Cost Curve Research Articles

Financial impact of biosimilar adoption in the oncology care model for the U.S. Oncology Network.

e18749 Background: Biosimilar adoption offers the potential to save significant cost while maintaining high quality of care. The Oncology Care Model (OCM) is structured to reward practices for decreasing total cost of care (TCOC) compared to historical baseline prices that are adjusted by a trend factor accounting for the rising cost of oncology care. Practices are rewarded for bending the cost curve by utilizing lower cost medications. Biosimilar options were introduced for 3 mainstay therapeutic classes in oncology, bevacizumab and trastuzumab in 2019, and rituximab in 2020. This led to a concerted approach for biosimilar adoption in The US Oncology Network (The Network) as an opportunity to reduce TCOC. Here we describe utilization and the financial impact of biosimilar adoption in The Network during OCM performance period 8 (PP8) which enrolled patients from 1/2/2020 to 7/1/2020. Methods: Claims data for the 14 US Oncology Network practices participating in the OCM were used to determine biosimilar utilization for bevacizumab, trastuzumab, and rituximab therapeutic classes. The impact of biosimilar utilization on TCOC was measured by comparing the cost of each dose of a biosimilar vs the estimated cost if the more expensive innovator agent had been used instead. Results: Biosimilar utilization increased for each therapeutic class from prior performance periods. Biosimilar use for bevacizumab was 0% in PP6, 5% in PP7 and 43% in PP8. For trastuzumab, utilization was 0% in PP6, 4% in PP7 and 33% in PP8. Rituximab biosimilar utilization was 0% in PP6, 2% in PP7 and 41% in PP8. We compared the actual cost of these three medications in our OCM claims in PP8 vs. the calculated cost based on Medicare reimbursement as ASP + 6% minus sequestration if the more expensive innovator drugs had been used entirely. The difference in the actual cost vs estimated cost led to a savings of $6.61 million by using the biosimilars over innovator drugs. Conclusions: The increased utilization of biosimilars in The Network generated significant savings in OCM. Continued adoption of biosimilars will increase savings to Medicare in the Oncology Care Model. We estimate that 100% Network adoption of these three biosimilars could save nearly 1.5% of the TCOC in our Network compared to continued use of innovator drugs.

Financial impact of generic therapeutic interchange of abiraterone in the oncology care model for the U.S. Oncology Network.

e17019 Background: Therapeutic interchange (TIC) to lower-cost generic products offers significant cost savings to payers and patients while maintaining equivalent quality of care. The Oncology Care Model (OCM) is a value-based care program from Medicare that rewards practices for decreasing the total cost of care (TCOC) compared to historical baseline prices that are adjusted by a trend factor to account for rising cost trends in oncology. One strategy for bending the cost curve is to utilize lower cost medications. A generic version of abiraterone acetate (FDA approved for treatment of Prostate Cancer) was first introduced into the market in late 2018. This led to a concerted approach to interchange the brand product with the generic equivalent in The US Oncology Network (The Network) as an opportunity to reduce TCOC. Here we describe utilization and the financial impact of abiraterone TIC in The Network during OCM performance period 8 (PP8) which enrolled patients from 1/2/2020 to 7/1/2020. Methods: Prescription drugs dispense data for the 14 US Oncology Network practices participating in the OCM were used to determine brand and generic abiraterone utilization. The impact of TIC on TCOC was measured by comparing the cost of the generic product vs. the estimated cost of the brand agents. Results: TIC resulted in an increase in generic abiraterone use from prior performance periods. Generic abiraterone increased from 0% in PP4 to 24% in PP5, 55% in PP6, 67% in PP7 and 80% in PP8 within the Network. We compared the actual cost of all abiraterone dispenses in PP8 vs. the calculated cost (based on Average Wholesale Price less 17.5%) of the brand product if the more expensive brand drugs were used instead. The difference in the actual cost vs estimated cost led to a savings of $4.37 million in PP6, $6.34 million in PP7 and $9.37 million in PP8 (about 1% of TCOC) by using generic versus brand abiraterone alone. Conclusions: TIC from brand to generic abiraterone in The Network generated significant savings for OCM. Continued shift towards lower cost generics will increase savings to CMS in the Oncology Care Model. We estimate that 100% Network adoption of the generic abiraterone could save nearly 1.25% of the TCOC compared to continued use of brand product.

The EPLANoptMAC model to plan the decarbonisation of the maritime transport sector of a small island

Islands have been identified as living labs for implementing innovative solutions so as to boost the green energy transition. In this context, energy modelling and planning are key to optimally design the future energy system and identify the most appropriate technologies for both power generation and energy system management. In this research, a Marginal Abatement Cost curve method, by means of the EPLANoptMAC model, is applied to identify the optimal energy mix for the island of Favignana, Italy. The optimisation uses the carbon avoidance cost as objective function in order to consider both environmental and economic aspects. In addition to standard technologies such as photovoltaic and battery energy storage demand response technologies that include the maritime transport and heating sectors are optimised. Particularly, the decarbonisation of the maritime transport sector is of utmost importance since it represents the biggest energy consumption and source of carbon dioxide emissions for small islands. Results show that photovoltaic is the main priority, followed by battery energy storages. Electric ferries are preferrable to hydrogen ones when enough renewable energy from photovoltaic is produced. The decarbonisation of the maritime transportation is preferable to the heating sector and is required to reach decarbonisation targets higher than 40%.

A Preventive Replacement Policy for a System Subject to Bivariate Generalized Polya Failure Process

Numerous studies on preventive maintenance of minimally repaired systems with statistically independent components have been reported in reliability literature. However, in practice, the repair can be worse-than-minimal and the components of a system can be statistically dependent. The existing literature does not cover this important in-practice setting. Therefore, our paper is the first to deal with these issues by modeling dependence in the bivariate set up when a system consists of two dependent parts. We employ the bivariate generalized Polya process to model the corresponding failure and repair process. Relevant stochastic properties of this process have been obtained in order to propose and further discuss the new optimal bivariate preventive maintenance policy with two decision parameters: age and operational history. Moreover, introducing these two parameters in the considered context is also a new feature of the study. Under the proposed policy, the long-run average cost rate is derived and the optimal replacement policies are investigated. Detailed numerical examples illustrate our findings and show the potential efficiency of the obtained results in practice.

The role of energy supply in abatement cost curves for CO2 capture from process industry – A case study of a Swedish refinery

Carbon capture and storage (CCS) activities need to be ramped up to address the climate crisis. Abatement cost curves can help to identify low-cost starting points and formulate roadmaps for the implementation of CCS at industrial sites. In this work, we introduce the concept of energy supply cost curves to enhance the usefulness and accuracy of abatement cost curves. We use a multi-period mixed-integer linear programming (MILP) approach to find an optimal mix of heat sources considering the existing site energy system. For a Swedish refinery, we found that residual heat and existing boiler capacities can provide the heat necessary for CCS that avoids >75% of the site’s CO2 emissions. Disregarding the existing site energy system and relying on new heat supply capacities instead, would lead to capture costs that are 40–57% higher per tonne of CO2-avoided (excl. CO2 liquefaction, transport, and final storage). Furthermore, we estimated that temporal variations of heat sources (intermittent residual heat) increases the heat supply cost and emissions by 7–26% and 9–66%, respectively. The proposed method for optimization of the energy supply mix considering temporal variations of heat sources enables detailed estimates of energy supply costs for CO2 capture rates ranging from partial to full capture, and thus, improve abatement cost curves.

A hybrid repair-replacement policy in the proportional hazards model

Cox's proportional hazards model is widely used to describe the hazard rate of a system deteriorating with age and diagnostic covariates. Existing maintenance in the proportional hazards model was primarily concerned with replacement, resulting in excessive maintenance for many repairable systems. This paper develops a novel hybrid repair-replacement model in the proportional hazards model with a stochastically increasing Markovian covariate process. Preventive repair reduces both age and covariate, and the reduction rate decreases as the number of repairs increases. At an inspection epoch where the system age, covariate state, and repair number are available, the decision-maker considers three possible actions, i.e., no maintenance, preventive repair, and preventive replacement. The objective is to derive the optimal policy that minimizes the long-run average maintenance cost rate. The optimization problem is formulated in the semi-Markov decision process (SMDP) framework. The structural properties of the optimal policy are examined to reduce the policy space. Then a policy-iteration algorithm with a backward policy-improvement step is developed for efficiently finding the optimization results. A practical numerical example with sensitivity analysis is conducted to illustrate the effectiveness of the proposed approach. A comparison with two heuristic policies confirms the superiority of the proposed policy in reducing maintenance costs.

Optimal control of variable-speed wind turbines modeled as Markov jump systems

This paper shows the solution for the control problem of operating wind turbines through Markov jump nonlinear systems. The idea is to represent wind speed acting on the turbine blades into modes of Markov chain. The main result then characterizes the optimal control for the long-run average cost of turbine operation. As a byproduct, the turbine model becomes second-moment stable, a concept borrowed from the literature of stochastic systems. To illustrate the benefits of our approach, we performed simulations in the FAST 5-MW wind turbine; the simulated data suggest that our control outperform other control strategies from the literature—increasing the produced power by 0.53%.

Semi‐empirical development of a novel and versatile multiobjective optimization tool for co/trigeneration energy system design

This article describes a novel and implemented optimization routine for the analysis of combined cooling, heat, and power generation. The optimization model is briefly described both in terms of logic and operation and in terms of mathematical formalization. The model is able to optimize energy systems with different technologies used for the cogeneration unit, namely, SOFC and PEM fuel cells, internal combustion engines (ICEs), and micro-gas turbines (MGTs). The validation is performed both in terms of energy efficiency curves and in terms of unit costs, for all components within the energy system, including energy storage systems, heat pumps, and auxiliary boilers. The main key performance indicators included in the technical analysis of the CCHP units are the nominal electric and overall efficiency as a function of installed size and during a partial load operation, as well as the power-to-heat factor in dependence upon the nominal capacity/power. Two main storage technologies have been included, namely lithium-ion batteries (Li-ion BES) and lead-acid batteries (LA BES). The heat pump portfolio allows the choice between compression gas heat pumps (CGHPs) and absorption heat pumps (ABHPs), with their COP and EER described as a function of the external temperature and of a partial load operation. The validation of the modeled curves is performed in comparison with data of existing commercial units, presenting a correlation factor over 95% for most of the technologies. Highlights Presentation of a multiphysical design tool for CCHP systems; Economic, technical, and environmental analysis and optimization; PEMFC and SOFC are included in the tool technology portfolio; The model is based on validated and semi-empirical correlations; Evaluation of different energy storage systems and heat pump technologies. The present article describes the validation of an innovative multiobjective model aiming to optimize both the operating strategy of the plant and the sizing of the main equipment in a correlated way. The article focuses on the validation of the proposed model and, as a further novelty, includes a broad technological portfolio in terms of cogeneration units and describes the semi-empirical validation of the model by comparing the cost and performance curves modeled with existing market elements or literature data.

Modeling systems with revealing and non-revealing failures undergoing periodic inspection

In this study, the inspection policy for a single-unit randomly failing system with alternating operating and inactivity periods is proposed. Failures are detected instantly for the former case, whereas in the latter case, inspections are required for failure detection. This article focuses on evaluating the general expressions for limiting availability and long-run average cost of the system undergoing periodic inspections. The distinctive characteristic of the proposed model is that the inspections are not perfect. Furthermore, it is supposed that inspection and maintenance time are non-negligible, and corrective maintenance results in as good as new unit. The optimal inspection problem is developed predicated on maximizing the availability and minimizing the cost. Numerical example of electric motor corresponding to different life distributions is also presented to justify the obtained results.

The Energy Matching Economics Using Simulation Approach for Implementation of Nearly Zero Energy Building Design

The desire to approach a nearly Zero Energy (ZE) framework is affected by existing characterizations for two primary reasons: they only affect the energy based system, and They do not create a benchmark that assure designs are optimized for lower emissions and energy usage before using sustainable energy methods to achieve energy requirements. Energy matching defines the amount of load handled by the building's generation as well as the interface with neighboring grids. The optimization method adopts selection criteria of a large number of possible combinations and employs the most cost-effective choices to produce a minimal cost curve to attain efficient energy homes with good performance levels. The purpose of this study is to investigate a building's energy matching economics with specific balance choices that are classified to create the path for least cost of nearly zero energy building.

Asymptotically optimal idling in the GI/GI/N+GI queue

We formulate a control problem for a GI/GI/N+GI queue, whose objective is to trade off the long-run average operational costs with server utilization costs. To solve the control problem, we consider an asymptotic regime in which the arrival rate and the number of servers grow large. The solution to an associated fluid control problem motivates that non-idling service disciplines are not in general optimal, unless some arrivals are turned away. We propose an admission control policy designed to ensure that servers have sufficient idle time, which we show is asymptotically optimal.

How competitive is the Syrian banking sector? Empirical evidence from ‘pre/post’ war Syria

AbstractThis paper investigates the degree of market power in the Syrian banking sector over the period 2005–2016 where research on competitive conditions does not exist. The degree of competitiveness is assessed based on the revenue elasticity to input prices approach and is related to a set of market indicators. To test whether the Syrian crisis has altered the competitive conditions over the years of the sample, I divide the full sample into two subsamples, namely the pre‐crisis years (2005–2012) and the crisis years (2013–2016). The results suggest that banks in Syria earn their interest and total revenue under conditions of monopoly. I find that the trend of market structure—characterized by a monopoly—in the pre‐crisis years continues over crisis years. My findings provide robust evidence that a collusive behaviour among banks is in operation in the banking sector of Syria. The difficult penetration into/exit from the market has led to the existence of a profit‐curb mechanism for the Syrian banks, hence, an upward shift in the marginal cost curve will be associated with a reduction in revenue as a result of the optimal condition for these banks which act as monopolists.

AN OPTIMAL REPLACEMENT PROBLEM FOR A SHOCK MODEL WITH DIFFERENT MODES OF FAILURES USING ALPHA SERIES PROCESSES

This paper studies a shock model for a repairable system with two-type failures by assuming that two kinds of shocks in a sequence of random shocks will make the system failed, one based on the inter-arrival time between two consecutive shocks less than a given positive value and the other based on the shock magnitude of single shock more than a given positive value . Further it is assumed that the system after repair is not ‘as good as new’, but the consecutive repair times of the system form a stochastic increasing α-seires process. Under these assumptions, we determine an explicit expression for the average cost rate and an optimal placement policy N* based on the number of failure of the system is determined such that the long-run average cost per unit time is minimized. The explicit expression of long-run average cost per unit time is derived, and the corresponding optimal replacement policy can be determined analytically or numerically. Finally, a numerical example is given.

Exploring the Relationship Between Local Governmental Spending on the Social Determinants of Health and Health Care Costs of Privately Insured Adults

Greater investment in the social determinants of health (SDOH) is positively associated with improved health outcomes of both individuals and their communities, which in turn may help to bend the health care cost curve and reduce health care spending. The purpose of this study was to examine the relationship between local governments' spending on the SDOH and the health care costs of privately insured nonelderly adults. Annual spending by local governments on the SDOH for the years 2007-2017 was obtained from the Census of Governments. Annual health care costs for privately insured nonelderly adults for the years 2013-2017 was obtained from the Health Care Cost Institute. Bivariate and multivariate regression analyses were performed to examine the association between county-level local governments' per capita spending on the SDOH and the per member health care costs of privately insured adults living in these counties controlling for community characteristics. All analyses were conducted in 2021. For near-elderly adults ages 55-64, health care costs were significantly higher in counties with the lowest levels of local governmental spending on the SDOH. For adults ages 18-54, in contrast, health care costs were unrelated to local governmental spending. Investments of local governments in the SDOH may have rather limited potential to yield meaningful savings in health care costs for privately insured nonelderly adults at the population level, especially once such investments exceed a minimum threshold.

Stochastic Pinch Analysis to address multi-objective resources conservation problems with parametric uncertainties

An emphasis on sustainable growth helps decrease the initial capital and reduce the impact of industry on an ecologically sensitive environment through resource conservation. Pinch Analysis serves as a valuable tool to identify the potential for resource conservation, waste minimization, and resource costs. However, there are inherent uncertainties associated with these resource conservation networks. The concept of Pinch Analysis is broadened to multi-objective resource conservation problems incorporating parametric uncertainties in this paper. The objective functions are combined to form a single compound objective function using the weighted sum approach. For the compound objective function, prioritized sequences (or resource combinations) are generated through multi-objective prioritized costs to accommodate parametric uncertainties. The multi-objective prioritized cost vs. weights curve helps determine the unique prioritized sequences for different range of weights to achieve optimal solutions. An exact analytical expression for the maximum number of prioritized sequences is also derived. Furthermore, a method is presented to construct the complete Pareto-optimal front to represent all optimal solutions graphically. Two illustrative examples, the hydrogen conservation problem with two objectives (cost and emission) and the water conservation problem with three objectives (cost, land requirement, and emission), demonstrate the proposed method’s utility and efficacy.

Event-based optimization of service rate control in retrial queues

This paper deals with the problem of service rate control for a retrial queue where the controller can observe only the number of total customers in the system. Service rates are adjustable based on the partial information (the arrival or departure event) instead of the perfect system state (customer distribution among orbit and server). The goal is to find the optimal service rates that minimize the long-run average cost. This problem is formulated as an event-based optimization instead of a state-based optimization. Using the sensitivity-based optimization theory, we obtain interesting structures of the optimal service rate control policy, which show that the optimal policy is a bang-bang control and even has a threshold form under some mild conditions. The necessary and sufficient condition of optimal policies is also derived. Furthermore, by the difference formula of the system performances under any two policies, we develop a policy iteration-type algorithm to find the optimal policy for the case with general cost functions. With different initial values of service rates, our algorithm is demonstrated to efficiently find the optimal service rates. Moreover, we study the difference of system performances from the corresponding state-based optimization by numerical experiments, which indicates the managerial insights about the value of information for decision-makers.

Accuracy study of linearization methods for quadratic cost curves of thermal units in unit commitment problems

To solve the unit commitment (UC) problems with efficient mixed-integer linear programming solvers, the quadratic cost functions (QCFs) of thermal units are always approximated to piecewise linear (PWL) functions. This study examines the accuracies of different approximation methods for piecewise linearizing the QCFs of units in UC problems. We use five piecewise linearization methods—evenly spaced PWL interpolation, evenly spaced PWL tangent, evenly spaced PWL–ɛmax/2 shifted interpolation, tighter PWL interpolation, and evenly spaced PWL least-squares fit—to approximate the QCFs of units. The authors first perform a series of reproductivity studies to verify the program. Then, numerical tests are conducted using different methods on the systems with 10, 100, and 800 units. The results show that different approximation methods lead to considerable differences in operating costs and the tighter PWL interpolation, compared with the other methods, is preferred in terms of approximation accuracy.

The cost-benefit comparisons of China's and India's NDCs based on carbon marginal abatement cost curves

Incorporating co-benefits of carbon abatement policies can offset costs and inspire greater and faster reductions in emissions in many cases. Most studies on co-benefits are carried out within a partial equilibrium framework and ignore the general equilibrium effects. Therefore, using a computable general equilibrium model, this study incorporated the co-benefits of carbon abatement policies into the carbon marginal abatement cost curves (MACCs), and evaluated the total abatement costs and cost-saving effects for China and India to achieve their Nationally Determined Contributions (NDC) target. The results indicate that the original carbon MACCs of India in any given year are generally higher than those of China; however, after considering the air pollution-related co-benefits, the overall level of the revised MACCs in China is slightly higher than that of India from 2020 to 2030. In the composition of total co-benefits in China and India, the co-benefits of SO2 reductions account for more than 80% of national total co-benefits, followed by the co-benefits of NOx and PM2.5 reductions. Furthermore, if co-benefits are considered, whether it is China or India, the marginal abatement costs and total abatement costs to achieve NDC targets can be effectively offset; but in comparison, India has more significant cost-saving effects, while for China it will be more difficult to reduce emissions in the latter half of the process of achieving NDC targets. These findings are helpful for developing countries in coordinating and strengthening their ability to tackle climate change and environmental protection.

Climate change, extreme winter weather and electricity production costs

We analyze the characteristics of the Northeast US’s PJM wholesale electricity market production cost and supply curve during the most extreme winter weather event in modern meteorological history within the PJM territory. It is seemingly ironic that the cause of such extremely cold weather events is global warming. The experience from this event demonstrates that the annual-summer-peak-centric nature of the PJM capacity planning process does not address the adequacy of supply during the winter. The PJM production cost curve is estimated and compared across recent winter season peak months to understand how large the price responsiveness to load changes can be during extreme winter weather events and how winter weather events can reduce system reliability for a summer-capacity-planning-centric power market. Therefore, PJM and other power pools (such as ERCOT in Texas) have not considered secondary peak seasons in capacity planning.

Queueing Network Controls via Deep Reinforcement Learning

Novel advanced policy gradient (APG) methods, such as trust region policy optimization and proximal policy optimization (PPO), have become the dominant reinforcement learning algorithms because of their ease of implementation and good practical performance. A conventional setup for notoriously difficult queueing network control problems is a Markov decision problem (MDP) that has three features: infinite state space, unbounded costs, and long-run average cost objective. We extend the theoretical framework of these APG methods for such MDP problems. The resulting PPO algorithm is tested on a parallel-server system and large-size multiclass queueing networks. The algorithm consistently generates control policies that outperform state-of-art heuristics in literature in a variety of load conditions from light to heavy traffic. These policies are demonstrated to be near optimal when the optimal policy can be computed. A key to the successes of our PPO algorithm is the use of three variance reduction techniques in estimating the relative value function via sampling. First, we use a discounted relative value function as an approximation of the relative value function. Second, we propose regenerative simulation to estimate the discounted relative value function. Finally, we incorporate the approximating martingale-process method into the regenerative estimator.