Cost Variability Research Articles

The availability of gonadotropin therapy from FDA-approved pharmacies for men with hypogonadism and infertility.

Recent changes to the Biologics Price Competition and Innovation Act of 2009 have created barriers to accessing therapy for men utilizing gonadotropins for hypogonadism and infertility. In this study we sought to investigate ways to decrease disparities in the treatment of male hypogonadism by increasing access to gonadotropin therapy by identifying 503b outsourcing pharmacies which currently provide gonadotropin therapy. A review of 503b compounding pharmacies was performed using the online published registry available from the US Food and Drug Administration (FDA). Each pharmacy was contacted regarding their ability to provide gonadotropin therapy. Pharmacies were also queried regarding the impact of FDA-related legal changes and cost considerations. The study outcomes were the number and location of FDA-approved 503b compounding pharmacies supplying human chorionic gonadotrophin (hCG) and/or follicle-stimulating hormone (FSH) for the treatment of male hypogonadism and infertility. The 81 503b-compounding pharmacies approved by the FDA to produce hCG and FSH therapy were identified using the FDA registry. Seventy-five of the 81 pharmacies responded to the survey (response rate92.6%). Of the contacted pharmacies, 5 provided hCG (6.67%). Of the pharmacies offering compounded hCG, 4 offered FSH. No additional pharmacies offered compounded FSH. Eight pharmacies had previously provided hCG and FSH. Six of the 8 pharmacies that stopped making hCG and FSH cited the 2020 FDA mandate as the reason for halting compounding services. Of the 75 pharmacies that responded, only 1 pharmacy provided the cost for FSH ($287 per 100-IU vial), and 3 pharmacies provided the cost for hCG ($50-$83 per 10 000-IU vial). There are few FDA-approved outsourcing pharmacies currently providing male gonadotropin therapy, and increasing awareness of these pharmacies may decrease barriers to care for patients with male hypogonadism and infertility. The strengths of this article are the clinical utility of the data presented, as this article may serve as a tool for clinicians to increase patient access to therapy. All FDA-approved 503b outsourcing pharmacies were contacted, and 92.6% participated in this project. Limitations of this article were the following: no non-FDA-approved compounding pharmacies such as 503a pharmacies were contacted, participant-reported outcomes were utilized, and only 3 contacted outsourcing pharmacies provided a cost for FSH or hCG, allowing for an unknown degree of cost variability between outsourcing pharmacies. There currently exists limited access to FDA-approved compounded gonadotropin therapies for hypogonadism and male infertility, and these results demonstrate the barriers to hCG and FSH access and the need for additional treatment options for this vulnerable patient population.

Comparison of COVID-19 hospitalization costs across care pathways: a patient-level time-driven activity-based costing analysis in a Brazilian hospital

BackgroundThe COVID-19 pandemic raised awareness of the need to better understand where and how patient-level costs are incurred in health care organizations, as health managers and other decision-makers need to plan and quickly adapt to the increasing demand for health care services to meet patients’ care needs. Time-driven activity-based costing offers a better understanding of the drivers of cost throughout the care pathway, providing information that can guide decisions on process improvement and resource optimization. This study aims to estimate COVID-19 patient-level hospital costs and to evaluate cost variability considering the in-hospital care pathways of COVID-19 management and the patient clinical classification.MethodsThis is a prospective cohort study that applied time-driven activity-based costing (TDABC) in a Brazilian reference center for COVID-19. Patients hospitalized during the first wave of the disease were selected for their data to be analyzed to estimate in-hospital costs. The cost information was calculated at the patient level and stratified by hospital care pathway and Ordinal Scale for Clinical Improvement (OSCI) category. Multivariable analyses were applied to identify predictors of cost variability in the care pathways that were evaluated.ResultsA total of 208 patients were included in the study. Patients followed five different care pathways, of which Emergency + Ward was the most followed (n = 118, 57%). Pathways which included the intensive care unit presented a statistically significant influence on costs per patient (p < 0.001) when compared to Emergency + Ward. The median cost per patient was I$2879 (IQR 1215; 8140) and mean cost per patient was I$6818 (SD 9043). The most expensive care pathway was the ICU only, registering a median cost per patient of I$13,519 (IQR 5637; 23,373) and mean cost per patient of I$17,709 (SD 16,020). All care pathways that included the ICU unit registered a higher cost per patient.ConclusionsThis is one of the first microcosting study for COVID-19 that applied the TDABC methodology and demonstrated how patient-level costs vary as a function of the care pathways followed by patients. These findings can be used to develop value reimbursement strategies that will inform sustainable health policies in middle-income countries such as Brazil.

Cooperation or competition? The remanufacturing strategy with quality uncertainty in construction machinery industry

Construction machinery remanufacturing is energy-consuming and susceptible to quality uncertainty of used products, which increases the variability of cost and carbon emission and affects the production decisions of the original equipment manufacturer (OEM) in return. Therefore, surviving in such a situation by cooperating or competing with the third-party remanufacturer (TPR) is a vital decision for OEM. Incorporating the quality uncertainty, we first establish three remanufacturing models: integrated, outsourcing, and authorization remanufacturing. Secondly, the production decisions of the three models are derived and compared under different strategies (the complete remanufacturing strategy, CRS; the partial remanufacturing strategy, PRS; no remanufacturing strategy, NRS). Finally, the optimal remanufacturing mode and strategy are discussed according to the quantity of new and remanufactured products, profits and carbon emissions. The results suggest: (1) in terms of quantity of new products, integrated remanufacturing can be selected under CRS, and authorization remanufacturing under PRS; integrated remanufacturing generates the largest quantity of remanufactured products; (2) OEM has the highest profit in integrated remanufacturing whichever CRS or PRS is; (3) authorization remanufacturing generates the lowest carbon emissions while integrated remanufacturing generates the highest under CRS, which is the opposite under PRS; (4) as the quality threshold increases, the quantity of new and remanufactured products, profits and carbon emissions first increase and then decrease under CRS; however, new products and total carbon emissions increase, and remanufactured products and profits decrease under PRS; (5) as carbon price increases, the quantity of new products decreases under CRS and PRS, while the remanufactured ones decrease under CRS and increase under PRS; as a discount factor for remanufactured products increases, the quantity of new products increases under CRS and decreases under PRS, while remanufactured products increase under CRS and PRS.

8. Learning Curve of Wrist Level Tendon Repairs using a Novel Tendon Stapler Device vs. Traditional Suture Methods

Background: Tendon repairs in spaghetti wrists, wrist level replantation, or vascularized composite hand allotransplantations are time-consuming. Experience and skill greatly influence overall time in the operating room. A novel tendon stapler device (TSD) has the potential to mitigate these discrepancies among surgeons of varying levels of experience by enhancing the speed and consistency of primary tendon repairs. Methods: Participants of varying levels of surgical experience (novice, intermediate, expert) performed tendon repairs via suture method or TSD. Comparisons were performed on wrist level flexors and extensors from human cadaver arms. Suture repairs were performed using 3.0 braided polyester modified Kessler with horizontal mattress for a 4-strand repair. On matched, contralateral donor arms, TSD repairs were completed with a single simple 5.0 polypropylene stitch to approximate the tendon ends prior to device deployment. All repairs were timed by a non-participating data recorder. Learning curves were compared across repair types and surgeons with scatterplots fitted with Loess curves. Statistical analyses utilized one-way ANOVA, Tukey pairwise comparisons, two-sided independent samples t-test, and Fisher’s exact test. Results: A total of 228 tendon repairs from 12 donor arms were analyzed. Mean suture repair times were 3.25, 4.08, and 4.46 minutes for expert, intermediate, and novice, respectively with the expert being faster than the other two groups (p≤0.001). Mean TSD repair times were 1.32, 1.31, 1.28 minutes for expert, intermediate, and novice respectively, with no difference between groups. TSD repairs were significantly faster than suture repair (p<0.01). Suture repairs decreased to stable or “learned” level around repair #30, and then demonstrated small increases at the end of the session, suggesting user fatigue. TSD repairs showed faster, less variable time that stabilized around repair #23, consistent with a more efficient learning curve. Educational time required for both novice and intermediate groups to achieve competency was higher for the suture technique (106 and 54 minutes) than for the TSD (60 and 38 minutes). Conclusion: The TSD demonstrates tremendous potential for complex multi-tendon traumas by drastically reducing the learning curve, operative and warm ischemia times, overall cost, and surgeon variability based on experience and repair strength.

Male harm suppresses female fitness, affecting the dynamics of adaptation and evolutionary rescue.

One of the most pressing questions we face as biologists is to understand how climate change will affect the evolutionary dynamics of natural populations and how these dynamics will in turn affect population recovery. Increasing evidence shows that sexual selection favors population viability and local adaptation. However, sexual selection can also foster sexual conflict and drive the evolution of male harm to females. Male harm is extraordinarily widespread and has the potential to suppress female fitness and compromise population growth, yet we currently ignore its net effects across taxa or its influence on local adaptation and evolutionary rescue. We conducted a comparative meta-analysis to quantify the impact of male harm on female fitness and found an overall negative effect of male harm on female fitness. Negative effects seem to depend on proxies of sexual selection, increasing inversely to the female relative size and in species with strong sperm competition. We then developed theoretical models to explore how male harm affects adaptation and evolutionary rescue. We show that, when sexual conflict depends on local adaptation, population decline is reduced, but at the cost of slowing down genetic adaptation. This trade-off suggests that eco-evolutionary feedback on sexual conflict can act like a double-edged sword, reducing extinction risk by buffering the demographic costs of climate change, but delaying genetic adaptation. However, variation in the mating system and male harm type can mitigate this trade-off. Our work shows that male harm has widespread negative effects on female fitness and productivity, identifies potential mechanistic factors underlying variability in such costs across taxa, and underscores how acknowledging the condition-dependence of male harm may be important to understand the demographic and evolutionary processes that impact how species adapt to environmental change.

A Pharmacoeconomic Outlook of the Biological Drugs Marketed in India: A Cost Variance Analysis.

Introduction Biologic drugs are used to treat various illnesses like autoimmune diseases, cancers, hormonal irregularities, anemia, etc., and to prevent various diseases as vaccines. Though various biologic drugs are already available, they are still not within reach of the common man due to financial constraints. Through many search engines, studies evaluating the cost variation of different brands of biologics were investigated; however, only a few studies that address this problem were found. Hence, this study was planned with the objective of addressing the cost variation of various brands of biologic medicines available in the Indian market. Methods The website for the Current Index of Medical Specialties (CIMS) for India's location was used to obtain the prices of the different brands of biologic medicines in Indian National Rupee (INR) currency, which different manufacturers market with identical forms in strength and dosage. The percentage cost variation and cost ratio were calculated with the help of the minimum and maximum prices of various brands of biologic drugs. Results The prices of biologics belonging to six different classes that are available in 23 formulations were analyzed. The highest cost variability was shown by pegfilgrastim 6 mg at 1,022.92%, and the minimum-cost variation was shown by darbepoetin alfa 200 mcg at 13.07%. Conclusion Our research found a vast variance in the costs of various brands of biologic medicines in India. The government should address this cost variation problem by developing various policies, such as breaking the monopoly of manufacturers, providing tax incentives to nonprofit generic medicine manufacturers, and incorporating more biologic drugs under the protection of the Drugs Prices Control Order (DPCO).

Integrated risk averse and possibilistic humanitarian logistic model with social costs and material convergence

A stepwise development is proposed for the construction of a model that comprehensively solves the disaster planning and response problem. In addition to traditional logistics costs, the suggested approach incorporates response time sensitivity, social costs related to post-disaster deprivation, the impact of material convergence, and measurable and epistemic uncertainty. Starting from a stochastic model based on previously published scenarios, the global costs of the scenarios are minimized using the expected value of the parameters, in a risk-averse bi-objective function that also optimizes the robustness of the model by minimizing the variability of the parameters whose probabilities are known. Finally, the complexity arising from imprecise parameter values with unknown probabilities is addressed by using chance-constrained fuzzy programming to optimize the risk associated with inter-scenario cost variability and feasibility of the constraints. This formulation is applied to real case study data, demonstrating that both the overall costs and the social cost component of the costs decrease progressively from the base formulation. The model allows the decision-maker to test the sensitivity of the results and thus set the uncertainty parameters and the incorporation of multiple objectives accordingly.

Cost-effectiveness of Fibrinogen Concentrate vs Cryoprecipitate for Treating Acquired Hypofibrinogenemia in Bleeding Adult Cardiac Surgical Patients

Excessive bleeding requiring fibrinogen replacement is a serious complication of cardiac surgery. However, the relative cost-effectiveness of the 2 available therapies-fibrinogen concentrate and cryoprecipitate-is unknown. To determine cost-effectiveness of fibrinogen concentrate vs cryoprecipitate for managing active bleeding in adult patients who underwent cardiac surgery. A within-trial economic evaluation of the Fibrinogen Replenishment in Surgery (FIBERS) randomized clinical trial (February 2017 to November 2018) that took place at 4 hospitals based in Ontario, Canada, hospitals examined all in-hospital resource utilization costs and allogeneic blood product (ABP) transfusion costs incurred within 28 days of surgery. Participants included a subset of 495 adult patients from the FIBERS trial who underwent cardiac surgery and developed active bleeding and acquired hypofibrinogenemia requiring fibrinogen replacement. Fibrinogen concentrate (4 g per dose) or cryoprecipitate (10 units per dose) randomized (1:1) up to 24 hours postcardiopulmonary bypass. Effectiveness outcomes included number of ABPs administered within 24 hours and 7 days of cardiopulmonary bypass. ABP transfusion (7-day) and in-hospital resource utilization (28-day) costs were evaluated and a multivariable net benefit regression model built for the full sample and predefined subgroups. Patient level costs for 495 patients were evaluated (mean [SD] age 59.2 [15.4] years and 69.3% male.) Consistent with FIBERS, ABP transfusions and adverse events were similar in both treatment groups. Median (IQR) total 7-day ABP cost was CAD $2280 (US dollars [USD] $1697) (CAD $930 [USD $692]-CAD $4970 [USD $3701]) in the fibrinogen concentrate group and CAD $2770 (USD $1690) (IQR, CAD $1140 [USD $849]-CAD $5000 [USD $3723]) in the cryoprecipitate group. Median (interquartile range) total 28-day cost was CAD $38 180 (USD $28 431) $(IQR, CAD $26 350 [USD $19 622]-CAD $65 080 [USD $48 463]) in the fibrinogen concentrate group and CAD $38 790 (USD $28 886) (IQR, CAD $26 180 [USD $19 495]-CAD $70 380 [USD $52 409]) in the cryoprecipitate group. After exclusion of patients who were critically ill before surgery (11%) due to substantial variability in costs, the incremental net benefit of fibrinogen concentrate vs cryoprecipitate was positive (probability of being cost-effective 86% and 97% at $0 and CAD $2000 (USD $1489) willingness-to-pay, respectively). Net benefit was highly uncertain for nonelective and patients with critical illness. Fibrinogen concentrate is cost-effective when compared with cryoprecipitate in most bleeding adult patients who underwent cardiac surgery with acquired hypofibrinogenemia requiring fibrinogen replacement. The generalizability of these findings outside the Canadian health system needs to be verified.

Harnessing the Power of Artificial Intelligence: Revolutionizing Free Flaps Monitoring in Head and Neck Tumor Treatment.

Treating head and neck tumors has undergone significant advancements, focusing on improving the patient's quality of life after treatment. Reconstructive surgical techniques with free flaps have been vital in restoring anatomy, function, and aesthetics, reducing morbidity from locoregional treatments. Monitoring free flaps is crucial to detect and address any vascular compromise that may lead to flap failure. Various monitoring techniques have been employed in free flap monitoring. However, standardizing them presents a challenge due to the need for more consensus among surgeons and variability in techniques, costs, and training requirements. Artificial intelligence (AI) shows promise in standardizing monitoring practices and reducing the operator-dependent variability. AI techniques have been explored to improve monitoring and early detection of complications in free flap surgeries, and they have shown high accuracy in analyzing images and predicting flap outcomes. Despite the potential benefits, implementing AI in free flap monitoring remains challenging. Standardization of input data, interpretation, cost, training, and accounting for patient and flap variability are crucial considerations. Further research, including multicenter studies, validation, and collaboration amongst clinicians, researchers, and AI experts is needed to overcome these challenges.

Cost analysis of antipsychotics marketed in India – A pharmacoeconomic study

Background: Psychiatric disorders are one the leading causes of non-fatal disease in India. Among them, schizophrenia is one of the most common, distressing, and costly psychiatric disorders worldwide. There are large price differences between different brands of the same antipsychotics marketed and prescribed in India. Escalating drug costs are one of the causes of drug non-compliance, which can lead to adverse health outcome. Aims and Objective: The purpose of the study was to analyze the cost variability of different brands of oral antipsychotics marketed in India. Materials and Methods: The present study was an observational analytical one. The highest and lowest price of each antipsychotic drugs in the same strength, marketed by different pharmaceutical companies, were collected from the current index of medical specialties (CIMS), January to April 2023. Calculations were done in Indian Rupees per 10 tablets/capsules. For each antipsychotic drug, the cost ratio and percentage of price variation (PPV) were calculated. Results: The costs of a total of 14 drugs (10 single and 4 combined preparations) available in 337 dissimilar strengths were analyzed. Among typical antipsychotics, chlorpromazine 50 mg had the highest cost ratio and PPV of 5.36% and 435.62%, while haloperidol in the strength of 20 mg had the lowest cost ratio and PPV of 1.09% and 9.41%, respectively. On the other hand, atypical antipsychotic risperidone 1 mg had highest cost ratio and PPV of 6.97% and 596.99%, clozapine in the strength of 50 mg had lowest cost ratio and PPV of 1.13% and 13.18%, respectively. Among fixed-dose combinations, risperidone + trihexyphenidyl (2+2) mg had highest cost ratio and PPV of 2.47% and 147.53%, while trifluoperazine + trihexyphenidyl (10+2) mg had lowest cost ratio and PPV of 1.05% and 4.53%, respectively. Conclusion: This study showed large differences in cost ratio as well as PPV between various brands of the same antipsychotics. To reduce the financial burden and improve drug compliance, physicians must prescribe less expensive drugs. Furthermore, for the benefit of patients in general, MBBS students, future community prescribers, should also be trained in pharmacoeconomics to make them aware of the cost differences of the same drugs from different brands.

Expert opinion on a consensus-based checklist for the critical appraisal of cost-of-illness (COI) studies: qualitative interviews.

This study explored experts' views on the development of a proposed checklist for cost-of-illness (COI) studies. It also investigated experts' perspectives on the use of COI studies and quality/critical appraisal tools used for COI studies as well as their experiences with the use of these tools. Semi-structured, open-ended interviews were conducted with health economists and other experts working with COI studies and with experience of developing health economic guidelines or checklists. Participants were selected purposively using network and snowball sampling. A framework approach was applied for the thematic data analysis. Findings were reported narratively. Twenty-one experts from eleven different countries were interviewed. COI studies were found to be relevant to estimate the overall burden of a disease, to draw attention to disease areas, to understand different cost components, to explain cost variability, to inform decision making, and to provide input for full economic evaluations. Experts reported a lack of a standardized critical appraisal tool for COI studies. Their experience related predominantly to guidelines and checklists designed for full economic evaluations to review and assess COI studies. The following themes emerged when discussing the checklist: (i) the need for a critical appraisal tool, (ii) format and practicality, (iii) assessing the questions, (iv) addressing subjectivity, and (v) guidance requirements. The interviews provided relevant input for the development of a checklist for COI studies that could be used as a minimum standard and for international application. The interviews confirmed the important need for a checklist for the critical appraisal of COI studies.

Economic Sustainability of High–Speed and High–Capacity Railways

High–speed railways (HSRs or AV) and high–capacity railways (HCRs or AC, herein in the sense of open to freight trains) are crucial for the social and economic development of regions and nations. Their design, construction, and maintenance should comply with many requirements, including environment–, finance–, and policy–related ones. To this end, it is noted that the 2030 Agenda for Sustainable Development Goals (UN–SDGs, United Nations Member States, 2015) lists 17 targets, including decent work and economic growth (number 8), industry, innovation and culture (n. 9), and take urgent action to combat climate change and its impacts (n. 13). Despite the above, when analysing costs, many uncertainties arise. In light of the foregoing, the main objectives of the study presented in this paper have been confined to the definition of a model for the estimation of HSR and HSR/HCR infrastructure cost. Theoretical considerations and data derived from Italian (both HSR and HSR/HCR), Spanish, and French HSR projects were used to set up and validate the proposed model. Results demonstrate that, under given conditions, it is possible to explain cost variability in terms of four main factors, namely high capacity (ACF), speed (SF), national (NF), and freight train factor (K), where this latter mainly refers to the need for longer tracks when freight trains are the main type of traffic.

Extensible and swellable hydrogel-forming microneedles for deep point-of-care sampling and drug deployment

Microneedles are considered to be an effective, convenient, non-invasive, biosafety and compliant medical technology for vaccinations, biomarker testing, medical aesthetics and other related fields. Nonetheless, further clinical and commercial translation of regular microneedles is hampered by challenges in manufacturability, cost variability, insufficient comfort, contamination and so on. Recent innovations in functional biomaterials and chemical engineering technologies have been applied to develop extensible and swellable hydrogel-forming microneedles, achieving precise and controlled drug delivery and localized sampling from the target tissues. In this review, we systematically summarize the latest development of the extensible and swellable hydrogel-forming microneedles, including deep point-of-care testing, drug deployment, wound healing and mucoadhesion improvement. In addition, further analysis of the challenges and prospects for clinical application of current strategies is well presented. It is believed that the combined efforts of engineering, material, pharmaceutical and clinical research will contribute to the future success of this clinical and commercial translation.

Identifying Potential Supply Chain Bottlenecks Within the Ammunition Manufacturing Process for the Optionally Manned Fighting Vehicle

The U.S. Army is developing the Optionally Manned Fighting Vehicle (OMFV) to replace the Bradley Fighting Vehicle (BFV). The OMFV is slated to replace the BFV’s 25mm caliber gun with a 30mm or 50mm caliber gun. This analysis seeks to help inform the OMFV program's decision on round size by evaluating the supply chain aspects of each round. From stakeholder analysis, the primary supply chain concern is related to the casing material, with the 30 mm round using aluminum and the 50 mm round using steel. The manufacturing process for each round type was modeled in ProModel, including the supply chain issues related to steel and aluminum. The results indicated that both types of rounds are bottlenecked by steel limitations, since both rounds use steel projectiles. The study also indicated a larger variability in round cost for 50 mm rounds given the volatility of the steel market.

The Economic Value of Fuel Treatments: A Review of the Recent Literature for Fuel Treatment Planning

This review synthesizes the scientific literature on fuel treatment economics published since 2013 with a focus on its implications for land managers and policy makers. We review the literature on whether fuel treatments are financially viable for land management agencies at the time of implementation, as well as over the lifespan of fuel treatment effectiveness. We also review the literature that considers the broad benefits of fuel treatments across multiple sectors of society. Most studies find that fuel treatments are not financially viable for land management agencies based on revenue generated from forest products, biomass, or carbon credits at the time of implementation. Fuel treatments also tend to not be financially viable based on future management costs savings (fire suppression and rehabilitation costs) or averted losses in forest products from wildfire over the lifespan of treatment effectiveness. Similarly, most studies that consider benefits beyond those accruing to land management agencies find that the benefits from any single category (e.g., damage to structures and infrastructure, critical watersheds, air quality, or ecosystem values) are not sufficient to offset treatment costs. Overall, the recent literature suggests that fuel treatment projects are more likely to have benefits that exceed costs if they generate benefits in multiple categories simultaneously. The literature also documents tremendous variability in benefits and costs across regions and between projects within regions, which poses a challenge to reaching general conclusions about the benefits and costs of fuel treatments at programmatic scales, and suggests that practitioners should proceed with caution when trying to extrapolate the benefits and costs for a prospective fuel treatment project from estimates reported in the previous literature.

Learning optimization proxies for large-scale Security-Constrained Economic Dispatch

The Security-Constrained Economic Dispatch (SCED) is a fundamental optimization model for Transmission System Operators (TSO) to clear real-time energy markets while ensuring reliable operations of power grids. In a context of growing operational uncertainty, due to increased penetration of renewable generators and distributed energy resources, operators must continuously monitor risk in real-time, i.e., they must quickly assess the system’s behavior under various changes in load and renewable production. Unfortunately, systematically solving an optimization problem for each such scenario is not practical given the tight constraints of real-time operations. To overcome this limitation, this paper proposes to learn an optimization proxy for SCED, i.e., a Machine Learning (ML) model that can predict an optimal solution for SCED in milliseconds. Motivated by a principled analysis of the market-clearing optimizations of MISO, the paper proposes a novel just-in-time ML pipeline that addresses the main challenges of learning SCED solutions, i.e., the variability in load, renewable output and production costs, as well as the combinatorial structure of commitment decisions. A novel combined classification-plus-regression architecture is also proposed, to further capture the behavior of SCED solutions. Numerical experiments are reported on the French transmission system, and demonstrate the approach’s ability to produce, within a time frame that is compatible with real-time operations, accurate optimization proxies that produce relative errors below 1%. • Online risk estimation in power systems requires fast optimization proxies. • The variability of load, renewables, commitments, energy offers, complicates the learning. • The proposed just-in-time learning pipeline is grounded in ISOs’ operations. • A combined classification-then-regression architecture improves learning performance. • Experiments are conducted on real, industrial network, over multiple days.

Prediction of total healthcare cost following total shoulder arthroplasty utilizing machine learning.

Given the increase in demand in treatment of glenohumeral arthritis with anatomic total (aTSA) and reverse shoulder arthroplasty (RTSA), it is imperative to improve quality of patient care while controlling costs as private and federal insurers continue its gradual transition toward bundled payment models. Big data analytics with machine learning shows promise in predicting health care costs. This is significant as cost prediction may help control cost by enabling health care systems to appropriately allocate resources that help mitigate the cause of increased cost. The Nationwide Readmissions Database (NRD) was accessed in 2018. The database was queried for all primary aTSA and RTSA by International Classification of Diseases, Tenth Revision (ICD-10) procedure codes: 0RRJ0JZ and 0RRK0JZ for aTSA and 0RRK00Z and 0RRJ00Z for RTSA. Procedures were categorized by diagnoses: osteoarthritis (OA), rheumatoid arthritis (RA), avascular necrosis (AVN), fracture, and rotator cuff arthropathy (RCA). Costs were calculated by utilizing the total hospital charge and each hospital's cost-to-charge ratio. Hospital characteristics were included, such as volume of procedures performed by the respective hospital for the calendar year and wage index, which represents the relative average hospital wage for the respective geographic area. Unplanned readmissions within 90 days were calculated using unique patient identifiers, and cost of readmissions was added to the total admission cost to represent the short-term perioperative health care cost. Machine learning algorithms were used to predict patients with immediate postoperative admission costs greater than 1 standard deviation from the mean, and readmissions. A total of 49,354 patients were isolated for analysis, with an average patient age of 69.9±9.6 years. The average perioperative cost of care was $18,843±$10,165. In total, there were 4279 all-cause readmissions, resulting in an average cost of $13,871.00±$14,301.06 per readmission. Wage index, hospital volume, patient age, readmissions, and diagnosis-related group severity were the factors most correlated with the total cost of care. The logistic regression and random forest algorithms were equivalent in predicting the total cost of care (area under the receiver operating characteristic curve=0.83). After shoulder arthroplasty, there is significant variability in cumulative hospital costs, and this is largely affected by readmissions. Hospital characteristics, such as geographic area and volume, are key determinants of overall health care cost. When accounting for this, machine learning algorithms may predict cases with high likelihood of increased resource utilization and/or readmission.

Pengaruh Ukuran Perusahaan, Variabilitas Harga Pokok Penjualan, Likuiditas, dan Laba Sebelum Pajak Terhadap Pemilihan Metode Akuntansi Persediaan:

This study aims to examine the size of company, the variability of cost of goods sold, liquidity, and profit before tax on the selection of inventory accounting methods. To test the hypothesis used secondary data with a purposive sampling method. Data analysis technique used multiple linier regression analysis with company size, variability of the cost of inventory, liquidity, and profit before tax as variable (X) and the selection of inventory accounting methods as variable (Y). After data was analyzed, it was determined that partially the variability of cost of goods sold and liquidity had an effect on the selection of inventory accounting methods, while firm size and profit before tax had no effect on inventory accounting methods. Simultaneously the size of the company, the variability of cost of goods sold, liquidity and profit before tax together have a significant effect on the selection of inventory accounting methods.&#x0D; Keywords : firm size, variability of cost of goods sold, liquidity, profit before tax, selection of inventory accounting methods.

Damage or no damage from traffic: Re-examining marginal cost pricing for rail signalling maintenance

This paper re-examines the implementation of the short run marginal cost (SRMC) pricing principle with respect to rail infrastructure usage and empirically tests if there are rail infrastructure maintenance costs triggered by traffic but not caused by asset damage from traffic. This is important because current EU legislation stipulates that only costs related to infrastructure wear and tear from traffic are eligible for the direct cost-based element of track access charges. An econometric approach is applied to French panel data on signalling maintenance costs. The results show that the SRMC for infrastructure provision of these assets is not only related to asset damage costs caused by traffic, but can also be due to economic factors linked to increased line capacity utilisation: 1) higher cost per maintenance activity, and/or 2) increased preventative maintenance to curb delays. Our work offers an explanation as to why econometric and engineering approaches give different views of rail infrastructure cost variability and suggests that EU legislation on track access pricing may need to be revised.

Economic costs and Predictors of occupation-related Injuries in Ethiopian sugar industries from the Employer’s perspective: top-down approach and friction method

BackgroundFor many industrial workers, occupational injuries are a common health and safety concern. However, sufficient information on the economic costs and predictors of occupation-related injuries from the perspective of employers is lacking in developing countries, including Ethiopia. The objective of this study was to close this gap by quantifying the economic costs and predictors of occupation-related injuries in Ethiopian manufacturing industries from the employer’s perspective.MethodsA cross-sectional study was employed to estimate the employer-side economic cost of occupation-related injuries from December 2021 to March 2022. This study used a top-down approach to compute direct costs, while the friction method was used for indirect cost estimation. Injury data were obtained from the Bureau of Labour and Social Affairs and the industries, while cost data were from workers’ compensation records. The insurance company’s injury compensation record was triangulated with industries’ data. The study collected primary data via an interview-administered, semi-structured questionnaire from 1136 randomly selected injured cases. Statistical analysis was carried out with STATA version 14 software. The study employed a generalized linear model to identify predictors of total cost by considering the non-normal distribution of the total cost. Exponentiate coefficients with a 95% confidence interval were used to express the direction and strength of the association.ResultsThe survey participation rate was 100%. From the perspective of the employers, the total cost of occupation–related injury was 22,587,635.32 Ethiopian birr (537,800.84 $).Indirect and direct costs accounted for 65.86 and 34.14% of the overall expenses, respectively. Long-term absence from work (exp (b) = 0.85), having a sleeping disorder (exp (b) = 0.90), co-morbidity (exp (b) = 0.85), and severity (type) of injury (exp (b) = 1.11) were predictors significantly associated with the total cost variability in the fully adjusted model.ConclusionsEmployers’ toll of occupation-related injuries has severe economic implications. The influential factors that elevated the total cost variation were: long-term absence from work, unsafe acts of the workers, having a sleeping disorder, co-morbidity, and severity (type) of injury. Therefore, the identified modifiable factors are the areas of intervention to reduce the cost of occupation-related injuries.