Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

Critical Examination of Modeling Approaches Used in Economic Evaluations of First-Line Treatments for Locally Advanced or Metastatic Non-Small Cell Lung Cancer Harboring Epidermal Growth Factor Receptor Mutations: A Systematic Literature Review

BACKGROUND

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with up to 32% of patients with NSCLC harboring an epidermal growth factor receptor (EGFR) mutation. NSCLC harboring an EGFR mutation has a dedicated treatment pathway, with EGFR tyrosine kinase inhibitors and platinum-based chemotherapy often being the therapy of choice.

OBJECTIVE

The aim of this study was to systemically review and summarize economic models of first-line treatments used for locally advanced or metastatic NSCLC harboring EGFR mutations, as well as to identify areas for improvement for future models.

METHODS

Literature searches were conducted via Ovid in PubMed, MEDLINE, MEDLINE In-Process, Embase, Evidence-Based Medicine Reviews: Health Technology Assessment, Evidence-Based Medicine Reviews: National Health Service Economic Evaluation Database, and EconLit. An initial search was conducted on 19 December 2022 and updated on 11 April 2023. Studies were selected according to predefined criteria using the Population, Intervention, Comparator, Outcome and Study design (PICOS) framework.

RESULTS

Sixty-seven articles were included in the review, representing 59 unique studies. The majority of included models were cost-utility analyses (n = 52), with the remaining studies being cost-effectiveness analyses (n = 4) and a cost-minimization analysis (n = 1). Two studies incorporated both a cost-utility and cost-minimization analysis. Although the model structure across studies was consistently reported, justification for this choice was often lacking.

CONCLUSIONS

Although the reporting of economic models in NSCLC harboring EGFR mutations is generally good, many of these studies lacked sufficient reporting of justification for structural choices, performing extensive sensitivity analyses and validation in economic evaluations. In resolving such gaps, the validity of future models can be increased to guide healthcare decision making in rare indications.

Simplified Methods for Modelling Dependent Parameters in Health Economic Evaluations: A Tutorial

BACKGROUND

In health economic evaluations, model parameters are often dependent on other model parameters. Although methods exist to simulate multivariate normal (MVN) distribution data and estimate transition probabilities in Markov models while considering competing risks, they are technically challenging for health economic modellers to implement. This tutorial introduces easily implementable applications for handling dependent parameters in modelling.

METHODS

Analytical proofs and proposed simplified methods for handling dependent parameters in typical health economic modelling scenarios are provided, and implementation of these methods are illustrated in seven examples along with the SAS and R code.

RESULTS

Methods to quantify the covariance and correlation coefficients of correlated variables based on published summary statistics and generation of MVN distribution data are demonstrated using examples of physician visits data and cost component data. The use of univariate normal distribution data instead of MVN distribution data to capture population heterogeneity is illustrated based on the results from multiple regression models with linear predictors, and two examples are provided (linear fixed-effects model and Cox proportional hazards model). A conditional probability method is introduced to handle two or more state transitions in a single Markov model cycle and applied in examples of one- and two-way state transitions.

CONCLUSIONS

This tutorial proposes an extension of routinely used methods along with several examples. These simplified methods may be easily applied by health economic modellers with varied statistical backgrounds.

Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

Budget Impact of Introducing Fixed-Duration Mosunetuzumab for the Treatment of Relapsed or Refractory Follicular Lymphoma After Two or More Lines of Systemic Therapy in the USA

OBJECTIVE

This study aimed to assess the budget impact of introducing fixed-duration mosunetuzumab as a treatment option for adult patients with relapsed or refractory follicular lymphoma after at least two prior systemic therapies and to estimate the total cumulative costs per patient in the USA.

METHODS

A 3-year budget impact model was developed for a hypothetical 1-million-member cohort enrolled in a mixed commercial/Medicare health plan. Comparators were: axicabtagene ciloleucel, tisagenlecleucel, tazemetostat, rituximab plus lenalidomide, copanlisib, and older therapies (rituximab or obinutuzumab ± chemotherapy). Costs per patient comprised treatment-associated costs including the drug, its administration, adverse events, and routine care. Dosing and safety data were ascertained from respective package inserts and clinical trial data. Drug costs (March 2023) were estimated based on the average wholesale acquisition cost reported in AnalySource®, and all other costs were based on published sources and inflated to 2022 US dollars. Market shares were obtained from Genentech internal projections and expert opinion. Budget impact outcomes were presented on a per-member per-month basis.

RESULTS

Compared with a scenario without mosunetuzumab, its introduction over 3 years resulted in a budget increase of $69,812 (1% increase) and an average per-member per-month budget impact of $0.0019. Among the newer therapies, mosunetuzumab had the second-lowest cumulative per patient cost (mosunetuzumab = $202,039; axicabtagene ciloleucel = $505,845; tisagenlecleucel = $476,293; rituximab plus lenalidomide = $263,520; tazemetostat = $250,665; copanlisib = $127,293) and drug costs, and its introduction only increased total drug costs by 0.1%. By year 3, the cumulative difference in the per patient cost with mosunetuzumab was -$303,805 versus axicabtagene ciloleucel, -$274,254 versus tisagenlecleucel, -$61,481 versus rituximab plus lenalidomide, -$48,625 versus tazemetostat, and $74,747 versus copanlisib. Older therapies were less costly with 3-year cumulative costs that ranged from $36,512 to $147,885.

CONCLUSIONS

Over 3 years, the estimated cumulative per patient cost of mosunetuzumab is lower than most available newer therapies, resulting in a small increase in the budget after its formulary adoption for the treatment of relapsed or refractory follicular lymphoma.

Markov modeling for cost-effectiveness using federated health data network

OBJECTIVE

To introduce 2 R-packages that facilitate conducting health economics research on OMOP-based data networks, aiming to standardize and improve the reproducibility, transparency, and transferability of health economic models.

MATERIALS AND METHODS

We developed the software tools and demonstrated their utility by replicating a UK-based heart failure data analysis across 5 different international databases from Estonia, Spain, Serbia, and the United States.

RESULTS

We examined treatment trajectories of 47 163 patients. The overall incremental cost-effectiveness ratio (ICER) for telemonitoring relative to standard of care was 57 472 €/QALY. Country-specific ICERs were 60 312 €/QALY in Estonia, 58 096 €/QALY in Spain, 40 372 €/QALY in Serbia, and 90 893 €/QALY in the US, which surpassed the established willingness-to-pay thresholds.

DISCUSSION

Currently, the cost-effectiveness analysis lacks standard tools, is performed in ad-hoc manner, and relies heavily on published information that might not be specific for local circumstances. Published results often exhibit a narrow focus, central to a single site, and provide only partial decision criteria, limiting their generalizability and comprehensive utility.

CONCLUSION

We created 2 R-packages to pioneer cost-effectiveness analysis in OMOP CDM data networks. The first manages state definitions and database interaction, while the second focuses on Markov model learning and profile synthesis. We demonstrated their utility in a multisite heart failure study, comparing telemonitoring and standard care, finding telemonitoring not cost-effective.

The peer effects of resident stock market participation: Evidence from 2019 CHFS

Limited resident's participation in the stock market has become a key constraint to the capital market development. Utilizing the 2019 China Household Financial Survey (CHFS) data, our paper designs probit models to examine the peer effects of residents' stock market participation and explore the intermediary mechanisms with a multiple intermediary model. We find that: (1) Resident involvement in stock market decision-making exhibits significant peer effects. (2) Heterogeneity analysis reveals that males and rural residents display more pronounced peer effects than females and urban residents. Additionally, middle-aged residents demonstrate more potent peer effects than their younger and older counterparts, with the intensity of peer effects correlating with education levels. (3)We observe that the peer effects of market participation operate by altering economic expectations and enhancing residents' financial literacy. (4) Further investigation establishes that individuals engaging in stock market investments manifest peer effects when deciding whether to diversify their stock portfolio. This study holds reference value for analyzing the impact of social interaction on financial behaviors and regulating individuals' financial conduct.

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study

Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it.

Pricing decision and channel selection of fresh agricultural products dual-channel supply chain based on blockchain

The application of blockchain can effectively improve the efficiency of fresh agricultural product circulation and consumer trust, but it can also increase investment costs. In this context, this paper introduces parameters such as blockchain unit variable cost, the level of blockchain technology investment, and consumer channel preference in two dual-channel supply chain systems dominated by fresh agricultural product manufacturers: online direct sales and distribution. It compares and analyzes pricing and channel selection strategies in both cases of not using and using blockchain. The research shows that when blockchain is used, manufacturer profits are higher in the direct sales model than in the distribution model. Traditional retailers' profits are lower in the direct sales model than in the distribution model. Total supply chain profits are higher in the direct sales model than in the distribution model, and they exhibit an inverted "U" shape as the level of blockchain investment increases. In the online direct sales model, if the blockchain technology unit variable cost is within a certain threshold range, manufacturer profits, traditional retailer profits, and total supply chain profits are all higher than when blockchain technology is not used. In the online distribution model, when the blockchain variable cost and blockchain usage level meet certain conditions, manufacturers, traditional retailers, and online distributors all have higher profits when using blockchain technology than when not using it. This study provides theoretical guidance for the practical application of blockchain technology in dual-channel fresh agricultural product supply chains.

Cross-sectional analysis of use of real-world data in single technology appraisals of oncological medicine by the National Institute for Health and Care Excellence in 2011-2021

OBJECTIVES

This study aims to identify how real-world data (RWD) have been used in single technology appraisals (STAs) of cancer drugs by the National Institute for Health and Care Excellence (NICE).

DESIGN

Cross-sectional study of NICE technology appraisals of cancer drugs for which guidance was issued between January 2011 and December 2021 (n=229). The appraisals were reviewed following a published protocol to extract the data about the use of RWD. The use of RWD was analysed by reviewing the specific ways in which RWD were used and by identifying different patterns of use.

PRIMARY OUTCOME MEASURE

The number of appraisals where RWD are used in the economic modelling.

RESULTS

Most appraisals used RWD in their economic models. The parametric use of RWD was commonly made in the economic models (76% of the included appraisals), whereas non-parametric use was less common (41%). Despite widespread use of RWD, there was no dominant pattern of use. Three sources of RWD (registries, administrative data, chart reviews) were found across the three important parts of the economic model (choice of comparators, overall survival and volume of treatment).

CONCLUSIONS

NICE has had a long-standing interest in the use of RWD in STAs. A systematic review of oncology appraisals suggests that RWD have been widely used in diverse parts of the economic models. Between 2011 and 2021, parametric use was more commonly found in economic models than non-parametric use. Nonetheless, there was no clear pattern in the way these data were used. As each appraisal involves a different decision problem and the ability of RWD to provide the information required for the economic modelling varies, appraisals will continue to differ with respect to their use of RWD.

A short-term price prediction-based trading strategy

Quantitative investment theory has emerged as a prominent and widely researched domain within the financial markets, where investors predominantly focus on discerning the intricate influences of market dynamics. In this paper, we proposed a short-term prediction-based trading strategy, which can equiponderate between return and risk, considerations while accounting for investor risk preferences. This strategy employs GM(1,1) to capture nuanced features of price dynamics in short-term intervals and update the GM(1,1) model with the latest data. Subsequently, a multi-objective planning equation is formulated to optimize asset allocations by determining the optimal holding that strikes between specific returns and risk mitigation. In the end, this work conducts a case study and sensitivity analysis using five years of gold and bitcoin price data spanning from 2016 to 2021. This empirical examination serves to affirm the efficacy and resilience of the proposed trading strategy. The case study reveals that proficient short-term price forecasting serves as a potent means to proactively mitigate risk, facilitating, judicious and objective trading practices. Moreover, it underscores the strategy's tangible utility as a guide for real-world investment decisions.

An Economic Model of Gambling Behaviour: A Two-Stage Approach

Gambling can cause significant harms and these can result in a net negative utility from participation, although lower levels of participation have potential benefits and can yield positive net utility. It is therefore important to understand and distinguish between these two stages of gambling behaviour. Currently, economic models have had limited focus on explaining why someone would gamble despite it yielding a negative utility. Here, we present a two-stage model, motivated by empirical literature and intuitive assumptions, that improves on existing economic models by distinguishing between the likelihood of gambling participation and of gambling that yields a negative utility. The model's predictions are empirically testable, consistent with existing literature, and add new insights. The model's ability to distinguish between the two stages helps to inform interventions that aim to reduce the prevalence of gambling-related harm while avoiding the need for restrictive approaches that aim to eliminate gambling altogether.

Measuring Costs of Cardiovascular Disease Prevention for Patients with Familial Hypercholesterolemia in Administrative Claims Data

INTRODUCTION

Familial hypercholesterolemia is a common genetic condition that significantly increases an individual's risk of cardiovascular events such as heart attack, stroke, and cardiac death and is a candidate for population-wide screening programs. Economic analyses of strategies to identify and treat familial hypercholesterolemia are limited by a lack of real-world cost estimates for screening services and medications for reducing cardiovascular risk in this population.

METHODS

We estimated the cost of lipid panel testing in patients with hyperlipidemia and the cost of statins, ezetimibe, and PCKS9 inhibitors in patients with familial hypercholesterolemia from a commercial claims database and report costs and charges per panel and prescription by days' supply.

RESULTS

The mean cost for a 90-day supply for statins was $183.33, 2.3 times the mean cost for a 30-day supply at $79.35. PCSK9 inhibitors generated the highest mean costs among medications used by patients with familial hypercholesterolemia.

CONCLUSIONS

Lipid testing and lipid-lowering medications for cardiovascular disease prevention generate substantial real-world costs which can be used to improve cost-effectiveness models of familial hypercholesterolemia screening and care management.

Digital economy and green total factor productivity in China

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy.

Emergent invariance and scaling properties in the collective return dynamics of a stock market

A key metric to determine the performance of a stock in a market is its return over different investment horizons (τ). Several works have observed heavy-tailed behavior in the distributions of returns in different markets, which are observable indicators of underlying complex dynamics. Such prior works study return distributions that are marginalized across the individual stocks in the market, and do not track statistics about the joint distributions of returns conditioned on different stocks, which would be useful for optimizing inter-stock asset allocation strategies. As a step towards this goal, we study emergent phenomena in the distributions of returns as captured by their pairwise correlations. In particular, we consider the pairwise (between stocks i, j) partial correlations of returns with respect to the market mode, ci,j(τ), (thus, correcting for the baseline return behavior of the market), over different time horizons (τ), and discover two novel emergent phenomena: (i) the standardized distributions of the ci,j(τ)'s are observed to be invariant of τ ranging from from 1000min (2.5 days) to 30000min (2.5 months); (ii) the scaling of the standard deviation of ci,j(τ)'s with τ admits good fits to simple model classes such as a power-law τ-λ or stretched exponential function [Formula: see text] (λ, β > 0). Moreover, the parameters governing these fits provide a summary view of market health: for instance, in years marked by unprecedented financial crises-for example 2008 and 2020-values of λ (scaling exponent) are substantially lower. Finally, we demonstrate that the observed emergent behavior cannot be adequately supported by existing generative frameworks such as single- and multi-factor models. We introduce a promising agent-based Vicsek model that closes this gap.

Social Risk and Dialysis Facility Performance in the First Year of the ESRD Treatment Choices Model

Importance

The End-Stage Renal Disease Treatment Choices (ETC) model randomly selected 30% of US dialysis facilities to receive financial incentives based on their use of home dialysis, kidney transplant waitlisting, or transplant receipt. Facilities that disproportionately serve populations with high social risk have a lower use of home dialysis and kidney transplant raising concerns that these sites may fare poorly in the payment model.

Objective

To examine first-year ETC model performance scores and financial penalties across dialysis facilities, stratified by their incident patients' social risk.

Design, Setting, and Participants

A cross-sectional study of 2191 US dialysis facilities that participated in the ETC model from January 1 through December 31, 2021.

Exposure

Composition of incident patient population, characterized by the proportion of patients who were non-Hispanic Black, Hispanic, living in a highly disadvantaged neighborhood, uninsured, or covered by Medicaid at dialysis initiation. A facility-level composite social risk score assessed whether each facility was in the highest quintile of having 0, 1, or at least 2 of these characteristics.

Main Outcomes and Measures

Use of home dialysis, waitlisting, or transplant; model performance score; and financial penalization.

Results

Using data from 125 984 incident patients (median age, 65 years [IQR, 54-74]; 41.8% female; 28.6% Black; 11.7% Hispanic), 1071 dialysis facilities (48.9%) had no social risk features, and 491 (22.4%) had 2 or more. In the first year of the ETC model, compared with those with no social risk features, dialysis facilities with 2 or more had lower mean performance scores (3.4 vs 3.6, P = .002) and lower use of home dialysis (14.1% vs 16.0%, P < .001). These facilities had higher receipt of financial penalties (18.5% vs 11.5%, P < .001), more frequently had the highest payment cut of 5% (2.4% vs 0.7%; P = .003), and were less likely to achieve the highest bonus of 4% (0% vs 2.7%; P < .001). Compared with all other facilities, those in the highest quintile of treating uninsured patients or those covered by Medicaid experienced more financial penalties (17.4% vs 12.9%, P = .01) as did those in the highest quintile in the proportion of patients who were Black (18.5% vs 12.6%, P = .001).

Conclusions

In the first year of the Centers for Medicare & Medicaid Services' ETC model, dialysis facilities serving higher proportions of patients with social risk features had lower performance scores and experienced markedly higher receipt of financial penalties.

Benefits of a digital health technology for older nursing home residents. A de-novo cost-effectiveness model for digital health technologies to aid in the assessment of toileting and containment care needs

The aim of this study was first, to introduce a comprehensive, de-novo health economic (HE) model incorporating the full range of activities involved in toileting and containment care (T&CC) for people with incontinence, capturing all the potential benefits and costs of existing and future Digital Health Technologies (DHT) aimed at improving continence care, for both residential care and home care. Second, to use this novel model to evaluate the cost-effectiveness of the DHT TENA SmartCare Identifi in the implementation of person-centred continence care (PCCC), compared with conventional continence care for Canadian nursing home residents. The de-novo HE model was designed to evaluate technologies across different care settings from the perspective of several stakeholders. Health states were based on six care need profiles with increasing need for toileting assistance, three care stages with varying degrees of toileting success, and five levels of skin health. The main outcomes were incremental costs and quality-adjusted life years. The effectiveness of the TENA SmartCare Identifi was based primarily on trial data combined with literature and expert opinion where necessary. Costs were reported in CAD 2020. After 2 years, 21% of residents in the DHT group received mainly toileting as their continence care strategy compared with 12% in the conventional care group. Conversely, with the DHT 15% of residents rely mainly on absorbent products for incontinence care, compared with 40% with conventional care. On average, residents lived for 2.34 years, during which the DHT resulted in a small gain in quality-adjusted life years of 0.015 and overall cost-savings of $1,467 per resident compared with conventional care. Most cost-savings were achieved through reduced costs for absorbent products. Since most, if not all, stakeholders gain from use of the DHT-assisted PCCC, widespread use in Canadian residential care facilities should be considered, and similar assessments for other countries encouraged.

Breeding objectives for Central Highland goats using participatory and bio-economic modelling approaches

The breeding objectives of Central Highland goats rearing under a low-input production system were defined through a participatory proportional piling method and bio-economic model. Additionally, the economic values and relative economic value of the breeding objective traits were derived. A participatory proportional piling method was used to estimate the relative weights of farmers attached to a list of goat traits identified, and the relative weights were statistically evaluated using a generalized multinomial logit model analysis. A bio-economic model was used to compute the economic values of the identified traits. The most important traits for selection of does according to farmer's preference were body size, coat colour, post-weaning growth rate and weaning rate with a relative weight (odds ratio) of 1.58, 1.38, 1.37 and 1.13, respectively. Goats with dark red followed by light red coat colour were the most preferred (p < 0.001) by goat keepers compared with white-coloured goats. Farmers were more likely (p < 0.001) to allocate higher scores for does-bearing twins than for single and triplet-bearing does. Using the bio-economic model (economic value and relative economic value), post-weaning growth rate, weaning rate, and six-month weight (body size) were identified as the most important traits and if the mean of these traits is changed by one genetic standard deviation, the change in profit will range from 2.06 to 3.03 $ doe-1  year-1 . Therefore, the most important traits for the selection of Central Highland goats according to the economic-based method were post-weaning weight gain, weaning rate and body size (six-month weight). Besides, coat colour was the second preferred trait by goat keepers next to body size. Thus, this aesthetical trait should be included in the designed breeding programme besides economically important quantitative traits. The combination of the participatory proportional piling method and bio-economic model would give better insights to explore the trait preferences of farmers and enhance profitability. The economic values of traits estimated in this study can be used for the construction of selection indices for Central Highland goats.

Scaling up the transition: The role of corporate governance mechanisms in promoting circular economy strategies

This study examines the role of corporate governance (CG) mechanisms in promoting Circular Economy (CE) strategies among non-financial listed companies in manufacturing industries, which is still a relatively unexplored topic in the CE literature. Our findings indicate that the presence of stakeholder engagement practices, sustainability reporting, and environment management teams have a direct impact on the adoption of CE strategies, while the presence of a CSR committee, adherence to the United Nations' Global Compact, and executives' compensation linked to environmental, social, and corporate governance performance do not have a direct effect but support CE strategies through other mechanisms. Overall, this study provides valuable insights for policymakers and managers as it shows that CG mechanisms can be used to promote the adoption of CE business models, thus contributing to climate risk mitigation objectives.

A Scoping Review and Taxonomy of Epidemiological-Macroeconomic Models of COVID-19

OBJECTIVES

The COVID-19 pandemic placed significant strain on many health systems and economies. Mitigation policies decreased health impacts but had major macroeconomic impact. This article reviews models combining epidemiological and macroeconomic projections to enable policy makers to consider both macroeconomic and health objectives.

METHODS

A scoping review of epidemiological-macroeconomic models of COVID-19 was conducted, covering preprints, working articles, and journal publications. We assessed model methodologies, scope, and application to empirical data.

RESULTS

We found 80 articles modeling both the epidemiological and macroeconomic outcomes of COVID-19. Model scope is often limited to the impact of lockdown on health and total gross domestic product or aggregate consumption and to high-income countries. Just 14% of models assess disparities or poverty. Most models fall under 4 categories: compartmental-utility-maximization models, epidemiological models with stylized macroeconomic projections, epidemiological models linked to computable general equilibrium or input-output models, and epidemiological-economic agent-based models. We propose a taxonomy comparing these approaches to guide future model development.

CONCLUSIONS

The epidemiological-macroeconomic models of COVID-19 identified have varying complexity and meet different modeling needs. Priorities for future modeling include increasing developing country applications, assessing disparities and poverty, and estimating of long-run impacts. This may require better integration between epidemiologists and economists.

A systematic review of cost-effectiveness analysis of different screening strategies for familial hypercholesterolemia

AIMS

Diagnosis rate of familial hypercholesterolemia (FH) remained less than 10 % globally and the economic evaluation results of different FH screening strategies varied. This study aimed to systematically review the methodology and results of cost effectiveness analysis (CEA) of FH screening, which will provide evidence support for health-related decision-making.

METHODS

The Medline/PubMed, Embase, Cochrane Library, Web of science, National Health Service Economic Evaluation Database (NHSEED) and CEA Registry databases were electronically searched to collect full economic evaluation from the establishment of the databases to June 30, 2022. The quality of included studies was evaluated by the Consolidated Health Economic Evaluation Reporting Standards statement 2022 (CHEERS 2022) checklist.

RESULTS

Among 232 retrieved studies, 18 economic evaluations were included and all of them are from developed countries, with an average quality score of 0.73. The decision tree model and/or Markov model were constructed by thirteen articles (72 %). Twelve studies (67 %) adopted the healthcare perspective and the lifetime horizon to compare the costs and health outcome of different screening strategies. The results of eight studies indicated that cascade screening was a cost-effective strategy compared with no screening, which was more pronounced in younger adults. Universal screening in young adults aged 16 years or 18-40 years (n=3) and in children aged 1-2 years combined with reverse cascade screening (n=3) are both cost-effective. The probability of being cost-effective for cascade screening (n=6) and universal screening (n=1) of young aged 18-40 years were greater than 95 %.

CONCLUSIONS

Our review demonstrated the economic advantages of cascade screening, universal screening of young adults, and universal screening of newborns combined with reverse cascade screening. Further health economic evaluation is needed in children and in low- and middle-income countries.

The Difference Method Approach for Sampling Order Constrained Parameters: An Improved Implementation and Important Limitations

A health economic model may include a set of related inputs whose true values are uncertain, but that can be assumed to follow a logical order. Various approaches are available for performing probabilistic sensitivity analysis while preserving the order constraint, one such approach is known as the difference method. The difference method approach appears to have many of the required properties, has been endorsed by good practice guidelines, and is likely to prove a popular approach. However, the proposed implementation of the difference method approach is cumbersome, requiring numerical estimation, which might present a barrier to its adoption. Furthermore, it is unclear whether the method can always be applied to three or more model inputs and whether it is unbiased across all possible input values. This study has investigated these three issues for ordered inputs bounded between 0 and 1. An analytic solution is given that allows for more straightforward and compact implementation. The difference method approach cannot always be applied to a set of three or more model inputs, and this depends on the relative size of the variances of the logit-transformed Beta distributions fitted to each variable. The approach can also produce samples with biased means and variances under certain combinations of input means and variances. It is recommended that the difference method approach be used where appropriate; however, an understanding of its limitations is necessary to identify such cases.

Effect of network size on comparing different stock networks

We analyzed complex networks generated by the threshold method in the Korean and Indian stock markets during the non-crisis period of 2004 and the crisis period of 2008, while varying the size of the system. To create the stock network, we randomly selected N stock indices from the market and constructed the network based on cross-correlation among the time series of stock prices. We computed the average shortest path length L and average clustering coefficient C for several ensembles of generated stock networks and found that both metrics are influenced by network size. Since L and C are affected by network size N, a direct comparison of graph measures between stock networks with different numbers of nodes could lead to erroneous conclusions. However, we observed that the dependency of network measures on N is significantly reduced when comparing larger networks with normalized shortest path lengths. Additionally, we discovered that the effect of network size on network measures during the crisis period is almost negligible compared to the non-crisis periods.

Farm level bio-economic modelling of aquatic animal disease and health interventions

A farm level bio-economic model, for aquatic animal production, of the relationships between inputs (e.g. purchased animals), outputs (e.g. harvested animals) and gross margin (GM) was developed to assess ex-ante the economics of disease and animal health interventions. Feed costs were calculated from estimates of food conversion ratio (FCR), animals harvested and mortality. The model was applied to a typical grow-out rainbow trout (Oncorhynchus mykiss) farm on Lake Titicaca, Peru and a typical shrimp (Paenus vannamei) farm in the Mekong Delta, Vietnam. The model was used in two analyses. Firstly, an approach to assess the burden of disease developed by the Global Burden of Animal Diseases (GBADs) project was adopted. Output under conditions of 'ideal health' was estimated by reducing mortality to zero and removing health costs. GM in both systems increased by approximately 25% when production was kept constant (and stocking rates reduced) and more than doubled if production was allowed to rise (and initial stocking increased). The increase in GM under conditions of ideal compared with current production provided an estimate of the maximum possible benefit from improved health management. Secondly, break-even analysis was used to assess the economics of vaccination against infectious pancreatic necrosis (IPN) vaccine (rainbow trout - RBT) and probiotics (shrimp). If initial stocking was kept constant, and production allowed to rise, break-even points for the intervention (when GM was the same with and without the intervention) were achieved when mortality was reduced by 16% in RBT fry and juvenile and 28% in shrimp. If production was kept constant and benefit realised by reduced initial stocking, the break-even point was achieved for i) vaccination of RBT when mortality in fry and juveniles was reduced by 39%, and ii) probiotics in shrimp production when there was a 15% reduction in mortality (nursery and grow-out), 10% increase in shrimp weight at harvest and 10% improvement in FCR. The results demonstrate how relatively simple models, parameterised with basic farm production data, can assess the burden of disease and quantify ex-ante the potential benefit of interventions. In the absence of trial data, these analyses support decision-making by farmers. The models can be adapted for many aquaculture systems. Farm level results can be extrapolated to estimate disease burden, and benefits of interventions, at regional or national level and thus support informed decision-making and allocation of resources to health management.

Modelling Spillover Effects on Informal Carers: The Carer QALY Trap

The provision of informal (unpaid) care can impose significant 'spillover effects' on carers, and accounting for these effects is consistent with the efficiency and equity objectives of health technology assessment (HTA). Inclusion of these effects in health economic models, particularly carer health-related quality of life (QOL), can have a substantial impact on net quality-adjusted life year (QALY) gains and the relative cost effectiveness of new technologies. Typically, consideration of spillover effects improves the value of a technology, but in some circumstances, consideration of spillover effects can lead to situations whereby life-extending treatments for patients may be considered cost ineffective due to their impact on carer QOL. In this piece we revisit the classic 'QALY trap' and introduce an analogous 'carer QALY trap' which may have practical implications for economic evaluations where the inclusion of carer QOL reduces incremental QALY gains. Such results may align with a strict QALY-maximisation rule, however we consider the extent to which this principle may be at odds with the preferences of carers themselves (and possibly society more broadly), potentially leading decision makers into the carer QALY trap as a result. We subsequently reflect on potential solutions, highlighting the important (albeit limited) role that deliberation has to play in HTA.

Reflecting Parameter Uncertainty in Addition to Variability in Constrained Healthcare Resource Discrete Event Simulations: Worth Going the Extra Mile or a Road to Nowhere?

OBJECTIVES

Probabilistic sensitivity analysis (PSA) has been shown to reduce bias in outcomes of health economic models. However, only 1 existing study has been identified that incorporates PSA within a resource-constrained discrete event simulation (DES) model. This article aims to assess whether it is feasible and appropriate to use PSA to characterize parameter uncertainty in DES models that are primarily constructed to explore the impact of constrained resources.

METHODS

PSA is incorporated into a new case study of an Emergency Department DES. Structured expert elicitation is used to derive the variability and uncertainty input distributions associated with length of time taken to complete key activities within the Emergency Department. Potential challenges of implementation and analysis are explored.

RESULTS

The results of a trial of the model, which used the best estimates of the elicited means and variability around the time taken to complete activities, provided a reasonable fit to the data for length of time within the Emergency Department. However, there was substantial and skewed uncertainty around the activity times estimated from the elicitation exercise. This led to patients taking almost 3 weeks to leave the Emergency Department in some PSA runs, which would not occur in practice.

CONCLUSIONS

Structured expert elicitation can be used to derive plausible estimates of activity times and their variability, but experts' uncertainty can be substantial. For parameters that have an impact on interactions within a resource-constrained simulation model, PSA can lead to implausible model outputs; hence, other methods may be needed.

Effect of government revenue on economic growth of sub-Saharan Africa: Does institutional quality matter?

Following the approval of sustainable development goals at the global level, the link between fiscal policy, institutional quality, and economic growth has attracted special attention in economic literature. This study scrutinizes the effect of government revenue-institutional quality interaction on the economic growth of 43 Sub-Saharan Africa countries for the period of 2012-2022. Methodology-wise, the study employed the System Generalized Method of Moment (SGMM) to analyze the panel data gained from dependable data sources; the World Development Indicator and the Heritage Economic Freedom Index. The novelty of this study emanates from the estimation technique designated and the introduction of revenue-institutional quality into the economic growth model of SSA. The result of the study reveals that government revenue adversely affects economic growth while institutional quality positively enhances economic growth before interacting with each other. However, the interactive coefficient of government revenue and economic growth positively affected the real GDP growth rate of SSA countries over the study periods. Precisely, before interacting with institutional quality, a percentage change in government revenue, keeping all other things constant, leads to a 0.0866 percent decline in economic growth while it marks a 0.2329 percent upsurge in economic growth in the presence of institutional quality. The result of the study further shows that government revenue promotes the economic growth of the region when combined with institutional quality. On the other hand, foreign direct investment and openness to trade were the key sources of economic growth whereas the population growth rate adversely impacted economic growth in SSA countries. The policy implication of the study is that SSA needs to strengthen government revenue management. Further, the finding of the study implies that SSA countries need to improve institutional quality through promoting efficiency of the regulatory quality and the size of the SSA governments. In addition to this, the fast real GDP growth rate of SSA countries demands improved institutional quality indicators such as the rule of law and extended access to the open market.

Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China's leading stocks

The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learning techniques and investor sentiment analysis. To achieve this, a sentiment multi-classification dataset is for the first time constructed for China's stock market, based on four types of sentiments in modern psychology. The significant heterogeneity of sentiment changes in the sectors' leading stock markets is trained and mined using the Bi-LSTM-ATT model. The impact of multi-classification investor sentiment on stock price prediction was analyzed using the CNN-Bi-LSTM-ATT model. It finds that integrating sentiment indicators into the prediction of industry leading stock prices can enhance the accuracy of the model. Drawing upon four fundamental sentiment types derived from modern psychology, our dataset provides a comprehensive framework for analyzing investor sentiment and its impact on forecasting the stock prices of China's A-share market.

The dynamic impact of digital finance on green innovation: evidence enterprise-level empirical data in China

The digital finance generated by technology empowerment profoundly affects the innovative behavior of micro-enterprises. To estimate how digital finance influences the quantity and quality of green technological innovation, this paper introduces digital finance into endogenous economic growth model and further conducts correlation analysis with the combination of fixed effects model (FE) and panel threshold model (PTM). Moreover, this paper investigates the action mechanism from the perspective of financial constraint and financial cost. The empirical results indicate that digital finance improves the quantity and quality of green technological innovation significantly. Compared with the coverage of digital finance and digital services, the impact of usage depth on green innovation accumulates dynamically superimposed in the long term. The digital finance contributes to correct the mismatch of financial resources and further induces inclusive green innovation in the dimension of ownership, growth cycle, and enterprise scale. Mechanism analysis shows that the relief of financial constraint, environmental information disclosure, and increase of R&D investment are the main paths for digital finance to drive green innovation. With the different thresholds, the dynamic evolution process of green innovation driven by digital finance is characterized by "first quantitative change and then qualitative change." This paper suggests that the integration of technological innovation and digital financial services needs to build an inclusive digital financial service system, cultivate diversified financial formats, and improve the market environment of digital financial services.

The Changing Medical Publishing Industry: Economics, Expansion, and Equity

Medical journal publishing has changed dramatically over the past decade. The shift from print to electronic distribution altered the industry's economic model. This was followed by open access mandates from funding organizations and the subsequent imposition of article processing charges on authors. The medical publishing industry is large and while there is variation across journals, it is overall highly profitable. As journals have moved to digital dissemination, advertising revenues decreased and publishers shifted some of the losses onto authors by way of article processing charges. The number of open access journals has increased substantially in recent years. The open access model presents an equity paradox; while it liberates scientific knowledge for the consumer, it presents barriers to those who produce research. This emerging "pay-to-publish" system offers advantages to authors who work in countries and at institutes with more resources. Finally, the medical publishing industry represents an unusual business model; the people who provide both the content and the external peer review receive no payment from the publisher, who generates revenue from the content. The very unusual economic model of this industry makes it vulnerable to disruptive change. The economic model of medical publishing is rapidly evolving and this will lead to disruption of the industry. These changes will accelerate dissemination of science and may lead to a shift away from lower-impact journals towards pre-print servers.

Evaluating the health and health economic impact of the COVID-19 pandemic on delayed cancer care in Belgium: A Markov model study protocol

INTRODUCTION

Cancer causes a substantial burden to our society, both from a health and an economic perspective. To improve cancer patient outcomes and lower society expenses, early diagnosis and timely treatment are essential. The recent COVID-19 crisis has disrupted the care trajectory of cancer patients, which may affect their prognosis in a potentially negative way. The purpose of this paper is to present a flexible decision-analytic Markov model methodology allowing the evaluation of the impact of delayed cancer care caused by the COVID-19 pandemic in Belgium which can be used by researchers to respond to diverse research questions in a variety of disruptive events, contexts and settings.

METHODS

A decision-analytic Markov model was developed for 4 selected cancer types (i.e. breast, colorectal, lung, and head and neck), comparing the estimated costs and quality-adjusted life year losses between the pre-COVID-19 situation and the COVID-19 pandemic in Belgium. Input parameters were derived from published studies (transition probabilities, utilities and indirect costs) and administrative databases (epidemiological data and direct medical costs). One-way and probabilistic sensitivity analyses are proposed to consider uncertainty in the input parameters and to assess the robustness of the model's results. Scenario analyses are suggested to evaluate methodological and structural assumptions.

DISCUSSION

The results that such decision-analytic Markov model can provide are of interest to decision makers because they help them to effectively allocate resources to improve the health outcomes of cancer patients and to reduce the costs of care for both patients and healthcare systems. Our study provides insights into methodological aspects of conducting a health economic evaluation of cancer care and COVID-19 including insights on cancer type selection, the elaboration of a Markov model, data inputs and analysis.

MUSE-RASA captures human dimension in climate-energy-economic models via global geoAI-ML agent datasets

This article provides a combined geospatial artificial intelligence-machine learning, geoAI-ML, agent-based, data-driven, technology-rich, bottom-up approach and datasets for capturing the human dimension in climate-energy-economy models. Seven stages were required to conduct this study and build thirteen datasets to characterise and parametrise geospatial agents in 28 regions, globally. Fundamentally, the methodology starts collecting and handling data, ending with the application of the ModUlar energy system Simulation Environment (MUSE), ResidentiAl Spatially-resolved and temporal-explicit Agents (RASA) model. MUSE-RASA uses AI-ML-based geospatial big data analytics to define eight scenarios to explore long-term transition pathways towards net-zero emission targets by mid-century. The framework and datasets are key for climate-energy-economy models considering consumer behaviour and bounded rationality in more realistic decision-making processes beyond traditional approaches. This approach defines energy economic agents as heterogeneous and diverse entities that evolve in space and time, making decisions under exogenous constraints. This framework is based on the Theory of Bounded Rationality, the Theory of Real Competition, the theoretical foundations of agent-based modelling and the progress on the combination of GIS-ABM.

Context-Specific Estimation of Future Unrelated Medical Costs and Their Impact on Cost-Effectiveness Analyses

OBJECTIVES

This study constructed and applied procedures for the estimation of unrelated future medical costs (UFMC) of women with breast cancer in Israel (as a case study) and examined the influence of including UFMC in cost-effectiveness analyses (CEAs).

METHODS

Part I consisted of a retrospective cohort study based on patient-level claims data of both patients with breast cancer and matched controls during 14 years of follow-up. UFMC were estimated as (a) the annual average all-cause healthcare costs of the control subjects, and (b) as predicted values based on a generalized linear model (GLM) adjusted to patients' characteristics. Part II consisted of a CEA performed using a Markov simulation model comparing regimens of chemotherapy with/without trastuzumab, both excluding and including UFMC and for each of the UFMC estimates separately. All costs were adjusted to 2019 prices. Costs and QALYs were discounted at a yearly rate of 3%.

RESULTS

The average annual healthcare costs in the control group were $2328 (± $5662). The corresponding incremental cost-effectiveness ratio (ICER) was $53,411/QALY and $55,903/QALY, when UFMC were excluded or included, respectively. Hence, trastuzumab was not considered cost-effective compared with a threshold of willingness-to-pay of $37,000 per QALY, regardless of the inclusion of UFMC. When UFMC were estimated on the basis of the prediction model, the ICERs were $37,968/QALY and $39,033/QALY, when UFMC were excluded or included, respectively. Thus, in this simulation, trastuzumab was not considered cost-effective, independent of the inclusion of UFMC.

CONCLUSION

Our case study revealed that the inclusion of UFMC had modest effect on the ICERs, and thus did not alter the conclusion. Thus, we should estimate context-specific UFMC if they are expected to change the ICERs significantly, and transparently report the corresponding assumptions to uphold the integrity and reliability of the economic evaluation.

Cost-Effectiveness of Anti-retroviral Adherence Interventions for People Living with HIV: A Systematic Review of Decision Analytical Models

BACKGROUND

Although safe and effective anti-retrovirals (ARVs) are readily available, non-adherence to ARVs is highly prevalent among people living with human immunodeficiency virus/acquired immunodeficiency syndrome (PLWHA). Different adherence-improving interventions have been developed and examined through decision analytic model-based health technology assessments. This systematic review aimed to review and appraise the decision analytical economic models developed to assess ARV adherence-improvement interventions.

METHODS

The review protocol was registered on PROSPERO (CRD42022270039), and reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Relevant studies were identified through searches in six generic and specialized bibliographic databases, i.e. PubMed, Embase, NHS Economic Evaluation Database, PsycINFO, Health Economic Evaluations Database, tufts CEA registry and EconLit, from their inception to 23 October 2022. The cost-effectiveness of adherence interventions is represented by the incremental cost-effectiveness ratio (ICER). The quality of studies was assessed using the quality of the health economics studies (QHES) instrument. Data were narratively synthesized in the form of tables and texts. Due to the heterogeneity of the data, a permutation matrix was used for quantitative data synthesis rather than a meta-analysis.

RESULTS

Fifteen studies, mostly conducted in North America (8/15 studies), were included in the review. The time horizon ranged from a year to a lifetime. Ten out of 15 studies used a micro-simulation, 4/15 studies employed Markov and 1/15 employed a dynamic model. The most commonly used interventions reported include technology based (5/15), nurse involved (2/15), directly observed therapy (2/15), case manager involved (1/15) and others that involved multi-component interventions (5/15). In 1/15 studies, interventions gained higher quality-adjusted life years (QALYs) with cost savings. The interventions in 14/15 studies were more effective but at a higher cost, and the overall ICER was well below the acceptable threshold mentioned in each study, indicating the interventions could potentially be implemented after careful interpretation. The studies were graded as high quality (13/15) or fair quality (2/15), with some methodological inconsistencies reported.

CONCLUSION

Counselling and smartphone-based interventions are cost-effective, and they have the potential to reduce the chronic adherence problem significantly. The quality of decision models can be improved by addressing inconsistencies in model selection, data inputs incorporated into models and uncertainty assessment methods.

The roles of liquidity and delay in financial markets based on an optimal forecasting model

We investigate the roles of liquidity and delay in financial markets through our proposed optimal forecasting model. The efficiency and liquidity of the financial market are examined using stochastic models that incorporate information delay. Based on machine learning, we estimate the in-sample and out-of-sample forecasting price performances of the six proposed methods using the likelihood function and Bayesian methods, and the out-of-sample prediction performance is compared with the benchmark model ARIMA-GARCH. We discover that the forecasting price performance of the proposed simplified delay stochastic model is superior to that of the benchmark methods by the test methods of a variety of loss function, superior predictive ability test (SPA), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Using data from the Chinese stock market, the best forecasting model assesses the efficiency and liquidity of the financial market while accounting for information delay and trade probability. The rise in trade probability and delay time affects the stability of the return distribution and raises the risk, according to stochastic simulation. The empirical findings show that empirical and best forecasting approaches are compatible, that company size and liquidity (delay time) have an inverse relationship, and that delay time and liquidity have a nonlinear relationship. The most efficient have optimal liquidity.

Linking circular economy model and green supply chain practices to shape environmental performance: case of Vietnam

The circular economy (SE) is the foremost element for environmental improvement that reduces wastage and enhances production. This aspect requires the focus of new researchers and experienced policymakers. Hence, the current research examines the impact of SE practices such as internal environmental management (IEM), eco-design, and internal recovery on the SE targeted performance, such as environmental performance in Vietnam. The research also investigates the moderating role of environmental-oriented supply chain (SC) practices such as green purchases among IEM, eco-design, internal recovery, and environmental performance in Vietnam. The research collected the data from the respondents using survey questionnaires. The research also investigates the association among variables using smart-PLS. The outcomes revealed that the IEM, eco-design, and internal recovery are positively associated with environmental performance. The outcomes also exposed that the green purchases significantly moderates among IEM, eco-design, internal recovery, and environmental performance. The research guides the regulators in establishing regulations related to achieve the high SE targeted performance, such as environmental performance using circular economy practices such as IEM, eco-design, and internal recovery.

The estimation of health state utility values in rare diseases: do the approaches in submissions for NICE technology appraisals reflect the existing literature? A scoping review

BACKGROUND

Rare diseases negatively impact patients' quality of life, but the estimation of health state utility values (HSUVs) in research studies and cost-utility models for health technology assessment is challenging.

OBJECTIVES

This study compared the methods for estimating the HSUVs included in manufacturers' submissions of orphan drugs to the National Institute for Health and Care Excellence (NICE) with those of published studies addressing the same rare diseases to understand whether manufacturers fully exploited the existing literature in developing their economic models.

METHODS

All NICE Technology Appraisal (TA) and Highly Specialized Technologies (HST) guidance documents of non-cancer European Medicines Agency (EMA) orphan medicinal products were reviewed and compared with any published primary studies, retrieved via PubMed until November 2020, and estimating HSUVs for the same conditions addressed in manufacturers' submissions.

RESULTS

We identified 22 NICE TA/HST appraisal reports addressing 19 different rare diseases. Sixteen reports presented original HSUVs estimated using EQ-5D or Health Utility Index (n = 12), direct methods (n = 2) or mapping (n = 2), while the other six included values obtained from the literature only. In parallel, we identified 111 published studies: 86.6% used preference-based measures (mainly EQ-5D, 60.7%), 12.5% direct techniques, and 2.7% mapping. The collection of values from non-patient populations (using 'vignettes') was more frequent in manufacturers' submissions than in the literature (22.7% vs. 8.0%).

CONCLUSIONS

The agreement on methodological choices between manufacturers' submissions and published literature was only partial. More efforts should be made by manufacturers to accurately reflect the academic literature and its methodological recommendations in orphan drugs submissions.

The economics of early inequality

We examine three recent frameworks that attempt to explain early inequality. One explanation involves the emergence of dense and predictable resource patches in the Holocene, together with differential asset accumulation and inheritance by individuals or households. In this view, agriculture and pastoralism led to greater inequality because farmland and animal herds were readily inherited. Another explanation involves the distinction between ideal free and ideal despotic population distributions, together with factors that could trigger a transition from the former to the latter. We offer a third framework based on economic concepts. In our view, inequality initially arose across locations (insider-outsider inequality) and reflected geographical differences in resource endowments at those locations. As population densities increased, the barriers to individual migration across locations included fewer kinship linkages and the use of force by insiders to exclude outsiders. These barriers became important with the transition from mobile to sedentary foraging and predate agriculture. Insider-outsider inequality was followed by stratification within settlements (elite-commoner inequality), which arose at still higher population densities. We see these three theoretical approaches as distinct but complementary. While they overlap, each emphasizes some phenomena and processes ignored by the other two. This article is part of the theme issue 'Evolutionary ecology of inequality'.

The spatial impact of carbon trading on harmonious economic and environmental development: evidence from China

This research explores the endogenous influence of carbon trading on economic development, the ecological environment, and the coordinated development of both using panel data from 30 Chinese provinces and cities except Tibet from 2007 to 2017. First, we provide environmental production elements to build an economic model based on the endogenous growth model, and then we employ three-dimensional graphics to conduct theoretical derivation in a more accessible and tangible manner. Secondly, we build a comprehensive index of China's coordinated economic and environmental growth in the context of carbon trading and use the coupled coordination model to determine the coordinated coupling degree of each location. Thirdly, the S-DID model is built to investigate the local and geographical implications of carbon trading. The findings demonstrate that the policy has a local impact that is notably favorable on the economic and environmental levels of each Chinese province and the coordinated growth between them. The degree of environmental optimization and the degree of coordinated development between the economy and the environment are two additional areas where the carbon trading mechanism has a strong positive geographical spillover impact. This study adds to the body of knowledge on China's carbon trading system and advances the endogenous growth hypothesis.

Investing in nature can improve equity and economic returns

Sustainable development requires jointly achieving economic development to raise standards of living and environmental sustainability to secure these gains for the long run. Here, we develop a local-to-global, and global-to-local, earth-economy model that integrates the Global Trade Analysis Project (GTAP)-computable general equilibrium model of the economy with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model of fine-scale, spatially explicit ecosystem services. The integrated model, GTAP-InVEST, jointly determines land use, environmental conditions, ecosystem services, market prices, supply and demand across economic sectors, trade across regions, and aggregate performance metrics like GDP. We use the integrated model to analyze the contribution of investing in nature for economic prosperity, accounting for the impact of four important ecosystem services (pollination, timber provision, marine fisheries, and carbon sequestration). We show that investments in nature result in large improvements relative to a business-as-usual path, accruing annual gains of $100 to $350 billion (2014 USD) with the largest percentage gains in the lowest-income countries. Our estimates include only a small subset of ecosystem services and could be far higher with inclusion of more ecosystem services, incorporation of ecological tipping points, and reduction in substitutability that limits economic adjustments to declines in natural capital. Our analysis highlights the need for improved environmental-economic modeling and the vital importance of integrating environmental information firmly into economic analysis and policy. The benefits of doing so are potentially very large, with the greatest percentage benefits accruing to inhabitants of the poorest countries.

Prediction of complications in health economic models of type 2 diabetes: a review of methods used

AIM

Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions.

METHODS

PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated.

RESULTS

The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the 'sunflower method' (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly.

CONCLUSIONS

The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

Cost-effectiveness of somatrogon in the Spanish pediatric population with growth hormone deficiency

OBJECTIVE

To analyze the cost-effectiveness of weekly somatrogon compared to daily growth hormones (GH-d) in the pediatric population of Spain with growth hormone deficiency (GHD).

METHODS

Markov model with two states (patients with or without GH-d or somatrogon treatment) in prepubertal children (3 to 11 years and 3 to 12 years in girls and boys, respectively) with GHD in isolation or as part of multiple pituitary hormone deficiency and without previous treatment, from the perspective of the National Health System. The simulation of the economic model ends at the age of 18. The costs of hormones and monitoring were obtained from Spanish sources. The utilities were obtained from the literature. Spanish clinical experts validated the assumptions of the model.

RESULTS

In the deterministic analysis, somatrogon would be cost-effective, compared to GH-d, with a cost per QALY (quality-adjusted life year) gained of €19,259 and a clinically relevant QALY gain (0.336). This result was confirmed in deterministic sensitivity analyses. According to the probabilistic analysis, somatrogon would be the dominant treatment, with a 61% probability of a willingness to pay of €25,000 per QALY gained.

CONCLUSION

Compared to GH-d, somatrogon is cost-effective in the Spanish pediatric population with GHD.

Lessons learned from the application of the HEcoPerMed guidance to three modeling case studies

Background: The HEcoPerMed consortium developed a methodological guidance for the harmonization and improvement of economic evaluations in personalized medicine. Materials & methods: In three therapeutic areas, health economic models were developed to scrutinize the recommendations of the guidance. Results: Altogether, 20 of the 23 recommendations of the guidance were addressed by the models. Seven recommendations were applied in all studies, six in two of the studies and seven in one of the studies. Recommendations with an essential role on the final conclusions of the analyses were identified in each study. Conclusion: The guidance was found to be best used as a tool to identify and prioritize issues, verify solutions and justify decisions during the economic analysis of personalized interventions.

How does the integration of cultural and tourism industries impact the value added to tourism value chain: Evidences from Jiangsu Province of China

China has been fully implementing the policy of the cultural and tourism industrial integration since 2018. However, the value-added benefits of this policy are not prominent, and the relationship between industrial integration and the value added to the tourism value chain was seldom addressed by researchers. In the context of China's high-quality development, it is necessary to conduct the impact of the integration of cultural and tourism industries on the value added to tourism value chain. This paper proposed four theoretical hypotheses and the corresponding econometric models based on the panel data from 2013 to 2020 in China's Jiangsu Province. According to empirical results, the integration of cultural and tourism industries is spatially unbalanced, with notable imbalances between the south and the north. This paper identified a new connection between cultural and tourism integration and the tourism value chain. It is found that the integration of cultural and tourism industries can enhance the value added to tourism value chain either directly or indirectly through the information technology, with the direct effect being positively moderated by tourism agglomeration. Moreover, this paper may overturn how people generally think about the integration between cultural and tourism industries. It reveals a single-threshold effect that only when the integration of cultural and tourism industries reached a high level will it exert a positive effect. To be more specific, not all Chinese cities are suitable for implementing cultural and tourism integration, because the integration is likely to be ineffective in regions where the cultural industry is substantially less developed than the tourism industry.

Effectiveness of price limits: Evidence from China's ChiNext market

Starting from August 24, 2020, the daily stock price limits in China's ChiNext market have been adjusted from 10% to 20%. We use this reform to study the effectiveness of price limits in China's stock market. We test four hypotheses about price limits: delayed price discovery, volatility spillover, trading interference, and magnet effect. Using the event study method, we examine the differences in the behavior of stock price, trading volume, and volatility before and after the reform. We confirm the delayed price discovery, volatility spillover and trading interference hypothesis of price limits, and find that these negative effects of price limits are more serious when lower limits are hit. In addition, we examine the distribution of large price movements before and after the reform and find no evidence of the magnet effect of price limits. The present research has important implications for policymakers and investors in China's stock market.

An Economic Model to Establish the Costs Associated With Routes to Presentation for Patients With Multiple Myeloma in the United Kingdom

OBJECTIVES

Patients with myeloma often face significant diagnostic delay, with up to one-third of UK patients diagnosed after an emergency presentation (EP). Compared with other routes, patients presenting as an emergency have more advanced disease, increased complications, and poorer prognosis.

METHODS

An economic model was developed using a decision-tree framework and lifetime time horizon to estimate costs related to different presentation routes (EP, general practitioner [GP] 2-week wait, GP urgent, GP routine, and consultant to consultant) for UK patients diagnosed as having myeloma. After diagnosis, patients received one of 3 first-line management options (observation, active treatment, or end-of-life care). Inputs were derived from UK health technology assessments and targeted literature reviews, or based on authors' clinical experience where data were unavailable. Active treatment, complication, and end-of-life care costs were included.

RESULTS

The average per-patient cost of treating myeloma (across all routes) was estimated at £146 261. The average per-patient cost associated with EP (£152 677) was the highest; differences were minimal compared with GP 2-week wait (£149 631) and consultant to consultant (£147 237). GP urgent (£140 025) and GP routine (£130 212) were associated with marginally lower costs. Complication (£42 252) and end-of-life care (£11 273) costs were numerically higher for EP than other routes (£25 021-£38 170 and £9772-£10 458, respectively).

CONCLUSIONS

An economic benefit may be associated with earlier diagnosis, gained via reduced complication and end-of-life care costs. Strategies to expedite myeloma diagnosis and minimize EPs have the potential to improve patient outcomes and may result in long-term savings that could offset any upfront costs associated with their implementation.