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Projecting Medicines Expenditures in the English NHS Mestre-Ferrandiz AES 2013
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Projecting Medicines Expenditures in the English NHS Mestre-Ferrandiz AES 2013

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Projecting Medicines Expenditures in the English NHS Mestre-Ferrandiz AES 2013 Presentation Transcript

  • 1. Projecting Expenditure on Medicines in the NHS XXXIII Spanish Health Economics Association Meeting Santander, Spain • 18-21 June 2013 1 Office of Health Economics Jorge Mestre-Ferrandiz
  • 2. Editor’s note: A revised version of this paper has since been published as: O’Neill, P., Mestre‐Ferrandiz, J., Puig‐ Peiro, R. and Sussex, J. (2013) Projecting expenditures on medicines in the UK NHS. PharmacoEconomics. Online  First article. doi 10.1007/s40273‐013‐0082‐1. 2 This presentation is based on a recent OHE study. Available to download  at www.ohe.org
  • 3. • The model • Results • Conclusion Agenda 3
  • 4. • Bottom-up projection: built up from pack level to total market • Detailed company input on more dynamic therapy areas (covering approx. 80%+ of the market) • Public data and industry intelligence used to: • Generate current position • Define erosion curves post LoE • Identify possible future new products and their uptake • Keep model at list prices (based on IMS data) • Account for degree of ‘cannibalisation’ of sales from new launches, i.e. substitution effect • Scenarios – important to focus on ranges, rather than point estimates The Model 4
  • 5. Overview of Projection Method Whole Medicines Market Core Therapy Areas (~80% of market) Non-core Therapy Areas Key Subclasses/ Products Other Subclasses New Products Products Losing Exclusivity (LoE*) Retail • Alimentary • Cardiovascular • Central Nervous System • Respiratory Hospital • Cancer • Rheumatoid Arthritis • HIV • Other anti-infectives (non HIV) Homecare • EPO Biosimilars In-depth analysis • ATC4 level volumes • Epidemiology • Government policy • Clinical guidelines Product level pipeline analysis • Expected pipeline • Uptake curves based on historical analysis • Industry intelligence Product level LoE analysis • Specific price & volume erosion curves for key products • Set of erosion curves for - PC and Hospital - For easy vs. complex formulations - Biosimilars High level analysis • Trending based on historical performance Expected LoE • Set of erosion curves *Loss of Exclusivity (LoE) is defined as the time when a product has lost all legal protection and is expected to face generic competition High level analysis • Trending based on historical Performance Products Losing Exclusivity (LoE*) New Products 5 Product level pipeline analysis • Expected pipeline • Uptake curves based on historical analysis • Industry intelligence
  • 6. Four ‘types’ of products 1. LoE products between 2012 – 2015 • Distinguishing between generics and biosimilars 2. Future launches (launched between 2012 and 2015) 3. Recent launches (launched 2007-11) 4. Non-recent (launched before 2007), non-LoE products Building Blocks of the Model: Structure 6
  • 7. • Key issue for the forecast: how will generic competition evolve for those medicines losing patent protection between 2012 and 2015? • Four (price and volume) erosion curves, depending on manufacturing complexity (‘easy’ or ‘difficult’) and primary or secondary care • Primary care: Based on historical analysis for LoE products in primary care (2003-11) [IMS data sufficient] • Secondary care: case study approach • Use erosion curves to predict impact of generic competition LoE: Methodology 7
  • 8. LoE: Methodology Primary Care 8 Bases on historical analysis for LoE products between 2003 and 2011 (weighted by sales) RETAINED VOLUME by the originator RETAINED PRICE: Generic prices as a % of originator Formulation Year 1 Year 2 Year 3 Year 4 Year 5 Formulation Year 1 Year 2 Year 3 Year 4 Year 5 Easy 51% 25% 15% 13% 10% Easy 90% 54% 35% 26% 14% Difficult 59% 45% 35% 33% 30% Difficult 98% 96% 89% 81% 74% Number of observations Easy 71 53 44 35 26 Difficult 19 17 13 6 4 Note on sample size: There were only 6 and 4 observations for year 4 and year 5 respectively for ‘difficult’ formulations, and results derived for the volume erosion curve presented some anomalies. For this reason we decided to trend for years 4 and 5, assuming a 33% and 30% erosion rate respectively. For the price erosion curve, we used the result obtained from the historical analysis for year 4 and year 5 (81% and 74% respectively).
  • 9. • In secondary care, IMS data do not capture real prices as we know discounting takes place in hospitals. For this reason, we used a different approach to estimate erosion curves in secondary care. • Based on real examples of products that faced generic competition during the last few years in the hospital market, we constructed three case studies, representing the following market characteristics: • Existing biosimilars • ‘Easy to manufacture’ product • ‘Difficult to manufacture’ product • We used a panel of four hospital pharmacists to validate our 3 case studies (‘Delphi-type’ analysis) LoE: Methodology Secondary Care 9
  • 10. Building Blocks of the Model – LoE: Methodology Secondary Care 10 RETAINED VOLUME by the originator RETAINED PRICE: Generic prices as a % of originator Formulation Year 1 Year 2 Year 3 Year 4 Year 5 Formulation Year 1 Year 2 Year 3 Year 4 Year 5 Easy 15% 13% 11% 11% 11% Easy 15% 13% 9% 9% 9% Difficult 71% 48% 38% 35% 30% Difficult 80% 70% 60% 30% 30%
  • 11. • Great uncertainty. No good analogues to predict impact (similar feedback received by Delphi-type analysis) • Two areas: • Anti-TNFs (etanercept (Embrel), infliximab (Remicade): • Cancer • L01X3 (antineoplastic MABs; Herceptin, Mabthera, Erbitux): • L01X4 (A-NEO PROTEIN KINASE INH; Glivec, Iressa, Afinitor) and others • Use less aggressive curves for anti-TNFs and cancer relative to ‘2ry difficult’ • Cancer less aggressive than anti-TNFs (earlier years only) Future Biosimilars 11
  • 12. Future Biosimilars 12 RETAINEDVOLUMEby originator Therapeutic area Year 0 Year 1 Year 2 Year 3 Year 4 Year 5 Anti-TNFs 100% 90% 80% 70% 60% 40% Cancer 100% 90% 85% 80% 60% 40% Price of generics as a% of originator brand Therapeutic area Year 0 Year 1 Year 2 Year 3 Year 4 Year 5 Anti-TNFs 100% 85% 80% 75% 70% 65% Cancer 100% 95% 90% 85% 70% 65%
  • 13. • The model • Results • Conclusion Agenda 13
  • 14. • Baseline – status quo; no major changes in policy assumed • History is a good predictor of the future (with few adjustments) • High • Uptake of new medicines improves relative to past experience • Oncology biosimilars have little penetration • Low • Uptake of new medicines worsens • More aggressive generic and biosimilar competition Scenarios - Narrative 14
  • 15. Summary – Top Line Projections 15 CAGR 2011 - 15 Baseline 3.5% High 4.1% Low 3.1% Total UK NHS medicines bill: actual and forecast (£m) [at list prices] Total UK NHS Medicines Bill: CAGRs [At list prices]Total UK NHS Medicines Bill: CAGR 2007-11 CAGR 2007 - 11 IMS 3.9%
  • 16. Summary – Brands vs. Biosimilars vs. Generics [at list prices] 16 CAGR 2011 – 15 [At list prices] Brands 1.1 % Generics 10.2% Biosimilars 37.2%
  • 17. Elements Driving Total Expenditure: Baseline 17
  • 18. How Good Are Our Projections for 2012? 18 • For the total market: actual sales are within our projected range • For total brands: our baseline negative growth rate is higher in magnitude than the actual (-2.3% vs. -1.5%); but, again, actual sales are within our range • Generics: overall, we have slightly overestimated growth (14.0% in the baseline scenario vs. 12.6% actual) Growth Rates: 2012 vs. 2011 Actual Baseline High Low Total Market 1.3% 1.0% 4.4% 0.8% Total Brands -1.5% -2.3% 0.3% -2.6% Total Generics 12.6% 14.0% 27.4% 14.0% Total Primary Care -3.7% -1.9% 3.2% -2.2% Total Secondary Care 10.3% 6.5% 6.6% 6.3% Source: Actual: IMS BPI and HPAI (2012); Baseline, High and Low: author’s analysis
  • 19. Why Bother? 19 • The CAGR for the period 2003–2011 (3.8%) for the total medicines bill would lie within our projected growth range for 2011–2015 (3.1–4.1%) • Our projections for brands and generics are considerably lower and higher respectively than they were historically for the 2003–2011 period CAGR 2003−2011 CAGR 2011−2015e Total medicines bill 3.8% 3.1%‒4.1% Total brands 3.4% 0.5%‒1.8% Total generics 5.8% 10.0%‒11.0% Source: 2003–2011 authors’ calculations from IMS BPI and HPAI (2003–2011); 2011–2015 authors’ analysis
  • 20. • The model • Results • Conclusion Agenda 20
  • 21. • Method for projecting UK NHS expenditure on medicines over the medium term • The basis for our projections includes historical trends, knowledge of the unfolding lifecycles of existing medicines, published information about R&D pipelines that will produce future new medicines, and expert input • Key challenges: future impact of new launches and generic competition • Two broad approaches may be used to project future medicines expenditure in any health care system: bottom-up or top-down • The choice of approach depends on the reason for projecting medicines expenditure. • We have used a bottom-up model because we were particularly interested in exploring the impact of generic competition and new products over the medium term Summary and Conclusions 21
  • 22. To enquire about additional information and analyses, please contact Dr Jorge Mestre-Ferrandiz at jmestre-ferrandiz@ohe.org To keep up with the latest news and research, subscribe to our blog, OHE News. Follow us on Twitter @OHENews, LinkedIn and SlideShare. Office of Health Economics (OHE) Southside, 7th Floor 105 Victoria Street London SW1E 6QT United Kingdom +44 20 7747 8850 www.ohe.org OHE’s publications may be downloaded free of charge for registered users of its website. ©2013 OHE