Rob Hainsworth

Purpose. The diagnosis, management, and therapy of cancer are rapidly advancing and becoming more costly. Data linking the diagnosis, management, and prescription of systemic anticancer therapy (SACT) are not publicly available, are time-consuming to obtain, and are onerous to analyze. We aimed to illustrate how a simple expert elicitation method can be used rapidly to calculate the cost of diagnosing and treating a cancer by stage of disease. We illustrate the method using the example of melanoma. Methods. We designed a simple structured elicitation exercise for melanoma experts from the United Kingdom with the aim of describing the diagnosis, management, and SACT prescription for people with melanoma and the associated proportions offered each option. We modeled experts’ beliefs using scaled beta distributions. We used random-effects meta-analysis to combine the estimates. Published unit costs (£; 2024–2025) were multiplied by estimated proportions to calculate the mean costs for the diagnosis, management, and SACT by disease stage. Results. Seven dermatologists, 5 oncologists, and 4 surgeons participated (2022–2023). There was variation in the estimates of the proportions receiving possible diagnosis, management, or SACT options. Diagnosing suspicious lesions cost £424 to £699 depending on the investigations required. Mean (95% confidence intervals) management costs were £782 (£611, £981) for stage 0, £946 (£776, £1,145) for stage 1a, £4,729 (£4,532, £4,944) for stage 1b/2, £3,719 (£3,213, £4,150) for macroscopic 3, and £2,827 (£2,757, £2,904) for microscopic 3. Mean SACT costs were £65,289 (£50,581, £74,521) for stage 3 and £134,065 (£115,787, £152,938) for stage 4. Conclusions. Our new approach to eliciting and combining estimates balances practical and theoretical considerations. We illustrate how this method produces estimates of the costs, and associated uncertainty, of the diagnosis, management, and SACT for melanoma by stage of disease in the absence of readily accessible diagnosis, management, and prescribing data. These cost estimates were not validated against registry data. Highlights: Linked diagnosis, primary surgical management (hereafter “management”), and prescribing of systemic anticancer therapies (SACT) data describing the number and proportion of people with cancer offered these options are not readily available. The time to obtain data together with the time to conduct onerous analyses are a key barrier to obtaining values for use by decision analysts building economic models for evaluating new options for the early detection or prevention of cancer. We designed a simple expert elicitation exercise, completed in Excel. The exercise was designed to provide estimates of the total cost of cancer by stage of disease and crucially also to understand the extent of uncertainty around these estimates. We used melanoma as an exemplar cancer. Clinical experts (consultant dermatologists, surgeons, and oncologists) estimated the proportions of people offered possible diagnostic, management, or SACT options for melanoma (by stage of disease). Our study suggested that they found the expert elicitation exercise easy to understand and managed to complete the task. Random-effects meta-analysis used to pool the individual estimates indicated substantial heterogeneity across experts’ estimates of most elicited proportions. This finding suggested wide variation in possible approaches to the diagnosis, management, and use of SACT for melanoma.

Background: Clinical guidelines commonly recommend preventative treatments for people above a risk threshold. Therefore, decision-makers must have faith in risk prediction tools and model-based cost-effectiveness analyses for people at different levels of risk. Two problems that arise are inadequate handling of competing risks of death and failing to account for direct treatment disutility (i.e. the hassle of taking treatments). We explored these issues using two case studies: primary prevention of cardiovascular disease using statins and osteoporotic fracture using bisphosphonates. Objectives: Externally validate three risk prediction tools [QRISK®3, QRISK®-Lifetime, QFracture-2012 (ClinRisk Ltd, Leeds, UK)]; derive and internally validate new risk prediction tools for cardiovascular disease [competing mortality risk model with Charlson Comorbidity Index (CRISK-CCI)] and fracture (CFracture), accounting for competing-cause death; quantify direct treatment disutility for statins and bisphosphonates; and examine the effect of competing risks and direct treatment disutility on the cost-effectiveness of preventative treatments. Design, participants, main outcome measures, data sources: Discrimination and calibration of risk prediction models (Clinical Practice Research Datalink participants: aged 25–84 years for cardiovascular disease and aged 30–99 years for fractures); direct treatment disutility was elicited in online stated-preference surveys (people with/people without experience of statins/bisphosphonates); costs and quality-adjusted life-years were determined from decision-analytic modelling (updated models used in National Institute for Health and Care Excellence decision-making). Results: CRISK-CCI has excellent discrimination, similar to that of QRISK3 (Harrell’s c = 0.864 vs. 0.865, respectively, for women; and 0.819 vs. 0.834, respectively, for men). CRISK-CCI has systematically better calibration, although both models overpredict in high-risk subgroups. People recommended for treatment (10-year risk of ≥ 10%) are younger when using QRISK-Lifetime than when using QRISK3, and have fewer observed events in a 10-year follow-up (4.0% vs. 11.9%, respectively, for women; and 4.3% vs. 10.8%, respectively, for men). QFracture-2012 underpredicts fractures, owing to under-ascertainment of events in its derivation. However, there is major overprediction among people aged 85–99 years and/or with multiple long-term conditions. CFracture is better calibrated, although it also overpredicts among older people. In a time trade-off exercise (n = 879), statins exhibited direct treatment disutility of 0.034; for bisphosphonates, it was greater, at 0.067. Inconvenience also influenced preferences in best–worst scaling (n = 631). Updated cost-effectiveness analysis generates more quality-adjusted life-years among people with below-average cardiovascular risk and fewer among people with above-average risk. If people experience disutility when taking statins, the cardiovascular risk threshold at which benefits outweigh harms rises with age (≥ 8% 10-year risk at 40 years of age; ≥ 38% 10-year risk at 80 years of age). Assuming that everyone experiences population-average direct treatment disutility with oral bisphosphonates, treatment is net harmful at all levels of risk. Limitations: Treating data as missing at random is a strong assumption in risk prediction model derivation. Disentangling the effect of statins from secular trends in cardiovascular disease in the previous two decades is challenging. Validating lifetime risk prediction is impossible without using very historical data. Respondents to our stated-preference survey may not be representative of the population. There is no consensus on which direct treatment disutilities should be used for cost-effectiveness analyses. Not all the inputs to the cost-effectiveness models could be updated. Conclusions: Ignoring competing mortality in risk prediction overestimates the risk of cardiovascular events and fracture, especially among older people and those with multimorbidity. Adjustment for competing risk does not meaningfully alter cost-effectiveness of these preventative interventions, but direct treatment disutility is measurable and has the potential to alter the balance of benefits and harms. We argue that this is best addressed in individual-level shared decision-making. Study registration: This study is registered as PROSPERO CRD42021249959. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 15/12/22) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information.

Purpose: Evaluating interventions for cardiovascular disease (CVD) requires estimates of its effect on utility. We aimed to 1) systematically review utility estimates for CVDs published since 2013 and 2) critically appraise UK-relevant estimates and calculate corresponding baseline utility multipliers. Methods: We searched MEDLINE and Embase (April 22, 2021) using CVD and utility terms. We screened results for primary studies reporting utility distributions for people with experience of heart failure, myocardial infarction, peripheral arterial disease, stable angina, stroke, transient ischemic attack, or unstable angina. We extracted characteristics from studies included. For UK estimates based on the EuroQoL 5-dimension (EQ-5D) measure, we assessed risk of bias and applicability to a decision-analytic model, pooled arms/time points as appropriate, and estimated baseline utility multipliers using predicted utility for age- and sex- matched populations without CVD. We sought utility sources from directly applicable studies with low risk of bias, prioritizing plausibility of severity ordering in our base-case model and highest population ascertainment in a sensitivity analysis. Results: Most of the 403 studies identified used EQ-5D ( n = 217) and most assessed Organisation for Economic Co-operation and Development populations ( n = 262), although measures and countries varied widely. UK studies using EQ-5D ( n = 29) produced very heterogeneous baseline utility multipliers for each type of CVD, precluding meta-analysis and implying different possible severity orderings. We could find sources that provided a plausible ordering of utilities while adequately representing health states. Conclusions: We cataloged international CVD utility estimates and calculated UK-relevant baseline utility multipliers. Modelers should consider unreported sources of heterogeneity, such as population differences, when selecting utility evidence from reviews. Highlights: Published systematic reviews have summarized estimates of utility associated with cardiovascular disease published up to 2013. We 1) reviewed utility estimates for 7 types of cardiovascular disease published since 2013, 2) critically appraised UK-relevant studies, and 3) estimated the effect of each cardiovascular disease on baseline utility. Our review 1) recommends a consistent and reliable set of baseline utility multipliers for 7 types of cardiovascular disease and 2) provides systematically identified reference information for researchers seeking utility evidence for their own context.

Background: Direct treatment disutility (DTD) represents an individual’s disutility associated with the inconvenience of taking medicine over a long period of time. Objectives: The main aim of this study was to elicit DTD values for taking a statin or a bisphosphonate for primary prevention. A secondary aim was to understand factors which influence DTD values. Methods: Design: We used a cross-sectional study consisting of time-trade off exercises embedded within online surveys. Respondents were asked to compare a one-off pill (‘Medicine A’) assumed to have no inconvenience and a daily pill (‘Medicine B’) over 10 years (statins) or 5 years (bisphosphonates). Setting: Individuals from National Health Service (NHS) primary care and the general population were surveyed using an online panel company. Participants: Two types of participants were recruited. First, a purposive sample of patients with experience of taking a statin (n=260) or bisphosphonate (n=100) were recruited from an NHS sampling frame. Patients needed to be aged over 30, have experience of taking the medicine of interest and have no diagnosis of dementia or of using dementia drugs. Second, a demographically balanced sample of members of the public were recruited for statins (n=376) and bisphosphonates (n=359). Primary and secondary outcome measures: Primary outcome was mean DTD. Regression analysis explored factors which could influence DTD values. Results: A total of 879 respondents were included for analysis (514 for statins and 365 for bisphosphonates). The majority of respondents reported a disutility associated with medicine use. Mean DTD for statins was 0.034 and for bisphosphonates 0.067, respectively. Respondent characteristics including age and sex did not influence DTD. Experience of bisphosphonate-use reduced reported disutilities. Conclusions: Statins and bisphosphonates have a quantifiable DTD. The size of estimated disutilities suggest they are likely to be important for cost-effectiveness, particularly in individuals at low-risk when treated for primary prevention.

Background The use of indoor tanning devices causes melanoma and other skin cancers with resulting morbidity, mortality and increased healthcare costs. Policy-makers require robust economic evidence to inform decisions about a possible ban of such devices to mitigate these burdens. Objectives To assess the health costs and consequences of introducing a policy-based intervention across England to ban commercial indoor tanning with an accompanying public information campaign. Methods A cost-effectiveness analysis, adopting a healthcare system perspective, was conducted using a decision-model to track a national cohort of 18-year-olds over a lifetime time horizon. A nationwide ban on commercial indoor tanning combined with a public information campaign (the policy-based intervention) was compared with the status quo of availability of commercial indoor tanning. The expected costs (currency: GBP, price year: 2019) and Quality Adjusted Life Years (QALYs) were calculated. Net Monetary Benefit (NMB; net benefit measured in cost compared to an accepted threshold) and Net Health Benefit (NHB; net gain in QALYs compared to an accepted threshold) of implementation were calculated. A probabilistic sensitivity analysis was used to calculate the probability the intervention was cost-effective. Results Compared to the current situation, a ban on commercial indoor tanning combined with a public information campaign would result in 1,206 avoided cases of melanoma, 207 fewer melanoma deaths, and 3,987 averted cases of keratinocyte cancers over the lifetime of all 18-year-olds (n=618,873) living in England in 2019. An additional 497 QALYs would be realised along with healthcare cost-savings of £697,858. This intervention would result in an NMB of £10.6m and a NHB of 530 QALYS. Multiple sensitivity analyses confirmed the robustness of findings. At a cost-effectiveness threshold of £20,000, there is a 99% likelihood of this policy-based intervention being cost-effective. Conclusions The implementation of a ban on commercial indoor tanning across England with an accompanying public information campaign would be an effective use of healthcare resources.

Prior to 2011 legislation prohibiting children from using commercial sunbeds, the prevalence of sunbed use in 15- to 17-year-olds in some areas in England was as high as 50%. Despite significant decreases since 2011, children today still practice indoor tanning. We estimated current sunbed use in 11- to 17-year-olds in England, the number of available commercial sunbed units, and the associated cost of a ‘buy-back’ scheme to remove commercial sunbeds under a potential future policy to ban sunbeds. We undertook a calibration approach based on published prevalence rates in English adults and other sources. Internet searches were undertaken to estimate the number of sunbed providers in Greater Manchester, then we extrapolated this to England. Estimated mean prevalence of sunbed use was 0.6% for 11- to 14-year-olds and 2.5% for 15- to 17-year-olds, equating to 62,130 children using sunbeds in England. A predicted 2958 premises and 17,865 sunbeds exist nationally and a ‘buy-back’ scheme would cost approximately GBP 21.7 million. Public health concerns remain greatest for 11- to 17-year-olds who are particularly vulnerable to developing skin cancers after high ultraviolet exposure.