Treatment effect of semaglutide 2.4 mg on health-related quality of life from STEP 1 SF-6D derived from SF-36 with Australian weights.

AIM: This study aimed to determine the comparative treatment effects of semaglutide 2.4 mg and placebo on health utility index scores [6-dimension short-form survey (SF-6D)] with Australian weights in full analysis set (FAS) and in post-hoc subgroups of the STEP 1 trial, defined according to different body mass index (BMI) cut-off points and presence of comorbidities at baseline. The study also explored the correlation between baseline BMI and SF-6D in the STEP 1 trial population. METHODS: The 36-item SF survey (SF-36) scores from STEP 1 were mapped to SF-6D health states and converted to utility index scores using an Australian valuation algorithm. The change from baseline in SF-6D utility score (95% confidence intervals) was compared between semaglutide 2.4 mg and placebo at week 68 using the mixed model for repeated measurements approach. The relationship between utility scores and BMI at baseline was assessed by multiple linear regression analyses, controlling for demographic and clinical parameters. RESULTS: The estimated mean treatment difference in SF-6D utility score favoured semaglutide 2.4 mg, and, at week 68, it was 0.057 (0.038-0.076) for the FAS. A greater treatment effect was noted in subgroups with presence of symptomatic comorbidities, i.e. 0.077 (0.027-0.128) to 0.105 (0.030-0.179) at week 68. A 1-unit increase in BMI was associated with a utility loss of 0.0075 (-0.0089 to -0.0062) for the FAS population, while controlling for demographic and clinical parameters. CONCLUSION: To our knowledge, this is the first study showing statistically significant and clinically meaningful improvements in SF-6D utility scores with weight-loss pharmacotherapy in Australia.

Cost-effectiveness of weight-management pharmacotherapies in Canada: a societal perspective.

OBJECTIVES: This study aimed to assess the cost-effectiveness of weight-management pharmacotherapies approved by Canada Health, i.e., orlistat, naltrexone 32 mg/bupropion 360 mg (NB-32), liraglutide 3.0 mg and semaglutide 2.4 mg as compared to the current standard of care (SoC). METHODS: Analyses were conducted using a cohort with a mean starting age 50 years, body mass index (BMI) 37.5 kg/m2, and 27.6% having type 2 diabetes. Using treatment-specific changes in surrogate endpoints from the STEP trials (BMI, glycemic, blood pressure, lipids), besides a network meta-analysis, the occurrence of weight-related complications, costs, and quality-adjusted life-years (QALYs) were projected over lifetime. RESULTS: From a societal perspective, at a willingness-to-pay (WTP) threshold of CAD 50 000 per QALY, semaglutide 2.4 mg was the most cost-effective treatment, at an incremental cost-utility ratio (ICUR) of CAD 31 243 and CAD 29 014 per QALY gained versus the next best alternative, i.e., orlistat, and SoC, respectively. Semaglutide 2.4 mg extendedly dominated other pharmacotherapies such as NB-32 or liraglutide 3.0 mg and remained cost-effective both under a public and private payer perspective. Results were robust to sensitivity analyses varying post-treatment catch-up rates, longer treatment durations and using real-world cohort characteristics. Semaglutide 2.4 mg was the preferred intervention, with a likelihood of 70% at a WTP threshold of CAD 50 000 per QALY gained. However, when the modeled benefits of weight-loss on cancer, mortality, cardiovascular disease (CVD) or osteoarthritis surgeries were removed simultaneously, orlistat emerged as the best value for money compared with SoC, with an ICUR of CAD 35 723 per QALY gained. CONCLUSION: Semaglutide 2.4 mg was the most cost-effective treatment alternative compared with D&E or orlistat alone, and extendedly dominated other pharmacotherapies such as NB-32 or liraglutide 3.0 mg. Results were sensitive to the inclusion of the combined benefits of mortality, cancer, CVD, and knee osteoarthritis.

Once-Weekly Subcutaneous Semaglutide 2.4 mg Injection is Cost-Effective for Weight Management in the United Kingdom.

OBJECTIVES: The objective of the current preliminary study was to present the cost-effectiveness analyses submitted to the National Institute for Health and Care Excellence (NICE) (TA10765) that deemed semaglutide 2.4 mg subcutaneous (s.c.) injection a cost-effective option for weight management in the United Kingdom (UK) alongside diet and exercise (D&E). METHODS: The study was conducted from the National Health Service (NHS) and Personal Social Services perspective and based on the NICE reference case. The clinical safety and efficacy of semaglutide 2.4 mg s.c. injection were obtained from the Semaglutide Treatment Effect in People with Obesity (STEP) 1 trial. The previously published and validated Core Obesity Model was used to project lifetime occurrence of obesity complications, their costs and quality of life consequences over 40 years. The base case cohort had a mean starting age of 48 years and BMI of 38.7 kg/m2. The confidential NHS price for semaglutide 2.4 mg s.c. injection was provided by Novo Nordisk. The incremental cost-effectiveness ratios (ICERs) were expressed as cost/quality-adjusted life-year (QALY). Uncertainty was assessed through sensitivity analyses, including a scenario analysis using clinical data from the STEP 2 trial and a previously published and validated Core Diabetes Model to investigate a cohort with type 2 diabetes at baseline. RESULTS: Semaglutide 2.4 mg s.c. injection showed higher total costs and health benefits compared with D&E, with an ICER of £14,827/QALY gained. The probabilistic sensitivity analysis showed that semaglutide 2.4 mg s.c. injection was cost-effective in 90% of cases at a willingness-to-pay threshold of £20,000/QALY. The ICER from the scenario analysis for the diabetic population was £16,613/QALY gained, using the Core Diabetes Model. CONCLUSION: Semaglutide 2.4 mg s.c. injection is a cost-effective therapy compared to D&E alone for patients with obesity and weight-related comorbidities in the UK. Sensitivity and scenario analyses confirm the robustness of the analyses.

A novel decision model to predict the impact of weight management interventions: The Core Obesity Model.

AIMS: Models are needed to quantify the economic implications of obesity in relation to health outcomes and health-related quality of life. This report presents the structure of the Core Obesity Model (COM) and compare its predictions with the UK clinical practice data. MATERIALS AND METHODS: The COM is a Markov, closed-cohort model, which expands on earlier obesity models by including prediabetes as a risk factor for type 2 diabetes (T2D), and sleep apnea and cancer as health outcomes. Selected outcomes predicted by the COM were compared with observed event rates from the Clinical Practice Research Datalink-Hospital Episode Statistics (CPRD-HES) study. The importance of baseline prediabetes prevalence, a factor not taken into account in previous economic models of obesity, was tested in a scenario analysis using data from the 2011 Health Survey of England. RESULTS: Cardiovascular (CV) event rates predicted by the COM were well matched with those in the CPRD-HES study (7.8-8.5 per 1000 patient-years across BMI groups) in both base case and scenario analyses (8.0-9.4 and 8.6-9.9, respectively). Rates of T2D were underpredicted in the base case (1.0-7.6 vs. 2.1-22.7) but increased to match those observed in CPRD-HES for some BMI groups when a prospectively collected prediabetes prevalence was used (2.7-13.1). Mortality rates in the CPRD-HES were consistently higher than the COM predictions, especially in higher BMI groups. CONCLUSIONS: The COM predicts the occurrence of CV events and T2D with a good degree of accuracy, particularly when prediabetes is included in the model, indicating the importance of considering this risk factor in economic models of obesity.

External Validation of the Core Obesity Model to Assess the Cost-Effectiveness of Weight Management Interventions.

BACKGROUND: For economic models to be considered fit for purpose, it is vital that their outputs can be interpreted with confidence by clinicians, budget holders and other stakeholders. Consequently, thorough validation of models should be carried out to enhance confidence in their predictions. Here, we present results of external dependent and independent validations of the Core Obesity Model (COM), which was developed to assess the cost-effectiveness of weight management interventions. OBJECTIVE: The aim was to assess the external validity of the COM (version 6.1), in line with best practice guidance from the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making. METHODS: For validation, suitable sources and outcomes were identified, and used to populate the COM with relevant inputs to allow prediction of study outcomes. Study characteristics were entered into the COM to replicate either the studies used to develop the model (dependent validation) or those not included in the model (independent validation). The concordance between predicted and observed outcomes was then assessed using established statistical methods and generation of mean error estimates. RESULTS: For most outcomes, the predictions of the COM showed good linear correlation with observed outcomes, as evidenced by the high coefficients of determination (R2 values). The independent validation revealed a degree of underestimation in predictions of cardiovascular (CV) disease and mortality, and type 2 diabetes. CONCLUSION: The predictions generated by the risk equations used in the COM showed good concordance both with the studies used to develop the model and with studies not included in the model. In particular, the concordance observed in the external dependent validation suggests that the COM accurately predicts obesity-related event rates observed in the studies used to develop the model. However, the impact of existing CV risk, as well as mortality, is a key area for future refinement of the COM. Our results should increase confidence in the estimates derived from the COM and reduce uncertainty associated with analyses using this model.