© Henley Business School 2008

Dr. Giampiero Favato

Henley Business School University of Reading

SEFAP University of Milan

Long term sustainability of SSN pharmaceutical coverage in ItalyA twenty year outlook (2005-2025)

2

Long term sustainability of current SSN level of pharmaceutical coverage

Source: OSMED data 2007

?

3

Key determinants of demand for pharma in public healthcare systems*

• Ageing– Increase Rx

volume

• Price– Generic

substitution– Shift to more

expensive alternatives

– Shift in demand• Morbidity• Mortality• Chronic illness rate• Physicians’

prescribing behaviour• Disposable income• Education• Access to healthcare* Majeed A, Malcom I (1999)

Source: OSMED data 2007

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Study objective

• To determine the long term impact of age and generic substitution on the Italian public pharmaceutical spending.

• Key assumptions:– Base year: 2005– Fundamental level of SSN pharmaceutical coverage

unchanged– No drastic shifts in pharmaceutical demand– All other determinants unchanged– Generics price = 40% of branded off-patent

5

Quantifying the impact of ageing:the ASSET study (2007)

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ASSET outcomes• Patient and cost data were

obtained directly from computerised prescription records for a two year period, from January 2004 to December 2005.

• The ASSET sample totalled 3,175,691 residents.

• The ASSET mean costs were applied to the Istat projections of the Italian population (intermediate scenario)

* Favato G, Mariani P, Mills RW, Capone A, Pelagatti M, et al (2007) ASSET

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Impact of ageing on demand:+24% over twenty years (2005-2025)

0

20

40

60

80

100

120

2005

= 1

00

Ageing

Ageing 100 101 103 104 105 106 107 109 110 111 112 114 115 116 117 118 119 120 121 122 124

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

124

100

8

Goodness of fit

Polynomial regression model:y = -0.0055x2 + 1.2906x + 98.759R2 = 0.9999

0

20

40

60

80

100

120

2005

= 1

00

Ageing Poly. (Ageing)

Ageing 100 101 103 104 105 106 107 109 110 111 112 114 115 116 117 118 119 120 121 122 124

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

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Quantifying the impact of generics• Population: ISTAT projections 2005-2025 (intermediate

scenario) at year end.

• Impact of generic substitution: 40% price reduction, calculated at year end for each molecule going off patent on its average cost by age group (source: ASSET)

• The calculated reduction is then carried forward to the following year.

Patent expiration date2006 start A02BA03 A02BC03 A10BB12 C09BA01 G04CA02 J01CR01 J01CR02 M01AC02 M05BA04 N03AX09 N06AB06 Total 40% off end 2006

0-14 38.64 0.0000% 0.0471% 0.0032% 0.0021% 0.0251% 0.0187% 6.1652% 0.0003% 0.0079% 0.3506% 0.0408% 6.6610% 3.9966% 37.6115-24 42.98 0.0010% 0.8133% 0.0279% 0.0199% 0.1512% 0.0524% 5.8104% 0.0081% 0.0988% 1.2858% 1.7045% 9.9733% 5.9840% 41.2725-34 57.70 0.0012% 0.6058% 0.0208% 0.0148% 0.1126% 0.0390% 4.3280% 0.0060% 0.0736% 0.9578% 1.2697% 7.4293% 4.4576% 55.9935-44 85.64 0.0045% 0.7995% 0.0546% 0.0240% 0.1231% 0.0315% 3.1438% 0.0089% 0.0741% 0.5814% 1.2909% 6.1365% 3.6819% 83.5445-54 147.93 0.0109% 0.8867% 0.1702% 0.0595% 0.2999% 0.0226% 1.8354% 0.0114% 0.1578% 0.2755% 0.9277% 4.6576% 2.7946% 145.1755-64 288.84 0.0112% 0.8788% 0.2366% 0.1546% 1.0469% 0.0175% 0.9953% 0.0121% 0.4379% 0.1241% 0.5823% 4.4973% 2.6984% 283.6465-74 465.95 0.0100% 0.9625% 0.2342% 0.2460% 1.5817% 0.0162% 0.6080% 0.0135% 0.7837% 0.0555% 0.4978% 5.0091% 3.0054% 456.6175> 546.61 0.0088% 1.3595% 0.1699% 0.3137% 1.4274% 0.0156% 0.4676% 0.0127% 0.9847% 0.0390% 0.5816% 5.3804% 3.2282% 534.85

01/09/2006 01/04/2006 01/08/2006 01/12/2006 01/09/2006 01/10/2006 01/07/2006 01/02/2006 01/12/2006 01/08/2006 01/01/2006

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Time distribution of patent expiration

0

11

20

28

22

13

20

15

8 87

34

0 0 0 0 0 0 0 00

5

10

15

20

25

30

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

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Expected impact of patent expirationby ATC class 2006-2017*

ATC Number of Cost savedclass products %

1st level off-patent classC 40 25.3%J 23 7.3%A 16 7.3%N 27 5.6%G 8 3.2%L 9 2.7%M 7 2.0%R 10 1.9%B 3 0.9%S 7 0.8%H 2 0.5%V 1 0.1%D 6 0.1%P 1 0.0%

Total 160 24.3%

* Patent expiration dates provided by AIFA

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Top ten molecules off patent by 2017

2005 % total cost 2025 % total costC10AA05 2.2777% atorvastatin 1 C10AA05 2.3445%C10AA01 2.2215% simvastatin 2 C10AA01 2.3165%C08CA01 1.9977% amlodipine 3 C08CA01 2.0945%A02BC01 1.9350% omeprazole 4 A02BC01 2.0054%A02BC05 1.2734% esomeprazole 5 C10AA03 1.2868%C10AA03 1.2387% pravastatin 6 A02BC05 1.2496%C09A05 1.1438% ramipril 7 L02BB03 1.2093%L02BB03 1.0975% bicalutamide 8 C09A05 1.1991%G04CA02 1.0468% tamsulosin 9 G04CA02 1.1138%C02CA04 1.0095% doxazosin 10 C02CA04 1.0515%A02BC03 0.9855% lansoprazole 11 C09DA04 1.0515%C09DA04 0.8762% irbesartan and diuretics 12 A02BC03 1.0240%Top 10 15.24% Top 10 15.8709%40% saved 6.10% 40% saved 6.3484%

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Generic substitution to offset theimpact of ageing population

0

20

40

60

80

100

120

2005

= 1

00

Ageing Ageing & generics Poly. (Ageing & generics) Poly. (Ageing)

Ageing 100 101 103 104 105 106 107 109 110 111 112 114 115 116 117 118 119 120 121 122 124

Ageing & generics 100 99 96 94 93 92 91 91 91 92 93 94 94 95 96 97 98 99 100 101 101

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

100

124

101

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Goodness of fit

0

20

40

60

80

100

120

2005 =

100

Ageing Ageing & generics Poly. (Ageing & generics) Poly. (Ageing)

Ageing 100 101 103 104 105 106 107 109 110 111 112 114 115 116 117 118 119 120 121 122 124

Ageing & generics 100 99 96 94 93 92 91 91 91 92 93 94 94 95 96 97 98 99 100 101 101

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

Polynomial regression model:y = 0.0846 x2 – 1.6324x + 100.19R2 = 0.8854

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Sensitivity to generics’ price

0

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40

60

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100

120

2005

= 1

00

Generic price 20% off Base case (40% off) Generic price 80% off

Generic price 20% off 100 100 100 99 99 99 99 100 100 101 102 103 104 105 106 107 108 109 110 111 112

Base case (40% off) 100 99 96 94 93 92 91 91 91 92 93 94 94 95 96 97 98 99 100 101 101

Generic price 80% off 100 97 91 84 82 80 77 76 76 76 76 77 77 78 78 79 80 81 81 82 83

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

112

101

83

124

No generics

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The actual trend 2005-2008E* seems to confirm the ageing generic model

0

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40

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120

2005

= 1

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Ageing Ageing & generics Actual

Ageing 100 101 103 104 105 106 107 109 110 111 112 114 115 116 117 118 119 120 121 122 124

Ageing & generics 100 99 96 94 93 92 91 91 91 92 93 94 94 95 96 97 98 99 100 101 101

Actual 100 100 95 93

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

* 2008 estimates based on Jan-Jun (-1.4% vs. 2007). Source: Il Sole 24 Ore

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Net effect of generic substitution:2001-2004 equilibrium

• ATC4 class growing:– 32.6% of drugs off-patent UP

leaders– 18.6% of drugs off-patent DOWN shift

• ATC4 class declining– 13.9% of drugs off-patent UP low impact– 34.5% of drugs off-patent DOWN shift

47%

53% sustainablecost reduction

potentialcost increase(therapy shift)

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Conclusions• Ageing and price are key determinants of pharmaceutical

demand in a state funded, open access social healthcare system like the Italian SSN.

• All else equal, by 2025 the expected ageing of the Italian population would increase the cost of the current public pharmaceutical coverage by 24%.

• Generic substitution could offset the upward trend driven by ageing.

• Cholesterol inhibitors and anti hypertensive agents could reduce the total cost of cardiovascular treatments by 25% .

• Due to time to patent expiration, the next couple of years would provide critical indications about the sustainability of the current pharmaceutical coverage provided by the SSN in Fascia A.

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All else equal… a major limitationof the age/generic substitution model

CHANGE

• Shift in pharmaceutical demand

• Accelerated shift to more expensive treatment options

• Physicians behaviour

• Patients behaviour

POLICY IMPLICATIONS

• Redefine levels of SSN coverage

• Outcome based prescribing guidelines

• Rationale prescribing

• Reduce moral hazard:– Link reimbursement to

compliance– Education: health as

capital