Becky Pennington

Current methods for modelling spillover effects on carers in economic evaluation include four main methods: (absolute) utilities, disutilities, increments and multipliers. Each of these approaches assumes that the spillover effect is one-dimensional. We aimed to develop a new approach that better reflects the complexity of caring and the nuances of how a new treatment may impact the caregiver. We propose a new method based on the established concepts of the ‘family effect’ (or caring about someone) and the ‘caregiving effect’ (providing care for someone). These effects can be disentangled through analysis of carer-patient dyads or using patient and carer (dis)utilities and estimates from the literature. We consider case studies in Duchenne Muscular Dystrophy, Spinal Muscular Atrophy and Alzheimer’s Disease. Our approach models a small carer health-related quality of life (HRQoL) gain for each intervention, whereas the utility approach consistently models a substantial carer HRQoL gain, and the disutility approach models a carer HRQoL loss in two case studies. Our method allows explicit consideration of the benefits to carers of extending patient survival or improving patient health, with the negative HRQoL impact of increased caregiving burden. We propose that our method can be used with published data at present, and further research should analyse the family and caregiving effects in different conditions.

The cost effectiveness of pharmacotherapies for obesity (such as semaglutide, tirzepatide, liraglutide, and newer agents) is increasingly being appraised by health technology assessment (HTA) bodies. Modelling is required to extrapolate weight change observed over relatively short clinical trial durations to long-term weight loss and associated cardio-metabolic outcomes and costs. Extrapolation is a common issue in HTA, but there is a unique challenge for anti-obesity drugs because of the number of interacting uncertainties. This is a particular concern given the substantial eligible population sizes and associated high financial decision risk of providing lifetime treatment. We describe four key challenges in modelling pharmacotherapies for obesity: (1) modelling long-term body mass index (BMI) trajectories with and without obesity pharmacotherapy, (2) modelling time on treatment, (3) using risk equations to link changes in BMI to clinical outcomes, and (4) modelling clinical outcomes not (solely) related to BMI changes. We discuss each of these challenges and the impact they have had in global HTA appraisals for pharmacotherapies. We speculate how these challenges relating to short-term clinical trials could be overcome to more robustly predict long-term outcomes and the role that observational data may play. As clinical trial and real-world evidence for technologies for obesity evolves, analysts and decision-makers need to determine which evidence sources are most appropriate and how they should be combined.

Background: Guidelines for modelling in economic evaluation recommend that it may be necessary to consider costs and outcomes until all modelled patients have died. Some guidelines also recommend that carers’ health-related quality of life (HRQoL) outcomes should be included. However, it is unclear whether economic evaluations should continue to include carers’ HRQoL after patients have died, and whether there is any evidence to support an additional bereavement effect for carers. Methods: We used the UK Household Longitudinal Study waves 1–12. We used Difference-in-Differences to estimate the short- and long-term bereavement effects on the SF-6D for people who reported that they did and did not provide care to a household member who then died. We assumed parallel trends conditional on age, sex, long-term health conditions, education, and household income. Results: Carers and non-carers experienced a significant loss in HRQoL in the year immediately following bereavement. Carers potentially experienced a loss in HRQoL in the year before bereavement, whereas the bereavement effect may have lasted longer for non-carers. For both groups, HRQoL became comparable to the non-bereaved population around 3 years after bereavement. Conclusions: Bereavement has a statistically significant negative impact on HRQoL in the short-term, for both carers and non-carers. However, the effect size is small and is not sustained, suggesting that including bereavement in economic evaluation would make little difference to results.

There has been increasing interest in including carers’ health-related qualify of life (HRQoL) in decision models, but currently there is no best practice guidance as to how to do so. Models thus far have typically assumed that carers’ HRQoL can be predicted from patient health states, as we illustrate with three examples of disease-modifying treatments. However, this approach limits the mechanisms that influence carers’ HRQoL solely to patient health and may not accurately reflect carers’ outcomes. In this article, we identify and discuss challenges associated with modelling intervention effects on carers’ HRQoL: attaching carer utilities to patient disease states, the size of the caring network, aggregation of carer and patient HRQoL, patient death, and modelling longer-term carer HRQoL. We review and critique potential alternatives to modelling carers’ HRQoL in decision models: trial-based analyses, qualitative consideration, cost-consequence analysis, and multicriteria decision analysis, noting that each of these also has its own challenges. We provide a framework of issues to consider when modelling carers’ HRQoL and suggest how these can be addressed in current practice and future research.

The provision of informal (unpaid) care can impose significant ‘spillover effects’ on carers, and accounting for these effects is consistent with the efficiency and equity objectives of health technology assessment (HTA). Inclusion of these effects in health economic models, particularly carer health-related quality of life (QOL), can have a substantial impact on net quality-adjusted life year (QALY) gains and the relative cost effectiveness of new technologies. Typically, consideration of spillover effects improves the value of a technology, but in some circumstances, consideration of spillover effects can lead to situations whereby life-extending treatments for patients may be considered cost ineffective due to their impact on carer QOL. In this piece we revisit the classic ‘QALY trap’ and introduce an analogous ‘carer QALY trap’ which may have practical implications for economic evaluations where the inclusion of carer QOL reduces incremental QALY gains. Such results may align with a strict QALY-maximisation rule, however we consider the extent to which this principle may be at odds with the preferences of carers themselves (and possibly society more broadly), potentially leading decision makers into the carer QALY trap as a result. We subsequently reflect on potential solutions, highlighting the important (albeit limited) role that deliberation has to play in HTA.

Including health outcomes for carers as well as patients in economic evaluations can change the results and conclusions of the analysis. Whilst in many disease areas there can be clear justification for including carers’ health-related quality of life (HRQL) in health technology assessments (HTAs), we believe that, in general, the perspective of carers is under-represented in HTA. We were interested in the extent, and methods by which, HTA bodies include carers’ HRQL in economic evaluation. We reviewed guidance from 13 HTA bodies across the world regarding carers’ HRQL. We examined five interventions, as case studies, assessed by different HTA bodies, and extracted information on whether carers’ HRQL was included by the manufacturers or assessors in their dossiers of evidence, the data and methods used, and the impact on the results. We developed recommendations to guide analysts on including carers’ HRQL in economic evaluations. When reviewing the methods guides: two bodies recommend including carers’ HRQL in the base case, two referred to outcomes for all individuals, two preferred to exclude carers, three said it depended on other conditions, and it was unclear for four. Across the five case studies: five source studies for carers’ HRQL and two different modelling approaches were used. Including carers’ HRQL increased incremental quality-adjusted life-years (QALYs) in 19/23 analyses (decreased it in two); there was substantial variation in the magnitude of change. We recommend: (1) the inclusion of carers is clearly justified, (2) the use of HRQL data from the population under comparison where possible, (3) the use of data from another disease area or country is clearly justified (and transferability/applicability issues are discussed), (4) the use of external data to derive comparisons for cross-sectional data is justified, (5) assumptions and implications of the modelling approach are explicit, and (6) disaggregated results for patients and carers are presented.

The impact of time on the applicability and relevance of historical economic evaluations can be considerable. Ignoring this may lead to the use of weak or invalid evidence to inform important research questions or resource allocation decisions, as historical economic evaluations may have reached different conclusions compared to if a similar study had been conducted more recently. There are multiple factors that contribute towards evidence becoming outdated including changes to the relevant decision problem (e.g. comparators), changes to parameters (such as costs, utilities and resource use) and methodological updates (e.g. recommendations on uncertainty analysis). Researchers reviewing economic evaluations need to consider whether changes over time would influence the study design and results if the evaluation were repeated, to the extent that it is no longer helpful or informative. In this paper, we summarise these key issues and make recommendations about how and whether researchers can future proof their economic evaluations.

Background: We aimed to assess the cost effectiveness of intravitreal ranibizumab (Lucentis), aflibercept (Eylea) and bevacizumab (Avastin) for the treatment of macular oedema due to central retinal vein occlusion. Methods: We calculated costs and quality-adjusted life-years from the UK National Health Service and Personal Social Services perspective. We performed a within-trial analysis using the efficacy, safety, resource use and health utility data from a randomised controlled trial (LEAVO) over 100 weeks. We built a discrete event simulation to model long-term outcomes. We estimated utilities using the Visual-Functioning Questionnaire-Utility Index, EQ-5D and EQ-5D with an additional vision question. We used standard UK costs sources for 2018/19 and a cost of £28 per bevacizumab injection. We discounted costs and quality-adjusted life-years at 3.5% annually. Results: Bevacizumab was the least costly intervention followed by ranibizumab and aflibercept in both the within-trial analysis (bevacizumab: £6292, ranibizumab: £13,014, aflibercept: £14,328) and long-term model (bevacizumab: £18,353, ranibizumab: £30,226, aflibercept: £35,026). Although LEAVO did not demonstrate bevacizumab to be non-inferior for the visual acuity primary outcome, the three interventions generated similar quality-adjusted life-years in both analyses. Bevacizumab was always the most cost-effective intervention at a threshold of £30,000 per quality-adjusted life-year, even using the list price of £243 per injection. Conclusions: Wider adoption of bevacizumab for the treatment of macular oedema due to central retinal vein occlusion could result in substantial savings to healthcare systems and deliver similar health-related quality of life. However, patients, funders and ophthalmologists should be fully aware that LEAVO could not demonstrate that bevacizumab is non-inferior to the licensed agents.