Research on emergency logistics information traceability model and resource optimization allocation strategies based on consortium blockchain.

In response to increasingly complex social emergencies, this study realizes the optimization of logistics information flow and resource allocation by constructing the Emergency logistics information Traceability model (ELITM-CBT) based on alliance blockchain technology. Using the decentralized, data immutable and transparent characteristics of alliance blockchain technology, this research breaks through the limitations of traditional emergency logistics models and improves the accuracy and efficiency of information management. Combined with the hybrid genetic simulated Annealing algorithm (HGASA), the improved model shows significant advantages in emergency logistics scenarios, especially in terms of total transportation time, total cost, and fairness of resource allocation. The simulation results verify the high efficiency of the model in terms of timeliness of emergency response and accuracy of resource allocation, and provide innovative theoretical support and practical scheme for the field of emergency logistics. Future research will explore more efficient consensus mechanisms, and combine big data and artificial intelligence technology to further improve the performance and adaptability of emergency logistics systems.

Development of big data assisted effective enterprise resource planning framework for smart human resource management.

The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs.

Energy-efficient UAV communication: A NOMA scheme with resource allocation and trajectory optimization.

This work investigates a downlink nonorthogonal multiple access (NOMA) scheme with unmanned aerial vehicle (UAV) aided wireless communication, where a single UAV was regarded as an air base station (ABS) to communicate with multiple ground users. Considering the constraints of velocity and maneuverability, a UAV energy efficiency (EE) model was proposed via collaborative design resource allocation and trajectory optimization. Based on this, an EE maximization problem was formulated to jointly optimize the transmit power of ground users and the trajectory of the UAV. To obtain the optimal solutions, this nonconvex problem was transformed into an equivalent convex optimization problem on the basis of three user clustering algorithms. After several alternating iterations, our proposed algorithms converged quickly. The simulation results show an enhancement in EE with NOMA because our proposed algorithm is nearly 99.6% superior to other OMA schemes.

Research on the impact of digital economy on labor resource allocation: Evidence from China.

This paper establishes a coherent framework for delineating the nexus between the digital economy and the subjective efficacy of labor resource allocation. It elucidates the theoretical underpinnings of the digital economy's impact and its channel effects on the efficiency of labor allocation. Within the digital economy landscape, the phenomena of survivorship bias, digital divide, and algorithmic hegemony wield substantial sway over the efficiency of labor market allocation. Empirical analysis, conducted through a cross-sectional data model, validates the theoretical framework. The findings demonstrate that the digital economy markedly diminishes the subjective efficiency of labor allocation. Notably, this inhibitory effect is more pronounced among female workers, households with multiple residences, the non-unmarried demographic, and individuals over the age of 40, with the most pronounced effect observed among those aged over 60. In the examination of the causative mechanisms, it is discerned that the digital economy attenuates the subjective efficiency of labor allocation by workers through three conduits: alterations in social and economic status, shifts in living standards, and modifications in workplace comfort.

Oriented feature pyramid network for small and dense wheat heads detection and counting.

Wheat head detection and counting using deep learning techniques has gained considerable attention in precision agriculture applications such as wheat growth monitoring, yield estimation, and resource allocation. However, the accurate detection of small and dense wheat heads remains challenging due to the inherent variations in their size, orientation, appearance, aspect ratios, density, and the complexity of imaging conditions. To address these challenges, we propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes. In order to facilitate the development and evaluation of our proposed method, we introduce a novel dataset named the Rotated Global Wheat Head Dataset (RGWHD). This dataset is constructed by manually annotating images from the Global Wheat Head Detection (GWHD) dataset with oriented bounding boxes. Furthermore, we incorporate a Path-aggregation and Balanced Feature Pyramid Network into our architecture to effectively extract both semantic and positional information from the input images. This is achieved by leveraging feature fusion techniques at multiple scales, enhancing the detection capabilities for small wheat heads. To improve the localization and detection accuracy of dense and overlapping wheat heads, we employ the Soft-NMS algorithm to filter the proposed bounding boxes. Experimental results indicate the superior performance of the OFPN model, achieving a remarkable mean average precision of 85.77% in oriented wheat head detection, surpassing six other state-of-the-art models. Moreover, we observe a substantial improvement in the accuracy of wheat head counting, with an accuracy of 93.97%. This represents an increase of 3.12% compared to the Faster R-CNN method. Both qualitative and quantitative results demonstrate the effectiveness of the proposed OFPN model in accurately localizing and counting wheat heads within various challenging scenarios.

Variable resource allocation pattern, biased sex-ratio, and extent of sexual dimorphism in subdioecious Hippophae rhamnoides.

Evolutionary maintenance of dioecy is a complex phenomenon and varies by species and underlying pathways. Also, different sexes may exhibit variable resource allocation (RA) patterns among the vegetative and reproductive functions. Such differences are reflected in the extent of sexual dimorphism. Though rarely pursued, investigation on plant species harbouring intermediate sexual phenotypes may reveal useful information on the strategy pertaining to sex-ratios and evolutionary pathways. We studied H. rhamnoides ssp. turkestanica, a subdioecious species with polygamomonoecious (PGM) plants, in western Himalaya. The species naturally inhabits a wide range of habitats ranging from river deltas to hill slopes. These attributes of the species are conducive to test the influence of abiotic factors on sexual dimorphism, and RA strategy among different sexes. The study demonstrates sexual dimorphism in vegetative and reproductive traits. The sexual dimorphism index, aligned the traits like height, number of branches, flower production, and dry-weight of flowers with males while others including fresh-weight of leaves, number of thorns, fruit production were significantly associated with females. The difference in RA pattern is more pronounced in reproductive traits of the male and female plants, while in the PGM plants the traits overlap. In general, habitat conditions did not influence either the extent of sexual dimorphism or RA pattern. However, it seems to influence secondary sex-ratio as females show their significant association with soil moisture. Our findings on sexual dimorphism and RA pattern supports attributes of wind-pollination in the species. The observed extent of sexual dimorphism in the species reiterates limited genomic differences among the sexes and the ongoing evolution of dioecy via monoecy in the species. The dynamics of RA in the species appears to be independent of resource availability in the habitats as the species grows in a resource-limited and extreme environment.

Ethnic disparities and temporal trends in health resource allocation: a retrospective decadal analysis in Sichuan, a multi-ethnic Province of Southwest China (2009-2019).

BACKGROUND: Research on health resource allocation trends in ethnic minority and impoverished areas in China is limited since the 2009 Medical Reform. This study aimed to investigate the variations and inequalities in health resource distribution among ethnic minority, poverty-stricken, and non-minority regions in Sichuan Province, a multi-ethnic province in Southwest China, from 2009 to 2019. METHODS: The numbers of beds, doctors and nurses were retrospectively sourced from the Sichuan Health Statistics Yearbook between 2009 and 2019. All the 181 counties in Sichuan Province were categorized into five groups: Yi, Zang, other ethnic minority, poverty-stricken, and non-minority county. The Theil index, adjusted for population size, was used to evaluate health resource allocation inequalities. RESULTS: From 2009 to 2019, the number of beds (Bedp1000), doctors (Docp1000), and nurses (Nurp1000) per 1000 individuals in ethnic minority and poverty-stricken counties consistently remained lower than non-minority counties. The growth rates of Bedp1000 in Yi (140%) and other ethnic minority counties (127%) were higher than in non-minority counties (121%), while the growth rates of Docp1000 in Yi (20%) and Zang (11%) counties were lower than non-minority counties (61%). Docp1000 in 33% and 50% of Yi and Zang ethnic counties decreased, respectively. Nurp1000 in Yi (240%) and other ethnic minority (316%) counties increased faster than non-minority counties (198%). The Theil index for beds and nurses declined, while the index for doctors increased. Key factors driving increases in bed allocation include preferential policies and economic development levels, while health practitioner income, economic development levels and geographical environment significantly influence doctor and nurse allocation. CONCLUSIONS: Preferential policies have been successful in increasing the number of beds in health facilities, but not healthcare workers, in ethnic minority regions. The ethnic disparities in doctor allocation increased in Sichuan Province. To increase the number of doctors and nurses in ethnic minority and poverty-stricken regions, particularly in Yi counties, more preferential policies and resources should be introduced.

Needs assessment in long-term care: expression of national principles for priority setting in service allocation.

BACKGROUND: Long-term care services for older adults are characterised by increasing needs and scarce resources. Political strategies have led to the reorganisation of long-term care services, with an increased focus on "ageing in place" and efficient use of resources. There is currently limited research on the processes by which resource allocation decisions are made by service allocators of long-term care services for older adults. The aim of this study is to explore how three political principles for priority setting in long-term care, resource, severity and benefit, are expressed in service allocation to older adults. METHODS: This qualitative study uses data from semi-structured individual interviews, focus groups and observations of service allocators who assess needs and assign long-term care services to older adults in Norway. The data were supplemented with individual decision letters from the allocation office, granting or denying long-term care services. The data were analysed using reflexive thematic analysis. RESULTS: The allocators drew on all three principles for priority setting when assessing older adults' long-term care needs and allocating services. We found that the three principles pushed in different directions in the allocation process. We identified six themes related to service allocators' expression of the principles: (1) lowest effective level of care as a criterion for service allocation (resource), (2) blanket allocation of low-cost care services (resource), (3) severity of medical and rehabilitation needs (severity), (4) severity of care needs (severity), (5) benefit of generous service allocation (benefit) and (6) benefit of avoiding services (benefit). CONCLUSIONS: The expressions of the three political principles for priority setting in long-term care allocation are in accordance with broader political trends and discourses regarding "ageing in place", active ageing, an investment ideology, and prioritising those who are "worse off". Increasing attention to the rehabilitation potential of older adults and expectations that they will take care of themselves increase the risk of not meeting frail older adults' care needs. Additionally, difficulties in defining the severity of older adults' complex needs lead to debates regarding "worse off" versus potentiality in future long-term care services allocation. TRIAL REGISTRATION: Not applicable.

Accounting for future populations in health research.

The research we fund today will improve the health of people who will live tomorrow. But future people will not all benefit equally: decisions we make about what research to prioritize will predictably affect when and how much different people benefit from research. Organizations that fund health research should thus fairly account for the health needs of future populations when setting priorities. To this end, some research funders aim to allocate research resources in accordance with disease burden, prioritizing illnesses that cause more morbidity and mortality. In this article, I defend research funders' practice of aligning research funding with disease burden but argue that funders should aim to align research funding with future-rather than present-disease burden. I suggest that research funders should allocate research funding in proportion to aggregated estimates of disease burden over the period when research could plausibly start to yield benefits until indefinitely into the future.

Monetary valuation of one year in full capability and health based on demographics, health status, income and well-being.

BACKGROUND: Budget constraints in health-care systems have led to the popularity of Cost Effectiveness Thresholds (CET) to achieve efficient allocation of resources. The capability approach has been hailed for its potentially richer evaluative capabilities compared to the QALY in terms of thresholds. Extensive research, however, is still limited. RESEARCH DESIGN AND METHODS: This study estimated the monetary value of a year in full capability (YFC) and compared it to monetary value of a QALY for the Hungarian population. Data was collected from a large, cross sectional, representative online survey on the adult Hungarian population. Applying the wellbeing valuation method, health, capability, and income were then regressed against wellbeing to estimate 'shadow prices' for one QALY and YFC controlling for gender, age, employment, education, marital and social support. To examine 'core' regression coefficients, a robustness check was conducted. RESULTS: Health (VAS) and capability (ICECAP-A) had a positive and significant effect on Subjective Well-Being. The monetary values of one QALY and one YFC were 39 459 EUR and 58 148 EUR respectively. CONCLUSIONS: These tools provide a systematic approach to determining 'compensating income' for certain illnesses, disabilities and levels of pain. The capability approach shown to be broader than the QALY.

An ALNS-based approach for the traffic-police-routine-patrol-vehicle assignment problem in resource allocation analysis of traffic crashes.

OBJECTIVES: Imbalances between limited police resource allocations and the timely handling of road traffic crashes are prevalent. To optimize resource allocations and route choices for traffic police routine patrol vehicle (RPV) assignments, a dynamic crash handling response model was developed. METHODS: This approach was characterized by two objective functions: the minimum waiting time and the minimum number of RPVs. In particular, an adaptive large neighborhood search (ALNS) was designed to solve the model. Then, the proposed ALNS-based approach was examined using comprehensive traffic and crash data from Ningbo, China. RESULTS: Finally, a sensitivity analysis was conducted to evaluate the bi-objective of the proposed model and simultaneously demonstrate the efficiency of the obtained solutions. Two resolution methods, the global static resolution mode, and real-time dynamic resolution mode, were applied to explore the optimal solution. CONCLUSIONS: The results show that the optimal allocation scheme for traffic police is 13 RPVs based on the global static resolution mode. Specifically, the average waiting time for traffic crash handling can be reduced to 5.5 min, with 53.8% less than 5.0 min and 90.0% less than 10.0 min.

Supporting equitable and responsible highway safety improvement funding allocation strategies - Why AI prediction biases matter.

The existing methodologies for allocating highway safety improvement funding closely rely on the utilization of crash prediction models. Specifically, these models produce predictions that estimate future crash hazard levels in different geographical areas, which subsequently support the future funding allocation strategies. In recent years, there is a burgeoning interest in applying artificial intelligence (AI)-based models to perform crash prediction tasks. Despite the remarkable accuracy of these AI-based crash prediction models, they have been observed to yield biased prediction outcomes across areas of different socioeconomic statuses. These biases are primarily attributed to the inherent measurement and representation biases of AI-based prediction models. More precisely, measurement bias arises from the selection of target variables to reflect crash hazard levels, while representation bias results from the issue of imbalanced number of samples representing areas with different socioeconomic statuses within the dataset. Consequently, these biased prediction outcomes have the potential to perpetuate an unfair allocation of funding resources, contributing to worsen social inequality over time. Drawing upon a real-world case study in North Carolina, this study designs an AI-based crash prediction model that utilizes previous sociodemographic and crash-related variables to predict future severe crash rate of each area to reflect the crash hazardous level. By incorporating a fair regression framework, this study endeavors to transform the crash prediction model to become both fair and accurate, aiming to support equitable and responsible safety improvement funding allocation strategies.

The study on setting priorities of zoonotic agents for medical preparedness and allocation of research resources.

The aim of this study is to develop a scoring platform to be used as a reference for both medical preparedness and research resource allocation in the prioritization of zoonoses. Using a case-control design, a comprehensive analysis of 46 zoonoses was conducted to identify factors influencing disease prioritization. This analysis provides a basis for constructing models and calculating prioritization scores for different diseases. The case group (n = 23) includes diseases that require immediate notification to health authorities within 24 hours of diagnosis. The control group (n = 23) includes diseases that do not require such immediate notification. Two different models were developed for primary disease prioritization: one model incorporated the four most commonly used prioritization criteria identified through an extensive literature review. The second model used the results of multiple logistic regression analysis to identify significant factors (with p-value less than 0.1) associated with 24-hour reporting, allowing for objective determination of disease prioritization criteria. These different modeling approaches may result in different weights and positive or negative effects of relevant factors within each model. Our study results highlight the variability of zoonotic disease information across time and geographic regions. It provides an objective platform to rank zoonoses and highlights the critical need for regular updates in the prioritization process to ensure timely preparedness. This study successfully established an objective framework for assessing the importance of zoonotic diseases. From a government perspective, it advocates applying principles that consider disease characteristics and medical resource preparedness in prioritization. The results of this study also emphasize the need for dynamic prioritization to effectively improve preparedness to prevent and control disease.

A fuzzy interval dynamic optimization model for surface and groundwater resources allocation under water shortage conditions, the case of West Azerbaijan Province, Iran.

The allocation of water in areas which face shortage of water especially during hot dry seasons is of utmost importance. This is normally affected by various factors, the management of which takes a lot of time and energy with efforts falling infertile in many cases. In recent years, scholars have been trying to investigate the applicability of fuzzy interval optimization models in attempts to address the problem. However, a review of literature indicates that in applicating such models, the dynamic nature of the problem has mostly been overlooked. Therefore, the aim of the present study is to provide a fuzzy interval dynamic optimization model for the allocation of surface and groundwater resources under water shortage conditions in West Azerbaijan Province, Iran. In so doing, an optimization model for the allocation of water resources was designed and then was validated by removing surface and groundwater resources and analyzing its performance once these resources were removed. The model was then applied in the case study of ten regions in West Azerbaijan Province and the optimal allocation values and water supply percentages were determined for each region over 12 periods. The results showed that the increase in total demand has the greatest effect while the increase in groundwater industrial demand has the least effect on the supply reduction rate. The increase of uncertainty up to 50% in the fuzzy interval programming would lead to subsequent increases in groundwater extraction by up to 19% and decreases in water supply by up to 10%. The increase of uncertainty in the fuzzy interval dynamic model would cause an increase in groundwater extraction to slightly more than 10% and a decrease in water supply to 0.05%. Therefore, implementing the fuzzy interval dynamic programming model would result in better gains and would reduce uncertainty effects. This would imply that using a mathematical model can result in better gains and can provide better footings for more informed decisions by authorities for managing water resources.

Regional differences in health resource allocation: a longitudinal study in the Chengdu-Chongqing economic circle, China.

OBJECTIVE: To analyse regional differences in health resource allocation in the Chengdu-Chongqing economic circle. DESIGN: A longitudinal analysis that collected data on health resource allocation from 2017 to 2021. SETTING: The number of beds, health technicians, licensed (assistant) physicians, registered nurses and financial allocations per 1000 population in the 42 regions of Chengdu-Chongqing economic circle were used for the analysis. METHODS: The entropy weight technique for order preference by similarity to an ideal solution (TOPSIS) method and the rank sum ratio (RSR) method were used to evaluate the health resource allocation. RESULTS: The number of licensed (assistant) physicians per 1000 population in the Chengdu-Chongqing economic circle (3.01) was lower than the average in China (3.04) in 2021. According to the entropy weight-TOPSIS method, Yuzhong in Chongqing had the largest C-value and the highest ranking. Jiangbei in Chongqing and Chengdu and Ya'an in Sichuan Province had higher C-values and were ranked in the top 10. Jiangjin, Hechuan, Tongnan and Zhongxian in Chongqing and Guang'an in Sichuan Province had lower C-values and were all ranked after the 30th place. According to the RSR method, the 42 regions were divided into three grades of good, medium and poor. The health resource allocations of Yuzhong, Jiangbei, Nanchuan, Jiulongpo and Shapingba in Chongqing and Chengdu and Ya'an in Sichuan Province were of good grade, those of Tongnan, Jiangjin, Yubei and Dazu in Chongqing and Guang'an and Dazhou in Sichuan Province were of poor grade, and the rest of the regions were of medium grade. CONCLUSION: The regional differences in health resource allocation in the Chengdu-Chongqing economic circle were more obvious, the health resource allocation in Chongqing was more polarised and the health resource allocation in Sichuan Province was more balanced, but the advantaged regions were not prominent enough.

Agent-based systems in healthcare.

This Special Issue is dedicated to discussing which are the advantages, challenges and open issues in the application of the agent-based approach as a part of the digital transformation in the healthcare sector. Agent-based technology in healthcare optimises resource allocation and coordination and supports clinical decision-making. Challenges, such as model reliability and interdisciplinary collaboration, must be addressed for widespread adoption. Embracing this technology promises improved healthcare delivery and better patient outcomes. Six papers, out of the many submitted, have been accepted for publication, each one discussing an aspect of this broad field.

Priority-setting for hospital funding of high-cost innovative drugs and therapeutics: A qualitative institutional case study.

OBJECTIVES: Rising costs of innovative drugs and therapeutics (D&Ts) have led to resource allocation challenges for healthcare institutions. There is limited evidence to guide priority-setting for institutional funding of high-cost D&Ts. This study sought to identify and elaborate on the substantive principles and procedures that should inform institutional funding decisions for high-cost off-formulary D&Ts through a case study of a quaternary care paediatric hospital. METHODS: Semi-structured, qualitative interviews, both virtual and in-person, were conducted with institutional stakeholders (i.e. staff clinicians, senior leadership, and pharmacists) (n = 23) and two focus groups at The Hospital for Sick Children in Toronto, Canada. Participants involved in, and impacted by, high-cost off-formulary drug funding decisions were recruited through stratified, purposive sampling. Participants were approached for study involvement between July 27, 2020 and June 7, 2022. Data was analysed through reflexive thematic analysis. RESULTS: Institutional resource allocation for high-cost D&Ts was identified as ethically challenging but critical to sustainable access to novel therapies. Important substantive principles included: 1) clinical evidence of safety and efficacy, 2) economic considerations (direct costs, opportunity costs, value for money), 3) ethical principles (social justice, professional/organizational responsibility), and 4) disease-specific considerations. Multidisciplinary deliberation was identified as an essential procedural component of decision-making. Participants identified tension between innovation and the need for evidence-based decision-making; clinician and institutional responsibilities; and value for money and social justice. Participants emphasized the role of health system-level funding allocation in alleviating the financial and moral burden of decision-making by institutions. CONCLUSIONS: This study identifies values and processes to aid in the development and implementation of institutional resource allocation frameworks for high-cost innovative D&Ts.

Optimizing health resource allocation for improving timely HIV diagnosis in China.

INTRODUCTION: The Joint United Nations Programme on HIV/AIDS (UNAIDS) updated the 95-95-95 targets for the HIV endgame in 2030. To achieve the first target in a timely manner, we investigate the optimized strategy of resource allocation to maximize timely HIV diagnosis in 14 populations in China. METHODS: We developed a mathematical model by integrating epidemiological, demographical and behavioural data from 12 high-risk and two general populations to evaluate the impact of various resource allocation strategies of HIV testing on HIV incidence in China. We identified the optimized allocation strategy that maximizes the number of HIV diagnoses at an estimated total spending on HIV tests in China and calculated the per-capita cost of new HIV case detection. RESULTS: We estimated that 144,795 new HIV cases may occur annually in 14 populations in China, with a total annual spending of US$2.8 billion on HIV testing. The largest proportion of spending was allocated to general males (44.0%), followed by general females (42.6%) and pregnant women (5.1%). Despite this allocation strategy, only 45.5% (65,867/144,795, timely diagnosis rate) of annual new infections were diagnosed within a year of acquisition, with a cost of $42,852 required for each new HIV case detection. By optimizing the allocation of HIV testing resources within the same spending amount, we found that general females received the highest proportion of spending allocation (45.1%), followed by low-risk men who have sex with men (13.9%) and pregnant women (8.4%). In contrast, the proportion of spending allocation for the general males decreased to 0.2%. With this optimized strategy, we estimated that 120,755 (83.4%) of annual new infections would be diagnosed within a year of acquisition, with the cost required for one HIV case detection reduced to $23,364/case. Further spending increases could allow for significant increases in HIV testing among lower-risk populations. CONCLUSIONS: Optimizing resource allocation for HIV testing in high-risk populations would improve HIV timely diagnosis rate of new infections and reduce cost per HIV case detection.

It Is Time to Reconsider the 3% Discount Rate.

OBJECTIVES: Health technology assessment (HTA) guidance often recommends a 3% real annual discount rate, the appropriateness of which has received limited attention. This article seeks to identify an appropriate rate for high-income countries because it can influence projected cost-effectiveness and hence resource allocation recommendations. METHODS: The author conducted 2 Pubmed.gov searches. The first sought articles on the theory for selecting a rate. The second sought HTA guidance documents. RESULTS: The first search yielded 21 articles describing 2 approaches. The "Ramsey Equation" sums contributions by 4 factors: pure time preference, catastrophic risk, wealth effect, and macroeconomic risk. The first 3 factors increase the discount rate because they indicate future impacts are less important, whereas the last, suggesting greater future need, decreases the discount rate. A fifth factor-project-specific risk-increases the discount rate but does not appear in the Ramsey Equation. Market interest rates represent a second approach for identifying a discount rate because they represent competing investment returns and hence opportunity costs. The second search identified HTA guidelines for 32 high-income countries. Twenty-two provide no explicit rationale for their recommended rates, 8 appeal to market interest rates, 3 to consistency, and 3 to Ramsey Equation factors. CONCLUSIONS: Declining consumption growth and real interest rates imply HTA guidance should reduce recommended discount rates to 1.5 to 2+%. This change will improve projected cost-effectiveness for therapies with long-term benefits and increase the impact of accounting for long-term drug price dynamics, including reduced prices attending loss of market exclusivity.

Health Professional vs Layperson Values and Preferences on Scarce Resource Allocation.

Importance: COVID-19 prompted rapid development of scarce resource allocation (SRA) policies to be implemented if demand eclipsed health systems' ability to provide critical care. While SRA policies follow general ethical frameworks, understanding priorities of those affected by policies and/or tasked with implementing them is critical. Objective: To evaluate whether community members and health care profesionals (HCP) agree with SRA protocols at the University of California (UC). Design, Setting, and Participants: This survey study used social media and community-partnered engagement to recruit participants to a web-based survey open to all participants aged older than 18 years who wished to enroll. This study was fielded between May and September 2020 and queried participants' values and preferences on draft SRA policy tenets. Participants were also encouraged to forward the survey to their networks for snowball sampling. Data were analyzed from July 2020 to January 2024. Main Outcomes and Measures: Survey items assessed values and preferences, graded on Likert scales. Agreement was tabulated as difference in Likert points between expressed opinion and policy tenets. Descriptive statistics were tested for significance by HCP status. Free text responses were analyzed using applied rapid qualitative analysis. Results: A total of 1545 participants aged older than 18 years (mean [SD] age 49 [16] years; 1149 female participants [74%], 478 health care practitioners [30%]) provided data on SRA values and preferences. Agreement with UC SRA policy as drafted was moderately high among respondents, ranging from 67% to 83% across domains. Higher agreement with the interim policy was observed for laypersons across all domains except health-related factors. HCPs agreed more strongly on average that resources should not be allocated to those less likely to survive (HCP mean, 3.70; 95% CI, 3.16-3.59; vs layperson mean, 3.38; 95% CI, 3.17-3.59; P = .002), and were more in favor of reallocating life support from patients less likely to those more likely to survive (HCP mean, 6.41; 95% CI, 6.15-6.67; vs layperson mean, 5.40; 95% CI, 5.23-5.58; P < .001). Transparency and trust building themes were common in free text responses and highly rated on scaled items. Conclusions and Relevance: This survey of SRA policy values found moderate agreement with fundamental principles of such policies. Engagement with communities affected by SRA policy should continue in iterative refinement in preparation for future crises.