VA L U E I N H E A LT H
price and net effective selling price. Methods: A demand model was constructed to evaluate the affordability of price to patient inclusive of mark-ups across 20 UMI & LMI markets by leveraging detailed income distributions across the markets to inform how far down the wealth pyramid recent launch products may be able to reach. A mix of industry stakeholders responded to the model and provided initial reactions to potential policy implications. Results: Our research evaluated the principles of tiered pricing which are based on sound equity principles (Pricing using HDI- and GNI-based indexes). The model outputs determined that a significant proportion of patients cannot access modern innovations in many LMI markets and in payer driven markets negotiated pricing can fall out of equitable bands resulting in UMI markets paying less than LMI markets. Conclusions: Existing approaches for tiered pricing neglect to incorporate local funding and income distribution dynamics. Although the vaccines industry has been able to leverage NGOs to enhance patient access a gap continues to exist when it comes to pharmaceuticals. There is precedent in many cash pay markets for the role of local access programs to enable patient access to expand down the wealth pyramid. Tiered pricing was once seen as the answer for equitable access but a fresh look is required by manufactures if they are to live up to the ATMI goals and aspirations. On its own tier pricing is not enough to expand access to medicines down the wealth pyramid. PCN317 Acute Lymphoblastic Leukaemia’s Burden Of Disease In Portugal Paquete AT, Alarcão J, Fiorentino F, Guerreiro R, Silva Miguel L, Borges M Center for Evidence Based Medicine, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
Objectives: Acute lymphoblastic leukaemia (ALL) is a haematological malignancy with a rapid progression, affecting both children and adults. Although rare, it is the most common leukaemia in childhood. The main objective of this study was to estimate the burden of disease impact of ALL measured by Disability Adjusted Life Years (DALY) in Portugal, for 2015. Methods: DALY combines Years Lost due to Disability (YLD), and Years of Life Lost (YLL) due to premature death. Deaths due to ALL were estimated based on lymphoblastic leukaemia (LL) mortality data from national statistics. The disability weights used to estimate YLD were based on the 2015 Global Burden of Disease (GBD 2015). Four disease states were considered: 1) diagnosis and primary therapy; 2) controlled disease; 3) relapsed/refractory disease; and 4) terminal phase. The incidence of states 1) and 4) was based on national registries and statistics for LL, and on international registries for ALL. The incidence of states 2) and 3) was estimated for children and adults separately through partitioned survival models based on clinical trials in ALL patients. State 1) was assumed to last 2 years (experts’ opinion), and other states durations were based on GBD 2015. Results: In 2015, 130 new ALL cases and 58 ALL deaths were estimated to have occurred in Portugal. The total disease burden attributable to ALL was estimated at 1,039 DALY, with 89% due to YLL and the remaining due to YLD. Per average ALL patient, a burden of 1.78 DALY was estimated. Children and adults share YLD equally, but 69% of YLL took place in adults. Conclusions: ALL is an important cause of disease burden, both in children and adults, with a higher impact on YLL in Portuguese adults. ALL is therefore an important target for health policy interventions due to its burden by average patient. PCN318 Review Of Real-World Evidence To Assess The Burden Of Illness Of Mantle Cell Lymphoma Monga N1, Garside J2, Quigley JM3, O’Rourke JM3, O’Donovan P3, Padhiar A3, Parisi L4, Tapprich C5 Global Oncology, Toronto, ON, Canada, 2Janssen, High Wycombe, UK, 3ICON Health Economics & Epidemiology, Abingdon, UK, 4Janssen Global Oncology, Raritan, NJ, USA, 5Janssen Pharmaceuticals, Neuss, Germany
1Janssen
Objectives: Mantle cell lymphoma (MCL), a rare and aggressive disease, accounts for approximately 5% of all B-cell non-Hodgkin’s lymphomas. This review aimed to synthesize the global burden of disease for MCL, which is generally considered to be incurable, by characterizing its epidemiology, natural history, economic, societal, and humanistic burden using real-world evidence (RWE). Methods: Searches were run in EMBASE, Medline, NHSEED, and ECONLit from January 2007–January 2017. Outcomes of interest were incidence, prevalence, quality of life, costs, resource use, mortality, and long-term prognosis. The review was restricted to RWE, selecting cross-sectional or observational studies and reviews of clinical registries. Data from clinical trials were not considered. In total, 1692 abstracts and 44 full-texts were reviewed; 25 studies met the inclusion criteria. Results: Standardized MCL incidence rates ranged from 0.1/100,000–1.27/100,000 and varied by sex (female: 0.05/100,000–0.7/100,000, male: 0.18/100,000–1.4/100,000) and geography (0.1/100,000 in Japan, 1.27/100,000 in Denmark). Overall survival (OS) rates of patients at 3 years differed according to age at diagnosis (≤ 65 years: 76–81%, > 65 years: 46–64%) and disease stage (stage I: 73–80%, stage IV: 48–53%), but not by sex. Median OS in patients receiving first-line chemotherapy±rituximab ranged from 27–68 months and in relapsed/refractory (R/R) patients from 4–19 months. Only 1 RWE study reported median progression-free survival for chemotherapy±rituximab (2 months in R/R setting). Median number of hospitalization days with first-line chemotherapy ranged from 0–29 depending on treatment. No quality of life or economic burden data were identified. Conclusions: Our burden of illness review demonstrated that MCL is a rapidly progressing disease with poor outcomes following relapse and low survival rates (age, disease stage, and line of therapy all adversely affected survival). New treatments are needed to improve patient outcomes and reduce the global burden of disease for MCL patients. PCN319 Assessing The Implications Of The Nice Budget Impact Test: How Many Oncology Regimens Will Be Affected And What Will Be The Impact On Patient Outcomes? Tuson HA, Dunsch AK, Song X PRMA Consulting, Fleet, UK
20 (2017) A399–A811
A469
Objectives: In April 2017, NICE introduced the budget impact (BI) test for technology appraisals (TAs), resulting in delays of up to 3 years in funding for treatments with a BI of over £20 million in 1 of the first 3 years of use. This study aimed to identify whether oncology regimens assessed in the last year would pass the BI test and to understand the potential impact of delays on patient outcomes. Methods: TAs for oncology drugs being launched in the UK with final guidance documentation published between May 2016 and May 2017 were identified. BI data were extracted from the submission documents and the number of oncology regimens with a BI of > £20 million (i.e., those that would fail the BI test) was determined. The number of additional deaths due to delays in funding was estimated from data on survival gains and eligible patient population projections based on market share assumptions. Results: Eighteen relevant TAs were identified and routine funding was recommended by NICE for thirteen of these. No BI data were identified for four drugs; of the remaining nine assessments, eight would pass the BI test. Nivolumab combined with ipilimumab for advanced melanoma (TA 400) was estimated to have a BI (based on list prices) of approximately £22 million (2017), increasing to approximately £32 million (2019). Assuming a confidential patient access scheme for ipilimumab would not decrease the BI below £20 million, access to this combination would be delayed. For a delay of 18 months (midpoint of 0–3 years), the impact was estimated to be 206 or 412 additional deaths, assuming uptake of 25% or 50%, respectively. Conclusions: Although the number of oncology treatments affected by the new BI test may be small, the impact of delayed access to potentially costeffective treatments on patient outcomes may be considerable. PCN320 Relevance Of Real World Data In German Amnog Submissions In Oncology Haas JS, Borchert K, Loepmeier J, Braun S, Mittendorf T Xcenda GmbH, Hannover, Germany
Objectives: Real World Evidence (RWE) in AMNOG assessments potentially could address various data needs during the process and also thereafter as epidemiology might be limited in oncology. Areas of RWE use are evidence generation for epidemiology (e.g. incidence, prevalence, unmet need) but also the demonstration of effectiveness in daily life settings later on. Aim was to assess how RWE is currently integrated in AMNOG submissions in oncology. Methods: German AMNOG dossiers submitted until March 2017 were screened and evaluated if they relate to any oncology field and implemented RWE evidence on incidence or prevalence. Findings were then analyzed and stratified by indication and the applied data sources were described. Results: Out of n= 273 submitted AMNOG dossiers until March 2017, n= 95 (35%) were submitted in oncology comprising of 168 subpopulations. Per definition, more than one subpopulation is included if the drug is launched in ≥ 1 target application. The most prevalent indication was NSCLC with n= 37 subpopulations, followed by chronic lymphatic leukemia (n= 23), and melanoma (n= 19). RWE for prevalence/incidence assessments was used in n= 161 subpopulations, 3 dossiers were not available, and the remaining 4 subpopulations did not include RWE. Use of claims data was reported in n= 12 (7.5%) populations, n= 159 (98.8%) used registry data, and n= 22 (13.7%) used other data sources such as IMS data, Delphi panels, Kantar Health data, Insight Health data, and Megapharm data. Conclusions: RWE is commonly used in AMNOG dossiers in oncology forming an integral part of the epidemiology section of the available evidence package. Registry data today is the data source that is applied predominantly. With a rising trend, claims data becomes an important data source adding evidence on epidemiology in German AMNOG assessments. New products in the pipeline will create additional need for epidemiological insights from various data sources to inform future AMNOG dossiers. PCN321 A Review of The Approaches Used To Model Subsequent Treatments In Nice Economic Evaluations: Examples Within Multiple Myeloma And Renal Cell Carcinoma Lee D1, Gudala K2, Morgan P1 1BresMed Health Solutions Ltd., Sheffield, UK, 2BresMed Health Solutions Ltd., Gurugram, India
Objectives: Chronic diseases often require multiple lines of treatment. Inappropriate selection or modelling of subsequent treatments can complicate submissions or result in sub-optimal decisions. Accurate modelling of subsequent treatment is particularly important when a clinical trial used in cost-effectiveness analysis deviates from clinical practice. We investigated the implementation of, and reaction to modelling of, subsequent treatments. We focussed on two particularly crowded disease areas where multiple NICE technology appraisals have been conducted: multiple myeloma (MM) and renal cell carcinoma (RCC). Methods: The NICE website was searched to identify both manufacturer submissions and the associated evidence review group (ERG)/final appraisal documents published from January 2015 to May 2017 in MM and RCC. If subsequent treatments were included in the model, the following information was extracted: approach, data source (trial versus clinical practice), adjustments made to costs and effects due to subsequent treatments, ERG critique and committee recommendations. Results: Six out of eight submissions reviewed have modelled subsequent treatments. Three submissions each included subsequent therapies using market share data from trials and from clinical practice. In base case analysis, five submissions included impact on costs, and only two included impact on utilities. None of the submissions adjusted the effectiveness observed in the trial (particularly overall survival) for use of subsequent treatments. The ERG and committee regularly criticised the modelling approach for not reflecting clinical practice (three submissions) or biased overall survival estimates (four submissions). Conclusions: The choice of subsequent treatments should reflect clinical practice. Impact on both costs and effects should also be considered. Where the trial does not reflect clinical practice, methods similar to those used for crossover adjustment could be applied and/or published literature could be used to supplement trial data. The effect of exclusion of subsequent treatments should be explored in scenario analysis to determine model sensitivity.