Total Daily Cost Research Articles

Multi-objective energy management using a smart charging technique of a microgrid with the charging impact of plug-in hybrid electric vehicles

The Microgrid (MG) concept is being developed to better integrate renewable energy sources and automate distribution networks. Microgrids combine distributed generating units (DGs) and energy storage systems to achieve this. This research paper aims to simultaneously minimize the daily operational cost and net environmental pollution of a small MG system, factoring in the charging demand from Plug-in-Hybrid Electric Vehicles (PHEVs) and consumer load demands. The proposed energy management process not only minimizes operational costs and emissions, but also determines the optimal battery size for the energy storage system. The analysis also explores the importance of two critical variables - the operation and maintenance costs of the DGs, and the total daily cost of the battery energy storage system. The demand for PHEV charging is managed using an intelligent charging approach. Given the complexity of the optimization, a recently developed metaheuristic algorithm, Slime Mould Algorithm (SMA), is applied. The performance of SMA is compared against the Grasshopper Optimization Algorithm and Sine Cosine Algorithm. To solve the multi-objective problem, a weighted sum method maintaining non-dominance and a fuzzy decision-maker technique are employed alongside the suggested algorithms. Three different scenarios verify the proposed method's effectiveness.

Optimization of capacity configuration and comprehensive evaluation of a renewable energy electrolysis of water for hydrogen production system

The global green hydrogen industry is experiencing rapid growth, but the high production costs are hindering its widespread adoption. To address this challenge, it is particularly important to rationally configure a renewable energy hydrogen production system. For this purpose, the study proposes a model for capacity optimization configuration of a renewable energy hydrogen production system, which integrates wind power, photovoltaic (PV) power, and concentrating solar power (CSP) with alkaline electrolyzer. It conducts capacity optimization configuration and comprehensive evaluations of the hydrogen production system across various scenarios. To minimize the total daily consumption cost, the CPLEX solver is utilized to solve the objective function and determine the capacity configuration of the renewable energy electrolysis of water hydrogen production system generator set under various scenarios. This approach achieves a utilization rate of over 99% for renewable energy. Through comprehensive evaluation, research has found that renewable energy-coupled hydrogen production significantly reduces generator capacity and electricity generation costs compared to separate hydrogen production, enhancing the economic efficiency of the system. The Wind-PV-CSP coupling hydrogen production system has the smallest generator assembly capacity and the lowest hydrogen production cost, which is 18.84 CNY·kg−1, significantly lower than the cost of PV-CSP coupling hydrogen production (25.78 CNY·kg−1) and wind-PV coupling hydrogen production (25.86 CNY·kg−1). It has good development prospects and plays an important role in exploring the development path of large-scale on-site consumption of new energy.

The Impact of a High Sensitivity Troponin HEART Pathway Based Clinical Decision Protocol on Observation Visits.

Use of high-sensitivity troponin (hs-cTn) might lead to an increase in hospital observation visits due to higher number of abnormal troponin levels. To determine the impact of incorporating hs-cTn into a chest pain clinical decision protocol (CDP) on observation visits in a large academic health system. This is a retrospective observational cohort study of all chest pain observation patients in four hospitals in an academic health system over 24 months. All hospitals used the Beckman Coulter Unicel Dxi instrument, and all shared the same emergency department (ED) chest pain protocol, which used the HEART pathway and serial troponins and directed ED dispositions to either an observation stay, ED discharge, or inpatient admission. Outcomes studied before and after introduction of a hs-cTn protocol included daily chest pain observation census, cost, observation hours, and inpatient admit rate. Census was reported as the daily chest pain observation census and as a proportion of all observation visits. Data was retrieved from a health system data warehouse and a cost accounting program. There were 6,712 chest pain observation visits over 24-months, with 4,087 visits before and 2,634 visits after the hs-cTn protocol implementation. Comparison groups were similar in terms of age, gender, and type of insurance. There were 10.59 (95% CI: 10.24 - 10.95) daily chest pain observation visits before and 7.66 (95% CI: 7.34 - 7.97) visits after implementation, with a 28% (95% CI: 35% - 20%) decrease in the total daily census. As a portion of all observation visits, there was a 22% drop in the proportion that were observed for chest pain. The daily number of chest pain patients requiring inpatient admission was unchanged. The daily total direct cost for chest pain observation decreased with an effective daily cost savings of $4,313 USD (95% CI: $1,534 - $6,998). The total daily number of chest pain observation bed hours also decreased by 41.5 hours (95% CI 13.4 - 96.4 hr). Implementation of a hs-cTn chest pain protocol was associated with a significant decrease in the number and proportion of observation visits, a decrease in total daily cost and bed hours used, and no increase in inpatient admissions.

Multi-objective optimization of a hydrogen supply chain network: Wind and solid biomass as primary energy sources for the public transport in Sicily

Europe’s heavy reliance on diesel power for nearly half of its railway lines, for both goods and passengers, has significant implications for carbon emissions. To address this challenge, the European Union advocates for a shift towards hydrogen-based mobility, necessitating the development of robust and cost-effective hydrogen supply chains at regional and national levels. Leveraging renewable energy sources such as wind farms and solid biomass could foster the transition to sustainable hydrogen-based transportation. In this study, a mixed-integer linear programming approach integrated with an external heavy-duty refueling station analysis model is employed to address the optimal design of a new hydrogen supply chain. Through multi-objective optimization, this study aimed to minimize the overall daily costs and emissions of the supply chain. By applying the model to a case study in Sicily, different scenarios with varying supply chain configurations and wind curtailment factors were explored. The findings revealed that increasing the wind curtailment factor from 1% to 2% led to reductions of 12% and 15% in the total daily emission costs and network costs, respectively. Additionally, centralized biomass-based plants dominated hydrogen production, accounting for 96% and 94% of the total production under 1% and 2% wind curtailment factors, respectively. Furthermore, transporting gaseous hydrogen via tube trailers proved more cost effective than using tanker trucks for liquid hydrogen when compressed gaseous hydrogen is required at the dispenser of forecourt refueling stations. Finally, the breakdown of the levelized cost for the hydrogen refuelling station strongly depends on the form of hydrogen received at the gate, namely, liquid or gaseous. Specifically, for the former, the dispenser accounts for 60% of the total cost, whereas for the latter, the compressor is responsible for 58% of the total cost. This study highlights the importance of preliminary and quantitative analyses of new hydrogen supply chains through model-based optimization.

Real-time energy management strategy of the hydrogen-coupled microgrid based on Informer model prediction results and deep reinforcement learning

Abstract Microgrids (MG) powered by emerging distributed energy resources (DERs) are increasingly pivotal in driving the decarbonization of the power system. Due to the uncertainty of renewable energy sources, electricity generated from wind and solar is typically stored, either in batteries or in the form of high-pressure hydrogen gas within hydrogen storage tanks to meet the refuelling requirements of fuel cell vehicles (FCVs). For FCVs, the substantial operational expenses associated with hydrogen refuelling stations pose a significant barrier to their widespread adoption, with a notable portion of these costs attributed to hydrogen transportation. To address this challenge, this paper employs the Informer model based on a transformer for day-ahead prediction of photovoltaic power generation, as well as load demand forecasting. Subsequently, the Twin-delayed deep deterministic (TD3) policy gradient algorithm is used for real-time energy management of the microgrid. While ensuring that the State of Charge (SOC) remains above 45% daily to meet future hydrogen demands, the MG pursues an optimal strategy to minimize the total daily cost, including electricity procurement, carbon emissions and equipment degradation. Compared to outcomes derived from rule-based approaches, the algorithm introduced in this paper minimizes solar waste and equipment degradation, facilitating hydrogen production during low-cost electricity periods and enhancing economic feasibility.

Energy Bus-Based Matrix Modeling and Optimal Scheduling for Integrated Energy Systems

Integrated energy systems (IESs) can easily accommodate renewable energy resources (RESs) and improve the utilization efficiency of fossil energy by integrating various energy production, conversion, and storage technologies. However, the coupled multi-energy flows and the uncertainty of RESs bring challenges regarding optimal scheduling. Therefore, this study proposes an energy bus-based matrix-modeling method and a coordinated scheduling strategy for the IES. The matrix-modeling method can be used to formulate the steady- and transient-state balances of the multi-energy flows, and the transient model can clearly express the multi-time-scale characteristics of the different energy flows. The model parameters are fitted with data from experiments and the literature. To address the inherent randomness of the RESs and loads, a coordinated scheduling strategy is designed that contains two components: day-ahead optimization and rolling optimization. Day-ahead optimization uses the system steady-state model and multiple scenarios from the RES and load forecast data to minimize the operation cost while rolling optimization is based on the system’s transient-state model and aims to achieve the optimal real-time scheduling of the energy flows. Finally, a case study is conducted to verify the advantages and effectiveness of the proposed model and optimization method. The results show that stochastic optimization reduces the total daily cost by 1.48% compared to deterministic optimization when considering the prediction errors associated with the RESs and loads, highlighting the stronger adaptability of stochastic optimization to prediction errors. Moreover, rolling optimization based on the system’s transient-state model can reduce the errors between day-ahead scheduling and rolling correction.

Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods

The reverse osmosis (RO) process is a well-established desalination technology, wherein energy-efficient techniques and advanced process control methods significantly reduce production costs. This study proposes an optimal real-time management method to minimize the total daily operation cost of an RO desalination plant, integrating a storage tank system to meet varying daily freshwater demand. Utilizing the dynamic model of the RO process, a cascade structure with two reinforcement learning (RL) agents, namely the deep deterministic policy gradient (DDPG) and deep Q-Network (DQN), is developed to optimize the operation of the RO plant. The DDPG agent, manipulating the high-pressure pump, controls the permeate flow rate to track a reference setpoint value. Simultaneously, the DQN agent selects the optimal setpoint value and communicates it to the DDPG controller to minimize the plant’s operation cost. Monitoring storage tanks, permeate flow rates, and water demand enables the DQN agent to determine the required amount of permeate water, optimizing water quality and energy consumption. Additionally, the DQN agent monitors the storage tank’s water level to prevent overflow or underflow of permeate water. Simulation results demonstrate the effectiveness and practicality of the designed RL agents.

Development of a Variable Cost Prediction Model for Hospitalized Critically Ill Patients in ICU

Introduction: Hospitalization of critically ill patients in the ICU is one of the most expensive areas of hospital care. Aim: The creation of a variable cost prediction model for hospitalized patients in ICU. Material and method: A retrospective observational study was carried out, using the bottom-up costing methodology. The study sample consisted of 204 hospitalized patients in the 7-bed ICU of 251 AGH, from January 1, 2016 to December 31, 2017. The demographic and clinical characteristics of the patients were recorded and the variable cost of hospitalization was accurately calculated, through an analytical recording of all the resources required for their treatment. Multivariable linear regression was implemented to investigate the impact of variables, derived from a systematic review conducted on determinants of intensive care costs. Gender, age, length of stay, APACHE II severity scale, type of patient (medical/surgical), and renal replacement therapy were used as independent variables. The average total daily cost of hospitalisation and the average daily costs of laboratory tests, medication, and consumables were used as dependent variables. Results: An increase in APACHE II by one unit and length of ICU stay by one day is associated with an increase in total daily cost by 2.74% and 1.82% respectively, while an increase in age by one year with a decrease of 0.4 %. A medical patient shows 11.62% higher total daily cost than a surgical patient, while renal replacement therapy increases the cost by 79.49%. Conclusions:Variable ICU cost is related to gender, age, severity scale,type of event and need for renal replacement therapy.

Investment principles in value-target processes of electricity consumption at iron ore enterprises

Purpose. To establish the influence of investment on cost-target processes of electric consumption of iron ore enterprises by applying economic and mathematical models. Methodology. As a result of the research, system and critical analysis was applied in the synthesis of formation of economic and analytical indicators and processes of electricity consumption at iron ore enterprises. Methods of comparative analysis, functional-cost, economic-statistical research were used to determine cost-target characteristics when building economic-mathematical models. The general basis of the conducted study is a complex approach. Findings. The capitalization of relations in Ukraine requires a more careful organization of the practice of investing, in relation to the cost policy of electricity consumption at industrial enterprises in Ukraine. When mining iron ore, optimal matching of the cost of electricity consumption and investment investments, which make it possible to ensure the necessary volumes of production, is of great importance. Due to this, in modern conditions, the necessary digital support of investment processes is of great importance. An economic-mathematical model of the impact of investments on the cost-target problems of electric energy consumption is developed, which allows numerically determining the interrelationship of the selected indicators. The combination of theoretical study with practical implementation made it possible to investigate the conditions under which there is the best ratio of investment investments and the cost of electricity consumption. Originality. The method of influencing investment on the cost of electricity consumption by iron ore enterprises has received further development. An economic-mathematical model has been constructed that allows the proposed method to be implemented. Practical value. The analysis of the results of the cost of electricity consumption at iron ore enterprises allows us to estimate the statistical dependence of the cost of electricity consumption on the amount of investment. The conducted analysis made it possible to investigate the dependence of the values of the total average daily costs of consumed electricity, the cost of consumed electricity, the specific cost of electricity consumption on the amount of investment contributions at the enterprises of Kryvyi Rih Iron Ore Combine on the basis of regression models. It is expedient to determine the question of choosing an optimization option on the basis of an analysis of the real economic situation in accordance with the volume and cost of electricity consumption at iron ore enterprises. The practice of applying the proposed research results makes it possible to determine the most optimal option for energy efficiency management.

Cost minimizing decisions on equipment and charging schedule for electric buses in a single depot

Combined decisions on equipment and charging schedule for electric buses (e-buses) are studied in this paper. The most representative day is considered, in which each e-bus fulfills a transfer task composed of a series of passenger transportation trips. Each trip starts and terminates at given times in the same single depot where e-buses re-charge their batteries. The problem is to determine battery variants for the e-buses, number of chargers of different types and daily cyclic charging schedule of e-buses such that a linear combination of (a) the total daily cost of the charging equipment, batteries wear and consumed energy, and (b) the total number of e-bus charging interruptions (switchings) is minimized. The constraints include an upper bound on the supplied electric power, restoration of the initial battery energy state before the next day transfer task for each e-bus and an upper bound on the maximal number of switchings for each e-bus in each depot visit over all e-buses and visits. A bi-level decomposition approach is proposed, in which decisions and constraints related to the detailed charging schedule and the number of switchings are moved to the lower-level problem. The lower level problem is formulated as Mixed Integer Linear Programming (MILP) problem. The upper-level problem is reduced to a MILP problem using linearization techniques. An exact decomposition scheme is developed for the upper-level MILP problem. A case study and comprehensive computer experiments are provided.

Spatially-explicit optimization of an integrated wind-hydrogen supply chain network for the transport sector: The case study of Sicily

Almost half of the European railways are served by diesel-powered trains used for both cargo and passenger transport. Similar considerations apply to the bus sector in urban and suburban areas. The decarbonization of the transport sector can be achieved by using hydrogen as a fuel for trains and buses. However, the main barrier to adopting hydrogen fuel cell trains and buses is the construction of adequate infrastructure. Thus, to foster a sustainable energy transition, existing renewable plants should be considered when designing new hydrogen supply chains. In the proposed Sicilian case study, existing wind farms are chosen as electricity production plants. Most of them have been shut down due to grid unbalancing issues; still, the curtailed renewable electricity could be used to produce zero-carbon hydrogen via water electrolysis, significantly reducing costs. To account for this opportunity, this study models the hydrogen production, transportation, and refueling stages, employing a mixed-integer linear programming approach to find the optimal location and capacities of hydrogen infrastructure, while minimizing the total daily cost of the supply chain. The model is applied to Sicily and different scenarios for varying hydrogen demands for trains and buses are analyzed and discussed. Results show that capital expenses cover more than 90% of the total cost of the supply chain, with a hydrogen cost dispensed at the filling station ranging from 6.32 to 9.02 €/kgH2. The environmental analysis shows that as hydrogen demand rises, so do the carbon emissions from the distribution stage. However, the impact of using wind-generated electricity in terms of avoided emissions is notably greater. Finally, it is shown that transporting hydrogen in gaseous form using tube trailers is economically more attractive than transportation in liquid form with tanker trucks in the early stages of the network.

Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control

Considering the integration of distributed energy resources (DER) such as distributed generation, demand response, and electric vehicles, day-ahead scheduling plays a significant role in the operation of active distribution systems. Therefore, this article proposes a comprehensive methodology for the short-term operational planning of a distribution company (DisCo), aiming to minimize the total daily operational cost. The proposed methodology integrates on-load tap changers, capacitor banks, and flexible loads participating in demand response (DR) to reduce losses and manage congestion and voltage violations, while considering the costs associated with the operation and use of controllable resources. Furthermore, to forecast PV output and load demand behind the meter at the MV/LV distribution transformer level, a short-term net load forecasting model using deep learning techniques has been incorporated. The proposed scheme is solved through an efficient two-stage strategy based on genetic algorithms and dynamic programming. Numerical results based on the modified IEEE 13-node distribution system and a typical 37-node Latin American system validate the effectiveness of the proposed methodology. The obtained results verify that, through the proposed methodology, the DisCo can effectively schedule its installations and DR to minimize the total operational cost while reducing losses and robustly managing voltage and congestion issues.

Robust Operation Scheme of EV Charging Facility With Uncertain User Behavior

With the widespread of electric vehicles (EV), the necessity of installing EV charging facility has rapidly increased. Since operating EV charging facility are costly and complex, choosing a proper power operation scheme is critical in implementing a cost-effective charging service. Therefore, this paper proposes an efficient power operation scheme for EV charging facility by jointly analyzing not only monetary issues with various power rate policies and battery wear-out costs of the energy storage system (ESS) but also the uncertainty issues caused by EV users' behavior under different charging requirements. By considering the issues, this paper analyzes and formulates the problem with robust optimization techniques. By using the real-world EV operation datasets, the effectiveness of the proposed scheme is analyzed in terms of daily peak power and total cost. In addition, by applying the proposed scheme in an actual power system, it is shown that the proposed scheme is able to reduce the overall cost by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20.5\%$</tex-math></inline-formula> and reduce peak power by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$17.3\%$</tex-math></inline-formula> compared to other benchmark models.

A three-stage mechanism for flexibility-oriented energy management of renewable-based community microgrids with high penetration of smart homes and electric vehicles

A multi-stage mechanism for flexibility-oriented energy management (FOEM) of the distribution system is developed in this article, which main novelty is providing the flexibility requirements of the main grid by resources within renewable-based microgrids (RBMGs), including distributed generations (DGs), storage systems (SDs), internet-of-things enabled smart homes (IESHs), and plug-in electric vehicles (PEVs). In the first stage, IESHs are programmed in a decentralized space subject to satisfying the residents' satisfaction index (SI). In the second stage, RBMGs are programmed according to the announced plan of IESHs. Ultimately, in the third stage, the main grid operator calculates the required flexibility capacities of the system according to the announced plans of RBMGs, where a risk-averse strategy is adopted to manage the risk of programming. Next, the main grid operator announces the required flexibility capacities to RBMGs and IESHs so that they can re-plan with the possibility of participating in flexibility markets. The numerical results clarify that the participation of RBMGs, IESHs, and plug-in electric vehicles (PEVs) in providing flexibility capacities not only reduces the dependence of the distribution system on the upstream grid (UG), but also reduces the total daily costs by 30.7%.

Integrating wind energy and compressed air energy storage for remote communities: A bi-level programming approach

The integration of compressed air energy storage and wind energy offers an attractive energy solution for remote areas with limited access to reliable and affordable energy sources. This study presents a design approach for an energy system comprising wind turbines, compressed air energy storage, and diesel generators. The proposed method is based on bi-level programming, enabling the simultaneous optimization of the size and operation of the system while considering the interaction between them. Detailed mechanical design and configuration of the compressed air energy storage system are considered, including the number and size of compressors and turbines, valves, recuperator operating conditions, etc. In contrast with conventional compressed air energy storage systems, operating once a day for peak shaving, the proposed compressed air energy storage system aims to mitigate wind fluctuations. Therefore, it would operate under partial load conditions most of the time, and as a result, the system's off-design modeling is also considered. The results indicate that the proposed system is a promising, cost-effective, reliable energy solution for remote areas, significantly decreasing the average daily total cost and CO2 emissions by 69 % and 76 %, respectively. Additionally, by studying the system's performance under both design and off-design conditions, it is concluded that considering the off-design conditions is critical to ensure a more realistic performance of the system as the system is less likely to utilize the compressed air energy storage in low charging and discharging opportunities.

Design and implementation of a control system for a steel plate cutting production line using programmable logic controller

&lt;span lang="EN-US"&gt;Most of the old machines installed in different industries are managed manually by system operators. This action makes their productivity low and not suitable to cover the needs in other sectors. In addition, these machines are not without risks due to the proximity of the operators to them. The installing a new full system instead of the old one on the same site requires very high costs. So, modernizing the same old system by making its operation automatic has become necessary to reduce the economic cost significantly. In this paper, an automatic control system based on &lt;a name="_Hlk124492942"&gt;&lt;/a&gt;programmable logic controller (PLC) for the cutting steel plate machine that had been managed manually in a factory is designed. The system includes an Encoder that is used to specify the length of the steel plate to be cut by sending a signal to the PLC. The system operation is completed by using human machine interface (HMI) unit to monitor and control the system performance by the system operator. A practical test for the developed system offered more productivity (more than 30%), more safety, reduces human efforts and the total daily production cost (less than one third).&lt;/span&gt;

Coordinated Optimal Power Dispatch Incorporating the Scheduling of Distributed Energy Resources Under the Virtual Power Plant Concept

In this paper, a method is proposed for coordinated optimal power dispatch (OPD) incorporating the scheduling of distributed energy resources (DERs (COPD-IDS). The proposed COPD-IDS aims to minimize the total daily operating cost of a power system by considering the optimal scheduling of DERs. In the problem formulation, the DERs are considered dispatchable limited energy units and treated as a virtual power plant (VPP). The OPD is solved for total hourly cost minimization, using quadratic programming (QP) as a subproblem in COPDIDS. Meanwhile, the total daily operating cost minimization incorporating the scheduling of DERs is solved by particle swarm optimization (PSO) and compared to a genetic algorithm (GA). The proposed COPD-IDS is tested on the modified IEEE 30-bus system under a practical load and the daily profiles of DERs. The simulation results show that the proposed method can minimize the total daily operational cost of the electricity system with the dispatchable condition of DERs using the VPP concept.

A three-layer game theoretic-based strategy for optimal scheduling of microgrids by leveraging a dynamic demand response program designer to unlock the potential of smart buildings and electric vehicle fleets

The proliferation of the number of Smart Buildings (SBs) and the fleet of Electric Vehicles (EVs) in Distribution Systems (DSs) makes the need for new strategies to coordinate them with microgrid (MG) scheduling inevitable. Therefore, this article proposes a three-layer risk-averse game theoretic-based strategy to coordinate SBs and EV fleets with MGs scheduling. In the first layer of this strategy, a Demand Response Program (DRP) is designed for SBs where dynamic incentive tariffs are calculated based on the consumption pattern of subscribers. Then, in the second layer, the scheduling of SBs and EV fleets is done in a decentralized space and considering their participation in the designed DRP. Eventually, in the third layer, the operators of MGs have received the power exchange information of SBs in order to carry out their scheduling in accordance with it. In this layer, the Day-Ahead (DA) scheduling of MGs and DS is done through the implementation of a cooperative game theory. Fluctuations of uncertain operational parameters such as load demand, radiation and wind are embedded in the model by scenario-based technique where a Risk-Averse (RA) strategy is adopted to manage them. Running the proposed three-layer strategy on a 69-node DS containing four MGs showed that this strategy can use the potential of SBs and EV fleets to improve the voltage characteristics in the high-demand period and reduce total daily costs by 13.66% with designing a dynamic-tariff DRP. Moreover, the results reveal that MGs using the Peer-to-Peer (P2P) power exchange option have not only reduced the power losses in the system but also reduced the total daily costs by about 8%.

Costs of psychotropics for outpatients with bipolar disorder in Japan; the MUSUBI 2016 survey

Background Although the costs of bipolar disorder (BD) treatments are associated with local and universal factors, data from non-Western countries remain limited. The associations between clinical features and costs of outpatient pharmacotherapy have not been well characterize. To estimate the costs of outpatient BD treatments and their associations with clinical features in a Japanese population, we investigated with special reference to the costs of medicines constituted the bulk of the total healthcare expense and were steadily increasing. Methods The Multicenter Treatment Survey for Bipolar Disorder (MUSUBI) retrospectively evaluated 3130 patients with BD who visited 176 Japanese psychiatric outpatient clinics in 2016. Clinical features and drug prescriptions were recorded, and the total daily costs of psychotropic drug treatment were calculated. The annual medical costs related to outpatient BD treatments in Japan were estimated based on the corresponding demographics. The associations between daily medical costs and patients’ clinical features were analyzed using multiple regression analysis. Results The daily costs of psychotropic drugs ranged from zero to JPY3245 (mean, JPY349 equivalent to USD32.5) and were exponentially distributed. The annual costs for outpatients BD treatments were approximately 51.9 billion Japanese yens (519 million US dollars). Subsequent multiple regression analysis revealed that social adjustment, depressive symptoms, age, rapid cycling, psychotic symptoms, and comorbid mental disorders correlated strongly with the daily cost of psychotropic drugs. Conclusion The estimated annual costs for outpatient BD treatment in Japan were equivalent to those in OECD countries (except for the US) and higher than those in some Asian countries. The cost of psychotropic treatments was associated with individual characteristics and psychopathological conditions. Key Messages Psychotropic treatment for an outpatient with bipolar disorder has a daily cost approximately JPY350. The annual outpatient treatment cost for bipolar disorder in Japan was estimated to 51.9 billion Japanese yen in 2016. Individual characteristics and psychopathological conditions affected the cost of drug treatment.

Comparison of hospital delivery costs between cesarean section and natural delivery and analysis of influencing factors.

The increasing costs of hospital delivery have increased the economic burden of pregnant women, and the mode of delivery is the main factor affecting the costs of hospital delivery. This study aims to explore the difference in costs between cesarean section and natural delivery, and to provide reference for controlling the increase of hospital delivery costs. The data of inpatient delivery in the Hunan Maternal and Child Health Care Hospital from January 2016 to December 2020 were selected to compare the total inpatient costs and average daily costs of cesarean section and natural delivery. The linear trend model was used to analyze the trend change of inpatient delivery costs and the generalized linear model was used to analyze the influential factors for inpatient delivery costs. The average hospitalization costs of cesarean section (10 447.25 yuan) were higher than that of natural delivery (5 567.95 yuan), and the average daily costs of cesarean section (1 902.57 yuan) were higher than those of natural delivery (1 666.40 yuan). There was no significant increase or decrease in trend for cesarean section, while the average annual growth rate of the costs of natural delivery was 11.79%. The main factors affecting the hospitalization costs of cesarean section and natural delivery included age, occupation, medical insurance, route of admission, length of stay, premature delivery and complications (all P<0.05). The total hospitalization costs and average daily costs of cesarean section are higher than those of natural delivery, but the costs of natural delivery show a faster growth trend, and the hospitalization costs of cesarean section and natural delivery should be controlled by targeted measures.