Value in Health最新文献

Objective: The use of cost-effectiveness methods to support policy decisions has become well established but difficulties can arise when evaluating a new treatment which is indicated to be used in combination with an established "backbone treatment." If the latter has been priced close to the decision maker's willingness to pay threshold, this may mean that there is no headroom for the new treatment to demonstrate value, at any price, even if the combination is clinically effective. Without a mechanism for attributing value to component treatments within a combination therapy, the health system risks generating negative funding decisions for combinations of proven clinical benefit to patients. The aim of this work was to define a value attribution methodology which could be used to allocate value between the components of any combination treatment.

Methods: The framework is grounded in the standard decision rules of cost-effectiveness analysis and provides solutions according to key features of the problem: perfect/imperfect information about component treatment monotherapy effects and balanced/unbalanced market power between their manufacturers.

Results: The share of incremental value varies depending on whether there is perfect/imperfect information and balance/imbalance of market power, with some scenarios requiring the manufacturers to negotiate a share of the incremental value within a range defined by the framework.

Conclusions: It is possible to define a framework that is independent of price and focuses on benefits expressed as Quality-Adjusted Life-Year (QALY) gains (and/or QALY equivalents for cost-savings), a standard metric used by many HTA agencies to evaluate novel treatments.

Objectives: To assess the indirect economic impacts on caregivers resulting from mental health problems in their children and to explore the association with characteristics of the young people and their caregivers.

Methods: Data from 1,158 caregivers of young people aged 14-23 with mental health problems in a Brazilian cohort were analysed. We assessed productivity losses, additional household tasks, out-of-pocket expenses, and own healthcare utilisation due to the young person's mental health problems over the past 6 months. Costs of productivity losses and household tasks were estimated in terms of caregivers' earnings. Logistic regression models identified factors associated with reported impacts. Generalised linear models examined clinical and caregiver characteristics associated with the economic impact on caregivers.

Results: Nearly 40% of caregivers (n=458) experienced economic impacts due to mental health issues in their children over the previous 6 months. The total economic impact among these 458 caregivers who reported incurring costs amounted to half of their earnings, and this was consistent across socioeconomic groups. Factors associated with reporting impacts differed from those affecting their costs. Externalising and comorbid diagnoses, service use, higher impairment, and female caregiver increased the likelihood of impacts, while the greatest economic impacts were associated with internalising conditions and service use.

Conclusions: While these findings need to be interpreted with caution due to inherent limitations, they underscore the substantial economic impacts borne by caregivers of young people with mental health problems, suggesting the need for targeted policy interventions to promote equitable caregiving and provide more comprehensive childcare support.

Objective: Hypoglycaemia impacts the health-related quality of life (HRQoL) of people living with diabetes (PwD), and existing preference-weighted measures do not capture all important aspects. The study aimed to generate a preference-weighted measure capturing the HRQoL impact of hypoglycaemia in PwD.

Methods: Items for the health state classification system were selected from the hypoglycaemia-specific Hypo-RESOLVE QoL measure using: relevance in cognitive interviews, translatability, suitability for valuation, endorsement by patient advisors and experts, and psychometric performance in a large survey of PwD. Second, an online valuation survey using discrete choice experiment (DCE) with survival attribute was conducted with members of the UK public. DCE data was modelled using conditional logit analysis, and results scaled to produce preference weights for the classification system on a scale where 1 is equivalent to full health, 0 is equivalent to dead, and below zero is worse than dead.

Results: The health state classification system consists of eight items reflecting the factors of the Hypo-RESOLVE QoL (psychological, social and physical aspects). The valuation survey was completed by 1000 members of the UK public, representative for age and sex. Good understanding of DCE tasks was demonstrated. The item "do what I want to do in my life" had the largest preference weight, and "find it hard to stop thinking about my glucose levels" had the smallest.

Conclusions: This study generated Hypo-RESOLVE QoL-8D, a preference-weighted measure capturing the HRQoL impact of hypoglycaemia in PwD, with UK general public preference-weights. The measure can be generated from Hypo-RESOLVE QoL data.