Cost Estimates Research Articles

Opioid-related clinical outcomes and associated healthcare costs following abuse/misuse of oxycodone formulations: A HEOR analysis from real-world data.

The United States (US) opioid epidemic is a continued burden on the healthcare system and on the lives of individuals affected by the consequences of opioid abuse/misuse. The objective of this study was to use real-world data from intentional abuse/misuse exposures managed by US poison centers to compare clinical outcomes and quantify healthcare costs among three study cohorts: -exposures that involved Xtampza ER®, other oxycodone extended-release (ER), and oxycodone immediate-release (IR). A real-world, observational study. Descriptive statistics were used to describe patient and exposure characteristics. Drug utilization-adjusted rates of intentional abuse/misuse and clinical outcomes were used to determine relative risk. Healthcare cost estimates were calculated by extrapolating average charge per opioid-related disorder emergency department (ED) visit and per inpatient stay based upon case disposition rates, adjusted for population and drug utilization. Compared to Xtampza ER, exposures that involved other oxycodone ER were 7.4 times more likely to be intentional abuse/misuse, 25.9 times more likely to result in major effect or death, 9.7 times more likely to require a visit to the ED, and 14.3 times more likely to result in hospital admission. Similar results were found for oxycodone IR when compared to Xtampza ER. This study is the first of its kind to synthesize clinical outcomes with opioid-related healthcare costs, suggesting that even when Xtampza ER is abused/misused, the rates of major effect/death, ED visits, and hospital admissions were significantly lower than those for other oxycodone-containing medications, resulting in relatively low downstream opioid-related healthcare costs.

Db2une: Tuning Under Pressure via Deep Learning

Modern database systems including IBM Db2 have numerous parameters, "knobs," that require precise configuration to achieve optimal workload performance. Even for experts, manually "tuning" these knobs is a challenging process. We present Db2une, an automatic query-aware tuning system that leverages deep learning to maximize performance while minimizing resource usage. Via a specialized transformer-based query-embedding pipeline we name QBERT, Db2une generates context-aware representations of query workloads to feed as input to a stability-oriented, on-policy deep reinforcement learning model. In Db2une, we introduce a multi-phased, database meta-data driven training approach---which incorporates cost estimates, interpolation of these costs, and database statistics---to efficiently discover optimal tuning configurations without the need to execute queries. Thus, our model can scale to very large workloads, for which executing queries would be prohibitively expensive. Through experimental evaluation, we demonstrate Db2une's efficiency and effectiveness over a variety of workloads. We compare it against the state-of-the-art query-aware tuning systems and show that the system provides recommendations that surpass those of IBM experts.

Nationwide cost and capacity estimates for sedimentary basin geothermal power and implications for geologic CO2 storage

Introduction: Sedimentary basins are naturally porous and permeable subsurface formations that underlie approximately half of the United States. In addition to being targets for geologic CO2 storage, these resources could supply geothermal power: sedimentary basin geothermal heat can be extracted with water or CO2 and used to generate electricity. The geothermal power potential of these basins and the accompanying implication for geologic CO2 storage are, however, understudied.Methods: Here, we use the Sequestration of CO2 Tool (SCO2TPRO) and the generalizable GEOthermal techno-economic simulator (genGEO) to address this gap by a) estimating the cost and capacity of sedimentary basin geothermal power plants across the United States and b) comparing those results to nationwide CO2 sequestration cost and storage potential estimates.Results and discussion: We find that across the United States, using CO2 as a geothermal heat extraction fluid reduces the cost of sedimentary basin power compared to using water, and some of the lowest cost capacity occurs in locations not typically considered for their geothermal resources (e.g., Louisiana, South Dakota). Additionally, using CO2 effectively doubles the sedimentary basin geothermal resource base, equating to hundreds of gigawatts of new capacity, by enabling electricity generation in geologies that are otherwise (with water) too impermeable, too thin, too cold, or not deep enough. We find there is competition for the best sedimentary basin resources between water- and CO2-based power, but no overlap between the lowest-cost resources for CO2 storage and CO2-based power. In this way, our results suggest that deploying CO2-based power may increase the cost of water based systems (by using the best resources) and the cost of CO2 storage (by storing CO2 in locations that otherwise may not be targeted). As such, our findings demonstrate that determining the best role for sedimentary basins within the energy transition may require balancing tradeoffs between competing priorities.

The evolution of competition and efficiency in U.S. Banking: new results from a novel two-step nonparametric method

ABSTRACT We examine the evolution of competition and efficiency in the U.S. banking industry over the past two decades by employing a novel two-step nonparametric method to estimate cost efficiency and the Lerner index for banks with multiple outputs. In the first step, data envelopment analysis is used to estimate the cost efficiency and the cost frontier. In the second step, the nonparametric local linear method is employed to smooth the estimated cost frontier, obtaining estimates of the marginal cost for each output and the Lerner index for each bank. Our innovative two-step nonparametric method has shed light on the evolving landscape of competition and efficiency that the ongoing consolidation in the U.S. banking sector is accompanied with the increasing trend of the concentration and the decreasing trend of efficiency. Further, small banks are more willing to set prices above their marginal costs but may not gain as much profit from a cost efficiency perspective. This phenomenon is a long-standing and enduring characteristic of the U.S. banking industry, reflecting the diverse strategies and priorities of banks operating in different market contexts.

Mars Ascent Propellants and Life Support Resources - Take it or Make it?

Studies of Mars missions over the past thirty years lacked credible cost estimates, so the total mass of materiel delivered to Low-Earth Orbit (LEO) was typically used as a rough measure of relative mission cost because the complexity of the mission was thought to be roughly proportional to the initial mass in LEO (IMLEO). Historically, high launch costs led to large investments in space hardware development which led to high space mission costs. Reducing mass became the central theme of space mission engineering. We are now entering a new era where launch costs no longer have the impact that they would have two decades ago. Launch costs are coming down to the point where we must ask ourselves whether it now makes sense to bring ascent propellants and life support resources from Earth (with higher reliability as a bonus), as opposed to using in situ propellant production and cycling of life support resources. This paper compares various options for bringing ascent propellants and life support resources from Earth vs. developing in situ. In short, it examines the “take it or make it” options for both technologies. For ISPP, the answer is clear: Mars ISPP is not worth the investment when launch costs are low. For life support, the most robust option is to bring life survival resources from Earth, and only use cycling to upgrade the quality of life for the crew.

A 3-year retrospective review of hospital admissions involving opioid toxicity in South Australia.

This article aims to characterise hospital admissions involving opioid toxicity across South Australia to guide future implementation and evaluation of risk mitigation strategies. International Classification of Diseases, 10th Edition codes (T40.0-T40.4) were used to identify admissions involving pre-hospital opioid toxicity in public hospitals across South Australia from 1 June 2017 to 30 August 2020. Demographic and episode of care data were extracted and summarised using descriptive statistics. Admission cost estimates were calculated using Independent Hospital Pricing Authority data. A total of 2046 cases met the criteria for inclusion; over half (56%) were female and median age on admission was 44 years (interquartile range 27 years). Where opioid toxicity was the primary diagnosis, 70% of admissions did not specify the responsible opioid and 23% were related to heroin use. One-fifth of admissions occurred outside of metropolitan Adelaide. Individuals living in an area of relative socio-economic disadvantage were over-represented. Over half of admissions required a stay >24 h; 19% were admitted for ≥5 days, 22% required intensive care and ~10% required mechanical ventilation. The total estimated cost of admissions involving opioid toxicity in South Australia over the 3-year period was $18,230,546.50, equating to $5.6 million per annum. These findings highlight the significant personal, fiscal, and systemic impacts of opioid toxicity-related hospital admissions in South Australia and provide a baseline to evaluate the effectiveness of initiatives to reduce opioid-related harm, including real-time prescription monitoring and take-home naloxone supply.

Web-Based Application for Sales of Water Depot Accessories and Equipment at VQ Air Store

An online shop website refers to a website that is equipped with an e-commerce system, allowing users to engage in transactions. In addition to the requirement of loading product offerings, the website must also possess inventory management capabilities, automated shipping cost estimates, and support for manual and automated payments using a Payment Gateway. This research aims to develop an application that facilitates the sale of accessories and supplies required for a gallon of drinking water depot at the VQ WATER store. This application will be directly linked to the website and provide comprehensive information about the VQ Water Depot Gallon Shop, including prices and purchasing instructions. The aim is to enhance user convenience when locating depot equipment. The research methodology employed in this final project is the Waterfall approach, which incorporates UML modeling. The attained outcomes manifest as an internet-based sales application accessible through a website, facilitating the exploration of depot equipment requirements. The final project entails the development of a website-based online sales application that facilitates the convenient purchase of gallon depot products. This application provides users with visual representations of accessories and equipment, their respective costs and purchasing instructions.

Construction of Virtual Simulation Experiment Platform for Intelligent Construction Based on Statistical Machine Learning System Modelling

In the construction of a virtual simulation experiment platform for intelligent construction, cutting-edge technologies converge to revolutionize traditional project management methodologies. By harnessing the power of virtual reality, statistical modeling, and machine learning, this platform empowers stakeholders to predict, optimize, and simulate construction projects with unprecedented accuracy and efficiency. This paper introduces the Virtual Statistical Machine Learning (VS-ML) platform and demonstrates its application in intelligent construction processes. Through comprehensive experimentation and simulation, the VS-ML platform accurately estimates construction project parameters, optimizes resource utilization, schedules tasks efficiently, and classifies project outcomes with high accuracy. Numerical results from our study showcase the platform's effectiveness in various aspects of construction project management. For instance, in construction projects estimation, scenarios ranging from Scenario 1 to Scenario 10 exhibit project durations between 100 to 150 days, cost estimates ranging from $470,000 to $550,000, and safety ratings varying from "Good" to "Excellent". Furthermore, labor efficiency and material waste estimations across scenarios demonstrate percentages ranging from 85% to 93% and 3% to 7%, respectively, with corresponding safety ratings. Additionally, task computations elucidate the durations, start dates, end dates, and resource allocations for individual tasks within construction projects. Lastly, classification results exhibit the predicted probabilities and class labels for samples, showcasing the platform's ability to accurately predict project outcomes. Overall, the findings underscore the potential of VS-ML in revolutionizing traditional construction practices through data-driven approaches, leading to improved project management, cost savings, and enhanced safety standards in the construction industry.

Comparative Analysis Of Cost Calculation With Activity-Based Costing And Traditional Methods

In the modern business world, accurate cost calculation is crucial for effective decision-making. Traditional methods often fail to reflect actual resource consumption, leading to inaccurate pricing and resource allocation. This research aims to analyze the use of Activity-Based Costing (ABC) and compare it with traditional methods. The research approach is qualitative, employing literature review methods. The findings show that ABC, which allocates costs based on activities performed, provides more realistic and accurate cost estimates. ABC helps companies identify non-value-added activities, improve efficiency, and reduce waste. Although ABC is more complex and expensive to implement, it provides more precise cost information, supporting optimal decision-making regarding pricing, product strategy, and resource allocation. Conversely, traditional methods are simpler but often result in less accurate cost allocation and do not reflect variations in activity consumption. Therefore, ABC is superior in terms of accuracy and effectiveness in identifying waste and inefficient activities, despite requiring technological support and higher initial costs.

Net Monetary Benefit Lines Augmented with Value-of-Information Measures to Present the Results of Economic Evaluations under Uncertainty.

Methods to present the result of cost-effectiveness analyses under parameter uncertainty include cost-effectiveness planes (CEPs), cost-effectiveness acceptability curves/frontier (CEACs/CEAF), expected loss curves (ELCs), and net monetary benefit (NMB) lines. We describe how NMB lines can be augmented to present NMB values that could be achieved by reducing or resolving parameter uncertainty. We evaluated the ability of these methods to correctly 1) identify the alternative with the highest expected NMB and 2) communicate the magnitude of parameter and decision uncertainty. We considered 4 hypothetical decision problems representing scenarios with high variance or correlated cost and effect estimates and alternatives with similar cost-effectiveness ratios. We used these decision problems to demonstrate the limitations of existing methods and the potential of augmented NMB lines to resolve these issues. CEPs and CEACs/CEAF could falsely imply the lack of sufficient evidence to identify the optimal option if cost and effect estimates have high variance, are correlated across alternatives, or when alternatives have similar cost-effectiveness ratios. The augmented NMB lines and ELCs can correctly identify the option with the highest expected NMB and communicate the potential benefit of resolving uncertainties. Like ELCs, the augmented NMB lines provide information about the value of resolving parameter uncertainties, but augmented NMB lines may be easier to interpret for decision makers. Our analysis supports recommending the augment NMB lines as an important method to present the results of economic evaluation studies under parameter uncertainty. The results of cost-effectiveness analyses (CEAs) when the cost and effect estimates of alternatives are uncertain are commonly presented using cost-effectiveness planes (CEPs), cost-effectiveness acceptability curves/frontier (CEACs/CEAF), and expected loss curves (ELCs).Although currently not often used, net monetary benefit (NMB) lines could present the results of cost-effectiveness to identify the alternative with the highest expected NMB values given the current level of uncertainty. Furthermore, NMB lines can be augmented to 1) show metrics of value of information, which measure the value of additional research to reduce or eliminate the decision uncertainty, and 2) display the confidence intervals along the NMB lines to ensure that NMB values are estimated accurately using a sufficiently large number of parameter samples.Using several decision problems, we demonstrate the limitation of existing methods to present the results of CEAs under parameter uncertainty and how augmented NMB lines could resolve these issues.Our analysis supports recommending augmented NMB lines as an important method to present the results of CEA under uncertainty since they 1) correctly identify the alternative with the highest expected NMB value given the current evidence, 2) provide information about the potential value of additional research to improve the decision by reducing or resolving uncertainty in model parameters, 3) assist the analysis to visually ensure that enough parameter samples are used to estimate the expected NMB of alternatives, and 4) are easier to interpret for decision makers compared with other methods.

Cost of cardiovascular disease events in patients with and without type 2 diabetes and factors influencing cost: a retrospective cohort study

BackgroundCardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) are non-communicable diseases that impose a significant economic burden on healthcare systems, particularly in low- and middle-income countries. The purpose of this study was to evaluate the hospital treatment cost for cardiovascular disease events (CVDEs) in patients with and without diabetes and identify factors influencing cost.MethodWe conducted a retrospective, cross-sectional study using administrative data from three public tertiary hospitals in Malaysia. Data for hospital admissions between 1 March 2019 and 1 March 2020 with International Classification of Diseases 10th Revision (ICD-10) codes for acute myocardial infarction (MI), ischaemic heart disease (IHD), hypertensive heart disease, stroke, heart failure, cardiomyopathy, and peripheral vascular disease (PVD) were retrieved from the Malaysian Disease Related Group (Malaysian DRG) Casemix System. Patients were stratified by T2DM status for analyses. Multivariate logistic regression was used to identify factors influencing treatment costs.ResultsOf the 1,183 patients in our study cohort, approximately 60.4% had T2DM. The most common CVDE was acute MI (25.6%), followed by IHD (25.3%), hypertensive heart disease (18.9%), stroke (12.9%), heart failure (9.4%), cardiomyopathy (5.7%) and PVD (2.1%). Nearly two-thirds (62.4%) of the patients had at least one cardiovascular risk factor, with hypertension being the most prevalent (60.4%). The treatment cost for all CVDEs was RM 4.8 million and RM 3.7 million in the T2DM and non-T2DM group, respectively. IHD incurred the largest cost in both groups, constituting 30.0% and 50.0% of the total CVDE treatment cost for patients with and without T2DM, respectively. Predictors of high treatment cost included male gender, non-minority ethnicity, IHD diagnosis and moderate-to-high severity level.ConclusionThis study provides real-world cost estimates for CVDE hospitalisation and quantifies the combined burden of two major non-communicable disease categories at the public health provider level. Our results confirm that CVDs are associated with substantial health utilisation in both T2DM and non-T2DM patients.

Dynamic financial modeling and feasibility studies for affordable housing policies: A conceptual synthesis

The escalating demand for affordable housing necessitates innovative approaches to financial modeling and feasibility analysis to ensure the success and sustainability of housing policies. This abstract presents a conceptual synthesis of dynamic financial modeling and feasibility studies, exploring their pivotal role in shaping effective affordable housing policies. Dynamic financial modeling involves creating flexible and adaptive financial models that account for varying economic conditions, policy changes, and market fluctuations. These models are essential for predicting financial outcomes, assessing the viability of housing projects, and guiding investment decisions. By incorporating real-time data and scenario analysis, dynamic financial models help stakeholders understand potential risks and opportunities, enabling more informed decision-making. Key components of dynamic financial modeling include cash flow analysis, sensitivity testing, and scenario planning, which together provide a comprehensive view of a project's financial viability over time. Feasibility studies complement financial modeling by evaluating the practical aspects of affordable housing projects, such as site selection, regulatory compliance, and market demand. These studies assess the alignment of housing projects with community needs and regulatory requirements, ensuring that proposed developments are both feasible and beneficial. Feasibility studies often include market analysis, cost estimates, and risk assessments, providing a detailed understanding of project constraints and opportunities. The synthesis of dynamic financial modeling and feasibility studies offers a robust framework for evaluating affordable housing policies. It allows for the integration of financial projections with practical considerations, ensuring that policies are not only economically viable but also aligned with social and regulatory objectives. Case studies from various regions demonstrate the effectiveness of this approach in identifying successful strategies and addressing common challenges. In conclusion, dynamic financial modeling and feasibility studies are crucial tools for developing and implementing effective affordable housing policies. They provide a comprehensive understanding of financial and practical dimensions, facilitating informed decision-making and enhancing the sustainability and impact of affordable housing initiatives. This conceptual synthesis underscores the importance of integrating these methodologies to achieve long-term success in addressing housing affordability challenges. Keywords: Policies, Affordable Housing, Feasibility Studies, Dynamic Financial Modeling, conceptual Synthesis.

Analisis Perawatan Mesin Bubut Baoji 1660A dengan Metode Reliability Centered Maintenance (RCM)

PT Swadaya Graha is a company that works in the field of construction, fabrication and steel manufacturing. One of the machines used in carrying out its operational activities is the Baoji 1660A lathe. The lathe often fails during the production process. This study aims to determine critical components, critical component reliability values, maintenance scheduling intervals and maintenance cost estimates of the Baoji 1660A lathe using the Reliability Centered Maintenance (RCM) method. The results of this study show that the electrical system with an RPN value of 240, v-belt with an RPN value of 168, and gearbox with an RPN value of 60 as critical components. The reliability value of the electrical system approaches 0.5 at a time interval of 640 hours or 80 working days, the reliability value of the v-belt approaches 0.5 at a time interval of 208 hours or 26 working days, and the reliability value of the gearbox approaches 0.5 at a time interval of 384 hours or 48 working days. From the scheduling interval, the estimated total maintenance cost in 2025 is IDR 7,019,092, in 2026 is IDR 3,615,386, in 2027 is IDR 4,187,484, in 2028 is IDR 4,276,752, and in 2029 is IDR 3,615,386.

Software cost estimation tool: A App based application, estimate the cost of software project

This paper presents the design and implementation of a software cost estimation tool integrated into a mobile application developed using Flutter. The tool incorporates various techniques for software cost estimation, including expert judgment, function point analysis, 3D point analysis, and the COCOMO model. The purpose of the program is to give software engineers and project managers a practical and effective tool for calculating the time and money needed for software development projects. The paper provides a thorough explanation of each estimation technique’s implementation, along with a discussion of the app’s main features and functionalities. Because of the app’s intuitive and user-friendly design, users can quickly enter project data and get precise cost estimates. The tool’s efficacy is assessed using case studies and contrasts with other software cost estimation methods currently in use. The outcomes show that the app can produce trustworthy and precise cost estimates, which makes it an important resource for software development projects.

Thermo-economic examination of ocean heat-assisted pumped thermal energy storage system using transcritical CO2 cycles

In response to the challenges posed by the growth of renewable energy electricity to the security of the power grid, energy storage technology has rapidly developed in recent years. Pumped thermal energy has been recognized as an attractive massive energy storage technology with the superiority being independent of site. An ocean heat-assisted pumped thermal energy storage system using transcritical CO2 cycles is proposed in this study. State-of-the-art thermo-economic models are developed and described to simulate the system processes and conduct cost estimates. The originality of this system lies in the ability to effectively improve its efficiency by configuring seawater at different depths at the low temperature end of the system. Research has found that, if the seawater used in the charge process always comes from the surface of the ocean, the system efficiencies are 48.1 %, 61.3 % and 66.4 % respectively when the depth of seawater during the discharge process are 50 m, 100 m and 1000 m. When the seawater employed in the entire system comes from 1000 m underwater, the efficiency of the system is only 53.5 %. In addition, for the 50 MW/10 h system, the minimum value of levelized cost of storage is 0.143 $/kWh. The uncertainty analysis of cost for the proposed system is carried out using the Monte Carlo method. Results show that with the charge/discharge duration ratio rising from 4 h/10 h to 10 h/4h, the total capital cost falls from 245 ± 76 M$ to 102 ± 34 M$ and the levelized cost of storage reaches the minimum value when this ratio is 1. The larger the energy storage capacity, the lower the levelized cost of storage. In summary, the system has shown satisfactory results in both thermodynamics and economic performance, and further research is worthwhile in the future.

Medical costs of lung cancer by stage, histology and first-line treatment modality in the Netherlands (2012–2021)

IntroductionLung cancer is a leading cause of mortality worldwide, with lung cancer treatment presenting a significant financial burden. The treatment landscape has recently shifted, seeing an increase in targeted- and immunotherapies. Such treatments are expensive, but estimates of the medical costs of the lung cancer treatment pathway largely predate their introduction. MethodsWe link medical expenditures of individuals resident in the Netherlands (n = 19.2 m) for 2013–2021 to tumour-level (n = 137,129, incident 2012–2021) Netherlands Cancer Registry data. We estimate lung cancer-attributable costs by phase of care (initial, continuing and terminal), stratified by cancer stage and histology, and observe trends in medical costs over time. ResultsWe estimate mean costs over the lung cancer treatment pathway to be €48,443 per patient. Total medical costs are highest in the initial phase, followed by the terminal and continuing phase. Monthly treatment for stage IV lung cancer is significantly more expensive than for early-stage disease (€8293 per month of initial care relative to €3228 for stage IA). Stage IV lung cancer has become significantly more expensive to treat 2018–2021 relative to 2013–2017, with monthly expenditures rising 55 % in initial care and 148 % in continuing care. Population-wide, we find €900.6 million spent on lung cancer care in 2021, €433 million more than in 2016, of which €307.3 million is attributed to per-patient expenditure trends. ConclusionsTreatment advances are quickly inflating medical costs for late-stage lung cancer. Policy makers should carefully evaluate the cost-effectiveness of novel treatments, and incorporate stage-specific treatment costs in evaluating interventions for early detection.

Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective

In today’s industry, H2 is mostly produced from fossil fuels such as natural gas (NG), oil, and coal through various processes. However, all these processes produce both carbon dioxide (CO2) as well as H2, making them questionable in terms of climate change mitigation efforts. In addition to efforts to increase the conversion efficiency of green H2 technologies, work is also underway to make H2 production from fossil fuels more environmentally friendly by reducing/avoiding CO2 emissions. In this framework, these technologies are combined with geologic carbon storage. In a further step, the use of depleted hydrocarbon reservoirs for in situ H2 production is being investigated, with the co-generated CO2 remaining permanently in the reservoir. The objective of this paper is to provide a brief overview of the technologies that can be used to produce H2 from depleted and depleting hydrocarbon reservoirs (DHRs) in various ways. We evaluate the required processes from a reservoir engineering perspective, highlighting their potential for H2 generation and their technology readiness level (TRL) for applications. We also investigate the possibility of permanently storing the co-produced CO2 in the reservoir as a means of mitigating emissions. In addition, we provide a preliminary cost analysis to compare these methods with conventional hydrogen production techniques, as well as an assessment of operational risks and associated cost estimates.

Smart e-waste management: a revolutionary incentive-driven IoT solution with LPWAN and edge-AI integration for environmental sustainability.

Managing e-waste involves collecting it, extracting valuable metals at low costs, and ensuring environmentally safe disposal. However, monitoring this process has become challenging due to e-waste expansion. With IoT technology like LoRa-LPWAN, pre-collection monitoring becomes more cost-effective. Our paper presents an e-waste collection and recovery system utilizing the LoRa-LPWAN standard, integrating intelligence at the edge and fog layers. The system incentivizes WEEE holders, encouraging participation in the innovative collection process. The city administration oversees this process using innovative trucks, GPS, LoRaWAN, RFID, and BLE technologies. Analysis of IoT performance factors and quantitative assessments (latency and collision probability on LoRa, Sigfox, and NB-IoT) demonstrate the effectiveness of our incentive-driven IoT solution, particularly with LoRa standard and Edge AI integration. Additionally, cost estimates show the advantage of LoRaWAN. Moreover, the proposed IoT-based e-waste management solution promises cost savings, stakeholder trust, and long-term effectiveness through streamlined processes and human resource training. Integration with government databases involves data standardization, API development, security measures, and functionality testing for efficient management.

Longitudinal economic burden of incident complications among metabolic syndrome populations

BackgroundThis study quantifies the longitudinal economic burden for a wide spectrum of incident complications, metabolic syndrome (MS)-related risk factors, and comorbidities in patients with MS.MethodsThis retrospective study utilized linked data from the 2013 National Health Interview Survey and the 2012–2021 National Health Insurance Research Database to identify MS individuals and their characteristics. The incidence rate of each complication was calculated as the number of complication events in the study period divided by the total person-years during follow-up. The healthcare costs of complications were analyzed using a generalized estimating equation model to determine the cost impact of complications after adjustment for patients’ characteristics. Sensitivity analyses on variables with high missing rates (i.e., cause of death, body mass index) were performed.ResultsAmong 837 identified MS individuals over 8.28 (± 1.35) years of follow-up, the most frequent complications were microvascular diseases (incidence rate for nephropathy/retinopathy/neuropathy: 6.49/2.64/2.08 events per 100 person-years), followed by cardiovascular diseases (2.47), peripheral vascular diseases (2.01), and cancers (1.53). Death was the costliest event (event-year cost per person: USD 16,429) and cancers were the most expensive complications (USD 9,127−11,083 for non-MS- and MS-related cancers). Developing non-MS/MS-related cancers, cardiovascular diseases, and obesity-related medical conditions increased annual costs by 273% (95% CI: 181−397%)/175% (105−269%), 159% (118−207%), and 140% (84−214%), respectively. Microvascular diseases had the lowest cost impact on annual costs (i.e., 27% [17−39%]/27% [11−46%]/24% [11−37%] increases for nephropathy/neuropathy/retinopathy, respectively). Having existing comorbidities increased annual costs by 20% (osteoarthritis) to 108% (depression). Having morbid obesity (i.e., body mass index ≥ 35 kg/m2) increased annual costs by 58% (30−91%).ConclusionsThe economic burden from costly incident complications (i.e., cardiovascular diseases, peripheral vascular diseases, cancers), MS-related risk factors (i.e., morbid obesity), and comorbidities (i.e., depression) highlight the urgent need for early intervention to prevent MS and its progression. The comprehensive cost estimates reported in this study can facilitate the parameterization of economic analyses to identify cost-effective interventions for these patients.

Estimating Stockout Costs and Optimal Stockout Rates: A Case on the Management of Ugly Produce Inventory

Efficiently managing inventories requires an accurate estimation of stockout costs. This estimation is complicated by challenges in determining how to compensate consumers monetarily to ensure they will maintain the same level of utility they would have obtained had stockouts not occurred. This paper presents an analysis of these compensation costs as applied to the design of optimal stockout rates by an online retailer marketing to consumers aesthetically substandard fruits and vegetables rejected by mainstream grocery chains. Because growers face high uncertainty in their harvesting conditions and in the aesthetic quality of their crops and there are little data on hand to predict the value consumers attach to the availability of subpar produce, it is difficult to optimally match the supply of these products with consumers’ demand. Our analysis draws from a multiple discrete-continuous extreme value (MDCEV) choice model to calculate consumer compensations that the retailer can use to estimate the opportunity costs of stockouts to manage its inventory. We show that not taking into account these compensation costs could unduly inflate the optimal stockout rates for these products. Armed with these compensation cost estimates, we show how these costs can serve as incentives for retailers to source greater inventory amounts of imperfect produce from growers and how this will ultimately translate into less waste in the supply chain. This paper was accepted by Charles Corbett, operations management. Funding: Funding from Agriculture and Food Research Initiative (National Institute for Food and Agriculture, USDA) [Grant 2016-09900] is gratefully acknowledged. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03174 .