Abstract

Background/Objective
Sickle cell disease (SCD) is a group of inherited health conditions affecting 7.74 million people worldwide. Regular automated red blood cell exchange (aRBCX) transfusions have been shown to improve control and management of SCD compared with manual RBCX (mRBCX). The aim of this study was to estimate the lifetime clinical and economic impact of aRBCX versus mRBCX in two United Kingdom-based populations with SCD (paediatrics initiated aged 5 years and adults initiated aged 38 years) that were clinically indicated for chronic disease-modifying transfusions (DMTs).

Methods
An individual patient-level simulation model was developed to estimate lifetime quality-adjusted life years (QALYs) and healthcare costs. DMT administration programmes aligned with recommended treatment schedules. Monte Carlo methods determined baseline characteristics and clinical event occurrence. Pragmatic review findings and expert opinion informed model parameters and assumptions. Second-order probabilistic sensitivity analysis (PSA) was performed for 1000 individuals’ lifetimes over 500 iterations.

Results
Per individual, aRBCX reduced acute clinical events by 19% in both populations versus mRBCX. The time spent receiving chelation therapy reduced by 63 and 32 months for paediatric-initiated and adult-initiated individuals, respectively. Total lifetime DMT costs were reduced by £71,217 and £30,740 for paediatric-initiated and adult-initiated individuals, respectively. Overall, aRBCX increased QALYs and reduced costs by 0.29 and £112,811 in paediatric-initiated individuals and 0.24 and £61,895 in adult-initiated individuals. aRBCX was cost-effective in 100% of PSA iterations for both populations.

Conclusion
aRBCX shows potential to improve health outcomes and reduce healthcare costs for individuals with SCD initiating a chronic DMT programme.

Abstract

Abstract Introduction: The life cycle of health technologies contribute to air pollution, ecotoxicity, and resource depletion, impacting the environment and human health. Increasing healthcare resource use globally increases emissions that accelerate climate change and negatively affect the health of current and future generations. HealthTechnologyAssessment(HTA)shouldinformdecisionmakerstoprioritizetheadoption of technologies demonstrating value in terms of health benefits, costs, and other relevant dimensions such as environmental sustainability. This paper reports on a multistakeholder approach to guiding an international working group for Environmental Sustainability in Health Technology Assessment (ESHTA) that has been formed by Health Technology Assessment international. Methods: A multistakeholder online workshop was held with 32 participants in May 2024 to define the critical issues to be considered. The resulting report underwent consultation among the ESHTA members and in a broader group of 90 additional worldwide stakeholder representatives. Results: Theworkshopparticipantsrecognized definingframeworks, mechanisms,andtoolsfor embedding environmental sustainability into HTA as an opportunity to support sustainable development and quality improvement in healthcare. Achieving this requires (1) consensus on what environmental sustainability in healthcare means, (2) reconcilement with other healthcare and environmental policies, and (3) methods that are useful and applicable within HTA frameworks. Conclusion: This novel collaboration aims to align the global HTA community on the role of environmental sustainability in HTA. The report provides a path for the way forward for incorporating environmental sustainability into HTA based on broad perspectives from global multistakeholders.

Abstract

BACKGROUND: Primary immunodeficiency diseases (PIDDs) are rare genetic disorders impairing immunity. Studies evaluating diagnostic rates of PIDDs in historically marginalized US populations are limited.

OBJECTIVE: To conduct a scoping review that identifies the extent of race and ethnicity reporting in US-based observational studies of people with PIDDs, and the demographic composition of study populations compared with the broader US population.

METHODS: We conducted pragmatic searches of MEDLINE in April 2024 and ultimately included studies dating back 10 years. Results were screened and extracted against prespecified eligibility criteria by a single reviewer. Included data were compared with US census data using χ2 tests.

RESULTS: We identified 126 publications publishing observational PIDD studies that report patient characteristics, 62 of which (49%) reported race or ethnicity data. After grouping for data source and PIDD type to avoid overlapping studies, 25 publications were prioritized for extraction. Of these, seven were fully compliant with current Food and Drug Administration–recommended reporting guidelines. The populations of the extracted studies were not statistically representative of the broader US population, with overrepresentation of non-Hispanic White patients.

CONCLUSIONS: Primary immunodeficiency disease cohort and other studies inconsistently report demographic data on patient race and ethnicity according to current Food and Drug Administration recommendations. Efforts to improve understanding of the prevalence, characteristics, and diagnostic rates of PIDD in different US populations (as well as differences among study populations and overall US demographics) would likely be facilitated by a greater effort toward comprehensive demographic reporting.

Abstract

BACKGROUND: In levodopa-treated individuals with Parkinson’s disease (PD) and end-of-dose motor fluctuations, the BIPARK-I randomized controlled trial (RCT) demonstrated that opicapone is noninferior to entacapone in reducing OFF-time. Furthermore, the BIPARK-II RCT demonstrated that opicapone is well tolerated and significantly reduces OFF-time compared with placebo. This study developed a cost-effectiveness model (CEM) of opicapone compared with entacapone from the perspective of the English National Health Service (NHS) and personal social services (PSS).

METHODS: The CEM used a Markov model with three health states, including “<25% OFF-time,” “≥25% OFF-time,” and “dead,” as individuals spending less than 25% of their awake time experiencing OFF-time have previously been shown to have a significantly improved health-related quality of life and to accumulate fewer healthcare costs. The CEM had a 25-year time horizon, expressed costs as 2021/22 Great British Pounds (GBPs), and health outcomes as quality-adjusted life years (QALYs). Both costs and health outcomes were discounted at 3.5% annually, and a cost-effectiveness threshold of £20,000 per QALY was used. Probabilistic sensitivity analysis (PSA) considered parameter uncertainty. RESULTS: The deterministic base case indicates that an individual treated with opicapone accrues fewer costs and more QALYs compared with each entacapone comparator and, therefore, is considered cost-effective. The PSA indicates that the probability that opicapone is cost-effective ranges from 87.2% to 98.0%, depending on the choice of entacapone comparator. CONCLUSIONS: Opicapone is cost-effective when compared with entacapone for levodopa-treated PD patients experiencing end-of-dose motor fluctuations.

Abstract

Health economics is an important field not just in the UK but globally. Decision-makers can use it to prioritise resources used in healthcare systems to maximise population health whilst ensuring value for money. Since 1999, the National Institute for Health and Care Excellence (NICE) has been responsible for these decisions in the UK. There is a need to provide these decision-makers with the best possible evidence for new treatments to support well-informed decisions.

The vast majority of health economic modelling is still conducted using Markov models to capture disease progression. However, these models rely on restrictive assumptions that may not accurately capture real-world disease progression. This talk will discuss two examples from a recent publication
where regression modelling was used to improve the health economic model. The first example will cover using a multinomial logistic regression to predict the changes in haemoglobin health states over time in patients with chronic kidney disease. Traditional Markov models assume a fixed disease progression rate which fails to account for patient subgroups and time-varying rates. Regression models enable dynamic transitions based on patient characteristics like age, sex, and baseline haemoglobin levels. This leads to better modelling of disease progression and subsequent decision-making.

The second example will cover using a generalised linear mixed model (GLMM) to estimate drug doses for each haemoglobin health state whilst adjusting for subgroup characteristics. This allows for a more granular approach to cost-effectiveness analysis. Unlike Markov models, which often require simplifying assumptions to capture drug doses, a GLMM better reflects the uncertainty in the target-population.

As real-world evidence and individual patient data (IPD) become more accessible, alongside increasing computational power, statisticians must apply and share advanced modelling approaches. By doing so, we can enhance healthcare decision-making, ultimately leading to better policies and improved population health.

Abstract

OBJECTIVES: Probabilistic sensitivity analysis (PSA) is used to characterise uncertainty in cost-effectiveness models. A model was developed using R and Shiny to explore the impact of different parameter correlation structures on PSA outputs.

METHODS: A Markov model was built in R to compare a hypothetical treatment and comparator. Three options were built into the model: no correlation (inputs varied independently); part correlation (correlation within but not between costs, utilities and transition matrices); and full correlation (correlation between all inputs). A Shiny interface allowed users to explore the impact of the correlation options with different model parameters. Features of the Shiny model included preloading base case results, running the Markov model with an arbitrary number of health states and costs, and displaying results for different subsets of correlation options. A scenario analysis was included in the Shiny model to determine the circumstances in which correlation had the largest impact by varying the treatment cost as a proxy for the ICER.

RESULTS: While the ICER was comparable across all correlation options, the likelihood of cost-effectiveness differed substantially from 61% to 93%. In all scenarios, the 'no correlation' option displayed the most certain likelihood (closest to either 0 or 1) of cost-effectiveness, while the least certain was produced by the full correlation option. Counterintuitively, correlating inputs increased uncertainty because it allowed for a greater number of 'extreme' scenarios to be generated, whereas allowing independent generation of large numbers of inputs tends to lead to a 'cancelling out' effect. This effect was most pronounced when the ICER is moderately close to the willingness-to-pay threshold.

CONCLUSION: This analysis demonstrates that input correlation can have a substantial impact on the level of certainty in model outputs, and by ignoring this, the model may be over- or under-stating the true level of confidence.

Abstract

OBJECTIVES: Health Technology Assessment (HTA) guidelines in the UK mention the importance of uncertainty in cost-effectiveness analysis (CEA). The presence of missing data within data sets used to provide inputs for CEA can be a source of uncertainty. One setting in which missing data may be prevalent is for utility scores and these are often used in CEA. The intention of this analysis is to formalise recommendations for dealing with missingness and outline a minimum accepted reporting standard for missing utility data.

METHODS: A simulated patient level dataset (SIPD) was created in an oncology setting. The SIPD included utility scores for hypothetical individuals and treatments. Key prognostic variables were also simulated. Missing data was generated for utility for 10% and 30% of the observations, for missing completely at random (MCAR), at random (MAR) and not at random (MNAR) missingness mechanisms. Four methods for addressing missing data were analysed, complete case analysis (CCA), mean score estimation (MSE), multiple imputation via chained equations (MICE) and linear mixed modelling (LMM). The outcome of interest was the mean difference between the true utility value and the estimated utility value from each method.

RESULTS: MICE tended to result in the lowest mean difference across levels and mechanism. For the 10% level and MCAR mechanism, CCA resulted in negligible differences from the true utility value. MNAR data tended to result in substantial differences from the true utility value regardless of the level of missingness and method used. LMMs suppressed the standard deviation of utility.

CONCLUSION: Differing results across levels and mechanisms for each method used to deal with missingness were observed here. This suggest that the level and mechanism of missingness should be investigated and reported as a minimum standard for all data sets that inform economic models for HTA in the UK.