Total Social Cost Research Articles

Risk-averse distributionally robust optimization for construction waste reverse logistics with a joint chance constraint

The uncertainty of the amount of construction and demolition waste (CDW) generation affects the CDW reverse logistics network service level. We investigate a CDW reverse logistics network location-routing problem considering uncertainties. To minimize the total social cost, a two-stage risk-averse distributionally robust optimization model is developed, which aims to optimize the location and number of CDW disposal facilities and the CDW transportation scheme. We introduce the mean-conditional value at risk measure and a joint chance constraint into our model to consider the government’s risk aversion. The above model is approximated as a standard second-order cone programming model (SOCP) considering a special case. To exactly solve the SOCP, we design an outer approximation algorithm. Several performance tests and a case study are proposed, and sensitivity analysis provides helpful managerial insights.

Truthful interval covering

We initiate the study of a novel problem in mechanism design without money, which we term Truthful Interval Covering (TIC). An instance of TIC consists of a set of agents each associated with an individual interval on a line, and the objective is to decide where to place a covering interval to minimize the total social or egalitarian cost of the agents, which is determined by the intersection of this interval with their individual ones. This fundamental problem can model situations of provisioning a public good, such as the use of power generators to prevent or mitigate load shedding in developing countries. In the strategic version of the problem, the agents wish to minimize their individual costs, and might misreport the position and/or length of their intervals to achieve that. Our goal is to design truthful mechanisms to prevent such strategic misreports and achieve good approximations to the best possible social or egalitarian cost. We consider the fundamental setting of known intervals with equal lengths and provide tight bounds on the approximation ratios achieved by truthful deterministic mechanisms. For the social cost, we also design a randomized truthful mechanism that outperforms all possible deterministic ones. Finally, we highlight a plethora of natural extensions of our model for future work, as well as some natural limitations of those settings.

Differentiated health insurance policy for coordinating healthcare referral in hierarchical healthcare systems with an internet diagnosis platform

The integration of the Internet with traditional medical services is poised to transform health insurance policies. This study aims to explore the coordinating role of differentiated health insurance policies within the context of Internet healthcare. A four-stage sequential game decision-making model is developed within a queuing framework to address scenarios involving online patient referrals and misdiagnoses. The model begins by analyzing the equilibrium arrival strategy of patients, followed by the determination of optimal service capacity strategies for a nonprofit community health center (CHC) and optimal pricing strategies for a for-profit general hospital (GH). Additionally, the model describes an optimal differentiated subsidy strategy for the government aimed at minimizing total social costs. Analysis reveals that under certain conditions, an increase in the service price at GH relative to CHC can lead to a higher influx of online patients visiting GH in person. Furthermore, when the number of online patients exceeds a specific threshold, it not only prompts the government to increase the disparity in health insurance subsidies between the two hospital tiers but also encourages GH to reduce its service prices and offer free services to online patients. Numerical experiments explore the effects of government budgets, sharing ratios, and other variables on the system's equilibrium state, providing several managerial insights. Notably, when patients' misdiagnosis costs are partially covered, increasing GH's misdiagnosis cost-sharing ratio not only enhances the patient arrival rate but also enhances GH's profitability.

A new model for comprehensively evaluating the economic and environmental effects of vehicle-to-grid(V2G) towards carbon neutrality

Vehicle-to-grid (V2G) technology enables electric vehicles (EVs) to serve as flexible load storage resources, which is expected to play a pivotal role in pursuing carbon neutrality. However, existing studies on the effect of V2G at different stages of carbon neutrality is not sufficient, and there is a lack of discussion on the optimal adoption period for V2G in the context of electricity marketization trading. To fill this gap, a new methodology is proposed in this study to analyze multi-dimension effects of V2G towards carbon neutrality. The model is a novel attempt of applying partial market equilibrium model to depict the interaction between electricity suppliers and V2G adopters. By applying in a China's case, the results demonstrate that: (1) EVs with V2G can substitute 22.2 %–30.1 % energy storage and accelerate the phase-out of coal-fired power. (2) V2G can effectively mitigate electricity price fluctuations, moreover, more fast charging infrastructure will strengthen such effect. (3) V2G only become attractive when renewable energy penetration rate reaches 80 %, otherwise, it cannot effectively reduce the total social cost and carbon emission. (4) In the carbon neutrality scenario with limited emission, the emission reduction effect of V2G is weakened, however, its economic benefit keeps increasing.

Carbon emission allocation policy making in liner shipping: A novel approach toward equitable and efficient maritime sustainability

Maritime shipping, an integral component of global trade, accounts for nearly 3% of global carbon dioxide emissions and faces significant challenges in mitigating its carbon footprints. This study addresses the shortcomings of current carbon emission surcharges levied by shipping companies and the deficiencies of allocation methods utilized by the government, such as the inequity of uniform carbon emission charges across diverse origin–destination (od) pairs and the reliance on potentially inaccurate self-reported emissions data to assign carbon emission allowances. We propose a novel approach to carbon emission allocation per twenty-foot equivalent unit (TEU) container per od pair in liner shipping from the perspective of the government, aiming to enhance government oversight and minimize social costs. Leveraging a bi-level programming model, our research navigates the complex interplay between government regulatory objectives and shipping companies’ operational decisions, identifying a socially optimal carbon allocation policy. Our approach introduces three innovative carbon allocation methods that advance beyond the traditional metric that typically focuses solely on emissions and cargo volume. Our first two innovative methods integrate shipping distances into the carbon allocation process, specifically considering both the actual traveling distance and the shortest sailing distance. Furthermore, we propose the third innovative “harmonic distance,” which is a convex combination of the actual traveling distance and the shortest sailing distance. This measure effectively reflects both the operational realities and equity concerns associated with shipping services. Our findings reveal the effectiveness of utilizing the shortest sailing distance for carbon allocation per TEU per od pair, offering a straightforward, route-independent, and equitable solution. This approach diverges from current industrial practices, favoring a fairer emission allocation basis and balancing the interests of governments, shipping companies, and consumers. Through comprehensive experiments, we demonstrate the superiority of this method in minimizing total social costs and providing a viable path toward environmental sustainability and economic efficiency in liner shipping.

A new two-stage emergency material distribution framework for urban rainstorm and flood disasters to promote the SDGs

Under the coupled effects of climate change and urbanization, urban rainstorm and flood has become more frequent and intense. The effect of the flood disasters emergency response has a direct impact on the success or failure of the rescue. Emergency material distribution is one of the most critical steps in the emergency response phase and is crucial to achieve the Sustainable Development Goals (SDGs). This study proposes a two-stage emergency material distribution framework for urban rainstorm and flood disasters. The entropy weight method and technique for order preference by similarity to ideal solution (EWM-TOPSIS) was adopted to incorporate the difference in relief-demand urgency into the emergency material distribution principle. The mathematical model was established by minimizing emergency travel time, inequity and social costs (logistic costs and deprivation costs) as objective functions. The gamultiobj algorithm was used to solve the model. The proposed framework is further applied in Xi'an city of China. The results showed that the affected sites were categorized into disaster risk levels I, II and III. The Pareto solution set was obtained by optimizing each conflicting objective. The emergency material distribution plan was selected from the Pareto solution set based on the actual situation. In the plan, the flood disasters emergency travel time totaled 6.48 h, the inequity totaled 3.69, and the total social costs reached 226,584 CNY. The proposed framework is more consistent with the needs of the actual rescue by comparing the four models generated plans. Decision-makers should not only consider minimizing dispatch time and social costs, but also the trade-off between equity and prioritization of distribution. This study helps to provide new ideas for sustainable urban development and emergency management. Urban rainstorm and flood disasters change with the changing environment dynamically, and it is difficult to collect dynamic disaster information on the affected sites. Thus our further study will focus on improving the prediction accuracy of dynamic urban rainstorm and flood disasters.

Gasoline and electric vehicle ownership rationing over time: Lottery vs. First-come-first-served schemes

This paper addresses the ownership rationing issues of gasoline vehicles (GV) and electric vehicles (EV) in a given time horizon. A state-owned vehicle manufacturer is assumed to be the producer of these vehicles. Two typical rationing schemes for the GV and EV ownership that have been implemented in some Chinese megacities, namely a simultaneous lottery scheme for both GV and EV and a hybrid scheme consisting of lottery for GV and first-come-first-served (FCFS) for EV, are investigated. Multi-period social cost minimization models are proposed for determining the optimal auto quota (i.e., the number of vehicles to be produced) and the optimal frequency of ownership allocation for each scheme in the given time horizon. In the proposed models, residents’ heterogeneity is considered in terms of their values of time (VOTs). The properties of the proposed models are analytically explored, and a comparison between the lottery mode and the FCFS mode is made. The results show that the lottery mode outperforms the FCFS mode in terms of the total social cost. The VOT threshold of the subsequent participants in auto rationing schemes tends to be higher than that of the preceding participants. The decision of EV subsidy should be cautiously made due to a tug of war between the resultant decreased pollutant emissions and increased traffic congestion. The implementation of the auto ownership rationing schemes can cause inequity issue in terms of the changes of travel costs of different-income residents. A Pareto-improvement strategy is presented to balance equity and efficiency of the rationing schemes.

Stepped, evidence-based and integrated care service model vs. usual care for mental disorders: A randomized controlled trial (RECOVER)

Stepped, evidence-based and integrated care service models have the potential to be used as a reference for mental health services. RECOVER aimed to evaluate cost savings, effectiveness, and cost-effectiveness of such a model within a two arm, assessor- and data analysist-blinded RCT in Hamburg, Germany. Participants aged 16–79 years with mental disorders were randomly assigned either to RECOVER or treatment as usual (TAU). Primary outcomes comprised costs, effectiveness (combined symptoms, functioning, quality of life), and cost-effectiveness, hierarchically ordered. Outcomes were evaluated according to the ITT principle, group differences regarding costs with adjusted generalized linear models, effectiveness with ANCOVA models, and cost-effectiveness with the incremental cost-effectiveness ratio (ICER) and cost-effectiveness acceptability curves (CEACs). Between 1/1/2018 and 12/31/2020, n = 891 were finally included (n = 477 in RECOVER, n = 444 in TAU). RECOVER was associated with significantly lower annual total costs (-22 %), health and social care costs (-25 %) and hospital costs (-50 %). Effectiveness analyses showed a significantly better outcome for RECOVER with the fully imputed data . The CEACs descriptively demonstrated that RECOVER was cost-effective with a probability of >95 %. Treatment in RECOVER resulted in substantial cost reductions with better cost-effectiveness. RECOVER can be recommended as a reference model for comprehensive and integrated mental health services.

Costs of care trajectories of people with dementia compared with matched controls. Longitudinal analysis of linked health and administrative data.

To provide insight into the health and social care costs during the disease trajectory in persons with dementia and the impact of institutionalization and death on healthcare costs compared with matched persons without dementia. Electronic health record data from family physicians were linked with national administrative databases to estimate costs of primary care, medication, secondary care, mental care, home care and institutional care for people with dementia and matched persons from the year before the recorded dementia diagnosis until death or a maximum of 4years after the diagnosis. Total mean health and social care costs among persons with dementia increased substantially during the disease trajectory, mainly due to institutional care costs. For people who remained living in the community, mean health and social care costs are higher for people with dementia than for those without dementia, while for those who are admitted to a long-term care facility, mean health and social care costs are higher for people without dementia than for those with dementia. The steep rise in health and social care costs across the dementia care trajectory is mainly due to increasing costs for institutional care. For those remaining in the community, home care costs and hospital care costs were the main cost drivers. Future research should adopt a societal perspective to investigate the influence of including social costs.

Agriculture models for restoring degraded land to enhance CO2 biosequestration and carbon credits in the Vindhyan region of India

The study's objective was to evaluate the status of converted degraded land into productive agricultural models by improving the physicochemical properties of the soil, soil organic matter (SOM), soil organic carbon (SOC) fractions (active and passive), and microbial biomass carbon (MBC), while also generating carbon (C) credit for additional farmers' income. Six models were analyzed, namely: (1) Arjun forest-based agroecosystems (AFBAE); (2) Lemon grass-based agroecosystems (LGBAE); (3) Legume-cereal-moong-based agroecosystems (LCMBAE); (4) Bael-black mustard-based agroecosystems (BMBAE); (5) Guava-wheat-based agroecosystems (GWBAE), and (6) Custard apple -lentil -based agroecosystems (CALBAE). These models were replicated three times in a randomized block design (RBD). Soil samples were collected from the study area at two depths (0–0.30 and 0.30–0.60 m). At a 0–0.30 m depth, the highest bulk density (ρb) of 1.50 Mg m−3 was observed in LCMBAE, while the lowest ρb of 1.43 Mg m−3 was recorded in BMBAE. The soil organic carbon (SOC) and SOC stock values exhibited a range of 4.2–7.7 g kg−1 and 19.0–33.4 Mg ha−1, respectively. In the AFBAE, the highest levels of 163.1 % MBC were found over LCMBAE. At a 0–0.30 m depth, the recalcitrant index (RI) and lability index (LI) ranged from 0.35–0.46 to 1.97–2.11, respectively. Additionally, the AFBAE exhibited the highest total biomass accumulation (39.23 Mg ha−1), carbon dioxide (CO2) biosequestration (287.9 Mg ha−1), and the total social cost of CO2 at US$ 277 ha−1. Furthermore, in the AFBAE, there was a 198.1 % increase in total C credit (US$ 161 ha−1) compared to LCMBAE (US$ 54 ha−1). However, at 0.30–0.60 m depths, GWBAE and CALBAE were statistically equivalent (p ≤ 0.05) in total C stocks. Principal component analysis (PCA) reveals that component-1 accounts for 77.4 % of the variability, while component-2 contributes 18.6 %. This article aimed to convert the degraded land into a sustainable agricultural module by increasing SOC and CO2 biosequestration and producing more C-credit, or climate currency, on underutilized land.

Dynamical vaccination behavior with risk perception and vaccination rewards.

Vaccination is the most effective way to control the epidemic spreading. However, the probability of people getting vaccinated changes with the epidemic situation due to personal psychology. Facing various risks, some people are reluctant to vaccinate and even prefer herd immunity. To encourage people to get vaccinated, many countries set up reward mechanisms. In this paper, we propose a disease transmission model combining vaccination behaviors based on the SIR (Susceptible-Infected-Recovered) model and introduce three vaccination mechanisms. We analyze the impact of the infection rate and the recovery rate on the total cost and the epidemic prevalence. Numerical simulations fit with our intuitive feelings. Then, we study the impact of vaccination rewards on the total social cost. We find that when vaccination rewards offset vaccination costs, both the total cost and the epidemic prevalence reach the lowest levels. Finally, this paper suggests that encouraging people to get vaccinated at the beginning of an epidemic has the best effect.

Preferences and Willingness to Pay for Preservation: The Case of a Proposed Hydropower Project in Southern Iceland

When decision makers use a financial approach to review the costs and benefits of new renewable energy projects, the economic value of changes in social welfare resulting from impacts on natural resources is not included in the analysis. Iceland’s policy objective for carbon neutrality by 2040 will require constructing new power plants. This study aimed to estimate the economic preservation value for the Hólmsá and Tungufljót river basins in southern Iceland using the contingent valuation method. A web-based survey of Icelandic residents was conducted between 13 November 2022 and 19 December 2022 using a stratified random sample of 2909 adults. The response rate was 46.6%, with 35.1% of respondents indicating a willingness to pay (WTP) to protect the area from hydropower development. After excluding protest voters and accounting for those with and without WTP, the estimated mean WTP equaled ISK 5515. When scaled to the adult population of Icelandic residents, that equates to a total environmental and social cost of between ISK 1.52 and 1.70 billion. Compared to the estimated levelized cost of energy for the Hólmsárvirkjun at Atley hydropower project, this aggregation equates to an estimated 5.6% markup in total project costs, excluding transmission lines. Accounting for these economic costs in decision making allows for a more holistic understanding of new energy project costs and net present benefits.

Capacity Allocation Method of Pumped-Storage Power Station for Multi-Level Market in New Power System

With the development of the electricity spot market, pumped-storage power stations are faced with the problem of realizing flexible adjustment capabilities and limited profit margins under the current two-part electricity price system. At the same time, the penetration rate of new energy has increased. Its uncertainty has brought great pressure to the operation of the power system. The ramp market and its market mechanism have emerged as the times require. To this end, this article proposes a bidding strategy for pumped-storage power stations to participate in multi-level markets such as the ramp market. Considering the demand calculation of ramping services, a two-layer model of pumped storage’s participation in multiple markets is constructed. The upper level makes trading decisions with the goal of maximizing pumped-storage revenue; the lower level aims to minimize the total social cost and jointly clears the primary and auxiliary markets. The income from pumped storage participating in the main energy and ramp-up auxiliary markets at the same time is significantly higher than the income from the two-part electricity price system. Its flexible adjustment ability can be quantified, reducing dependence on capacity electricity charges and providing a theoretical reference for cost recovery and profitability of future pumped-storage power stations.

Site Selection and Capacity Determination of Electric Hydrogen Charging Integrated Station Based on Voronoi Diagram and Particle Swarm Algorithm

In response to challenges in constructing charging and hydrogen refueling facilities during the transition from conventional fuel vehicles to electric and hydrogen fuel cell vehicles, this paper introduces an innovative method for siting and capacity determination of Electric Hydrogen Charging Integrated Stations (EHCIS). In emphasizing the calculation of vehicle charging and hydrogen refueling demands, the proposed approach employs the Voronoi diagram and the particle swarm algorithm. Initially, Origin–Destination (OD) pairs represent car starting and endpoints, portraying travel demands. Utilizing the traffic network model, Dijkstra’s algorithm determines the shortest path for new energy vehicles, with the Monte Carlo simulation obtaining electric hydrogen energy demands. Subsequently, the Voronoi diagram categorizes the service scope of EHCIS, determining the equipment capacity while considering charging and refueling capabilities. Furthermore, the Voronoi diagram is employed to delineate the EHCIS service scope, determine the equipment capacity, and consider distance constraints, enhancing the rationality of site and service scope divisions. Finally, a dynamic optimal current model framework based on second-order cone relaxation is established for distribution networks. This framework plans each element of the active distribution network, ensuring safe and stable operation upon connection to EHCIS. To minimize the total social cost of EHCIS and address the constraints related to charging equipment and hydrogen production, a siting and capacity model is developed and solved using a particle swarm algorithm. Simulation planning in Sioux Falls city and the IEEE33 network validates the effectiveness and feasibility of the proposed method, ensuring stable power grid operation while meeting automotive energy demands.

Dynamic pricing analysis of railway freight transport under different carbon emission penalties

in order to better promote the transportation industry to reduce carbon emissions and improve the competitiveness of railway freight in the whole freight market, to achieve the “double carbon” strategy and promote the “transit rail”. In this paper, considering the dynamic pricing of freight volume and freight price, a two-layer programming model is established under the minimum social total cost of railway and road. The upper level considers the income of railway freight, and the lower level considers the total social cost of freight transportation, so as to realize the increase of railway freight revenue and the decrease of the general total expenditure paid by freight owners. Considering the transportation of bulk commodity freight from shandong to sichuan, it is concluded that the railway freight market has a good development prospect under the consideration of the total social expenditure such as carbon emission. Under the carbon emission charge, the appropriate increase of railway freight rate will not lead to the reduction of railway freight volume. As rail freight becomes more competitive under increasingly stringent carbon emissions penalties, the volume of rail freight will continue to rise even after appropriate increases in rail freight fees. At this point, rail freight companies can transport more goods and gain higher returns, slightly increasing the overall cost borne by shippers in a broad sense, and helping to reduce carbon emissions in the process of freight transport.

Commercial tobacco endgame themes in the Australian media from 2000 to 2021

BackgroundConventional tobacco control is dominated by demand-reduction measures, whereas commercial tobacco endgame (endgame) policies address the key drivers that maintain the tobacco epidemic, such as Tobacco Industry interference in policymaking,...

The social benefits resulting from electric vehicle smart charging balancing economy and decarbonization

Smart charging is gaining more attention with the popularity of electric vehicles (EVs). Smart charging which aims only at reducing electricity costs or reducing carbon dioxide (CO2) emissions is efficient in achieving these objectives. However, the times of lower electricity costs and CO2 emissions are not identical in power grids, putting the two objectives in conflict, which may lower the social benefits of EV smart charging. This study investigates the potential social benefit improvement of smart charging by balancing the economy and decarbonization objectives. Considering five regions located in the United States as examples, it is deduced that smart charging balancing economy and decarbonization objectives can reduce the total social costs of EV charging by 14.5%–35.1%. Furthermore, this study analyzes the impacts of three factors on the social benefits change of EV smart charging, namely charging behavior, charger power, and the social costs of CO2. In the context of transport electrification, this study provides important guidance on how to adjust the charging mode of EVs in the future to improve social benefits.

AN INTEGRATED TRANSITIONAL CARE INTERVENTION IMPROVES PATIENT-REPORTED OUTCOMES IN OLDER ADULTS WITH STROKE

Abstract The transition between hospital and home is associated with significant challenges resulting in hospital readmissions and poor health outcomes. This study aimed to test, in real-world clinical practice, the effectiveness of a Transitional Care Stroke Intervention (TCSI) compared to usual care on patient-reported outcomes and health and social service use costs in older adults (> 55 years) with stroke and multimorbidity (> 2 chronic conditions). The TCSI was delivered virtually by an interprofessional (IP) team, and included care coordination/system navigation support, phone/video visits, monthly IP team conferences, and an online resource co-designed with patient partners to support self-management and system navigation. The primary outcome was risk of hospital readmission after six-months. Secondary outcomes included physical and mental functioning, stroke self-management, patient experience, and health and social service costs. Ninety participants were enrolled (44 intervention, 46 control); 11 (12%) participants were lost to follow-up, leaving 79 (39 intervention, 40 control). Differences favoring the intervention group were seen in physical functioning (SF-12 PCS mean difference: 5.10; 95% CI: 1.58-8.62, p=0.005), self-management (Southampton Stroke Self-Management Questionnaire mean difference: 6.00; 95% CI: 0.51—11.50, p=0.03), and patient experience (Person-Centred Coordinated Care Experiences Questionnaire mean difference: 2.64, 95% CI: 0.81, 4.47, p=0.005). No significant between-group differences were seen for baseline to six-month risk of hospital readmission or total health and social service costs. The TCSI produced greater gains in patient-reported physical health, self-management and experience compared with usual stroke care. These improvements were achieved without increasing total healthcare costs.

Optimal configuration of dynamic wireless charging facilities considering electric vehicle battery capacity

Dynamic wireless charging facilities deployed on the road network offer an effective charging method to alleviate the electric vehicle users’ range anxiety and thus facilitate the promotion of electric vehicles. The main benefit of wireless charging is that it extends EV’s driving range by enabling EV en-route recharging, which consequently reduces the required battery size for EVs. Basically, given densely distributed wireless charging lanes on the road network, smaller and less expensive batteries would be able to meet the travel demand. From the societal point of view, the reduction of battery size/capacity is capable of justifying more construction of wireless charging lanes. While previous research works on optimal deployment of wireless charging facilities ignored this benefit, this study aims to explicitly consider the tradeoff between the costs of building recharging infrastructure and manufacturing batteries from the societal point of view in the optimal configuration of dynamic wireless charging facilities. The problem is formulated into a bi-level programming model, wherein the upper-level model seeks to minimize the total social cost, and the lower-level model captures EV drivers’ choices in terms of battery size, travel route, and driving and charging behavior. To handle the problem, we first relax the path cost constraint and apply the linearization techniques to reformulate the problem into a mixed-integer linear programming, from which a lower bound of the problem can be obtained. An efficient surrogate optimization algorithm is then developed to solve the problem.

S7: Updates on Electroconvulsive Therapy practice and research

Electroconvulsive Therapy (ECT) has evolved into a highly safe, effective, and well tolerated treatment for several severe psychiatric conditions and has become a necessary tool in the armamentarium of modern psychiatry. In this symposium we will review current state of the art clinical practices in Electroconvulsive Therapy (ECT) and new data on research.More specifically Dr. Charles Kellner will review the elements of optimal patient selection/preparation in the ECT consultation and review clinical decision-making during a course of ECT (including electrode placement, stimulus dosing and other technical procedural details.)Dr Raphael Braga will review the evidence for the use of ECT as an augmentation to antipsychotic medications strategy for the treatment of patients with schizophrenia who are resistant to medications, including clozapine.Dr. Brent Forester will discuss the rationale and evidence base for the safety and efficacy of ECT for the treatment of Agitation in Dementia. Agitation is the most common neuropsychiatric symptom of dementia, accounts for about 12% of the total health and social care costs (approximately $31billion/year). It is implicated in a vicious cycle of negative events including deterioration of family and professional relationships, increased risk of death, and increased caregiver burden, which is a significant predictor for institutionalization or death of patients with dementia. His presentation will highlight the existing evidence base and method of ECT delivery to target symptoms of severe agitation, and review an ongoing multi-site NIA funded clinical trial of ECT for severe agitation in dementia.Dr Søren Dinesen Østergaard will discuss the suicide risk in patients receiving ECT which, despite the well-established anti-suicidal effect of ECT, remains high due to the severity of their underlying illness. He will present data quantifying and identifying risk factors for suicide among patients receiving ECT using data from nationwide Danish registers on 11,780 patients.Dr. Georgios Petrides is the organizer and will moderate this symposium