Incremental cost of premature birth - a public health care payer perspective from Hungary.

BACKGROUND: Preterm birth remains a significant burden to families, health systems and societies. The aim was to quantify the incremental prematurity-related public health expenditure in Hungary and to estimate the potential impact of a decrease in the prevalence of prematurity on the public payer's spending. METHODS: Over a 6-year time horizon, public financing data of inpatient, outpatient and pharmaceutical care for children born at ≥ 25 weeks of gestation in 2009/2010 were retrieved from the Hungarian National Health Insurance Fund database. In descriptive analysis, the public payer's spending was given as cost/capita. The impact of a decrease in prematurity prevalence was specified as the total budget impact. An exchange rate of 294 Hungarian forint/Euro was applied. RESULTS: A total of 93,124 children (including 8.6% who were premature babies) were included in the analysis. A strong negative relationship was shown between gestational age and per capita cost. The 6-year cost of care for the cohort born at 26 weeks of gestation (28,470 Euro per capita) was 24 times higher than that for the cohort born at 40 weeks. First-year inpatient spending accounted for the largest proportion of total health care spending across all gestational ages. All investigated prematurity complications (retinopathy of prematurity, necrotizing enterocolitis, bronchopulmonary dysplasia, intraventricular cerebral bleeding and leukomalacia) resulted in additional significant incremental spending. If 70% of pregnancies ending with preterm birth could be prolonged by 1 week, the savings would be almost 7.0 million Euros in the first 6 years of life. CONCLUSION: This comprehensive analysis of prematurity-related health care spending confirmed that premature infants have much higher costs for care than those born at term in Hungary. These quantitative outcomes can provide essential inputs for the cost-effectiveness analysis of medical technologies and public health interventions that can decrease the prevalence of premature birth. TRIAL REGISTRATION: Not applicable.

DSF Core: Integrated Decision Support for Optimal Scheduling of Lifetime Extension Strategies for Industrial Equipment.

This paper proposes a generic algorithm for industries with degrading and/or failing equipment with significant consequences. Based on the specifications and the real-time status of the production line, the algorithm provides decision support to machinery operators and manufacturers about the appropriate lifetime extension strategies to apply, the optimal time-frame for the implementation of each and the relevant machine components. The relevant recommendations of the algorithm are selected by comparing smartly chosen alternatives after simulation-based life cycle evaluation of Key Performance Indicators (KPIs), considering the short-term and long-term impact of decisions on these economic and environmental KPIs. This algorithm requires various inputs, some of which may be calculated by third-party algorithms, so it may be viewed as the ultimate algorithm of an overall Decision Support Framework (DSF). Thus, it is called "DSF Core". The algorithm was applied successfully to three heterogeneous industrial pilots. The results indicate that compared to the lightest possible corrective strategy application policy, following the optimal preventive strategy application policy proposed by this algorithm can reduce the KPI penalties due to stops (i.e., failures and strategies) and production inefficiency by 30-40%.

Machine learning for energy cost modelling in wastewater treatment plants.

Understanding the energy cost structure of wastewater treatment plants is a relevant topic for plant managers due to the high energy costs and significant saving potentials. Currently, energy cost models are generally generated using logarithmic, exponential or linear functions that could produce not accurate results when the relationship between variables is highly complex and non-linear. In order to overcome this issue, this paper proposes a new methodology based on machine-learning algorithms that perform better with complex datasets. In this paper, machine learning was used to generate high-performing energy cost models for wastewater treatment plants, using a database of 317 wastewater treatment plants located in north-west Europe. The most important variables in energy cost modelling were identified and for the first time, the energy price was used as model parameter and its importance evaluated.

Impact of Institutional Practices and Surgical Complexity on Sarcoma Surgery Costs: Driving Efficiency in Value-Based Healthcare.

BACKGROUND: Sarcomas present a unique challenge within healthcare systems due to their rarity and complex treatment requirements. This study explores the economic impact of sarcoma surgeries across three Swiss tertiary healthcare institutions, utilizing a consistent surgical approach by a single surgeon to eliminate variability in surgical expertise as a confounding factor. METHODS: By analyzing data from 356 surgeries recorded in a real-world-time data warehouse, this study assesses surgical and hospital costs relative to institutional characteristics and surgical complexity. RESULTS: Our findings reveal significant cost variations driven more by institutional resource management and pricing strategies than by surgical techniques. Surgical and total hospitalization costs were analyzed in relation to tumor dignity and complexity scores, showing that higher complexity and malignancy significantly increase costs. Interestingly, it was found that surgical costs accounted for only one-third of the total hospitalization costs, highlighting the substantial impact of non-surgical factors on the overall cost of care. CONCLUSIONS: The study underscores the need for standardized cost assessment practices and highlights the potential of predictive models in enhancing resource allocation and surgical planning. By advocating for value-based healthcare models and standardized treatment guidelines, this research contributes to more equitable and sustainable healthcare delivery for sarcoma patients. These insights affirm the necessity of including a full spectrum of care costs in value-based models to truly optimize healthcare delivery. These insights prompt a reevaluation of current policies and encourage further research across diverse geographical settings to refine cost management strategies in sarcoma treatment.

Cost Modelling for Recycling Fiber-Reinforced Composites: State-of-the-Art and Future Research.

Fiber-reinforced composites, such as carbon and glass fibers, are widely used across various industries. This is mainly a result of their outperforming properties in contrast with traditional materials. As a response to the environmental legal enforcement of the recycling of composite materials, several recycling methods such as mechanical, thermal, and chemical recycling, have been developed. Despite various merits, these recycling methods still face challenges, such as the heterogeneity of material, the quality of the recycled product, the high cost of recycling, and a lack of an established market. Since, in many cases, the financial aspect tends to be the major barrier to recycling composites, the appropriate cost modelling of the recycling process requires urgent consideration. To the knowledge of the authors, there is no prior research efforts on the reviewing of cost modelling techniques on composites recycling. Cost modelling research projects for different recycling technologies, with their findings and limitations, are sought from the literature and reported in this paper. It is found that recycling techniques still cannot compete with traditional landfilling in terms of cost, and are dependent on fiber recovery rates and plant capacities. Following a comprehensive literature review, research gaps are identified to formulate the research directions in this field.

Economic impact assessment indicators of circular economy in a decentralised circular water system - Case of eco-touristic facility.

The transition from a linear make-use-dispose model to a Circular Economy (CE) model has gained momentum in recent years. To date, substantive efforts have been put by researchers and practitioners on environmental assessment of circular water systems (CWS). Yet, the economic aspect of CWS has not received the same attention. This research is an attempt to bridge this gap by evaluating the economic viability of a decentralised hybrid rainwater- wastewater-greywater (HRWG) system. For this purpose, a framework of Shadow Pricing- Life Cycle Cost-Benefit (SLCCB) to analyse a CWS is proposed. Shadow pricing could compliment the established Life Cycle Costing (LCC) methods. The main parameters (costs and benefits) of the proposed SLCCB framework are divided into two types: Internal and External. The Internal pricing covers the capital expenditure (CAPEX) and operational expenditure (OPEX), while the External pricing covers the environmental and social costs-benefits of implementing CWS. The proposed SLCCB added to the classical Net Present Value (NPV) and Payback Period (PP) calculations could provide a more realistic evaluation of the economic performance of CWS. To demonstrate the efficacy of the new CE model, a new CWS in Greece was studied. A sensitivity analysis was conducted to assess the impact of the reclaimed water tariffs, internal costs, life span of the project, and the annual discount rate on the SLCCB. The results of the study reveal that the SLCCB of CWS is highly sensitive to these parameters. The economic feasibility of CWS boost with increasing discount rate and reclaimed water tariffs, as well as with decreasing project's life span and internal costs. The conclusion of this research demonstrates that investment in CWS is economically viable if External parameters are taken into consideration.

A Cost Modelling System for Recycling Carbon Fiber-Reinforced Composites.

Cost-effective and environmentally responsible ways of carbon fiber-reinforced composite (CFRP) recycling are increasingly important, owing to the rapidly increasing use of these materials in many industries such as the aerospace, automotive and energy sectors. Product designers need to consider the costs associated with manufacturing and the end-of-life stage of such materials to make informed decisions. They also need to understand the current methods of composite recycling and disposal and their impact on the end-of-life costs. A comprehensive literature review indicated that there is no such tool to estimate CFRP recycling costs without any prior knowledge and expertise. Therefore, this research paper proposed a novel knowledge-based system for the cost modelling of recycling CFRP that does not require in-depth knowledge from a user. A prototype of a cost estimation system has been developed based on existing CFRP recycling techniques such as mechanical recycling, pyrolysis, fluidized bed, and supercritical water. The proposed system has the ability to select the appropriate recycling techniques based on a user's needs with the help of an optimization module based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Estimating recycling costs has taken into consideration various factors such as different material types in different industries, transportation, and dismantling costs. The developed system can be employed to support early-stage designers and decision-making stakeholders in terms of understanding and predicting recycling costs easily and quickly.

Monitoring viral load for the last mile: what will it cost?

INTRODUCTION: Routine viral load testing is the WHO-recommended method for monitoring HIV-infected patients on ART, and many countries are rapidly scaling up testing capacity at centralized laboratories. Providing testing access to the most remote populations and facilities (the "last mile") is especially challenging. Using a geospatial optimization model, we estimated the incremental costs of accessing the most remote 20% of patients in Zambia by expanding the transportation network required to bring blood samples from ART clinics to centralized laboratories and return results to clinics. METHODS: The model first optimized a sample transportation network (STN) that can transport 80% of anticipated sample volumes to centralized viral load testing laboratories on a daily or weekly basis, in line with Zambia's 2020 targets. Data incorporated into the model included the location and infrastructure of all health facilities providing ART, location of laboratories, measured distances and drive times between the two, expected future viral load demand by health facility, and local cost estimates. We then continued to expand the modelled STN in 5% increments until 100% of all samples could be collected. RESULTS AND DISCUSSION: The cost per viral load test when reaching 80% patient volumes using centralized viral load testing was a median of $18.99. With an expanded STN, the incremental cost per test rose to $20.29 for 80% to 85% and $20.52 for 85% to 90%. Above 90% coverage, the incremental cost per test increased substantially to $31.57 for 90% to 95% and $51.95 for 95% to 100%. The high numbers of kilometres driven per sample transported and large number of vehicles needed increase costs dramatically for reaching the clinics that serve the last 5% of patients. CONCLUSIONS: Providing sample transport services to the most remote clinics in low- and middle-income countries is likely to be cost-prohibitive. Other strategies are needed to reduce the cost and increase the feasibility of making viral load monitoring available to the last 10% of patients. The cost of alternative methods, such as optimal point-of-care viral load equipment placement and usage, dried blood/plasma spot specimen utilization, or use of drones in geographically remote facilities, should be evaluated.

Modelling the energy costs of the wastewater treatment process: The influence of the aging factor.

Wastewater treatment plants (WWTPs) are aging and its effects on the process are more evident as time goes by. Due to the deterioration of the facilities, the efficiency of the treatment process decreases gradually. Within this framework, this paper proves the increase in the energy consumption of the WWTPs with time, and finds differences among facilities size. Accordingly, the paper aims to develop a dynamic energy cost function capable of predicting the energy cost of the process in the future. The time variable is used to introduce the aging effects on the energy cost estimation in order to increase the accuracy of the estimation. For this purpose, the evolution of energy costs will be assessed and modelled for a group of WWTPs using the methodology of cost functions. The results will be useful for the managers of the facilities in the decision making process.

Efficacy of antidepressants on measures of workplace functioning in major depressive disorder: A systematic review.

INTRODUCTION: Work-related disability and productivity loss in Major Depressive Disorder (MDD) are critical determinants of patient quality of life and contribute significantly to the human and economic costs of MDD. Notwithstanding the return to work and pre-morbid levels of functioning as a critical therapeutic objective among individuals with MDD, it is unclear whether antidepressant treatment significantly and reliably improves measures of workplace functioning. Herein, we investigate to what extent antidepressant treatment improves workplace functioning among adults with MDD. METHODS: We conducted a systematic review of randomized, double-blind, placebo-controlled or active comparator clinical trials primarily or secondarily investigating the efficacy of antidepressant agents on subjective ratings of workplace functioning and/or measures of work absence. RESULTS: Thirteen placebo-controlled and four active comparator clinical trials reported on the efficacy of agomelatine, bupropion, desvenlafaxine, duloxetine, fluoxetine, levomilnacipran, paroxetine, sertraline, venlafaxine, or vortioxetine on subjective measures of workplace impairment. Overall, antidepressant treatment improved standardized measures of workplace functioning (e.g., Sheehan Disability Scale-work item). One placebo-controlled trial of agomelatine and one clinical trial comparing the efficacy of vortioxetine to that of venlafaxine had mixed results on measures of work absence. LIMITATIONS: Included interventional trials evaluated work-related disability as a secondary outcome using subjective rating scales. CONCLUSION: Extant data suggest that antidepressant treatment improves workplace outcomes in MDD. The capability of antidepressants in improving measures of workplace functioning should be considered in cost-benefit analyses to better inform cost-modelling studies pertaining to antidepressant therapy.

Impact of a borderless sample transport network for scaling up viral load monitoring: results of a geospatial optimization model for Zambia.

INTRODUCTION: The World Health Organization recommends viral load (VL) monitoring at six and twelve months and then annually after initiating antiretroviral treatment for HIV. In many African countries, expansion of VL testing has been slow due to a lack of efficient blood sample transportation networks (STN). To assist Zambia in scaling up testing capacity, we modelled an optimal STN to minimize the cost of a national VL STN. METHODS: The model optimizes a STN in Zambia for the anticipated 1.5 million VL tests that will be needed in 2020, taking into account geography, district political boundaries, and road, laboratory and facility infrastructure. We evaluated all-inclusive STN costs of two alternative scenarios: (1) optimized status quo: each district provides its own weekly or daily sample transport; and (2) optimized borderless STN: ignores district boundaries, provides weekly or daily sample transport, and reaches all Scenario 1 facilities. RESULTS: Under both scenarios, VL testing coverage would increase to from 10% in 2016 to 91% in 2020. The mean transport cost per VL in Scenario 2 was $2.11 per test (SD $0.28), 52% less than the mean cost/test in Scenario 1, $4.37 (SD $0.69), comprising 10% and 19% of the cost of a VL respectively. CONCLUSIONS: An efficient STN that optimizes sample transport on the basis of geography and test volume, rather than political boundaries, can cut the cost of sample transport by more than half, providing a cost savings opportunity for countries that face significant resource constraints.