Decision Analysis Research Articles

Patients' Preferences for Sphingosine-1-Phosphate Receptor Modulators in Multiple Sclerosis Based on Clinical Management Considerations: A Choice Experiment.

Several sphingosine-1-phosphate receptor (S1PR) modulators are available in the US for treating relapsing forms of multiple sclerosis (RMS). Given that these S1PR modulators have similar efficacy and safety, patients may consider the clinical management characteristics of the S1PR modulators when deciding among treatments. However, none of the S1PR modulators is clearly superior in every aspect of clinical management, and for some treatments, clinical management varies based on a patient's comorbid health conditions (e.g., heart conditions [HC]). This study aimed to determine which S1PR modulator patients with relapsing-remitting multiple sclerosis (RRMS) would prefer based on clinical management considerations, and to estimate how different clinical management considerations might drive these preferences. Preferences were explored separately for patients with and without comorbid HC. A multicriteria decision analysis was conducted on S1PR modulators approved to treat RMS: fingolimod, ozanimod, siponimod, and ponesimod. Clinical management preferences of patients with RRMS were elicited in a discrete choice experiment (DCE) in which participants repeatedly chose between hypothetical S1PR modulator profiles based on their clinical management attributes. Attributes included first-dose observations, genotyping, liver function tests, eye examinations, drug-drug interactions, interactions with antidepressants, interactions with foods high in tyramine, and immune system recovery time. Preferences were estimated separately for patients with HC and without HC (noHC). Marginal utilities were calculated from the DCE data for each attribute and level using a mixed logit model. In the multicriteria decision analysis, partial value scores were created by applying the marginal utilities for each attribute and level to the real-world profiles of S1PR modulators. Partial value scores were summed to determine an overall clinical management value score for each S1PR modulator. Four hundred patients with RRMS completed the DCE. Ponesimod had the highest overall value score for patients both without (n=341) and with (n=59) HC (noHC: 5.1; HC: 4.0), followed by siponimod (noHC: 4.9; HC: 3.3), fingolimod (noHC: 3.4; HC: 2.8), and ozanimod (noHC: 0.9; HC: 0.8). Overall, immune system recovery time contributed the highest partial value scores (noHC: up to 1.9 points; HC: up to 1.2 points), followed by the number of drug-drug interactions (noHC: up to 1.2 points; HC: up to 1.7 points). When considering the clinical management of S1PR modulators, the average patient with RRMS is expected to choose a treatment with shorter immune system recovery time and fewer interactions with other drugs. Patients both with and without heart conditions are likely to prefer the clinical management profile of ponesimod over those of siponimod, fingolimod, and ozanimod. This information can help inform recommendations for treating RRMS and facilitate shared decision making between patients and their doctors.

Quality of accounting graduates and employers’ expectationsin Ghana

Purpose This paper aims to examine the skills expected by employers and the skills demonstrated by accounting graduates with data from 160 employers of accounting graduates in Ghana. From the perspective of employers, the authors compare the expected skills to the skills demonstrated by accounting graduates and identify any significant gaps. Design/methodology/approach One-way analysis of variance, T-tests and factor analysis are used to test significant gaps that exist between expected and demonstrated skills by employers and accounting graduates using survey questionnaires and purposive sampling. Findings The results reveal that there are significant gaps between the expected skills of employers and the demonstrated skills of accounting graduates. The authors find generic skills to be more expected by employers, while technical skills are demonstrated most by accounting graduates. The top five skills expected by employers are: business ethics and integrity; problem solving and decision analysis; written communication skills; learning skills; and application of accounting principles. Research limitations/implications The study focuses only on organizations that use fresh accounting graduates in Ghana. Further studies may consider other business-related fields like finance, human resource management and marketing. Practical implications The study provides insights on standards that serve as benchmarks for the conduct of accounting graduates as well as the development of accounting curriculum in Ghana. Accounting education should incorporate work-integrated learning as part of their curriculum to enable accounting graduates to solve problems and make decisions through a practical application of accounting principles, standards and concepts, especially in conducting audit and writing reports. Originality/value This study fills the gap in the literature on accounting education from employers’ perspectives in a developing country. It conducts a comparative analysis between the most important skills required by employers and the ones demonstrated by accounting graduates to provide insights on standards that should serve as benchmarks for the development of the accounting curriculum from an emerging economy perspective.

The Algorithmic Assignment of Incentive Schemes

The assignment of individuals with different observable characteristics to different treatments is a central question in designing optimal policies. We study this question in the context of increasing workers’ performance via targeted incentives using machine learning algorithms with worker demographics, personality traits, and preferences as input. Running two large-scale experiments, we show that (i) performance can be predicted by accurately measured worker characteristics, (ii) a machine learning algorithm can detect heterogeneity in responses to different schemes, (iii) a targeted assignment of schemes to individuals increases performance significantly above the level of the single best scheme, and (iv) algorithmic assignment is more effective for workers who have a high likelihood to repeatedly interact with the employer or who provide more consistent survey answers. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy [Grant EXC 2126/1-390838866]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03362 .

Cropping calendar, agroclimatic and agroecological zoning for rainfed maize (Zea mays L.) under different rainfall scenarios in a semi-arid region of Brazil

Context The productivity of crops is influenced by the available net water balance, which relies on a complex interplay of edaphic and climatic factors. In this sense, rainfall is the leading water input for crop growth and yield, especially in water-scarce regions under semi-arid climates worldwide. Aims This study aimed to define the optimum cropping calendar and the agroclimatic and agroecological zones for rainfed maize in the state of Ceará, which is predominantly located within the Brazilian semi-arid region, assuming different rainfall scenarios. Methods We considered the subdivision of the study area into eight homogeneous precipitation regions according to the regional rainfall pattern. The climatic water balance for each region over two decades was calculated from historical weather data series, assuming three rainy scenarios: dry, regular, and rainy. The agroclimatic zoning was defined through the crop water balance results, soil classification, land use and occupation, slope and temperature, weighted through a multi-criteria decision analysis based on the Analytic Hierarchy Process. Key results The recommended planting period was broader in rainy conditions. However, there may be some dry spells between two 10-day intervals of the calendar, which impair plant growth depending on their intensity. For the dry scenario, the agroclimatic zoning showed that the marginal water deficiency covered 96.55% of the study area. Conclusions Rainfed maize cultivated under the semi-arid climate of Ceará state is viable in rainy conditions, partially restricted in regular years, and not recommended in dry years. Implications This study emphasises the importance of climate monitoring in effective planning for rainfed maize cultivation in semi-arid regions.

EquiCity game: a mathematical serious game for participatory design of spatial configurations

We propose a mathematical framework for developing social-choice games that are designed to mediate decision-making processes for city planning, urban area redevelopment, and architectural configuration of urban housing complexes. The proposed framework features a digital serious gaming approach for participatory design to support transparency and inclusion in the process of decision-making and ensure an equitable balance of sustainable development goals in spatial design outcomes. The mathematical process consists of a Markovian design machine for balancing the design decisions of actors, a massing configurator equipped with fuzzy logic and multi-criteria decision analysis, algebraic graph-theoretical accessibility evaluators, and automated solar-climatic evaluators using geospatial computational geometry. We demonstrate the effectiveness of the framework by implementing a multi-player online game that facilitates a participatory decision-making workshop for forming multi-functional building complexes by providing a generative configurator equipped with automated appraisal/scoring mechanisms for revealing the aggregate impact of alternatives. The EquiCity game empowers a group of decision-makers to reach a fair consensual spatial design by mathematically simulating many rounds of reasonable trade-offs between their decisions, with different levels of interest or control over various types of investments. The novelty of the framework is in its capability to encompass decision-making about the most idiosyncratic aspects of a site related to its heritage status and cultural significance to the most generic aspects such as balancing access to sunlight for the site while respecting ‘the right to sunlight’ of the neighbours of the site, ensuring coherence of the entire configuration with regards to a network of desired closeness ratings, the satisfaction of a programme of requirements, and intricately balancing individual development goals in conjunction with communal goals and environmental design codes.

Correction to: Location selection for waste disposal boxes: a geographic information systems based Pythagorean fuzzy multi‑criteria decision analysis

Correction to: Location selection for waste disposal boxes: a geographic information systems based Pythagorean fuzzy multi‑criteria decision analysis

Cost effectiveness and decision analysis for national airport screening options to reduce risk of COVID-19 introduction in Uganda, 2020

IntroductionEarly during the COVID-19 outbreak, various approaches were utilized to prevent COVID-19 introductions from incoming airport travellers. However, the costs and effectiveness of airport-specific interventions have not been evaluated.MethodsWe evaluated policy options for COVID-19-specific interventions at Entebbe International Airport for costs and impact on COVID-19 case counts, we took the government payer perspective. Policy options included; (1)no screening, testing, or mandatory quarantine for any incoming traveller; (2)mandatory symptom screening for all incoming travellers with RT-PCR testing only for the symptomatic and isolation of positives; and (3)mandatory 14-day quarantine and one-time testing for all, with 10-day isolation of persons testing positive. We calculated incremental cost-effectiveness ratios (ICERs) in US$ per additional case averted.ResultsExpected costs per incoming traveller were $0 (Option 1), $19 (Option 2), and $766 (Option 3). ICERs per case averted were $257 for Option 2 (which averted 4,948 cases), and $10,139 for Option 3 (which averted 5,097 cases) compared with Option I. Two-week costs were $0 for Option 1, $1,271,431 Option 2, and $51,684,999 Option 3. The per-case ICER decreased with increase in prevalence. The cost-effectiveness of our interventions was modestly sensitive to the prevalence of COVID-19, diagnostic test sensitivity, and testing costs.ConclusionScreening all incoming travellers, testing symptomatic persons, and isolating positives (Option 2) was the most cost-effective option. A higher COVID-19 prevalence among incoming travellers increased cost-effectiveness of airport-specific interventions. This model could be used to evaluate prevention options at the airport for COVID-19 and other infectious diseases with similar requirements for control.

Employee Subordination of Civil Service Officials in the Context of Remote Work: Insights from Polish and Ukranian Experience

The concept of employee subordination is a fundamental aspect of the employment relationship, which is currently undergoing evolution towards greater employee independence. This article addressed the issue of employee subordination using civil servants as a case study. The selection of this professional group was based on the classic form of subordination within their employment relationship, which has deep historical roots. The COVID-19 pandemic normalised the remote work, including in administration, presenting a new phenomenon in Poland and Ukraine. The adoption of remote work in government administration marked a significant shift towards more flexible employment rules for civil servants. This study examined how Poland and Ukraine have integrated the remote work into their post-pandemic realities. The paper investigated whether heightened flexibility in employment regulations regarding remote work can align with the distinctive responsibilities of government officials. The methodology involved a comprehensive review and analysis of legal doctrines, judicial decisions, and scholarly literature related to subordination in labour law. Using legal frameworks and analyses, the article explored manifestations of subordination in labour law, focusing on the control and management exerted by employers over workers. Specific consequences of legal subordination, such as organisational prerogatives, regulatory powers, and disciplinary authority, were scrutinised within the employment relationships. The first section of the article explored subordination as a central feature of the employment relationship, highlighting indicators of subordination among civil servants in government administration, particularly considering employees appointed to their roles. The subsequent section discussed Polish and Ukrainian experiences with the remote work during the pandemic, alongside current legal provisions enabling the remote work in government administration post-pandemic. Drawing from legal frameworks in Poland and Ukraine, the article analysed the implications and intensity of legal subordination within employment relationships, examining employer powers like organisational control, regulatory oversight, and disciplinary actions that define the degree of subordination experienced by employees. In conclusion, the article contended that subordination should be understood not solely as a hierarchical relationship but as a system of ordered relations. The primary function of subordination is to establish order and structure within systems governed by legal norms, governmental bodies, or officials. This article was based on a paper presented at the LLRN-6 conference in Warsaw (June 25-27, 2023).

Decision Analysis of Cold Chain Supply Chain Based on a Stochastic Evolution Model

Accelerating the development of cold chain logistics for agricultural products is not only an objective requirement for the development of agricultural product circulation but also a necessary demand to promote rural revitalization and upgrade agricultural consumption. Addressing the evolutionary problems of cold chain transportation in uncertain environments, this study considers the actual stochastic disturbances in the cold chain supply chain system, introducing Gaussian white noise to reflect the random perturbations experienced during the evolution of the cold chain. A three-party stochastic evolutionary game model is established involving farmers, logistics enterprises, and government regulatory authorities. Based on this model, numerical simulation analysis is conducted to explore the effects of different factors such as the intensity of random disturbances and the loss transfer coefficient on the fluctuation of strategic choices of the entities and the convergence speed to a stable state during evolution. Finally, combining the results of numerical simulation, relevant policies and recommendations are proposed to provide insights and guidance for the development of cold chain logistics in agricultural products.

DGPRI, a new liver fibrosis assessment index, predicts recurrence of AFP-negative hepatocellular carcinoma after hepatic resection: a single-center retrospective study

Although patients with alpha-fetoprotein-negative hepatocellular carcinoma (AFPNHCC) have a favorable prognosis, a high risk of postoperative recurrence remains. We developed and validated a novel liver fibrosis assessment index, the direct bilirubin-gamma-glutamyl transpeptidase-to-platelet ratio (DGPRI). DGPRI was calculated for each of the 378 patients with AFPNHCC who underwent hepatic resection. The patients were divided into high- and low-score groups using the optimal cutoff value. The Lasso-Cox method was used to identify the characteristics of postoperative recurrence, followed by multivariate Cox regression analysis to determine the independent risk factors associated with recurrence. A nomogram model incorporating the DGPRI was developed and validated. High DGPRI was identified as an independent risk factor (hazard ratio = 2.086) for postoperative recurrence in patients with AFPNHCC. DGPRI exhibited better predictive ability for recurrence 1–5 years after surgery than direct bilirubin and the gamma-glutamyl transpeptidase-to-platelet ratio. The DGPRI-nomogram model demonstrated good predictive ability, with a C-index of 0.674 (95% CI 0.621–0.727). The calibration curves and clinical decision analysis demonstrated its clinical utility. The DGPRI nomogram model performed better than the TNM and BCLC staging systems for predicting recurrence-free survival. DGPRI is a novel and effective predictor of postoperative recurrence in patients with AFPNHCC and provides a superior assessment of preoperative liver fibrosis.

Exploring equality and sustainability trade-offs of energy transition outcomes in the United States in 2050

With increasing focus on equitable and just energy transition, it is critical to understand the trade-offs of different decarbonization outcomes across economic, environmental, and social sustainability criteria. In this analysis, we use a multi-criteria decision analysis to quantify sustainability outcomes across 32 decarbonization outcomes in 2050 in the U.S. The economic sustainability criteria we use are system cost, national average retail rate, and electricity system employment. The environmental sustainability criteria we use are life cycle greenhouse gas emissions, life cycle water depletion, life cycle land transformation, and air pollution fatalities. The social sustainability (distributional impacts) criteria we use are retail rate equality across states, electricity employment equality across low-income households, and air pollution disparities across census tracts. We evaluate performance across these criteria under eleven different stakeholder preference scenarios. We find that decarbonization policies with indefinitely extended tax credits have the highest sustainability score under equal criteria weighting, with greater investments in renewable energy technologies, and result in better environmental, system cost, job, and air pollution disparities compared to mid-case scenarios, that only include current policies and CO2 reduction targets. We also see that our multi-criteria decision analysis identifies decarbonization outcomes that would not have been identified as optimal under a single objective, which highlights the importance of trade-off analyses to understand decarbonization outcomes more holistically.

Energy Efficiency Optimisation of Joint Computational Task Offloading and Resource Allocation Using Particle Swarm Optimisation Approach in Vehicular Edge Networks

With the progression of smart vehicles, i.e., connected autonomous vehicles (CAVs), and wireless technologies, there has been an increased need for substantial computational operations for tasks such as path planning, scene recognition, and vision-based object detection. Managing these intensive computational applications is concerned with significant energy consumption. Hence, for this article, a low-cost and sustainable solution using computational offloading and efficient resource allocation at edge devices within the Internet of Vehicles (IoV) framework has been utilised. To address the quality of service (QoS) among vehicles, a trade-off between energy consumption and computational time has been taken into consideration while deciding on the offloading process and resource allocation. The offloading process has been assigned at a minimum wireless resource block level to adapt to the beyond 5G (B5G) network. The novel approach of joint optimisation of computational resources and task offloading decisions uses the meta-heuristic particle swarm optimisation (PSO) algorithm and decision analysis (DA) to find the near-optimal solution. Subsequently, a comparison is made with other proposed algorithms, namely CTORA, CODO, and Heuristics, in terms of computational efficiency and latency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating an 8% and a 5% increase in energy efficiency.

A multiple criteria decision analysis to establish the use cases and candidate point of care tests to enter into a platform trial of multiple in vitro diagnostic point of care tests in the prehospital environment

Background There are increasing demands on Emergency Medical Services. More efficient treatment pathways are required to support conveyance decision making and patient referral in prehospital care. Point of Care testing is increasingly available and utilised across the NHS to support optimal ways of working. We aimed to design and conduct a Multiple Criteria Decision Analysis to prioritise in vitro point of care tests and use cases for inclusion in a platform trial of in vitro point of care testing in UK Emergency Medical Services. Methods We designed a Multiple Criteria Decision Analysis that included systematic scoping reviews stakeholder recruitment, two stakeholder surveys and two stakeholder workshops to scope the use cases, explore criteria and map use cases, evaluate the criteria and measure the use cases against the criteria. Results We recruited 32 stakeholders. We developed a scoring matrix with 4 criteria for scoring the use cases and 8 criteria for scoring the point of care tests and applied weighting determined from survey results. Use cases were scored by the stakeholders against 4 criteria. The 3 highest scoring use cases were point of care troponin testing in: possible Acute Myocardial Infarction, lactate testing in suspected sepsis and in trauma. We developed the process for scoring the point of care tests to be completed close to a proposed trial to allow for a changes in technology. Conclusions We successfully designed a Multiple Criteria Decision Analysis to identify use cases and candidate tests for inclusion in a future platform trial of in vitro point of care testing in UK Emergency Medical Services. We identified 3 use cases for evaluation in a platform trial of in vitro point of care testing: troponin testing in possible acute myocardial infarction, lactate testing in suspected sepsis and lactate testing to identify occult haemorrhage in trauma.

Clinical and Genomic-Based Decision Support System to Define the Optimal Timing of Allogeneic Hematopoietic Stem-Cell Transplantation in Patients With Myelodysplastic Syndromes

PURPOSE Allogeneic hematopoietic stem-cell transplantation (HSCT) is the only potentially curative treatment for patients with myelodysplastic syndromes (MDS). Several issues must be considered when evaluating the benefits and risks of HSCT for patients with MDS, with the timing of transplantation being a crucial question. Here, we aimed to develop and validate a decision support system to define the optimal timing of HSCT for patients with MDS on the basis of clinical and genomic information as provided by the Molecular International Prognostic Scoring System (IPSS-M). PATIENTS AND METHODS We studied a retrospective population of 7,118 patients, stratified into training and validation cohorts. A decision strategy was built to estimate the average survival over an 8-year time horizon (restricted mean survival time [RMST]) for each combination of clinical and genomic covariates and to determine the optimal transplantation policy by comparing different strategies. RESULTS Under an IPSS-M based policy, patients with either low and moderate-low risk benefited from a delayed transplantation policy, whereas in those belonging to moderately high-, high- and very high-risk categories, immediate transplantation was associated with a prolonged life expectancy (RMST). Modeling decision analysis on IPSS-M versus conventional Revised IPSS (IPSS-R) changed the transplantation policy in a significant proportion of patients (15% of patient candidate to be immediately transplanted under an IPSS-R–based policy would benefit from a delayed strategy by IPSS-M, whereas 19% of candidates to delayed transplantation by IPSS-R would benefit from immediate HSCT by IPSS-M), resulting in a significant gain-in-life expectancy under an IPSS-M–based policy ( P = .001). CONCLUSION These results provide evidence for the clinical relevance of including genomic features into the transplantation decision making process, allowing personalizing the hazards and effectiveness of HSCT in patients with MDS.

Mapping Vulnerability to Potential Crisis Events in Surabaya City: A GIS-Based Approach

Background This study aims to develop a vulnerability map for Surabaya using GIS-based Multi-Criteria Decision Analysis (MCDA) to assess the city’s vulnerability to COVID-19. Methods Six key factors influencing vulnerability were identified and their relative importance determined through the Analytic Hierarchy Process (AHP) pairwise comparison matrix. GIS was utilized to classify Surabaya’s vulnerability into five levels: very low, low, medium, high, and very high. Results The resulting vulnerability map provides essential insights for decision-makers, healthcare professionals, and disaster management teams. It enables strategic resource allocation, targeted interventions, and formulation of comprehensive response strategies tailored to specific needs of vulnerable districts. Conclusions Through these measures, Surabaya can enhance its resilience and preparedness, ensuring the well-being of its residents in the face of potential emergency outbreaks.

Incentivizing Organ Donation Under Different Priority Rules: The Role of Information

This paper examines the incentive to register for deceased organ donation under alternative organ allocation priority rules, which may prioritize registered donors and/or patients with higher valuations for organ transplantation. Specifically, the donor priority rule grants higher priority on the organ waiting list to those who have previously registered as donors. The dual-incentive priority rules allocate organs based on donor status, followed by individual valuations within the same donor status, or vice versa. Both theoretical and experimental results suggest that the efficacy of the donor priority rule and the dual-incentive priority rules critically depends on the information environment. When organ transplantation valuations are unobservable prior to making donation decisions, the hybrid dual-incentive rules generate higher donation rates. In contrast, if valuations are observable, the dual-incentive priority rules create unbalanced incentives between high- and low-value agents, potentially undermining the efficacy of the hybrid dual-incentive rules in increasing overall donation rates. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: This research is supported by the National Natural Science Foundation of China [Grants 72173103, 72373127, and 71988101], the Singapore Ministry of Education (MOE) Academic Research Fund Tier 1 [RG57/20], and the Open Foundation of Key Laboratory of Interdisciplinary Research of Computation and Economics (Shanghai University of Finance and Economics), Ministry of Education of China. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.01530 .

Urban Echoes: Exploring the Dynamic Realities of Cities through Digital Twins

Digital twins (DTs) are highly valuable tools for urban planning as they provide a virtual replica of the physical city, integrating real-time data and simulations to enhance the decision-making and management processes. The use of DTs expands the possibilities for data integration and visualization in urban contexts. This includes real-time data measurements from multiple sources, such as sensors and IoT devices, facilitating comprehensive insights. DT’s virtual representation helps authorities and planners visualize urban dynamics and improve their understanding of urban ecosystems, energy efficiency, traffic management, emergency response, and more. DT supports the simulation and modeling of different scenarios in an urban built environment, enabling the predictive analysis of transformation decisions and the anticipation of future trends and challenges. This paper highlights the assumptions and ongoing progress in the development of a DT for the city of Turin (Italy), focusing on a range of applications, such as the extraction of built and natural environment features, land use data, road network and pavement quality, and signage, along with continuous model updates over time.

Economic Evaluation of HLA-B*15:02 Genotyping for Asian Australian Patients With Epilepsy

The HLA-B*15:02 allele has been associated with an increased risk of carbamazepine-induced Stevens-Johnson syndrome and toxic epidermal necrolysis in specific Asian populations (including Han Chinese, Malaysian, Thai, and Vietnamese individuals). While HLA-B*15:02 genotype testing in Asian populations is recommended by several international prescribing guidelines, it is not subsidized by the Medicare Benefits Schedule in Australia. To evaluate the cost-effectiveness of HLA-B*15:02 genotyping in Asian Australian patients with epilepsy. A model with components of decision analysis and Markov simulation was developed to simulate clinical trajectories of adult Asian Australian patients with newly diagnosed epilepsy being considered for carbamazepine treatment. Cost-effectiveness and cost-utility analyses over a lifetime time horizon were conducted from the perspective of the Australian health care sector. The study was conducted in May 2023 and data analysis was performed from August 2023 to November 2023. No HLA-B*15:02 genotyping and the empirical initiation of treatment with carbamazepine vs HLA-B*15:02 genotyping and the initiation of treatment with valproate in allele carriers. Life-years (LYs), quality-adjusted life-years (QALYs), and costs in 2023 Australian dollars (A$); incremental cost-effectiveness ratios. HLA-B*15:02 screening was associated with an additional mean cost of A$114 (95% CI, -A$83 to A$374; US$76; 95% CI, -US$55 to US$248) and a reduction in 0.0152 LYs (95% CI, 0.0045 to 0.0287 LYs) but improvement by 0.00722 QALYs (95% CI, -0.0247 to -0.01210) compared with no screening, resulting in an incremental cost-effectiveness ratio of A$15 839 per QALY gained (US$10 523 per QALY). Therefore, universal genotyping for Asian Australian individuals was cost-effective compared with current standards of practice at the A$50 000 per QALY willingness-to-pay threshold. Sensitivity analyses demonstrated that the intervention remained cost-effective across a range of costs, utilities, transition probabilities, and willingness-to-pay thresholds. At the A$50 000 per QALY willingness-to-pay threshold, universal screening was the preferred strategy in 88.60% of simulations. The results of this economic evaluation suggest that HLA-B*15:02 screening represents a cost-effective choice for Asian Australian patients with epilepsy who are being considered for treatment with carbamazepine.

Integrating socioeconomic and ecological data into restoration practice.

Driven by the United Nations Decade on Restoration and international funding initiatives, such as the Mangrove Breakthrough, investment in mangrove restoration is expected to increase. Yet, mangrove restoration efforts frequently fail, usually because of ad hoc site-selection processes that do not consider mangrove ecology and the socioeconomic context. Using decision analysis, we developed an approach that accounts for socioeconomic and ecological data to identify sites with the highest likelihood of mangrove restoration success. We applied our approach in the Biosphere Reserve Marismas Nacionales Nayarit, Mexico, an area that recently received funding for implementing mangrove restoration actions. We identified 468 potential restoration sites, assessed their restorability potential based on socioeconomic and ecological metrics, and ranked sites for implementation with spatial optimization. The metrics we used included favorable conditions for propagules to establish and survive under sea-level rise, provision of ecosystem services, and community dynamics. Sites that were selected based on socioeconomic or ecological metrics alone had lower likelihood of mangrove restoration success than sites that were selected based on integrated socioeconomic and ecological metrics. For example, selecting sites based on only socioeconomic metrics captured 16% of the maximum attainable value of functioning mangroves able to provide propagules to potential restoration sites, whereas selecting sites based on ecological and socioeconomic metrics captured 46% of functioning mangroves. Our approach was developed as part of a collaboration between nongovernmental organizations, local government, and academics under rapid delivery time lines given preexisting mangrove restoration implementation commitments. The systematic decision process we used integrated socioeconomic and ecological considerations even under short delivery deadlines, and our approach can be adapted to help mangrove restoration site-selection decisions elsewhere.

Кількісне оцінювання функціональної ефективності мережевих структур за аналізом чутливості оптимізаційної моделі до зовнішніх впливів

The real network organizational structure is constantly under the influence of external influences, which can cause unexpected consequences due to the cascading spread of relevant disturbances directed at critically important parameters. Parametric sensitivity analysis is the latest computational procedure, the results of which provide the researcher with an idea and quantitative assessment of the appropriate response of the structure in the form of an analytical platform as a composition and interaction of scenario analysis, cascade process development and optimization modeling technology. The main task is timely adaptation to expected changes in business analytics, oriented to optimal resources distribution. The generalized minimum cost problem is a typical example of widespread problems of optimal distribution and use of limited, in particular, energy resources, of any nature and purpose, using the scientific management and analysis of decisions, in its network model attention is paid to the potentials of nodes. The maximum flow problem is an example of the active and effective application of methods and models of flows optimization, where the processes and objects used a network organization, the energy systems and complexes investigated, where arc parameters are critical in its model. In the article, for these classic optimization problems, mathematical and spreadsheet models were built on concrete examples. Direct and dual mathematical programming problems were solved, the following stages were carried out - the organization of a computer experiment and the construction of an analytical platform for evaluating the behavior of the network structure under the influence of external influences and disturbances as their consequences. To take into account the specific conditions of real objects with a network structure at the stage of modification, the model supplemented with appropriate restrictions and correction of input data sets. The obtained results should be useful for planning and management personnel and top-management for discussion and decision-making regarding, for example, the optimal placement of energy equipment, the weighted distribution of energy flows in the "source-sink" system or the appointment of executors for work, automated project management using project networks, etc. Keywords: optimization modeling, maximum network flow, minimum cost flows, network organizational structures, decision making, spreadsheet modeling and analytics.