Value in Health最新文献

Objectives: The Weight-Specific Adolescent Instrument for Economic Evaluation (WAItE) is a weight-specific patient reported outcome measure for use in adolescence, consisting of seven domains, each with five response levels. The objective of this study was to generate a UK value set for the WAItE, enabling the calculation of utility values.

Methods: An online Discrete Choice Experiment (DCE) completed by an adult sample representative to the working population of the UK was used to estimate the preferences for the five levels of the seven domains. DCE data were analysed using Multinomial and Mixed Logit models. The latent values were then anchored onto the 0-1 death-full health Quality Adjusted Life Year (QALY) scale using two different anchoring techniques, the Time Trade Off (TTO) method and the DCE-Visual Analogue Scale (DCE-VAS) method.

Results: 1,004 adults from the UK were included in the final estimation sample for the DCE. From the latent estimates, the majority of the levels of the dimensions followed the monotonic nature of the WAItE, however some levels of the 'Tiredness', 'Walking' and 'Sports' related dimensions were not monotonic and combined to generate the final value set. The results from the TTO and DCE-VAS anchoring methods were similar, with values for the 'PITS state' (the worst health state possible from the WAItE) of 0.289 and 0.230.

Conclusions: This study has developed a value set for the WAItE based on the preferences of the UK population, enabling the use of the WAItE in cost utility analyses of interventions targeting obesity in adolescents.

Objectives To develop a value set for the EQ-5D-5L based on preferences of the general adult population of the United Arab Emirates (UAE). Methods The study followed the EuroQol EQ-5D-5L Valuation protocol and involved conducting interviewer-administered face-to-face or online interviews in Arabic or English, using the EuroQol Valuation Technology (EQ-VT) with a sample of 1005 adults representing the UAE general population. Sample recruitment involved a two-stage quota sampling strategy across the seven Emirates of the UAE, ensuring representation of nationals and expatriates. Various models using composite time-trade-off (cTTO) data only, discrete choice experiment (DCE) data only, and hybrid using both cTTO and DCE data were examined, along with various sensitivity analyses to examine the robustness of the models. Results Average age of respondents was 39 years (SD=10.8), 44.5% were female, and 11% were UAE nationals. The best performing model to generate the value set for the EQ-5D-5Lwas the hybrid Tobit model censored at -1.0, corrected for heteroskedasticity. Values ranged from - 0.654 for the worst health state (55555) to 1 for full health (11111) and 0.962 for 11211, with 15.3% of predicted values worse than dead. Mobility problems had the largest impact on health state preference values relative to other dimensions. Conclusion This value set will facilitate the application and use of the EQ-5D-5L instrument in the UAE population in generating local evidence on the cost-effectiveness of healthcare interventions, as well as to enhance other applications of EQ-5D in population health assessment and health systems.

Objectives Hospice services offer invaluable support to individuals facing life-limiting illnesses, however, quantifying their positive impact presents a challenge. As the demand for palliative care rises due to complex illnesses and an aging population, hospices face the need to prove their value. With funding primarily reliant on charitable donations and limited statutory support, they must demonstrate their effectiveness to secure additional resources in a competitive landscape. Methods This study employed the Social Return on Investment (SROI) framework to evaluate the social value generated by four hospice sites offering inpatient and day therapy services across North Wales. Through a mixed-methods approach, quantitative and qualitative data were collected to explore stakeholder experiences, values, and outcomes, facilitating a thorough examination of the broader social impact of hospice care. Results The average input and output values for the inpatient unit were £602,100 and £1,667,861 respectively, thus returning a base case ratio of £2.77: £1. The day therapy unit had average input and output costs of £155,928 and £1,847,347 respectively, hence a base case ratio of £11.85: £1. Sensitivity analysis yielded estimates of between £2.20: £1 and £6.83: £1 for the inpatient unit and between £2:44: £1 and £19:51: £1 for the day therapy unit. CONCLUSION As healthcare providers globally confront challenges with resource constraints, adopting value-driven methodologies becomes crucial. Embracing such methodologies fosters a more comprehensive understanding of value, transcending traditional metrics to encompass social, environmental, and long-term sustainability considerations.

Objectives: Cost-effectiveness analysis (CEA) is an accepted approach to evaluate cancer screening programmes. CEA estimates partially depend on modelling methods and assumptions used. Understanding common practice when modelling cancer relies on complete, accessible descriptions of prior work. This review's objective is to comprehensively examine published CEA modelling methods employed to evaluate Colorectal Cancer (CRC) screening from an aspiring modeller's perspective. It compares existing models highlighting the importance of precise modelling method descriptions, and essential factors when modelling CRC progression.

Methods: MEDLINE, EMBASE, Web of Science and Scopus electronic databases were used. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement and data items from previous systematic reviews formed a template to extract relevant data. Specific focus included model type, natural history, appropriate data sources and survival analysis.

Results: Seventy-eight studies, with 52 unique models found. Twelve previously published models were reported in 39 studies, with 39 newly developed models. CRC progression from the onset was commonly modelled, with only six models not including it as a model component.

Conclusions: Modelling methods needed to simulate CRC progression depend on the natural history structure and research requirements. For aspiring modellers, accompanying models with clear overviews and extensive modelling assumption descriptions is beneficial. Open-source modelling would also allow model replicability and result in appropriate decisions suggested for CRC screening programmes.

Objectives: We investigated how the Inflation Reduction Act (IRA) Medicare Part D benefit redesign may reduce out-of-pocket (OOP) drug expenditures for Medicare beneficiaries with dementia. Methods Design Utilizing data from the Health and Retirement Study (HRS) linked with Medicare claims, we simulated post-redesign OOP drug spending by applying the 2025 prescription drug cost-sharing rules to each beneficiary's pre-redesign Part D medication utilization data for 2016, adjusting for inflation. Participants Our study population comprised HRS respondents aged 65 and older in 2016, enrolled in Medicare fee-for-service, with at least one Part D drug claim in 2016, and diagnosed with dementia between 2000 and 2016 (n=1,677). Main Measures We compared pre- and post-redesign annual OOP drug expenditures stratified by: 1) low-income subsidy (LIS) eligibility status; 2) household income among non-LIS beneficiaries; 3) comorbidity count, and 4) cognitive impairment severity.

Results: Post-redesign, we project average annual OOP drug expenditures among LIS beneficiaries with dementia to decrease from $56 to $25 - representing a $31, or 55% reduction. In contrast, among non-LIS beneficiaries, average OOP drug expenditures is projected to decrease from $772 to $576, a $196, or 25% reduction. We project the reduction in OOP drug expenditures to be greater among beneficiaries with 6-8 comorbidities (45%) than among beneficiaries with fewer comorbidities (21-26%).

Conclusions: The IRA Medicare Part D benefit redesign will reduce OOP drug spending for beneficiaries with dementia, resulting in potentially marked savings not only for low-income beneficiaries and beneficiaries with a high comorbidity burden, but also for higher-income beneficiaries.

Objectives: To provide an overview of policy initiatives in high-income countries aimed at supporting the development and accessibility of treatments for rare diseases.

Methods: We examine how legislative, research, and pricing policies in high-income countries address barriers that have historically hindered innovation and access to rare disease treatments. By analysing examples from the EU, UK, US, Canada, Japan, and Australia, the article identifies ongoing initiatives, outlines current challenges, and explores proposed solutions to foster a sustainable, innovative, and accessible rare disease treatment ecosystem.

Results: The review highlights policies like legislative incentives in the EU, US, and Japan for orphan drug development, public-private partnerships to boost innovation, and patient registries to support research and clinical trials. Despite these efforts, major challenges persist, including high therapy costs, limited access to innovation for ultra-rare diseases, and diagnostic delays, with significant disparities across regions.

Conclusions: Overcoming these challenges will require sustainable pricing and reimbursement frameworks, alongside stronger collaboration between stakeholders, particularly for ultra-rare diseases. Advanced technologies like AI hold promise for improving diagnostic accuracy and data collection, supported by enhanced coding systems and registries to facilitate more robust research.

Objectives: The Recovering Quality of Life-Utility Index (ReQoL-UI) instrument was designed to measure the quality-of-life outcomes for people older than 16 years with mental health problems. We aimed to elicit societal preferences for the ReQoL-UI health states to facilitate better decision making in Australia.

Methods: A discrete choice experiment with duration was embedded in a self-completed online survey and administered to a representative sample (n = 1019) of the Australian adult population aged 18 years and older stratified by age, sex, and geographic location. A partial subset design discrete choice experiment was used with 3 fixed attributes and 5 varying attributes, containing 240 choice tasks that were divided into 20 blocks so that each respondent was assigned a block of 12 choice tasks. The value set was modeled using the conditional logit model with utility decrements directly anchored on the 0 to 1 dead-full health scale. Preference heterogeneity was tested using a mixed logit model.

Results: The final value set reflects the monotonic nature of the ReQoL-UI descriptive systems where the best health state defined by the descriptive system has a value of 1 and the worst state has a value of -0.585. The most important dimension was physical health problems, whereas the least important attribute was self-perception. Sensitivity and preference heterogeneity analyses revealed the stability of the value set.

Conclusions: The value set, which reflects the preferences of the Australian population, facilitates the calculation of an index for quality-adjusted life-years in mental health intervention cost-utility analyses.