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 1158 caregivers of young people aged 14 to 23 years with mental health problems in a Brazilian cohort were analyzed. We assessed productivity losses, additional household tasks, out-of-pocket expenses, and own healthcare utilization because of the young person's mental health problems over the past 6 months. The costs of productivity losses and household tasks were estimated in terms of caregivers' earnings. Logistic regression models identified factors associated with reported impacts. Generalized linear models evaluated clinical and caregiver characteristics associated with the economic impact on caregivers.
Results: Nearly 40% of caregivers (n = 458) experienced economic impacts because of 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. Externalizing and comorbid diagnoses, service use, higher impairment, and female caregivers increased the likelihood of impacts, whereas the greatest economic impacts were associated with internalizing conditions and service use.
Conclusions: Though these findings need to be interpreted with caution because of 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.
Objectives: Diversity and inclusion in clinical trials remains an important topic, particularly for participants with disabilities such as vision impairment. With advances in smartphone and tablet technologies, and their increasing use in clinical trials, accessibility features such as "pinch-to-zoom" are now at our fingertips. However, implementing such accessibility features when collecting electronic clinical outcomes assessments (eCOA) does not come without risks and must be designed with careful consideration and scientifically tested to ensure no impact to data integrity. Therefore, the objectives of this study were to determine the measurement equivalence of an eCOA questionnaire with and without a zoom accessibility feature and test its usability.
Methods: An eCOA app with a zoom accessibility feature was designed following industry standards for eCOA best design. Participants (n=53) with chronic or recent pain completed a questionnaire with standard response scales (verbal rating scale [VRS], numerical rating scale [NRS], visual analog scale [VAS]), with and without the zoom accessibility feature enabled, in a randomized crossover design. Intraclass Correlation Coefficients (ICCs) were determined. A subset of participants (n=10) with vision impairment participated in a usability testing interview.
Results: The ICC analysis showed high agreement (0.894-0.982) between zoomed and non-zoomed completions of the VRS, NRS and VAS. Participant usability testing showed good ease of use, ability to read the screen, and usefulness of the zoom feature, especially when not wearing corrective measures for vision impairment.
Conclusions: These findings support the use of a specially designed eCOA zoom accessibility feature for use in clinical trials.
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.