Short Time Horizon Research Articles

8.P. Scientific session: Lessons-learned from recent foresight studies to support policy making?

Abstract Though foresight is still relative new regarding applications to public health, their number is rapidly growing. Recently, several foresight studies have been published with different health angles. These foresight studies have in general the purpose to support and improve the policy making process. Policy making that is foresight-informed, e.g. policies that account for possible futures and the uncertainty they encompass, will hopefully be better in achieving policy goals than without foresight. However, policy makers in general have a relatively short time horizon, may not welcome uncertainty and have many other aspects to account for when developing their policies. In this workshop, the link of foresight studies with policy making, also referred to as knowledge translation will be the focal point. The workshop will start with and interactive Mentimeter session to get a better view on how the audience is familiar with foresight and knowledge translation. Then, a brief presentation is given as introduction to foresight and a systematic way of doing knowledge translation, applied to foresight, explaining the basic concepts to level understanding of the audience. This is followed by three presentations of foresight studies on specific topics (Screening, AMR, Substance use) focussing on their link withing policy making. Next, two broader foresight studies will be presented (i.e. Dutch PHFS 2024 and the future of public health in Europe). These provide good examples on how broad future public health are analyzed and can be linked to public health policy making. The workshop will be concluded with a discussion with presenters on the lessons learned regarding foresight and the link to policy making. Key messages • Foresight studies in the field of health are maturing by a stronger link to policy making. • We can learn from recent foresight studies how to do better knowledge translation regarding foresight.

Predicting the long-term effects of electronic cigarette use on population health: a systematic review of modelling studies

ObjectiveTo systematically review and synthesise the findings of modelling studies on the population impacts of e-cigarette use and to identify potential gaps requiring future investigation.Data source and study selectionFour databases...

Revisiting the Nexus Between Oil Prices and Economic Activity for Asian Economies Using MIDAS

The objective of this paper is to re-examine the causal relationship between oil prices and economic activity for five Asian economies. We apply the unrestricted MIDAS (U-MIDAS) model using monthly data for real oil prices and quarterly data for real GDP for the period from 1998Q1 to 2019Q4. The causal nexus between oil prices and economic activity is also studied by means of wavelet analysis to investigate whether the relationship between the variables changes over different time scales. The key empirical results under the MIDAS approach show that there is a clear significant causal link from oil prices to economic activity, which is not as clearly found under the standard VAR approach. In addition, our results using impulse response functions suggest that the five Asian economies respond, in general, positively in economic activity to an oil price shock at shorter time horizons (less than two years), while the positive responses switch to mostly negative ones at longer time horizons (from two to four years). Our results, in general, support that the (theoretically) expected negative causal nexus between oil price fluctuations and economic activity for oil importers is dominant over longer time horizons for our dataset. An exception to this pattern is China, which has a dissimilar country profile in terms of its oil market and production structure from the profiles of the other Asian economies in the study. JEL Classification: C32, E32, O53, Q43

Is primary trabeculectomy cost-effective for patients with advanced primary open angle glaucoma? Results from the Treatment of Advanced Glaucoma Study economic model

Background/aimsAdvanced primary open angle glaucoma (POAG) is a lifelong condition. The aim of this study is to compare medical treatment against trabeculectomy for patients presenting with advanced POAG using an...

Budget impact of resmetirom for the treatment of adults with non-cirrhotic non-alcoholic steatohepatitis (NASH) with moderate to advanced liver fibrosis (consistent with stages F2 to F3 fibrosis)

Aims This study assessed the budget impact of resmetirom as a treatment for adults with non-cirrhotic non-alcoholic steatohepatitis (NASH) with moderate-to-advanced liver fibrosis and estimated total costs for a hypothetical private payer in the United States. Materials and methods A three-year budget impact analysis based on an open cohort state transition model was developed for a hypothetical one-million-member private health plan. The comparator was Standard of Care (SOC), defined as routine care for non-cirrhotic NASH patients with moderate-to-advanced liver fibrosis. Each year, the number of resmetirom treatment-eligible patients was estimated through prevalent, incident, and diagnostic rate estimates. Costs included resources incurred by the medical and pharmacy benefits of private payers, including resmetirom drug acquisition costs, diagnosis and monitoring, other medical and other prescription costs stratified by disease progression status (i.e. non-cirrhotic vs. cirrhotic/advanced liver diseases). Resmetirom adverse event management costs were included in sensitivity analysis. Drug costs were estimated based on the average wholesale acquisition cost as of March 2024. Other costs were based on published sources and inflated to 2023 US dollars. Budget impact outcomes were presented in aggregate, net, and on a per-member per-month (PMPM) basis. Results Compared with a scenario without resmetirom, the introduction of resmetirom yielded results ranging from 50 to 238 treated patients, net budget impact of $2.2 to $9.5 million, and PMPM from $0.19 to $0.80 over years one and three. Net costs excluding resmetirom declined over time. In sensitivity analyses, results were most sensitive to diagnostic and epidemiologic inputs. Limitations Market shares are based on internal forecasts, a short time horizon, average treatment effects, and other limitations common to BIMs. Conclusion The adoption of resmetirom on the formulary for the treatment of non-cirrhotic NASH with moderate-to-advanced liver fibrosis resulted in a moderate increase in budget impact with declining costs related to NASH progression.

Cost-effectiveness of neuropsychological rehabilitation for acquired brain injuries: Update of Stolwyk etal.'s (2019) review.

Acquired brain injuries (ABI), resulting from stroke or traumatic brain injury, cause a range of neuropsychological impairments and many patients continue to experience neuropsychological deficits years after onset. The increasing average age of the population highlights the importance of effective management strategies for the consequences of ABI. Despite the well-documented impact of rehabilitation interventions, the cost-effectiveness of neuropsychological rehabilitation remains largely unknown. This study conducted a scoping review to update the findings of Stolwyk etal. (Neuropsychological Rehabilitation, 2021, 31, 316), focusing on the economic evaluations of neuropsychological rehabilitation for individuals with ABI. Following the PIO framework, PRISMA ScR guidelines, and systematic review reporting checklist, the review screened 1027 articles and included eight studies published between 2019 and 2024. The studies encompassed either language rehabilitation or general neuropsychological programs, including neuropsychological interventions. The economic analyses, including two cost-effectiveness, five cost-utility, and one cost-benefit study, mostly adhered to CHEERS guidelines, enhancing the transparency and methodological rigour of their reporting. These studies demonstrated varying degrees of cost-effectiveness for interventions targeting post-stroke language disorders and neuropsychological rehabilitation for ABI, with significant cost savings and health benefits observed, particularly for home-based rehabilitation interventions. The included studies suffered from a short time horizon, limiting the ability to capture the long-term economic impacts and effectiveness of the interventions. Future research should focus on longer-term follow-up data and include broader search strategies to enhance understanding and optimise health care interventions. A comprehensive implementation of these economic analyses is crucial for informing policymakers, enabling them to introduce rehabilitative interventions based on solid evidence.

Valuing the Future: Changing Time Horizons and Policy Preferences

AbstractThe short time horizons of citizens are a prominent explanation for why governments fail to tackle significant long-term public policy problems. Evidence for the influence of time horizons is mixed, complicated by the difficulty of determining how attitudes would differ if individuals were more concerned about the future. This paper approaches the challenge by leveraging a personal experience that leads people to place more value on the future: parenthood. The analysis compares new parents with otherwise similar individuals using a matched difference-in-differences design with a three-wave panel. The results show that parenthood increases support for stopping climate change. Falsification tests and two survey experiments suggest that longer time horizons explain part of this shift in support. Not only are scholars right to emphasize the role of individual time horizons, but changing valuations of the future offer a new way to understand how policy preferences evolve.

Bridging ‘infrastructural solutions’ and ‘infrastructures as solution’: Regional promises and urban pragmatism

The potential of infrastructure ‘as a solution’ is currently at the forefront of American political consciousness. Historic levels of investment in infrastructure proffer seismic material, economic, and symbolic transformations at a near-continental scale. However, the present policy context for infrastructure planning in the US is confounded by a mosaic of decision-making authorities that hamper the development of cohesive approaches to sustainable and equitable development. This situation underscores the need to identify how infrastructural futures are assembled and scaled as simultaneously continuous and emergent, old and new, and marked by the diverse capacities of various stakeholders. This paper makes a case for ‘seeing like a region’ when examining transformative approaches to infrastructural change, as infrastructure systems regularly transcend the boundaries of urban space and hence become enmeshed in the goals of broader constituencies and interests. Through a case study of the Southwestern Pennsylvania Commission, we question how infrastructural futures are understood and materialised by the region’s central planning stakeholders. Our analysis pays particular attention to the challenges faced by regional planning organisations when navigating the spatial–temporal frames of incremental and radical change. As the Southwestern Pennsylvania Commission operates with limited staff capacity, high regulatory burdens, and short time horizons for budgeting processes, incremental changes to infrastructure often are the best hope for solving regional challenges of structural inequality and uneven access to resources. This demonstrates how the solutions proffered by infrastructural development are confounded by the dynamics that come into focus when evaluated from the regional scale. Yet we also identify possibilities for regional approaches that foster equitable urban futures within the spatial envelopes created by infrastructural systems and imaginaries that transition from reactive ‘infrastructural solutions’ to a proactive materialisation of ‘infrastructures as solutions’.

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

Effectiveness of Using Candlestick Charts to Forecast Ethereum Price Direction: A Machine Learning Approach

Cryptocurrency is a form of decentralized digital currency. Ethereum is the second-largest cryptocurrency by market capitalization and the largest altcoin. Cryptocurrencies including Ethereum are highly volatile. Hence, shortterm directional forecasts in the cryptocurrency market have become a widely discussing topic. Candlestick charts are useful visualizations of the open, high, low and close prices which can identify patterns and gauge the near-term direction of prices. This research explores the effectiveness of forecasting hourly Ethereum closing price direction based on candlestick charts within a short time horizon. The proposed forecasting algorithm incorporates clustering methods such as fuzzy K-means, K-means and partition around medoids clustering to cluster candlestick chart properties namely upper shadow length, body length and lower shadow length. Classification methods such as random forest, support vector machine and K-nearest neighbour were used to forecast closing price direction using 16 different predictor variable sets including open, high, low and close prices, candlestick chart price direction, USL, BL and LSL. The accuracy for all considered cases was around 50%. Clustering improved the accuracy slightly and including the CPD with the predictor variable sets under consideration can increase the accuracy slightly. However, this approach is performing better in predicting the Down cases to the total number of actual Down cases because there is a higher sensitivity of 81.20% based on the SVM with Open, High, Low and Close at t in the clustering ignored method.

Banking on trust: exploring the relationship between Federal Reserve directors and financial institutions

Purpose This study aims to analyze the risk profile of banks whose managers sit on Federal Reserve district bank boards in 2023. In particular, to analyze the impact tha Federal Reserve bank directors have on their own banks. Design/methodology/approach Use a matched sample approach to perform univariate analysis and multiple regression methodology to study whether banks whose managers sit on Federal Reserve Bank boards differ in risk profile from banks whose managers do not sit on Federal Reserve district boards. Findings There is limited evidence that banks managed by Fed directors have different capital ratios and leverage ratios relative to non-Fed director banks. There does appear to be a slight difference in the growth of Held-to-Maturity (HTM) Securities between the two samples. Specifically, banks managed by a Fed director saw their HTM portfolio grow over the study period, while banks managed by non-Fed directors reduced their HTM securities. Overall, the results suggest that bank directors on Federal Reserve district boards do so with no apparent detriment to the banks that they manage. Research limitations/implications Results of this study suggest that stakeholder director relationships are not associated with higher risk-taking at director banks. This study is unique in that, rather than looking at how director ties might influence the firm that they are on the board of, the focus here is how the firm (the Fed district, in this case) might influence director affiliations. Limitations include a small sample size (70 banks, including the matched sample), and data over a short time horizon. Additional measures of risk can also be analyzed in future research. Practical implications While there has been much speculation in the industry and in the press regarding the conflict of interest involving bank directors on Fed district boards, this research suggests there is little evidence of any risk differential involving these directors and their specialties to the Fed. Originality/value This study involves a unique approach to corporate governance analysis, whereby any conflict of interest that might exist between directors and the firm is studied from an alternate angle – in particular, whether the association with a regulator’s board impacts the director firm’s risk. Furthermore, with the recent events in the banking industry involving the collapse of several banks, including Silicon Valley, the notion that bank management participating on the boards of directors of their own regulator seemed a worthwhile question as to whether this diminished the safety and soundness of the banks that they run.

Corruption and Co-Optation in Autocracy: Evidence from Russia

Do corrupt officials govern differently in elected office? This article develops a theoretical framework and analyzes new data from financial disclosures to estimate the governing costs of corruption. First, I uncover substantial hidden wealth held by roughly one quarter of the legislators in the Russian Duma; these “kompromat deputies” are vulnerable to damaging information being used against them by the regime. Analyzing their behavior in office, I find that these deputies are less active and more absent members of parliament. When called to vote, kompromat deputies from the opposition also more eagerly support the regime’s political agenda. Finally, kompromat deputies are less likely to win reelection, suggesting that they have shorter time horizons as well as that parties have incentives to rotate them out. Autocrats permit and then monitor corruption in order to co-opt potential challengers, who in turn trade loyalty to the regime in exchange for opportunities to self-enrich.

Integrated Optimisation Model of Daily Freight Train Scheduling and Dynamic Railcar Routing Based on a Two-Layer Space-Time Network

This paper focuses on daily freight train scheduling and dynamic railcar routing problems for rail freight transportation at the operational level. Two mixed integer linear programming models that adopted different strategies were formulated based on a continuous two-layer time-space network. We simultaneously considered the benefits of railroad company and service quality when setting the objective function. By solving the models, we can distribute the dynamic railcar flows to the train paths in the basic train timetable to obtain the daily train operation plan over a short time horizon (e.g. a day), which will be helpful for dispatchers to make decisions such as the empty railcar distribution and car routing (trip planning). Finally, we compared two models on a part of the Chinese railroad network. The results show that the second model can effectively improve the efficiency of railroad freight transportation.

Cross-category connectedness between Shanghai crude oil futures and Chinese stock markets related to the Belt and Road Initiative

This paper aims at investigating the cross-category time-varying frequency connectedness between Shanghai crude oil futures (INE), the CSI Belt and Road theme index, and seven Chinese stock markets affiliated with the Belt and Road Initiative (BRI), namely infrastructure, high-speed railway, green electricity, communication, ferrous metals, agriculture, and new energy markets. Using the novel time-varying parameter VAR (TVP-VAR) frequency connectedness model, we identify how the connectedness between INE and BRI-related stocks varies between the short-term, and long-term investment horizons. Our empirical findings reveal that the connectedness between INE and BRI-related stocks significantly increases during extreme market movements and mainly occurs at shorter time horizons. On the other hand, both INE and BRENT serve as net shock receivers, exhibiting relatively limited connectedness to BRI-related stocks. Notably, the correlation between INE and BRI-related stocks is significantly stronger compared to that observed with BRENT. We also observe that the INE exhibits a relatively strong correlation with infrastructure and ferrous metals compared to other stock markets, displaying a discernible bidirectional spillover effect. Our results document some practical enlightenment for investors and policymakers with various time horizons.

Does Support Price Policy Drive Yield Rate? An Econometric Investigation of Wheat and Sugarcane Crops in Pakistan

The governments of developing economies incentivize the cultivators of their most strategic crops by offering them minimum support price for their yield. This study aims at identifying distinctively the short-run dynamics and the long-run causal linkage of support price and yield rates for two major food crops of the country; wheat and sugarcane. Employing advance time-series econometric estimators of Error Correction Model (ECM) and Fully Modified OLS (FMOLS) cointegration regression, results indicate the valid existence of hypothesized relationship over both short and longer time horizons. Each year, the short-term divergence of two crop yields from their long-run equilibrium paths turns out to be corrected with high speed of adjustment. The long-run association between two variables is also found evident with decent magnitude and high degree of statistical significance. These findings favour the use of support prices as a long-term policy tool for encouraging cultivation of wheat and sugarcane crops in country. However, the overall welfare consequences of such a policy should never be over-rated in a society like Pakistan which is largely characterized by horrific levels of corruption and innumerable administrative bottlenecks.

Short-term impacts of Universal Basic Income on population mental health inequalities in the UK: A microsimulation modelling study.

Population mental health in the United Kingdom (UK) has deteriorated, alongside worsening socioeconomic conditions, over the last decade. Policies such as Universal Basic Income (UBI) have been suggested as an alternative economic approach to improve population mental health and reduce health inequalities. UBI may improve mental health (MH), but to our knowledge, no studies have trialled or modelled UBI in whole populations. We aimed to estimate the short-term effects of introducing UBI on mental health in the UK working-age population. Adults aged 25 to 64 years were simulated across a 4-year period from 2022 to 2026 with the SimPaths microsimulation model, which models the effects of UK tax/benefit policies on mental health via income, poverty, and employment transitions. Data from the nationally representative UK Household Longitudinal Study were used to generate the simulated population (n = 25,000) and causal effect estimates. Three counterfactual UBI scenarios were modelled from 2023: "Partial" (value equivalent to existing benefits), "Full" (equivalent to the UK Minimum Income Standard), and "Full+" (retaining means-tested benefits for disability, housing, and childcare). Likely common mental disorder (CMD) was measured using the General Health Questionnaire (GHQ-12, score ≥4). Relative and slope indices of inequality were calculated, and outcomes stratified by gender, age, education, and household structure. Simulations were run 1,000 times to generate 95% uncertainty intervals (UIs). Sensitivity analyses relaxed SimPaths assumptions about reduced employment resulting from Full/Full+ UBI. Partial UBI had little impact on poverty, employment, or mental health. Full UBI scenarios practically eradicated poverty but decreased employment (for Full+ from 78.9% [95% UI 77.9, 79.9] to 74.1% [95% UI 72.6, 75.4]). Full+ UBI increased absolute CMD prevalence by 0.38% (percentage points; 95% UI 0.13, 0.69) in 2023, equivalent to 157,951 additional CMD cases (95% UI 54,036, 286,805); effects were largest for men (0.63% [95% UI 0.31, 1.01]) and those with children (0.64% [95% UI 0.18, 1.14]). In our sensitivity analysis assuming minimal UBI-related employment impacts, CMD prevalence instead fell by 0.27% (95% UI -0.49, -0.05), a reduction of 112,228 cases (95% UI 20,783, 203,673); effects were largest for women (-0.32% [95% UI -0.65, 0.00]), those without children (-0.40% [95% UI -0.68, -0.15]), and those with least education (-0.42% [95% UI -0.97, 0.15]). There was no effect on educational mental health inequalities in any scenario, and effects waned by 2026. The main limitations of our methods are the model's short time horizon and focus on pathways from UBI to mental health solely via income, poverty, and employment, as well as the inability to integrate macroeconomic consequences of UBI; future iterations of the model will address these limitations. UBI has potential to improve short-term population mental health by reducing poverty, particularly for women, but impacts are highly dependent on whether individuals choose to remain in employment following its introduction. Future research modelling additional causal pathways between UBI and mental health would be beneficial.

Use of Persuasive Language in Communication of Risk during Prostate Cancer Treatment Consultations.

Physician treatment preference may influence how risks are communicated in prostate cancer consultations. We identified persuasive language used when describing cancer prognosis, life expectancy, and side effects in relation to a physician's recommendation for aggressive (surgery/radiation) or nonaggressive (active surveillance/watchful waiting) treatment. A qualitative analysis was performed on transcribed treatment consultations of 40 men with low- and intermediate-risk prostate cancer across 10 multidisciplinary providers. Quotes pertaining to cancer prognosis, life expectancy, and side effects were randomized. Coders predicted physician treatment recommendations from isolated blinded quotes. Testing characteristics of consensus predictions against the physician's treatment recommendation were reported. Coders then identified persuasive strategies favoring aggressive/nonaggressive treatment for each quote. Frequencies of persuasive strategies favoring aggressive/nonaggressive treatment were reported. Logistic regression quantified associations between persuasive strategies and physician treatment recommendations. A total of 496 quotes about cancer prognosis (n = 127), life expectancy (n = 51), and side effects (n = 318) were identified. The accuracy of predicting treatment recommendation based on individual quotes containing persuasive language (n = 256/496, 52%) was 91%. When favoring aggressive treatment, persuasive language downplayed side effect risks and amplified cancer risk (recurrence, progression, or mortality). Significant predictors (P < 0.05) of aggressive treatment recommendation included favorable side effect interpretation, downplaying side effects, and long time horizon for cancer risk due to longevity. When favoring nonaggressive treatment, persuasive language amplified side effect risks and downplayed cancer risk. Significant predictors of nonaggressive treatment recommendation included unfavorable side effect interpretation, favorable interpretation of cancer risk, and short time horizon for cancer risk due to longevity. Physicians use persuasive language favoring their preferred treatment, regardless of whether their recommendation is appropriate. Clinicians should quantify risk so patients can judge potential harm without solely relying on persuasive language. Physicians use persuasive language favoring their treatment recommendation when communicating risks of prostate cancer treatment, which may influence a patient's treatment choice.Coders predicted physician treatment recommendations based on isolated, randomized quotes about cancer prognosis, life expectancy, and side effects with 91% accuracy.Qualitative analysis revealed that when favoring nonaggressive treatment, physicians used persuasive language that amplified side effect risks and downplayed cancer risk. When favoring aggressive treatment, physicians did the opposite.Providers should be cognizant of using persuasive strategies and aim to provide quantified assessments of risk that are jointly interpreted with the patient so that patients can make evidence-based conclusions regarding risks without solely relying on persuasive language.

Prospective policy analysis-a critical interpretive synthesis review.

Most policy analysis methods and approaches are applied retrospectively. As a result, there have been calls for more documentation of the political-economy factors central to health care reforms in real-time. We sought to highlight the methods and previous applications of prospective policy analysis (PPA) in the literature to document purposeful use of PPA and reflect on opportunities and drawbacks. We used a critical interpretive synthesis (CIS) approach as our initial scoping revealed that PPA is inconsistently defined in the literature. While we found several examples of PPA, all were researcher-led, most were published recently and few described mechanisms for engagement in the policy process. In addition, methods used were often summarily described and reported on relatively short prospective time horizons. Most of the studies stemmed from high-income countries and, across our sample, did not always clearly outline the rationale for a PPA and how this analysis was conceptualized. That only about one-fifth of the articles explicitly defined PPA underscores the fact that researchers and practitioners conducting PPA should better document their intent and reflect on key elements essential for PPA. Despite a wide recognition that policy processes are dynamic and ideally require multifaceted and longitudinal examination, the PPA approach is not currently frequently documented in the literature. However, the few articles reported in this paper might overestimate gaps in PPA applications. More likely, researchers are embedded in policy processes prospectively but do not necessarily write their articles from that perspective, and analyses led by non-academics might not make their way into the published literature. Future research should feature examples of testing and refining the proposed framework, as well as designing and reporting on PPA. Even when policy-maker engagement might not be feasible, real-time policy monitoring might have value in and of itself.

Cost-effectiveness of abobotulinumtoxinA plus best supportive care compared with best supportive care alone for early treatment of adult lower limb spasticity following an acute event.

Spasticity is an incurable chronic condition, and patients with spasticity frequently experience symptoms such as muscle stiffness, restricted mobility, fatigue, spasms, and pain. The study objective was to assess the cost-effectiveness of abobotulinumtoxinA plus best supportive care compared with best supportive care alone for the early treatment of adult lower limb spasticity following an acute event (e.g. stroke or traumatic brain injury), from an Australian payer perspective. Using clinical data from published pivotal trials, an economic model based on a Markov model was developed to capture changes in treatment costs, healthcare resource use costs, functional outcomes, and health-related quality of life over a lifetime horizon. Scenario analyses and a probabilistic sensitivity analysis were conducted to explore the uncertainty in the model parameters and assumptions used in the base case. AbobotulinumtoxinA plus best supportive care was cost-effective versus best supportive care, yielding an incremental cost-effectiveness ratio of $35,721 per quality-adjusted life year gained. Sensitivity analyses confirm the robustness of the base case, with most results remaining below the commonly acceptable cost-effectiveness willingness-to-pay threshold of $75,000 per quality-adjusted life year for cost-effectiveness in Australia. Inputs and assumptions that produced the top four highest incremental cost-effectiveness ratios include the application of different health resource utilisation source, short time horizon, unweighted regression analyses to determine regression probabilities, and no stopping rule. AbobotulinumtoxinA plus best supportive care has a 74% probability of being cost-effective compared with best supportive care alone at the willingness to pay threshold. AbobotulinumtoxinA plus best supportive care treatment is cost-effective in Australia for the management of adult lower limb spasticity in patients treated within 2 years of an acute event.

Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling

Long-term river streamflow prediction and modeling are essential for water resource management and decision-making related to water resources. This research paper considers the importance of these predictions and proposes a model to address scarcity scenarios to support decision-making in water allocation, flood management, and drought prediction scenarios. Machine learning (ML) techniques offer promising alternatives for improving long-term streamflow prediction. However, most existing studies on ML models for streamflow prediction have focused on shorter time horizons, limiting their broader applicability. Consequently, there is a need for dedicated research that addresses the use of ML models in long-term streamflow prediction. Considering this research gap, this paper presents an ML-based approach that learns and replicates the natural flow dynamics of a river, allowing for the simulation of reduced flow scenarios (25 % and 50 % reduction). This capability allows for simulating drought scenarios of varying severity, providing valuable insights for water service managers. This study significantly contributes to the progress of predicting long-term river streamflow through the application of machine learning models. Moreover, this study offers valuable insights and recommendations for hydrologists to improve future research efforts.