single-arm studies to support drug reimbursement in … · •submissions claimed superior efficacy...

SINGLE-ARM STUDIES TO SUPPORT DRUG REIMBURSEMENT IN AUSTRALIA

Beth O’Leary Sarah Jane McKenna

Heather McElroy Adam Gordois

| ISPOR – Single Arm Studies | April 21st, 2015 1

PSD Analyses


Objectives

► To review Australian PBAC submissions that have used single-arm studies as the primary evidence to ascertain approaches to providing comparative evidence their success the PBAC’s comments on these submissions


Methods

N=694 • A database of 674 PSDs published between July 2005 and December 2013

N=23

• Searched for the term “single-arm” or “single arm” (N=22) • An additional submission (imatinib, rare diseases) was identified as presenting

case series as the primary clinical data and was included in the data set

N=11

• Trials which included a comparator arm were excluded (N=9) • As were drugs which were modifications of formulations already reimbursed based

on comparative evidence (N=2)

N=12 • Overall, 12 submissions were included in the primary analysis

Update search

• Update search conducted in March 2015 identified 5 additional submissions


Submissions with evidence from single arm trials


Generic name Requested indication

Reimbursed indications PBAC decision

Aprepitant Secondary prophylaxis of CINV associated with MEC

CINV associated with HEC

Recommended

Arsenic trioxide Acute promyelocytic leukaemia

Not listed Recommended

Imatinib ALL expressing the Ph+ or the transcript bcr-abl tyrosine kinase in newly diagnosed patients or patients with relapsed or refractory disease

Range of indications Recommended (newly diagnosed) Rejected (relapsed or refractory)

Nilotinib Ph+ CML in adult patients intolerant of or resistant to ≥ 1 prior therapy, including imatinib


Posaconazole Salvage treatment of life-threatening fungal infections


Ribavirin HCV; children and adolescents fulfilling certain criteria

HCV, adults Recommended

Dasatinib ALL expressing the Ph+ or the transcript bcr-abl kinase who are resistant to or intolerant of prior therapy

Not listed Rejected; recommended after resubmission

Eculizumab aHUS

PNH Rejected; recommended after resubmission

Imatinib 4 rare diseases

Range of indications Rejected; recommended after resubmission

Macrogol Faecal impaction, where conventional therapies have failed, and alternative treatments may require hospital admission

Not listed Rejected

Vorinostat Advanced CTCL after failure of 4 systemic therapies

Not listed Rejected

Infliximab RA dose escalation to 5 mg/kg

RA Rejected

Key findings

ESTABLISHING COMPARATIVE EFFICACY AND SAFETY


11 • submissions claimed superior efficacy and/or safety over the

comparator

9 • used published data to estimate the comparator’s effectiveness

and safety

2 • presented data that appeared to have been collected to inform

the submission

3 • presented no comparator data • efficacy and safety appears to have been inferred from pre- and

post-treatment differences for two of these submissions

Key findings

► For submissions presenting an economic evaluation, the ICER was variable: Three submissions < $15K/QALY or < $15K/life year

gained (LYG) Two submissions < $15 to $45K/LYG

One product with an ICER > $1M was recommended for listing on the LSDP

► All the products had a budget impact of < $10M per year with the exception of the drug recommended for the LSDP

ECONOMIC IMPLICATIONS


Key findings

OUTCOMES


Overall success rate = 9/12 = 75%

Three recommended after resubmission

Six of the 12 drugs recommended for reimbursement (five claimed superiority over the comparator)

Key findings

High clinical need

Evidence of effectiveness in other indications

Five of nine products were already listed for other indications

Low budget impact

FACTORS ASSOCIATED WITH SUCCESS


Other single-arm submissions ► PSDs for eight additional products used a single arm of a

comparative study to compare with the submission comparator treatment.

In general, submissions used similar techniques. Cetuximab used analysis of the Medicare Australia claims database to estimate survival in patients with colorectal cancer.

► Since the initial search, there have been further positive recommendations for products based on a primary evidence base of a single-arm study(s):

brentuximab for systemic anaplastic large cell lymphoma

denosumab for giant cell tumours

betaine for the treatment of homocystinuria

nitisinone for hereditary tyrosinaemia type 1 (HT-1)

ponatinib for CML and PH+ ALL


Study Limitations

► Search strategy may not have identified all relevant PSDs

► Information contained in the PSDs on the evidence base for some products is incomplete

► Public information on some submissions is not available, and submissions included with a recommendation to reject may be resubmitted in the future.


Conclusions


Half (6/12) of first-time submissions were approved for reimbursement by the PBAC, similar to the approval rate for all first-time submissions to the PBAC 75% (9/12) were eventually approved

While expressing concerns regarding the quality of the comparative evidence, the PBAC accepted the evidence base in the majority of cases

Only five drugs were listed for other indications – suggesting that while this may be helpful, it was not a prerequisite

The PBAC's acceptance of single-arm studies as the primary clinical evidence for drug reimbursement submissions may reflect a recognition of the practical and ethical challenges of undertaking comparative studies in some patient populations

A COMPARISON OF ASIAN AND GLOBAL PHARMACEUTICAL PRICES USING AN EKS METHOD

Peter Davey, Macquarie University; PRIMA Consulting; Emerge Health.

Overview of the presentation

•Background to the study •Methods •Results •Discussion

http://athomedadmatters.com/review-dads-awesome-science/

Background to the study • My experience

• In health and around pharmaceuticals for over 25 years • Projects overseas in a large number of countries which involved

also profiling funding arrangements • Brief period at IMS Health, largest supplier of pharmaceutical data

• What struck me

• Different policies in different markets. • Which are better/worse?

• Prices for the same products vary by market • Lack of consensus about where prices were high

and low

Published studies

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• 35 country prices published individually • Largest comparisons

• Cammeron (2009) 36 developing countries aggregated by region

• Sweeny (2003) 15 countries with individual prices • Most published studies:

• small numbers of developed countries; limited range of products; Japan only Asian country >1

• Problems: • Often unweighted; Often convenience sample of

prices; Rarely develop any formal index; limited number of countries; comparisons back to one country i.e. US vs rest.

Aggregated using average SD from mean

Summary results from published studies Average standard deviations from the mean for each study

Background to this part of the research • Aims of this analysis construct and report 2 international pharmaceutical price indices:

• Indices 1 - As broad a set of countries as possible, while covering a range of regions and countries with different levels of economic development as well as a broad sample of drugs available in each country.

• Indices 2 – On-patent medicines in a more narrow set of countries.

Methods – Challenges • To derive a valid price index it is necessary to have a price for each

country for every time point. • Availability of Molecules, form and strengths vary widely. Trade-off the more

countries the narrower the molecule grouping

• The availability of innovative new chemical entities first in wealthy countries. • newer, high cost therapies cannot be included in a broad international comparison

• Data on international prices and sales volumes are not readily available and all have limitations

• IMS • WHO • Ad Hoc price lists

• IMS most comprehensive data set

Methods – General approach • Products grouped by

outcomes • Equivalence according to

PBAC • Allowed a very broad

definition of equivalence • All products converted to a

daily dose using the WHO DDD

• IMS MIDAS data • March Quarter 2005 through

to the June Quarter 2011 • Sales / units

• Some variation in collection by country

Like molecules – equivalent outcome

EKS method (Elteto, Koves Szulc) P Price index Price per day

(Deflated by CPI)

Q Quantity index number of days

A Numerator Sum of the product of own countries P x Q for each molecule

B Denominator Sum of the product of own countries Q x every other countries P for each molecule

C Numerator Sum of the product of own countries P x every other countries Q for each molecule

D Denominator Sum of the product of every other countries P x every other countries Q for each molecule

Passche index Laspeyres index

Geometric mean of ((A/B),(C/D)) for each unique combination

Fishers Index for each country Geometric mean of all combinations for each country

AF Generate Fishers average

index for the whole set of P and Q

CAF Chain the Fishers average

index for the whole set of P and Q ie T1 x T2=CT2, CT2 x T3

EKS index Fishers index for each country x

CAF

Results – Index 1 Broad molecule

• 9 segments • 50 molecules. • Mostly community

medicines managed by GPs

• Off-patent medicines subject to generic competition.

• 69 Countries – Developed and developing

Segment molecules ace alacepril

alacepril

benazepril

captopril

cilazapril

delapril

enalapril

fosinopril

imidapril

lisinopril

moexipril

perindopril

quinapril

ramipril

spirapril

temocapril

trandolapril

zofenopril

AMISULPRIDE_RISP amisulpride

risperidone

amitriptyline__imipramine_desipramine amitriptyline

imipramine

desipramine

ARIPIPRAZOL_OLANZ_PALIP_ZIPRASIDONE aripiprazol

olanzapine

palip

ziprasidone

cimeti_famoti_nizatid_raniti_roxatid cimetidine

famotidine

nizatidine

ranitidine

roxatidine

fluoxetine_reboxetine_mirtazapine fluoxetine

mirtazapine

reboxetine

fluphenazine_chlorpromazine_trifluoperazine_haloperidol_pericyazine chlorpromazine

fluphenazine

haloperidol

periciazine

trifluoperazine

lansop_omepra_pantop_rabepr lansoprazole

omeprazole

pantoprazole

IN

DEX

1 P

rices

2008_Q1 2008_Q2 2008_Q3 2008_Q4 2009_Q1 2009_Q2 2009_Q3 2009_Q4 2010_Q1 2010_Q2 2010_Q3 2010_Q4 2011_Q1 2011_Q2 1. BULGARIA 3.03 2.96 3.02 3.12 3.45 3.23 3.43 3.76 4.03 3.87 4.01 4.39 4.42 4.32 2. POLAND 2.05 2.03 1.99 1.92 2.09 2.01 2.03 2.16 2.09 1.98 1.98 2.03 2.04 2.00 3. AUSTRIA 1.65 1.62 1.58 1.62 1.80 1.76 1.72 1.79 1.70 1.76 1.66 1.66 1.76 1.83 4. GREECE 2.01 1.78 1.81 1.87 2.17 2.12 1.99 2.05 2.04 1.98 1.96 1.90 2.00 1.82 5. BANGLADESH 2.51 2.34 2.24 2.29 2.31 2.09 1.96 2.13 1.90 1.81 1.94 1.95 1.87 1.79 6. ALGERIA 2.94 2.77 2.78 2.58 2.77 2.19 2.43 2.07 2.02 1.77 1.79 1.79 1.81 1.71 7. NORWAY 1.88 1.83 1.81 1.80 1.90 1.81 1.80 1.72 1.76 1.68 1.74 1.72 1.74 1.70 8. PERU 1.45 1.51 1.50 1.45 1.47 1.38 1.46 1.46 1.45 1.32 1.30 1.42 1.36 1.41 9. S. AFRICA 1.11 1.10 1.22 0.99 1.03 1.15 1.07 1.12 0.94 1.21 1.14 1.91 1.41 1.40 10. VENEZUELA 1.40 1.38 1.34 1.32 1.50 1.39 1.49 1.48 1.39 1.40 1.35 1.39 1.42 1.37 11. JORDAN 1.51 1.43 1.38 1.37 1.45 1.37 1.36 1.36 1.30 1.33 1.32 1.31 1.31 1.23 12. LATVIA 1.51 1.45 1.43 1.47 1.55 1.48 1.46 1.36 1.39 1.36 1.28 1.23 1.19 1.21 13. LUXEMBOURG 1.45 1.40 1.37 1.31 1.45 1.38 1.42 1.40 1.42 1.37 1.19 1.20 1.14 1.16 14. FRANCE 1.48 1.42 1.34 1.30 1.35 1.24 1.24 1.23 1.22 1.20 1.19 1.20 1.18 1.12 15. CANADA 1.09 1.03 1.05 0.99 1.00 0.98 1.01 1.09 1.02 1.07 1.10 1.14 1.15 1.10 16. PUERTO RICO 1.39 1.38 1.32 1.26 1.31 1.24 1.24 1.28 1.23 1.20 1.16 1.17 1.16 1.09 17. SINGAPORE 1.14 1.11 0.87 1.11 1.19 1.12 1.15 1.15 1.10 1.16 1.10 1.15 1.09 1.08 18. CHINA 1.05 1.01 0.98 0.95 1.09 1.00 1.04 1.10 1.06 1.07 1.09 1.15 1.06 1.07 19. RUSSIAN FED 1.32 1.30 1.20 1.17 1.29 1.21 1.15 1.19 1.12 1.11 1.09 1.09 1.09 1.07 20. NETHERLNDS 1.33 1.24 1.23 1.22 1.29 1.16 1.14 1.14 1.12 1.10 1.07 1.09 1.07 1.06 21. MEXICO 1.39 1.33 1.27 1.22 1.23 1.14 1.11 1.14 1.12 1.09 1.09 1.13 1.11 1.06 22. JAPAN 0.95 0.92 0.91 0.88 1.19 1.13 1.14 1.17 1.12 1.08 1.07 1.09 1.07 1.04 23. FR. W. AFRICA 0.98 0.96 0.87 1.03 1.07 1.10 0.94 1.05 0.94 1.07 1.07 1.17 1.06 1.03 24. KOREA 1.36 1.33 1.28 1.27 1.31 1.24 1.24 1.27 1.26 1.16 1.07 1.10 1.08 1.02 25. ARGENTINA 1.35 1.33 1.29 1.25 1.28 1.15 1.12 1.12 1.12 1.08 1.07 1.07 1.08 1.02 26. LEBANON 1.32 1.28 1.26 1.23 1.33 1.22 1.17 1.19 1.16 1.07 1.00 1.03 1.00 1.00 27. DENMARK 1.34 1.32 1.28 1.21 1.24 1.20 1.22 1.16 1.18 1.07 1.04 1.09 1.02 0.99 28. ESTONIA 1.19 1.22 1.16 1.16 1.20 1.14 1.10 1.14 1.13 1.07 1.08 1.06 1.04 0.99 29. URUGUAY 1.18 1.12 1.11 1.15 1.22 1.18 1.18 1.16 1.14 1.15 1.07 1.03 1.00 0.99 30. INDIA 1.04 1.02 1.00 0.94 1.11 1.04 1.01 1.03 1.00 1.00 0.99 1.02 1.00 0.96 31. CROATIA 1.04 1.05 1.00 0.99 1.05 1.00 1.00 1.02 0.97 0.99 0.96 0.97 0.97 0.96 32. LITHUANIA 1.13 1.10 1.03 0.99 1.02 1.00 0.95 1.00 1.02 0.96 0.95 0.99 0.95 0.92 33. ITALY 1.05 1.03 1.02 0.99 1.08 1.03 0.98 0.99 0.97 0.97 0.95 0.98 0.95 0.91 34. HUNGARY 1.15 1.11 1.03 1.00 1.02 0.92 0.93 0.93 0.92 0.92 0.91 0.94 0.93 0.89 35. SLOVAKIA 1.33 1.30 1.17 1.13 1.22 1.11 1.00 1.02 1.00 0.96 0.92 0.93 0.92 0.89 36. IRELAND 1.34 1.39 1.17 1.22 1.25 1.16 1.11 1.16 1.11 1.01 1.08 1.05 0.95 0.89 37. TURKEY 1.22 1.14 1.09 1.06 1.15 1.07 1.05 1.01 0.94 0.94 0.93 0.93 0.93 0.88 38. SWEDEN 1.29 1.25 1.26 1.10 1.20 1.03 1.06 1.06 0.93 0.96 0.98 0.96 0.94 0.88 39. BRAZIL 0.92 0.87 0.84 0.79 0.88 0.82 0.82 0.95 0.91 0.86 0.83 0.83 0.87 0.86 40. USA 0.94 0.91 0.87 0.83 0.95 0.85 0.83 0.86 0.84 0.80 0.79 0.84 0.83 0.80 41. GERMANY 0.88 0.86 0.81 0.85 0.82 0.77 0.72 0.79 0.74 0.73 0.74 0.81 0.78 0.76 42. MOROCCO 1.11 1.13 1.08 1.06 1.12 1.07 0.98 0.99 0.93 0.88 0.82 0.83 0.79 0.75 43. ROMANIA 0.86 0.85 0.82 0.81 0.87 0.82 0.85 0.88 0.84 0.80 0.82 0.82 0.80 0.74 44. NEW ZEALAND 1.27 1.18 1.07 1.02 1.03 0.96 0.93 0.94 0.87 0.84 0.85 0.81 0.73 0.69 45. SAUDI ARABIA 1.51 1.38 1.18 1.04 1.19 1.12 0.90 0.97 0.93 0.91 0.83 0.71 0.67 0.67 46. SLOVENIA 1.09 1.04 1.02 0.96 0.85 0.80 0.78 0.79 0.73 0.71 0.69 0.69 0.69 0.66 47. TAIWAN 0.77 0.77 0.82 0.79 0.84 0.76 0.78 0.77 0.74 0.77 0.70 0.71 0.66 0.63 48. DOMINICAN REP 0.87 0.85 0.84 0.81 0.95 0.74 0.72 0.72 0.69 0.70 0.62 0.64 0.63 0.62 49. VIETNAM 0.42 0.43 0.56 0.54 0.60 0.57 0.58 0.56 0.52 0.52 0.55 0.57 0.57 0.61 50. CZECH 0.63 0.62 0.62 0.61 0.64 0.62 0.55 0.56 0.60 0.58 0.60 0.60 0.61 0.61 51. PHILIPPINES 0.55 0.56 0.56 0.51 0.57 0.55 0.55 0.57 0.60 0.55 0.59 0.59 0.62 0.61 52. SPAIN 0.65 0.58 0.54 0.54 0.58 0.52 0.57 0.59 0.58 0.56 0.58 0.55 0.53 0.57 53. TUNISIA 0.81 0.82 0.83 0.75 0.81 0.73 0.68 0.73 0.65 0.67 0.65 0.62 0.60 0.56 54. AUSTRALIA 1.02 0.99 0.95 0.82 0.76 0.72 0.69 0.72 0.65 0.68 0.65 0.65 0.63 0.56 55. C. AMERICA 0.50 0.55 0.53 0.46 0.47 0.47 0.50 0.47 0.49 0.42 0.44 0.47 0.46 0.55 56. ECUADOR 0.60 0.61 0.58 0.56 0.57 0.57 0.57 0.57 0.55 0.55 0.53 0.53 0.54 0.55 57. PORTUGAL 0.62 0.62 0.62 0.58 0.61 0.56 0.57 0.56 0.55 0.53 0.52 0.53 0.50 0.49 58. EGYPT 0.63 0.61 0.59 0.57 0.61 0.56 0.54 0.54 0.53 0.51 0.50 0.51 0.51 0.48 59. PAKISTAN 0.62 0.62 0.62 0.62 0.60 0.59 0.60 0.61 0.55 0.53 0.55 0.53 0.50 0.48 60. UK 0.63 0.59 0.58 0.57 0.62 0.60 0.56 0.56 0.54 0.53 0.52 0.52 0.50 0.47 61. MALAYSIA 0.94 0.87 0.95 0.86 0.74 0.68 0.61 0.61 0.52 0.50 0.50 0.46 0.43 0.47 62. INDONESIA 0.60 0.55 0.55 0.52 0.56 0.53 0.46 0.48 0.49 0.50 0.45 0.45 0.46 0.46 63. FINLAND 0.46 0.45 0.44 0.43 0.44 0.41 0.41 0.41 0.41 0.40 0.39 0.41 0.45 0.43 64. COLOMBIA 0.36 0.34 0.34 0.32 0.33 0.31 0.31 0.32 0.34 0.33 0.33 0.34 0.35 0.34 65. THAILAND 0.45 0.43 0.42 0.39 0.41 0.37 0.37 0.37 0.35 0.34 0.36 0.35 0.32 0.31 66. BELGIUM 0.53 0.50 0.46 0.47 0.39 0.38 0.43 0.35 0.35 0.33 0.32 0.29 0.29 0.31 67. CHILE 0.31 0.29 0.30 0.28 0.29 0.28 0.28 0.28 0.28 0.28 0.30 0.29 0.30 0.29 68. HONG KONG 0.41 0.35 0.39 0.39 0.37 0.31 0.31 0.32 0.32 0.31 0.27 0.33 0.31 0.28 69. SWITZERLAND 0.39 0.32 0.33 0.33 0.35 0.31 0.31 0.31 0.29 0.31 0.26 0.25 0.24 0.24

Index 1 change in average price per quarter 2008 to 2011

Results – Index 2 On-Patent molecules • 25 segments • 27 molecules. • Mostly initiated in

hospital • On-patent medicines

not subject to generic competition.

• 9 Countries - Developed

Segment molecules bevacizumab bevacizumab bortezomib bortezomib cetuximab cetuximab dasatinib dasatinib duloxetine_venlofaxine_desvenlafaxine desvenlafaxine

duloxetine venlafaxine

erlotinib erlotinib etanercept etanercept ezetimibe ezetimibe gefitinib gefitinib imatinib imatinib insulin aspart insulin aspart insulin aspart protamine crystalline insulin aspart protamine crystalline insulin detemir insulin detemir insulin glargine insulin glargine insulin glulisine insulin glulisine insulin lispro protamine insulin lispro protamine lapatinib lapatinib maraviroc maraviroc nilotinib nilotinib oxaliplatin oxaliplatin raltegravir raltegravir rituximab rituximab sorafenib sorafenib sunitinib sunitinib tenofovir disoproxil tenofovir disoproxil

2010_Q1 2010_Q2 2010_Q3 2010_Q4 2011_Q1 2011_Q2

1.US 1.27 1.25 1.32 1.42 1.55 1.57 2. FRANCE 1.18 1.15 1.17 1.19 1.22 1.21 3. AUSTRIA 1.13 1.07 1.09 1.10 1.12 1.11 4. AUSTRALIA 1.08 1.07 1.08 1.09 1.09 1.10 5. SWEDEN 1.04 1.01 1.03 1.06 1.05 1.04 6. FINLAND 0.94 0.94 0.96 1.00 1.01 1.01 7. UK 0.93 0.91 0.92 0.93 0.95 0.94 8. JAPAN 0.90 0.89 0.92 0.90 0.92 0.89 9. GERMANY 0.67 0.65 0.66 0.67 0.69 0.69

US high and increasing

Results - Regional

Broad index – Index 1

On-patent index – Index 2

Discussion • This will be the largest multinational study comparing

indexes of pharmaceutical prices and the first to use the EKS index. A formal index pricing model which allows comparisons between all countries (transitive)

• The study uses wider definition of like molecule than has been previously published. Our definition of like is those pharmaceuticals which provide equivalent outcomes.

• The findings reported in this paper provide a basis for research into what are the factors that determine the level of prices in each country

Discussion • Analysis show that off-patent

pharmaceutical prices fall over time and on-patent increase slightly

• Asian prices for off-patent and possibly on-patent medicines low

• Published studies results are similar to the on-patent developed country results and both are different from the broad index

Discussion – limitations of the analysis • Data are not perfectly comparable

between countries. Some markets prices over-stated rebates and stock give-aways. Some MIDAS data are based on list prices and others invoiced prices.

• Indices are shaped by the molecules for which there is complete cover across our sample countries over the time period covered. So with broad sample limited cover.

End – thank you

School of Public Health and Community Medicine

NOT AS EASY AS IT SOUNDS: CHALLENGES IN ASSESSING THE VALUE FOR MONEY OF IMPLEMENTED VACCINATION PROGRAMS

1. School of Public Health and Community Medicine, UNSW Australia, Sydney, NSW, Australia

2. National Centre for Immunisation Research and Surveillance of Vaccine Preventable Diseases (NCIRS), University of Sydney, Westmead, NSW, Australia

3. Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium

Newall AT1 , Reyes JF1, Wood JG1, McIntyre P2, Menzies R2, Beutels P1,3

Pre-implementation modelling

• Significant uncertainty when predicting the real world impact of vaccine programs

• Nature of vaccine clinical trials – Vaccine efficacy measured in an idealised setting

• Dynamic population effects – Changes (shifts) in the causative organism – Herd protection effects (unvaccinated) – Dynamic transmission models (uncertainty)

• Model predictions (using vaccine efficacy est.) – Over or underestimate program impact

Published version now available: Newall AT, et al. Vaccine 2014;32(7):759-65

Post-implementation assessment

• Allows use of real world data on the impact of vaccine programs in assessing the cost-effectiveness – Surveillance data and healthcare statistics (e.g.

Notification, hospitalisation, mortality, adverse events) – Other data from program evaluation (e.g. Vaccine uptake)

• Leading to more robust cost-effectiveness est. -reduced uncertainty

• Research gap: – Issues for post-implementation economic evaluation of

vaccine programs have not been systematically explored


Methodological challenges

• Estimating the “no-program world” • Estimating the “program world” • Estimating the “future benefits”


Estimating the “no-program world”

• Q: what would have happened without vaccination? – Issue not exclusive to retrospective evaluations – Typically project forward pre-implementation rates

• Predicting trends in rates can be complex – ID rates may vary over time due to various factors

• Long-established vaccine programs – Using pre-implementation rates may be problematic – Alternative: model removal of vaccination using an

inflation factor on current “program world” rates


Estimating the program benefits accrued to date


Estimating the “program world”

• Q: what decline has occurred due to vaccination? – Real world data rather than predictive modeling using

vaccine efficacy est. (non-local setting)

• Surveillance data from setting of introduction – Captures features of population, disease epidemiology,

program implementation, herd impact (without the need for dynamic transmission modelling)

• Limitations of observational data – Attributing causation to decline in ID rates: statistical

methods to establish significance (temporal association) – Lack of routine testing to confirm diagnosis for many IDs


Estimating the “future benefits”

• Temptation to rely solely on available data (to date) – Full cost to vaccinate recent cohorts but only includes

benefits over a limited period (<duration of immunity) – Problematic when protection is long-lived or when

prevented events occur a long time into future – Issue that specifically affects prevention programs


Future benefits from doses given out to date


Benefits of retrospective evaluations

• Value for money achieved – Important given the growing costs of vaccine programs – Bargaining tool for pharmaceutical price negotiation

• Re-evaluation and future decision – Expansion or replacement of vaccine programs – Future marginally improved vaccines (non-inferiority)

• Validation of pre-implementation – Evaluate previous predictive cost-effective models by

comparison to the findings of our cases studies


Acknowledgments

– Australian Research Council Linkage grant (LP120200043) – Although they do not necessarily endorse the study and

its conclusions we gratefully acknowledge useful discussion with the Reference Group (including Jodie McVernon, Paul Scuffham, Rosalie Viney) for this grant.


Questions


Cost Effectiveness of Catheter-Based Renal Denervation for Treatment Resistant Hypertension – an Australian Payer Perspective Tilden D,1 McBride ME,2 Whitbourn R,3 Krum H,4 Walton T,5 Gillespie J2

1 THEMA Consulting Pty Ltd, Pyrmont, Australia, 2 Medtronic Australasia, North Ryde, Australia, 3 St Vincent's Hospital, Melbourne, Australia, 4 Monash University, Melbourne, Australia, 5 Epworth Hospital, Richmond, Australia. Australian Chapter ISPOR Encore 21st April 2015

Objectives •Patients with treatment resistant hypertension (TR-HTN) experience uncontrolled blood pressure despite optimal medical management (OMM) with 3 or more anti-hypertensive drugs including a diuretic. •TR-HTN patients remain at significantly increased risk of cardiovascular and renal morbidity and mortality,1 consume considerable healthcare resources2 and, with limited treatment options, experience significant unmet clinical need. •In randomised clinical trials,3 large registries4 and meta-analyses,5,6 catheter based renal denervation (RDN) has been shown to effectively reduce office systolic blood pressure (SBP) in patients with TR-HTN.

The objective of this analysis is to build upon an existing US economic evaluation,7 known associations between SBP and clinical events and local parameters to determine the cost-effectiveness of RDN from the perspective of the Australian healthcare payer.

1 Calhoun et al., (2008). Hypertension, 51: 1403-19 ; 2 Sapoval et al., (2013). Value in Health, 16(7): A520-1; 3 Esler et al., (2010). Lancet, 376(9756): 1903–9; 4 Schlaich et al., (2014). Am J Kidney Dis., 63(5):A1-A121; 5 Davis et al., (2013). J Am Coll Cardiol., 61(10):E1377; 6 Gosain et al., (2013). J Clin. Hypertens., 15(1):75-84p; 7 Geisler et al., (2012). J Am Coll Cardiol., 60 (14): 1271–7

Methods Population: TR-HTN patients (office SBP ≥ 160 mmHg despite >3 anti-hypertensive drugs including a diuretic). Analytical technique: Markov model with 30 health states reflecting established long-term consequences of hypertension. Model Inputs and Assumptions (Table 1):

• Health state utility values and transition probabilities from Geisler.7 • RDN treatment effect at 6 months from meta-analysis (June, 2013).8 • Australian life tables, resource use, costs and discount rates.

Comparative treatments: RDN+OMM vs OMM alone Analysis perspective: Australian healthcare payer Downstream Outcomes: ICER (cost per quality adjusted life year ($/QALY)) Deterministic Sensitivity Analyses: tested base case assumptions including extent of SBP reduction, durability of effect and baseline SBP.

7 Geisler et al., (2012). J Am Coll Cardiol., 60 (14): 1271–7; 8 McBride et al., (2014). HTAi Annual Conference, Washington, Abstract PO.085

Table 1: Base case model inputs and assumptions

Parameter Value Source

RDN+OMM reduction in SBP (vs OMM alone)

-28.1 mmHg (maintained for model duration)

Meta-analysis of 28 trials (n=896)8

Cost of RDN procedure

$10,724 (no repeat procedures required)

Australian costs a

Health state Utility values and event costs ($AUS)

Health State Hypertension (event free) Stroke MI – acute MI – 1st and ensuing years Heart Failure Angina Pectoris - stable Angina Pectoris - unstable ESRD

Utility 0.96 0.63 0.76 0.88 0.71 0.84 0.74 0.63

Cost/event n/a

$14,262 $15,838

n/a n/a

$20,981 $20,981

n/a

Cost/cycle b

$83.46 $1,783, $559

n/a $535

$2,556, $1,069 $535 $535

$6,688

Utility values from Geisler et al., 2012

Australian cost estimates

Discounting 5% (costs and outcomes) Local requirement c

Horizon lifetime (30 years)

MI = Myocardial infarction, ESRD = End stage renal disease a includes theatre/admission, capital equipment, ablation catheter, consumables, surgery time, angiography, anaesthetist services (and renal stenting in 5%

of patients) b Cost per cycle (1 month) (two values represent first and subsequent months), c Medical Services Advisory Committee (MSAC) Draft Guidelines (2012)

Cost item RDN+OMM OMM Incremental

RDN procedure $10,724.22 $0.00 $10,724.22

Hypertension OMM $9,634.86 $8,850.93 $783.93

Event costs

MI $5,713.66 $6,905.89 -$1,192.23

Stroke $10,370.34 $13,010.35 -$2,640.01

HF $7,269.64 $7,937.43 -$667.79

AP $10,451.65 $10,999.50 -$547.85

ESRD $1,656.68 $2,165.89 -$509.22

Total event costs $35,461.97 $41,019.07 -$5,557.10

Total costs $55,821.05 $49,870.00 $5,951.04

Table 2: Total healthcare costs for duration of the model

Results • Total costs over the duration of the model were $55,821 for

RDN+OMM and $49,870 for OMM alone (Table 2). • The upfront RDN procedure cost ($10,724) was partially offset over

the duration of the model by lower event costs ($5,557) due largely to the lower incidence of stroke (-0.0559) and MI (-0.0524).

Parameter RDN+OMM OMM Incremental

Cost $55,821.05 $49,870.00 $5,951.04

QALYs 11.5690 11.1394 0.4296

ICER $13,852

Table 3: Incremental cost per QALY gained

Results • When compared to OMM, treatment of each patient with RDN was

associated with greater cost ($5,951) and QALYs (0.4296) resulting in an incremental cost per QALY of $13,852 (Table 3).

• Amongst variables tested, results were most sensitive to the magnitude of SBP reduction, durability of effect and baseline SBP

Discussion • With a SBP reduction of -28.1 mmHg at 6 months, RDN was predicted

to reduce the incidence of all CV and renal events over the model duration.

• The lower probability of events with RDN was associated with improved quality adjusted life years and reduced healthcare costs.

• Clinical benefits of RDN in this analysis were conservatively restricted

to CV and renal events avoided as a consequence of reduced SBP. However, RDN also improves other prognostic indicators for CV events such as LV mass, cardiac output9 and central hemodynamics.10

• An ICER of $13,852/QALY would be considered cost effective from an

Australian payer perspective.

• Limitations – RDN treatment effect biased by unblinded studies in meta-analysis; SBP is a surrogate endpoint; durability of RDN not proven beyond 3 years. 9 Schirmer et al., (2014). J Am Coll Cardiol., 63(18):1916-23; 10 Brandt et al., (2012). J Am Coll Cardiol., 60(19):1956-65; 11 Krum et al.,

(2014). Lancet, 383(9917):622-9

Conclusions

• Based upon this analysis, RDN is a cost effective treatment option

for patients with TR-HTN in Australia. • Some uncertainty exists regarding the magnitude of SBP reduction

with RDN and the durability of the treatment effect. However, this should be considered in light of the significantly increased risk of cardiovascular and renal morbidity and mortality in this difficult to treat population.

Meanwhile in 2013…. • In the US – Symplicity HTN3 RCT trial of RDN underway

• Largest RDN RCT to date (N = 535), comparator is maximal medical

therapy (MMT) (maximally tolerated doses of at least three drugs, including a diuretic). Patient randomised 2:1 to RDN + MMT or Control: Sham + MMT – Patients blinded to procedure; blood pressure assessors blinded to study groups.

January 2014: 1st Medtronic announcement of Symplicity HTN31 results: Demonstrated safety profile RDN arm: clinically meaningful reductions in SBP from baseline RDN vs. Control: Primary efficacy endpoint not met

1. Bhatt et al (2014). A Controlled Trial of Renal Denervation for Resistant Hypertension. NEJM. Published online March 25.

Emerging evidence • April 2014: Medtronic present Global Symplicity Registry1 data (N=998) – In clinical

practice, renal denervation resulted in significant reductions in office and 24-hour BPs with a favourable safety profile. (Canada, Western Europe, Latin America, Eastern Europe, South Africa, Middle East, Asia, Australia, and New Zealand)

• September 2014 : Meta-analysis2 (5 studies, N=800) reports significant difference for SBP for RDN vs MMT – includes Symplicity HTN3 study

• Jan’ 2015: DENER HTN RCT3, RDN plus standardised stepped-care antihypertensive treatment (SSAHT) (n=53, ITT) vs SSAHT alone (n=53, ITT): RDN + SSAHT vs. SSAHT alone more effective in reducing ABPM (France)

• Jan’ 2015: Analysis of Symplicity HTN34 provides insight into key considerations for patient selection and ablation techniques.

• Feb’2015: PRAGUE-15 RCT5, RDN (n=52) vs reduces SBP comparable to intensified drug therapy (n=54) (Czech Republic)

Overall – RDN is a clinically effective treatment option for treatment resistant HTN, but….. there is more to learn about this therapy

1. Böhm et al (2015) First Report of the Global SYMPLICITY Registry on the Effect of Renal Artery Denervation in patients With Uncontrolled Hypertension. Hypertension. 65:766-774 2. Pancholy et al (2014) Meta-analysis of the effect of renal denervation on blood pressure and pulse pressure in patients with resistant systemic hypertension.Am J Cardiol. 2014 Sep 15;114(6):856-61 3. Aziz et al (2015) Optimum and stepped care standardised antihypertensive treatment with or without renal denervation for resistant hypertension (DENERHTN): a multicentre, open-label, randomised controlled trial. The Lancet Published online January 26. 4. Kandzari et al (2015) Predictors of blood pressure response in the SYMPLICITY HTN-3 trial. Eur Heart J. Jan 21;36(4):219-27 5. Rosa et al (2015) Randomized comparison of renal denervation versus intensified pharmacotherapy including spironolactone in true-resistant hypertension: six-month results from the Prague-15 study. Hypertension 65:407-413

Postscript – What does this all mean for HTA of medical devices ?...

• HTA for decision making related to funding is typically based on RCT evidence BUT…

– In the ‘real world’ the treatment effect may

differ… – How do we reconcile this Paradox? – How should RCT and non RCT evidence be

appropriately considered/weighted in decision making?

Postscript – What does this all mean for HTA of medical devices?...

• Technology, operator experience, patient selection and ongoing management – can all rapidly evolve – BUT..

– HTA processes may have an extended duration – how can we also consider the most up-to-date evidence reflective of technology used and experience in the HTA jurisdiction?.

– How should HTA processes manage disparate evidence and uncertainties where there is no effective alternative and an unmet clinical need, and the new therapy has no safety issues?....

There remains an unmet clinical need for RDN, with no alternative for patients who do not respond to drug therapy. Considering the demonstrated safety profile and demonstrated clinical effectiveness, RDN represents an example of a therapy where a managed entry scheme, under controlled conditions, would enable earlier access, and facilitate evidence collection to address uncertainties.

Back up slides

Parameter (base-case value) Variation

Costs Outcomes ICER

RDN + OMM

OMM Increment RDN + OMM

OMM Increment

Base-case $55,821 $49,870 $5,951 11.5690 11.1394 0.4296 $13,852

SBP reduction with RDN (–28.1 mm Hg)

Pooled 95% UCL (–31.6 mmHg) $54,935 $49,870 $5,065 11.6270 11.1394 0.4876 $10,388

Pooled 95% LCL (–24.5 mmHg) $56,685 $49,870 $6,815 11.5120 11.1394 0.3726 $18,292

Years until SBP benefit lost (linear decay after 3.5 years)

10 $58,243 $49,870 $8,373 11.2945 11.1394 0.1551 $53,989

20 $57,228 $49,870 $7,358 11.3846 11.1394 0.2452 $30,005

30 $56,718 $49,870 $6,848 11.4390 11.1394 0.2996 $22,859

Baseline SBP (178 mmHg) 140 $44,675 $42,482 $2,193 12.2849 11.7388 0.5461 $4,017

160 $51,069 $46,875 $4,193 11.9129 11.3579 0.5550 $7,556

180 $56,307 $50,122 $6,185 11.5370 11.1204 0.4166 $14,849

200 $59,730 $51,855 $7,874 11.2039 10.9870 0.2169 $36,307

220 $62,041 $56,104 $5,936 11.0292 10.6455 0.3838 $15,470

Table 4: Sensitivity Analyses

VALUING EQ-5D-5L:

DOES THE ORDERING OF THE HEALTH DIMENSIONS IMPACT ON HEALTH STATE VALUATIONS?

Brendan Mulhern1,2 & Koonal Shah2,3

1 Centre for Health Economics Research and Evaluation, University of Technology Sydney

2 School of Health and Related Research, University of Sheffield 3 Office of Health Economics, London

Acknowledgements

Collaborators: Nancy Devlin (Office of Health Economics) Ben van Hout (UoS) Bas Janssen (EuroQol Group) Louise Longworth (Brunel)

Study funded by EuroQol Group

Introduction

Time Trade Off and Discrete Choice methods used to value EQ-5D-5L

Most valuation studies use ‘standard’ order Extent to which dimension ordering impacts the

coefficients is unclear: Left to right bias (Spalek and Hammad, 2005)? DCE bottom-to-top bias (Shah et al., 2013)? No systematic pattern (Tsuchiya et al., 2014)? Relative importance of the first two EQ-5D dimensions

differs (Rand-Hendriksen and Augusted, 2012).

EQ-5D-5L

Aims

Important to systematically test the impact of reordering the dimensions on: Health state values provided Magnitude of the coefficients

Aim to assess the impact of different dimension orderings on the valuation of EQ-5D-5L health states using TTO and DCE preference elicitation methods.

Methods – Composite TTO

‘Classic’ TTO for states better than dead

Methods – Composite TTO

‘Lead time’ TTO for states worse than dead

Methods – DCE

EQ-5D-5L health state pairs

Methods – Study design

Three EQ-5D-5L orders: Standard (MO-SC-UA-PD-AD):

Used in most valuation studies Reverse (AD-PD-UA-SC-MO):

Provide clear comparison Block (PD-AD-MO-SC-UA):

Move block of functioning dimensions

Two ‘life A’ descriptors Full health and 11111

TTO states DCE state pairs

State 1 State 2

11111 42525 53422

11112 53242 44151

11121 31113 11331

11223 44222 22244

21111 44241 15244

21232 22331 22413

32442 23144 42452

34155

43331

55233

55555

Methods – Interview process

Face to face computer assisted personal interviews (CAPI) in respondents home

10 areas in South Yorkshire, UK Interview process

Background questions and the EQ-5D-5L for their own health.

4 c-TTO examples and practice tasks 10/11 c-TTO health state valuations 7 DCE pairs Feedback and follow up questions

Results - Sample

455 interviews, 20% response rate TTO data excluded for 13 respondents (all same value)

Results - TTO

Descriptive analysis Regression analysis

Results - DCE

Descriptive results Regression

Discussion

Health state dimension order key methodological issue in valuation of EQ-5D-5L/SF-6D-V2

Impact of order on: TTO values (standard vs. other) TTO and DCE coefficients, but not systematic

Implications for previous/future valuation studies? Between subject randomisation, but further research?

Limitations: Only 3 orders tested Sample size? Small number of states valued. Use full design?

For further information contact: [email protected]

References:

Rand-Hendriksen, K, Augestad LA. Time Trade-Off and Ranking Exercises Are Sensitive to Different Dimensions of EQ-5D Health States. Value in Health 2012; 15(5): 777–782.

Shah KK, Tsuchiya A, Hole AR, Wailoo AJ. Valuing health at the end of life: A stated preference discrete choice experiment. OHE Research Paper 12/04. London: Office of Health Economics; 2013.

Spalek TM, Hammad S. The left-to-right bias in inhibition of return is due to the direction of reading. Psychological Science 2005; 16(1): 15-18.

Tsuchiya A, Mulhern B, Bansback N, Hole AR. Using DCE with duration to examine the robustness of preferences across the five dimensions of the EuroQol instrument. EuroQol Plenary 2014; Stockholm.

Menzies

Health Economics

Group

Primary supervisor: Prof Andrew Palmer Encore presentation 6th Asia Pacific ISPOR Conference, Beijing, CHINA Sept 2014

Construct validity of

SF-6D health state

utility values in an

employed population

Siyan Baxter, Kristy Sanderson, Alison Venn, Petr Otahal, and

Andrew J. Palmer

Presenter: Siyan Baxter

Outline

AIM: To investigate the construct validity of SF-6D in a large and diverse Australian State Service workforce To investigate whether SF-6D provides preference based utility values in this population that reflects Australian working population norms

HYPOTHESIS: Utilities differentiate appropriately between health, socioeconomic and work characteristics • that negative associations exist for age, BMI, K10, ERI, and

comorbidities and • positive associations are shown for education and salary

Why is it important?

BACKGROUND: • Lack of utility data in working populations • Greater policy interest in the workplace as a setting for

public health strategies • To compete for public funding workplace health promotion

– follow economic guidelines for evidence-based decisions

– improve methodology of economic evaluations

– beyond business justification model

Must value the health benefits of participating employees

Methods – Data Collection

Tasmanian State Service

TSS (2010)

Source population: 27,659 Random Sample: 12,179 # responders: n= 3,408

(28% response rate) Cross-sectional

Self-reported questionnaire SF12v2

Age, BMI, Occupation, Education Lifestyle (smoking, alcohol, PA)

Mental health, Job stress, Comorbidity

Salary (HR data), Absenteeism

Normative working population

HILDA (2010)

Household Income and Labour Dynamics of Australia Survey

Wave 11

# cases: n = 17,612 General employed (n=11,234)

Public service subset (n=1,938)

Self-completed questionnaire (SCQ) SF36v1

Age, BMI, Occupation, Education Lifestyle (smoking)

Mental health Salary, Absenteeism

Methods – Data Collection

Tasmanian State Service

TSS (2010)

Source population: 27,659 Random Sample: 12,179 # responders: n= 3,407

(28% response rate) Cross-sectional

Self-reported questionnaire SF12v2

Age, BMI, Occupation, Education Lifestyle (smoking, alcohol, PA)

Mental health, Job stress, Comorbidity

Salary (HR data), Absenteeism

Normative working population

HILDA (2010)

Household Income and Labour Dynamics of Australia Survey

Wave 11

# cases: n = 17,612 General employed (n=11,234)

Public service subset (n=1,938)

Self-completed questionnaire (SCQ) SF36v1

Age, BMI, Occupation, Education Lifestyle (smoking)

Mental health Salary, Absenteeism

HEALTH

• SF-6D Health utility score: 0-1

• Kessler-10 Psychological Distress Scale; 4 week recall • Low (10-15), Very high (30+)

• Body Mass Index • Age (years) • Comorbidity (23 chronic dx)

SOCIOECONOMIC

• Salary (annual $AUD)

WORK CHARACTERISTICS

• Absenteeism (# days absent in 4 weeks; zero, any) • Job stress

• Low (0), High (>1)

Methods - Measures

• Construct validity tested both internally (TSS) and externally (HILDA) using 1. Mean utility by sample characteristics 2. Pearson rank correlations 3. Multivariable linear regression

• to identify relationship between utility and health, socioeconomic and work characteristics

• examine capability of SF-6D to discriminate between external factors and health status

Methods - Measures

TSS Results – Mean Health Utility

Females (n=2444) Males (n=964)

n mean n mean Mean SF-6D (SE) 2409 0.771 944 0.792

Age (yrs) ≤30 264 0.766 72 0.796

>60 119 0.803 68 0.835

BMI (kg/m2) < 25 1035 0.782 330 0.806

40+ 49 0.723 13 0.806

Comorbidities 0 519 0.819 264 0.825

3+ 647 0.715 197 0.735

K10 Low 1554 0.823 662 0.838

Very high 72 0.584 16 0.596

Salary ($AUD) <$40,000 150 0.775 67 0.794

$120K+ 22 0.813 53 0.807

Absenteeism Zero days 1885 0.788 761 0.804

Any days 510 0.713 180 0.742

Job stress (ERI) Low 746 0.814 311 0.833

High 800 0.714 286 0.738

Mean SF-6D by

characteristic

Low psychological distress

Low job stress

0.00 Death

High job stress

Zero comorbidities

SF-6D Health Utility

Female TSS employees on a health utility score continuum

1.00

0.82

0.72

0.71

0.58

Perfect health (1.7%)

Very high psychological distress

3+ comorbidities

Mean utility 0.771 (0.147)

TSS Results – Mean Utility

TSS Results – Correlations

Females

n=1780

Males

n=773

SF-6D 1 1

K10 -0.6332* -0.6585*

Age 0.0712 0.0484

BMI -0.1105* -0.0918*

Salary 0.0045 0.0509

Comorbidities -0.3958* -0.3318*

Job stress -0.3695* -0.3427*

Absenteeism -0.2539* -0.2101*

Pearson correlations demonstrated that SF-6D health utility was negatively associated with (listed by greatest to least strength) K10 Job stress Comorbidities Absenteeism BMI

Age and Salary were not associated

* p values are statistically significant (p<0.01)

Pearson rank correlations (bivariate)

TSS Results - Summary

• Higher psychological distress • Higher job stress • More co-morbid conditions • Absenteeism • Higher education (females) • Lower physical activity (females)

Associated with lower SF-6D:

No association between SF-6D and age or salary

TSS employee mean health utility is: Men: 0.792 (0.004) Women 0.771 (0.003)

External Validity

TSS

State Service

n=3408

HILDA

All employed

n=11,234

HILDA

Public service subset

n=1938

Mean SF-6D

Males 0.792 0.792 0.801 Females 0.771 0.775 0.784

Negative Association p<0.05

K10* Absenteeism*

K10* Age* BMI (females)* Absenteeism* Employment condition (males)

K10* Age (males)* Absenteeism (females)*

Positive Association p<0.05

Salary* Occupational type (males)*

*denotes p<0.01 Included in the model of normative sample: BMI, K10, age, occupation, employment condition, salary, smoking status, absenteeism

Linear regression

(multivariable)

Conclusions

SF-6D health utility scores appear to have construct validity

and can be used to measure health states within employee populations

• Male health utility scores are higher than females • Health Utility discriminates between

• health factors (comorbidity, body mass index, psychological distress, age),

• socioeconomic factors (salary, in populations within private enterprise),

• work characteristics (job stress, absenteeism, employment condition and occupational type)

Evaluators of workplace health promotion interventions can now: • Place value on employee health outcomes to improve

methodology of health economic evaluations

• Derive QALYs for policy decisions (compete for funding)

Bonus feature: SF-6D has previously been shown to exhibit sensitivity in detecting small changes in healthy individuals… … it may be sensitive to small health differences in working populations

Important Implications

Acknowledgements

pH@W Team: Lisa Jarman, Michelle Kilpatrick, Kim Jose, Kate Chappell, Tasmanian State Service & UTAS Partners Supervisors: Andrew Palmer, Kristy Sanderson, Alison Venn, Statistical Advisor: Petr Otahal HILDA Advisors: Nicole Watson (Melbourne University), Robert Bruenig (Australian National University) Menzies Health Economics Group: Andrew Palmer, Amanda Neil, Lei Si, Barbara De Graaff, Julie Nermut The study was approved by Tasmania Health & Medical Human Research Ethics Committee (EC00337): H0010501

Quality of Life Research: Volume 24, Issue 4 (2015), Page 851-870

From Regulatory Approval to Subsidized Patient Access in the Asia-Pacific Region: A Comparison of Systems Across Australia, China, Japan, New Zealand, South Korea, Taiwan and Thailand

GREG COOK, HANSOO KIM

DISCLAIMER

The views in this presentation do not necessarily reflect the policies of Bristol-Myers Squibb.

OBJECTIVE

To compare the processes and timings of regulatory and subsidised access systems for medicines across 7 jurisdictions within the Asia-Pacific region.

Australia China Japan New Zealand South Korea Taiwan Thailand

METHODS

A questionnaire was developed focussing on regulatory and Health Technology Assessment (HTA) based subsidised access processes and timings in each of the 7 surveyant’s jurisdictions.

RESULTS

Australia and Thailand are the only two jurisdictions that formally allow the subsidised access evaluation process to be conducted in parallel with the regulatory evaluation process. The Australian, Japanese, Korean, New Zealand and Taiwanese systems afford broad coverage, whereas the Chinese and Thai systems provide limited coverage for medicines under patent. The subsidised access systems for all jurisdictions except Thailand have an associated patient co-payment for each medicine/ prescription. The biggest disparity across the study group relates to time from regulatory submission to subsidised access of patented medicines – ranging from just over 1 year (Japan) to a minimum of 5 years (China).

RESULTS Jurisdiction Subsidised Patient

Access System Coverage Patient co-payment

Australia Pharmaceutical Benefits Scheme (PBS)

• Universal coverage of subsidised medicines for Australian residents

• AUD$37.70 [USD$29.39] (adult) • $AUD 6.10 [USD$4.76] (concession)

China Basic Health Insurance Scheme (BHIS) – urban & rural schemes

• Covering urban and rural residents

• Yes (amount depends on sub-insurance system)

Japan National Health Insurance (NHI) Scheme

• Coverage to all Japanese citizens

• 30% • 10% or 20% (elder and preschool child

South Korea National Health Insurance Scheme (NHI)

• Coverage to 99% of Korean citizens

• 30% • 5% oncology meds; 10% rare disease

meds New Zealand Pharmaceutical

Management Agency (PHARMAC)

• Universal coverage of subsidised medicines for New Zealand residents

• NZD$5 [USD$3.76] • Free (under 6 years of age)

Taiwan National Health Insurance Administration (NHIA)

• Coverage to 99.9% of Taiwanese citizens

• NTD20 [USD$0.64] for meds < NTD100 [USD$3.19]

• add NTD20 [USD$0.64] per additional NTD100 medication cost

• Cap for copayment at NTD200 [USD$6.37] Thailand National List of Essential

Medicines • Coverage of generics

and cost-effective patented medicines for Thai citizens

• No copayment

Fx rates: sourced March 3, 2015

RESULTS

Minimum time from

regulatory submission to subsidised access = 72

weeks

AUSTRALIA

TGA Registration (52 wks)

PBS Listing (37 wks: 17 wk PBAC evaluation + 20 wks)

MEDSAFE Registration (52 wks)

Minimum time from regulatory submission to subsidised access = 110

weeks

TAIWAN

TFDA Registration (80 – 104 wks) NHI Listing (30 – 78 wks)


weeks

SOUTH KOREA

MFDS Registration (52 – 78 wks: chemicals – biologics)

NHI Listing (52 wks)


weeks

JAPAN

PMDA Registration (52 wks) NHI Listing (8 – 12 wks)


weeks*

NEW ZEALAND

PHARMAC Listing (32 wks)

*indicative only as timelines not cited in guidelines # current process (August 2014); specific to urban population only.

Minimum time from regulatory submission to subsidised access = 5 yrs

(260 wks)

CHINA#

CFDA Registration (4 – 5 yrs) BHIS Listing (1 – 5 yrs)


weeks

THAILAND

Thai FDA Registration (64-78 wks: chemicals; 104 wks: biologics)

NLEM (104 – 156 wks)

Time to progress through regulatory & subsidised patient access systems across Asia-Pacific (Theoretical)

EXAMPLES

Jurisdiction Date of Registration

Date of Subsidised

Access

Time from Registration to Access

Australia July 2011 January 2012 21 weeks Taiwan August 2013 June 2014 42 weeks South Korea November 2011 January 2013 60 weeks New Zealand June 2013 n/a 60+ weeks Thailand November 2012 n/a 91+ weeks

Jurisdiction Date of Registration

Date of Subsidised

Access

Time from Registration to Access

Japan April 2010 June 2010 9 weeks Australia September 2013 December 2013 13 weeks South Korea May 2013 January 2014 30 weeks

Apixaban for VTE-P

Alogliptin for T2DM

CONCLUSIONS

There is consistency across the 7 jurisdictions studied in relation to regulatory and subsidised patient access processes:

regulatory approval is required prior to subsidised access review subsidised access coverage is broad, and the cost of medicine subsidisation is offset, in part, by patient co-payments.

While local differences will always exist in relation to budget and pricing negotiation, there may be efficiencies that can be applied across systems to improve time to subsidised access. Closer understanding of regulatory and subsidised access systems can lead to best-practice sharing and ultimately, timely access and better health outcomes for patients.

SUMMARY/ NEXT STEPS Parallel regulatory and subsidised access evaluation is one area where time efficiencies can be gained. Another potential way to reduce time to subsidised access is to consider how to remove duplication from the regulatory and subsidised access assessments. A recent Collaboration between the European Medicines Agency and the European network for HTA was established and is examining ways of improving the contribution of regulatory assessment reports to the assessment of medicinal products by HTA bodies. While the assessment of relative effectiveness should remain the remit of subsidised access agencies, there are data and review efficiencies that can be implemented. Recommended outcomes from collaborations such as the above could be used globally to improve communications within sponsor companies, expand dialogue between regulators and health technology assessment bodies, support policymaker decisions, and improve time to subsidised access for patients in the future.

PRICE DISCLOSURE IN AUSTRALIA

DELIVERING SAVINGS BUT INDIRECTLY BLOCKING

ACCESS TO INNOVATIVE MEDICINES?

James Harrison, Greg Cook, Hansoo Kim

Bristol-Myers Squibb Australia

1

ISPOR 6th Asia-Pacific Conference, Beijing September 2014

James Harrison, MPH

2

Disclaimer

The views in this presentation do not necessarily reflect the policies of Bristol-Myers Squibb.

Price disclosure – an overview

4

2007 Price disclosure (PD) introduced 2010 PD refined - Early and Accelerated Price Disclosure (EAPD) introduced 2014 EAPD refined – Simplified Price Disclosure (SPD) introduced

A price discounting mechanism to ensure the sustainability of the Pharmaceutical Benefits Scheme by moving the reimbursed price of off-patent drugs towards the competitive market price.

Docetaxel – total expenditure

5

Date Dispensed price per maximum amount* (AUD) October 2012 $3884.96

December 2012 $1002.81 April 2014 $237.54 April 2015 $158.59

*Maximum amount = 250mg; PBS item numbers: 5581R,5582T,5583W,5584X,5585Y,7281F,7282G,7283H,7284J,7285K

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

7,000,000

8,000,000

9,000,000To

tal B

enef

it Pa

id ($

AUD

) December 2012 Price Reduction ~75%

Implications – cost utility analysis

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• Drug price is a primary driver in cost effectiveness analysis


7



8


• Ceteris paribus, a reduction in comparator price will increase an ICER

• Decreases likelihood of a new drug being deemed ‘cost-effective’

A simple applied example

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The cost effectiveness of a new drug (NEW) compared with standard of care (SOC)

Variable Value Cost – SOC 100 Cost – NEW 15,000 Benefit – Alive 1 Benefit – Dead 0 Probability of remaining alive on SOC 10%

Probability of remaining alive on NEW 30%

Time to NEW loss of exclusivity 10

Time Horizon 30 Discount rate 5%

ICER = $21K/0.32QALY = $71k/QALY Assumed decision threshold of $50k/QALY

Multi-cohort structure

10

-30 Cohorts commencing in subsequent years -Each year discounted -Simulated patent expiry of NEW from year 10 -ICERs calculated per cohort

Price decay function - Extracted from Price Disclosure figures - Normalised against date of first reduction - Average price reduction at 0, 12 and 24 months determined - Applied to NEW from year 10 - NEW price floor equals price of SOC ($100)

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Cycle Weighted average discount

- -

0 16.00%

1 29.98%

2 20.80%

3 20.25%

n 20.00%

Year Applied discount

Price of NEW

9 - $15,000

10 16.00% $12,600

11 29.98% $8,823

12 20.80% $6,987

13 20.25% $5,572

14 20.00% $4,458

… … …

29 20.00% $100

Source: http://www.pbs.gov.au/info/industry/pricing/eapd

NEW Loss of exclusivity in year 10

12

$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

$80,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

ICER

($/Q

ALY)

Cohort / year

NEW LOE

Decision threshold of $50k/QALY

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$-

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

$80,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

ICER

Cohort / year

Aggregating costs and benefits

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- Over 30 years: aICER = $43,312/QALY

t=23 t=30

∑

- Over 23 years: aICER = $49,437/QALY

∑

∑

An aggregate ICER was calculated by summing horizontally costs and benefits across all cohorts for each year, discounting, totalling, then dividing aggregate costs by aggregate benefits

Summary Individual ICERs - ICERs ranged from $70,875/QALY to $100/QALY -Assuming Price disclosure from year 10, the ICER threshold of $50,000/QALY was reached in year 12 - Applying this as the sole criteria for funding decisions would result in a 12 year delay to access, despite tripling survival rates

Delayed access

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Aggregate ICERs - Over 30 years, the aggregate ICER was $43,312/QALY - Shortening the time horizon, the threshold was reached at year 23 at an aggregate ICER of $49,437

Earlier access?

single-arm studies to support drug reimbursement in … · •submissions claimed superior efficacy...

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