due to environmental corrosion. Therefore, this paper aims to comprehensively investigate the dynamic behavior of the bridge pier of a simply supported reinforced concrete (RC) bridge under the multi-hazard effect of rockfall impact and material corrosion. To achieve this, a comprehensive framework is proposed for analyzing the impact force and dynamic displacements of the bridge pier, which incorporates carbonation-induced corrosion models of concrete and steel, the bond-slip model, and the contact collision model for rockfall impact analysis. The study then explores the influences of various factors, including carbonation degree, rockfall weight, impact velocity, and impact angle, on the maximum impact force and dynamic displacements experienced by the bridge pier. Experimental data from a simply supported RC beam subjected to drop hammer impacts were employed to validate the correctness of the numerical modeling method. Subsequently, the numerical model of a two-span simply supported bridge was established to comprehensively investigate the dynamic behavior of the bridge pier under the combined effects of rockfall impact and material corrosion. The findings reveal that the impact force peak does not exhibit a clear relationship with the carbonation degree, but the displacement peak increases with the carbonation degree when the material corrodes over 60 years. Furthermore, the rockfall impact force peak and the displacement peak show a nonlinear increase in impact velocities, rockfall weight, and impact angle. The outcome of this study holds important implications for the design of resilient bridges and the development of effective maintenance strategies for a multitude of bridges exposed to rockfall hazards.

Application of Cross model for granular flow and impact analysis using three-dimensional B-spline material point method

Granular flow and impact, such as landslides and debris flows, are typical problems involving large deformations of non-Newtonian free surfaces, which are of great interest in geological engineering and also pose challenges for traditional mesh-based numerical methods. As an enhancement of the traditional Material Point Method (MPM), the B-Spline Material Point Method (BSMPM) can not only effectively reduce mesh-crossing and quadrature errors, but also facilitate the tracking and capturing of free surfaces and contact interfaces. In this paper, the non-Newtonian general Cross model, the rheological parameters of which are determined by the Bingham model and the Mohr-Coulomb yield criterion, is introduced into the three-dimensional BSMPM (3D BSMPM) for simulating granular flow and impact problems. Interparticle effects of granular materials are represented by artificial damping forces. Dynamic simulations are carried out on the collapse of sand column and glass beads, the flow of bentonite suspensions, and the impact between granular mass and elastic obstacles. The overall evolution of granular flow and impact behavior are presented and compared with existing experimental and/or original MPM results. It is found that the proposed method well reproduces the overall process of granular flow and impact behavior, including its various stages of formation, acceleration, deceleration, and deposition, as well as the impact pressure. The results show that the granular flow and impact behavior can be well represented by the proposed 3D BSMPM with the Cross rheological model, demonstrating the effectiveness of the proposed method in simulating complicated environmental fluid dynamics.

Budget impact analysis of temocillin insurance coverage for urinary tract infections caused by ESBL-producing pathogens in Iran.

One of the most prevalent infections with a significant disease burden is urinary tract infections (UTIs), which occurs in approximately 50% of women at least once in their lifetime. Antimicrobial resistance to pathogens causing UTIs is expanding worldwide and has been associated with increased use of broad-spectrum antibiotics, including carbapenems, leading to significant costs for insurance and healthcare systems. The emergence of resistance to carbapenems has led to an increasing need for and interest in carbapenem-sparing strategies, including the use of narrow-spectrum antibiotics, such as temocillin. Temocillin has a strong bactericidal effect, along with high tolerability and a good safety profile. It is also stable toward most extended-spectrum beta-lactamases (ESBL). The purpose of our study was to design a budget impact analysis (BIA) model and estimate the budget impact of temocillin insurance coverage for the treatment of UTIs caused by ESBL-producing bacteria from the perspective of the payer. The BIA model with insurance payer perspective was used to estimate the impact of temocillin insurance coverage on the treatment of UTIs caused by ESBL-producing bacteria over a 1-year time horizon in Iran. Direct medicine costs, hospitalization and paraclinical costs, and side effect costs were considered in this model. To assess the impact of uncertainty on the model inputs, 1-way sensitivity analyses were performed. The model demonstrates that inclusion of temocillin in insurance coverage, decreasing treatment costs from $36 million to $34 million, will result in overall savings of > $1.9 million and lead to > $9 million savings in insurance costs for antimicrobial resistance. The inclusion of temocillin in the insurance coverage in Iran for patients developing UTIs caused by ESBL-producing bacteria would be cost-saving for insurance and decrease the risk associated with emerging antimicrobial resistance.

A Discrete-Finite Element Analysis Model Based on Almen Intensity Test for Evaluation of Real Shot Peening Residual Stress.

In this study, a combined discrete-finite element model based on the Almen intensity measurement test was proposed to evaluate the real shot peening residual stress. The discrete element analysis was utilized to simulate the random behavior of numerous shot balls, while the finite element analysis was employed to quantitatively predict the residual stress induced by shot peening. Moreover, the Almen intensity, an essential factor in the actual shot peening process, was taken into account. Initially, an Almen strip analysis model was established, and the multi-random impact analysis was performed to validate the good agreement between the analytical Almen curve and experimental Almen curve. Subsequently, the unit cell discrete-finite element analysis model was expanded for predicting the peening residual stress, incorporating the Almen intensity. The analysis results showed a significant correlation between the predicted peening residual stress and the XRD (X-ray diffraction) experimental residual stress. Therefore, it was confirmed that the proposed discrete-finite element random impact analysis model in this study could serve as an effective analytical technique capable of substituting for the actual shot peening process.

EE27 A Novel Cost Impact Analysis Framework and Model to Evaluate Medications for Opioid Use Disorder within the US Criminal Justice System

Opioid use disorder (OUD) is common among incarcerated, but access to FDA-approved medications for OUD (MOUD) is limited. The purpose of this study was to develop a novel model framework to assess the cost impact of implementing a MOUD program within the criminal justice system (CJS).

Updated clinical and economic analysis of using follitropin alfa in combination with assisted reproductive technologies in 2022

Objective: to perform a clinical and economic analysis of using drugs containing follitropin alfa as an active substance and to assess the impact of the studied drugs on the budget of the state healthcare system of the Russian Federation considering cost data for 2022 in the population of women with infertility and poor ovarian response who are undergoing treatment with the use of assisted reproductive technologies (ART).Material and methods. Clinical and economic evaluation of follitropin alfa use in patients with infertility and poor ovarian response was performed by a cost minimization method, using data from the Uniform Information System for Procurement for the period from July to December 2022 inclusive. The budget impact analysis model compared the costs required for ovarian stimulation of patients with poor ovarian response receiving only a fixed dose combination (FDC) of follitropin alfa + lutropin alfa with the expected practice of switching 25% of these patients to follitropin alfa with a modeling time horizon of 1 year.Results. A cost analysis of an ovarian stimulation course using biosimilar follitropin alfa showed cost savings of 17% compared with the cost of therapy using the original follitropin alfa. Also, cost minimization analysis per cycle of ovarian stimulation demonstrated that the use of the biosimilar follitropin alfa was characterized by cost savings of 24,420 rubles (32.37%) compared to FCD of follitropin alfa + lutropin alfa. Compared to the 2021 data, in the current study based on 2022 data and including only biosimilar follitropin alfa, there was a 103% increase in savings. Budget impact analysis determined that switching 25% of the population from FCD of follitropin alfa + lutropin alfa to follitropin alfa would reduce direct medical costs by 17.3 million rubles (8.1%) per year. Savings in direct medical costs in the current study compared to 2021 increased by 117%.Conclusion. The clinical and economic analysis, as well as the budget impact analysis of using biosimilar follitropin alfa for ovarian stimulation in a population of women with infertility and poor ovarian response, confirm the economic expediency revealed in 2021. Moreover, according to 2022 data, the use of biosimilar follitropin alfa results in additional budgetary savings.

Evaluation of long-term impact of cereal rye as a winter cover crop in Illinois

Extensive tile drainage usage combined with excess nitrogen fertilization has triggered nutrient loss and water quality issues in Illinois, which over time endorsed the hypoxia formation in the Gulf of Mexico. Past research reported that the use of cereal rye as a winter cover crop (CC) could be beneficial in reducing nutrient loss and improving water quality. The extensive use of CC may aid in reducing the hypoxic zone in the Gulf of Mexico. The objective of this study is to analyze the long-term impact of cereal rye on soil water‑nitrogen (N) dynamics and cash crops growth in the maize-soybean agroecosystem in the state of Illinois. A gridded simulation approach was developed using the DSSAT model for the CC impact analysis. The CC impacts were estimated for the last two decades (2001–2020) for two fertilization scheduling (FA-SD = Fall and side-dress N and SP-SD = Spring pre-plant and side-dress N) comparing between CC scenario (FA-SD-C/SP-SD-C) with no CC (NCC) scenario (FA-SD-N/SP-SD-N). Our results suggest that the nitrate-N loss (via tile flow) and leaching reduced by 30.6 % and 29.4 %, assuming extensive adaptation of cover crop. The tile flow and deep percolation decreased by 20.8 % and 5.3 %, respectively, due to cereal rye inclusion. The model performance was relatively poor in simulating the CC impact on soil water dynamics in the hilly topography of southern Illinois. Generalizing changes in the soil properties (due to cereal rye inclusion) from the field scale to whole state (regardless of soil type) could be one of the possible limitations in this research. Overall, these findings substantiated the long-term benefits of cereal rye as a winter cover crop and found the spring N fertilizer application reduced nitrate-N loss compared to fall N application. These results could be helpful in promoting the practice in the Upper Mississippi River basin.

Budget Impact Analysis of Comprehensive Genomic Profiling in Advanced Non–Small Cell Lung Cancer in Taiwan

ObjectivesThe concept of precision oncology using genetic testing has become popular for cancer treatment in recent years. This research aimed to evaluate the financial impact of comprehensive genomic profiling (CGP) in patients with advanced non–small cell lung cancer before receiving any systemic treatments, compared with current practice using single-gene testing, in the hope that the findings can inform the National Health Insurance Administration the decision regarding CGP reimbursement. MethodsA budget impact analysis model was developed comparing the sum of gene testing costs, the first-line and subsequent systemic treatment costs, and other medical costs between the current practice of traditional molecular testing and the new test strategy of CGP. The evaluation time horizon is 5 years from the perspective of the National Health Insurance Administration. Outcome endpoints were incremental budget impact and life-year gained. ResultsThis research indicated CGP reimbursement would benefit 1072 to 1318 more patients receiving target therapies than the current practice and consequently had incremental 232 to 1844 life-years gained from 2022 to 2026. The new test strategy also led to higher gene testing cost and systemic treatment cost. Nevertheless, less medical resource utilization and better patient outcome were demonstrated. The incremental budget impact ranged from US dollar 19 to US dollar 27 million in the 5-year period. ConclusionThis research shows that CGP could pave the way for personalized healthcare with moderate increase of National Health Insurance budget.

Posture and Dynamics Analysis of Hydraulic Support with Joint Clearance under Impact Load

A hydraulic support is one of the key pieces of equipment in a fully mechanized coal-mining face. The shaft is seriously worn due to repeated support shifting in coal-mining operations, and the hydraulic support bears a large amount of impact load in the support process, leading to a threat to its dynamic stability. In order to study the impact load on the posture and dynamics of a hydraulic support, and considering the joint clearance of the hydraulic-support hinge point, based on the dynamics software Adams, the equivalent variable-stiffness damping system is used to replace the column, and the impact analysis model of the hydraulic support with joint clearance is established. The roof pressure is vertically applied to the load balance area of the top beam by static load, and the impact load is vertically applied to the top beam downward. Based on the above model, considering the different distributions of joint clearance, research about the influence of joint clearance on the posture and dynamic characteristics of the hydraulic support is carried out. The results show that when there is joint clearance on both sides of the hydraulic support, when the top beam bears the impact load, the X-axis PMR (position movement ratio) at different positions of the top beam is more than 80%, and the forward-tilt posture is obvious. When the hydraulic support has only unilateral clearance, the front end of the top beam bears the impact load, and the front end of the top beam moves laterally, while the rear end of the top beam mainly moves longitudinally. When the end of the top beam bears the impact load, the vertical PMR of the top beam is less than 1%. When the impact load acts on the side with joint clearance, the top beam has a certain degree of lateral offset, and the offset directions on both sides of the top beam are inconsistent. At this time, the top beam presents a torsional-bearing posture. When the front end of the top beam is loaded, the mechanical curve of each hinge point is higher than the rear-end load. For the hinge point on the side without clearance, the maximum load-fluctuation coefficient reaches 1.04, while for the hinge point on the side with clearance, the minimum load is 0 kN, which will mean some hinge points cannot to play a supporting role. The analysis results will be helpful to research hydraulic supports considering joint clearance.

Variational Autoencoders-BasedSelf-Learning Model for Tumor Identification and Impact Analysis from 2-D MRI Images.

Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist's task and the practitioners more comfortable assessing the tumor's progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result.