Transcript No Slide Title

The PAHO model for estimating
the impact of changes in
Intellectual Property Rights
Joan Rovira, Universitat de Barcelona
Ismail Abbas, Universitat Politècnica de Catalunya
Content
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Justification and objectives of the
project
Model specification and description
Illustrative results for Colombia and
Malaysia
Discussion
Acknowledgement:
This project has been funded by the PAHO-WHO
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Justification and objectives
of the project
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The changing environment
of IPR regimes
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Global trends and pressures towards an
international upwards harmonization of
IPR to developed countries’ standards
Regional agreements: Single European
Market-EU, NAFTA
The TRIPS agreement
Bilateral agreements
National implementation of agreements
and other policies
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The impact of changes in
IPR regimes might affect, with
varying levels of uncertainty:
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Market exclusivity - competition
Prices
Expenditure
Consumption – access – health
Foreign investment
Domestic production, employment
Imports – exports - balance of trade
R&D and innovation (global and domestic;
general and on country-specific diseases)
Technology transfer
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The timing of the impacts of
changes in IPR provisions
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Short-term
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Test data exclusivity
Conditions for compulsory licensing
Date of enforcement, transitory periods
...
Long term
(we’ll all be dead, according to J M Keynes?)
 Duration of patent (>20years)
 Scope of patentability
 ...
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Information needs of decision-makers
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Decision-makers, such as the negotiators of a trade
agreement, must often consider and take quick decisions
on proposals related to IPR, which might have a substantial
impact on the future access to pharmaceuticals and,
ultimately, on the health status in their countries.
Although it is increasingly apparent to most developing
countries’ negotiators that stronger (TRIPS plus) patent
protection is likely to have negative net effects on drug
accessibility and health status, trade agreements often
require trade-offs in a given sector in order to obtain
advantages (e.g. tariff reductions to agricultural exports) in
other sectors.
If concessions have to be considered or made in a given
sector, it is important to be able to identify and quantify as
precisely as possible the future impact, in order to compare
it with the expected benefits from other sectors.
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Objectives of the project
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To develop a user-friendly tool for
decision-makers that allows them to
estimate the future impact of changes in
IPR and related pharmaceutical policies
To collect the required information,
implement and test the model in several
countries (initially, Colombia, Costa Rica
and Guatemala, later Malaysia, Vietnam,
India, … )
To assess the information and knowledge
gaps and to propose an agenda for future
research
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Model specification
and description
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Development of a Model to Assess
the Impact of Changes in IPR
Version: 17-11-2006
Content
Justification and objectives of the project.
Introduction
Literature review
When to evaluate the impact
Options to evaluate
How to evaluate the impact
Impact variables
Presentation of the model of Impact of Changes in the IPR
Structure and general characteristics of the model of Impact of Changes in
the IPR
Operation of the model
Translating IPR changes into model parameters
Definition and calculation of impacts
Key assumptions of the model
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Development of a Model to Assess
the Impact of Changes in IPR
Version: 17-11-2006
Appendix 1: Model Specification
Appendix 2: Data required for the application of the IPR Impact Model to a
given country
Appendix 3: Suggested Reporting Format for Country Analysis
Appendix 4: Abbreviated User’s manual
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The PAHO IPR Impact Model
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A general model, not a country
application
Deterministic (sensitivity analysis)
Macroeconomic (agreggated)
Computer assisted simulation model
(Excel)
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The model estimates the impact of alternative scenarios
on pharmaceutical expenditure, consumption and market
share of the domestic industry over a defined time
horizon.
The baseline scenario estimates the evolution of the
variables under the existing IPR rules.
The alternative scenarios reflect the evolution of the
variables under other combinations of IPR rules that
might result from TRIPS enforcement, bilateral trade
agreements or autonomous IPR and pharmaceutical
policies, in general. The impact of a given alternative
scenario x is defined as the difference in a given outcome
variable between the baseline and the alternative scenario.
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Final impact variables
Impact of scenario x:
IMVx: Increase in expenditure/sales/market value
rCx: Relative reduction in consumption (in units)
RMSDx: Reduction in the sales of the domestic industry
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The model first computes the number and proportion of
AI under exclusivity for all years over the time horizon:
The data input required includes:
TAPto: Number of AI (active ingredients) on the market
at the beginning of the initial year
AIi: Number of AI entering the market each year
AOi: Number of AI exiting the market each year
AIPPi: Number of AI entering the market with patent
protection each year
AIDPi: Number of AI entering the market with test data
protection each year
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The duration of the effective exclusivity time derived from
a patent is computed as:
EEP = PD – DT + PDE + TTC + DGE,
where:
PD is the nominal patent duration
DT is the time elapsed between patent filling and
registration/market authorization
PDE is the extension of patent duration due to
compensation for delays in patent approval, which
might affect a certain proportion of AI, pPDE)
TTC is the time elapsed between the patent expiration
time and generic market entry (associated to Bolar
provision)
DGE is the delay in generic market entry due to the
linkage between registration and patent, which
might affect a certain proportion of AI, pDGE)
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The model then computes the average price differential of
medicines between the baseline and any alternative
scenario by means of a price index that takes the value 1
each year of the baseline scenario:
(Alternative scenarios x are indicated by the superindex (x)
Pi = 1
Pxi = [1+( pexi – pei)*(RPec – 1) + (1- pexi)*(pdi- pdxi)*(RPbd – 1)]
pei: Share of the relevant market under exclusivity in year i
RPec: Relative weighted average price of an AI under exclusivity
(APe) vs. its price under competition (APc)
pdxi: share of branded products in the non-exclusive segment
RPbd: Relative weighted average price of an branded vs. unbranded
(INN) AI
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Reduction in consumption (quantities demanded) and
expenditure increase are taken as proxis for reduction in
access caused by price increases. The demand function is
assumed to have a constant price-elasticity, e, and the
following expression:
q = k pe
where
q: units demanded
k: constant
p: price (index)
e: price-elasticity of demand; e = (dq/q)/(dp/p)
The price-elasticity of demand indicates the relative
change in the quantity demanded as a response to a 1%
change in price.
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The expenditure/sales/market value in the baseline
scenario is computed as:
MVi = MVi-1*(1+α)
The expenditure/sales/market value in the alternative
scenario x is computed as:
MVix = MVxi = MVi* Pxi e+1
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Impact of a change of the price on
consumption and expenditure
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The reduction in market sales of domestic industry under
scenario x, RMSDxi is
computed as:
RMSDx i = MVDi - MVDxi
= kde* pei* MVi + kdc*(1-pei)* MVi
- (kde*pei* MVxi + kdc*(1-pexi)* MVxi)
where
kde: market share of domestic industry in markets under exclusivity
kdc: market share of domestic industry in markets under competition
MVDi: Market sales of domestic industry in year i
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The main assumptions of the model are:
1. Under the alternative scenario x, the units of the
products now under exclusivity, (pexi – pei), will attain a
higher relative price than under competition, Rpec.
2. The market share (in monetary terms) of each AI is the
same for all AI throughout their market life
3. Competition in an AI market begins immediately or
some years after patent protection and test data
exclusivity end.
4. The average price of an AI instantaneously drops by a
given fixed proportion when competition starts.
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5. The model assumes that the domestic industry will
maintain all the time and under any scenario the initial
market share in the markets under exclusivity and under
competition.
As it is likely to have a higher share in the competitive
market, as long as the competitive market decreases in
relative terms, the share of the domestic industry in the
total market will come down. The absolute value of this
market share cannot be unambiguously predicted as it
depends, as well, on the growth of the market.
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Preliminary results for Colombia
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The time horizon for the simulation is 2005-2050.
In the baseline scenario the share of the market under
exclusivity rises until 2019 and remains stable thereafter at
43%
If patent duration is extended to 21 years, the market share
under exclusivity levels off a year later, in 2020, at a 47%,
and pharmaceutical expenditure is 5.7% higher than in the
baseline scenario.
Increasing by one year the duration of the exclusivity for
test data protection would imply a stabilization of the
market share under exclusivity in 2019 at 44% and a 1.5%
increase in expenditure over the baseline scenario.
If Colombia had not enforced product patent protection in
2000 and test data protection in 2002, it might have saved
an estimated 69% of the pharmaceutical expenditure over
the simulated period.
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IPR Impact Model
Preliminary Results in Malaysia
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Final purpose of analysis was to
estimate the likely impact a stronger
IPR protection on medicine prices in
Malaysia if Malaysia signs the USMalaysia FTA bilateral agreement
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Preliminary results based on
currently available data
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The time horizon for the simulation is 2007-2047
If all standard USFTA provisions are accepted, the
worst case scenario simulated would result in
increase in pharmaceutical expenditure of 46% or
a reduction in consumption of 56%
If data exclusivity alone is introduced, the worst
case scenario simulated would result in a
reduction of consumption by 50% or an increase
in expenditure of 38%
If the proportion of medicines that are patented
increases, the worst case scenario simulated is a
reduction in consumption of 36% or an increase
in expenditure of 28%
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Discussion
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The model has proved easy to understand and
use by decision-makers with no previous
experience in simulation models
Most of the information required for running the
model was available and relatively easy to collect
in the pilot test countries
The model results are very sensitive to the data
and assumptions on the initial number of AI, and
of those entering and exiting the market. The
number of existing AI should be adjusted in order
to ensure that it reflects the number of AI in the
market that have a market share similar to the
future new entrants.
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Discussion
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Additional analyses are also required on the price differential
of AI in competitive markets and in markets under exclusivity
conditions
Although the results look plausible, it is necessary to
improve the evidence base by undertaking systematic
reviews and, when required, new analyses of the impacts of
IPR changes.
The values of some of the parameters that define the
scenarios in the model directly reflect the options of the
decision maker, e.g. duration of the patent or year of
enforcement of pharmaceutical patents.
For other parameters the values are less straightforward,
and will require specific studies and subjective technical
judgments. E.g. how will an hypothetical broader scope of
patentability affect the proportion of AI that will enter the
market with patent protection?
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Generic Penetration of Brand Market:
Units Sales and Price per Unit
6 Months
Exclusivity
(Generic)
Generic Price Competition
(More Firms = Lower Prices)
Commodity Equilibrium Price Reached
(Modest Price Inflation Expected)
100%
Generic Units as
% of Total Unit Sold
90%
80%
70%
60%
50%
40%
30%
Generic Price as
% of Brand Price
20%
10%
0%
0
3
6
9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60
Months after First Generic Entry into Market
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SOURCE: Compiled by the PRIME Institute, University of Minnesota from data found in Kidder, Peabody
Estimating the price differential
exclusivity-competition
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The price of original drug under exclusivity is 100
The average price of the generics after three
years of patent expiration is 25
Market share of generics after three years is 85%
and that of the original product, 15%
The final average price of the AI is:
100 x 0,15 + 25 x 0,85 = 38,75
The price of the AI under exclusivity is 2,5 times
that of the price under competition
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Source: Danzon and Furukawa, Health Affairs, 2003
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Estimation of relative
average prices of drugs
Sales of unbranded generics (UBG) / Total sales
--------------------------------------------------------------------- =
Units of unbranded generics (UBG) / Total units
Units of UBG x average UBG price / Total units x average price
= -------------------------------------------------------------------------------------------- =
Units of unbranded generics (UBG) / Total units
average UBG price
= --------------------------- = RELATIVE PRICE OF UBG
average price
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Estimation of potential
savings from an INN policy
POTENTIAL SAVINGS (in % of total sales) =
SHARE OF BRANDED GENERICS SALES
x ( 1 - RELATIVE PRICE OF UBG / RELATIVE PRICE OF BG )
x 100
Assumptions:
In the absence of brand names, competition would reduce
the present prices of BG to the level of UBG.
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Diferenciales de precios de los medicamentos con patente y con marca de fantasia
Canada Chile Francia Alemania Italia Japón México R.U. U.S.
Precio relativo marca
Precio relativo DCI
Diferencial de precio marca/DCI
Reducción potencial del precio
Ahorro potencial en % de las ventas
0,43
0,45
0,97
-0,03
-0,45
1,62
0,35
4,60
0,78
32,88
0,52
0,43
1,22
0,18
2,00
0,58
0,50
1,16
0,14
3,50
Precio relativo proveedor único
2,84 3,25 1,86
Precio relativo competencia
0,95 1,92 0,56
Diferencial de precio proveedor único/competencia
2,98 1,69 3,35
Reducción potencial del precio
0,66 0,41 0,70
Ahorro potencial en % de las ventas
39,64 33,17 26,11
2,67
1,08
2,46
0,59
41,17
0,63
0,50
1,25
0,20
4,00
0,50
0,25
2,00
0,50
8,00
0,80
0,33
2,40
0,58
14,00
1,52 1,97
0,88 0,81
1,72 2,44
0,42 0,59
18,45 28,98
1,39
1,09
1,28
0,22
14,70
0,92
0,32
2,83
0,65
7,11
0,57
0,23
2,51
0,60
4,82
3,38 2,50
0,69 0,93
4,92 2,69
0,80 0,63
64,54 20,43
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Impact of generic competition on
pharmaceutical prices (1)
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(A substantial share of) the reduction of
ARV annual treatment costs between 2000
and 2003 from US$ 10.000 to 300 should
(probably) be accounted to generic
competition.
A recent WHO-HAI study, The price of
medicines, shows ratios between generic
and branded originals in the range of 1:2
(Armenia), 1:3 (Sri Lanka), and from 1:2
up to 1:7 in Kazakhstan
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Impact of generic competition on
pharmaceutical prices (2)
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A recent study applied WHO-HAI
methodology in Malaysia
Survey was conducted from October-2004
to January 2005.
48 drugs were surveyed by using
systematic sampling technique in four
geographical regions of Malaysia
Babar et al, A survey of medicine prices in
Malaysia by using WHO/HAI methodology.
UCSI-USM, October 2005.
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Median MPRs of IB, MSG and LPG in Public for
Procurement Sector, Private Sector Retail Pharmacies
and Dispensing Doctors Sector
Drugs
Public
Sector
PSRP
DDS
Innovator
Brands
2.41
16.35
15.40
Most Sold
Generic
1.56
6.89
7.76
Lowest
Price
Generic
1.09
6.57
7.76
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Private Retail Pharmacies
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
For additional information, please
contact Joan Rovira
[email protected]
Thanks for your attention
45
Fixed parameters
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YI: Initial year of simulation period
YL: Last year of simulation period
TAPtn: Number of existing active ingredient (AI) in the market in year
tn; tn <YI
AIi: Number of AI entering the market in year i; i > tn
AOi: Number of AI exiting the market in year i; i > tn
DT: Time from patent filling (or approval) of an AI to market
registration (approval) of the original product
MVto: Total sales/expenditure of the relevant market in year to
α: annual rate of growth of MV
RPec: Relative weighted average price of an AI under exclusivity (APe)
vs. its price under competition (APc)
d: discount rate
kde: market share of domestic industry in markets under exclusivity
kdc: market share of domestic industry in markets under competition
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Scenario-dependent parameters
YP: initial year of product patent enforcement
pp: Percentage of patented (product patent) AI entering the
market in year i
PD: Patent duration in years
YDP: initial year of data protection enforcement
AIDPi: Number of AI entering the market in year i with test
data protection
DE: Duration of exclusivity due to test data protection
TTC: Time from patent expiration to generic competition
e: price-elasticity of demand
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Impact variables
Impact of scenario x:
IMVxi: Increase in total sales/expenditure of the
relevant market in year i.
RICi: Reduction in consumption (units) in year i
RMSDxi: Reduction in market sales of domestic
industry in year i
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ASEF ASIA-EUROPE FOUNDATION
CULTURES &CIVILISATIONS DIALOGUE
“8th Talks on the Hill”
Re-righting Intellectual Property: Economic and
Social policy challenges in Asia and Europe
Singapore, 7th-9th of May 2006
SELECTED ASEF ROUND TABLE
RECOMMENDATIONS:
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Allowing for greater
flexibilities in using TRIPS
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Many of the Europeans felt that their
governments should be more
forthcoming about telling the
developing countries that it is
acceptable for them to use the
flexibilities that TRIPS provides. In
addition to governments, companies
should also start to recognise the
flexibilities that are built in TRIPS –
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for example parallel imports.
Releasing the Pressure
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The group also called upon
governments and patent companies
to refrain from using pressure tactics
that result in developing countries
adopting higher standards of
intellectual property protection and
enforcement than they need to or are
able to.
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Understanding the depth of
Intellectual Property
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Many of the participants from
developing countries recognised the
need to gain more concrete advice
before signing on the treaties such
as the Patent Cooperation Treaty
(PCT) and indicated that their
governments need to realise that
intellectual property is far more
important and pervasive than
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previously thought.
Distinguishing between
obligations and pressures
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Linked to this point was the need for
developing countries to focus on
dissecting much of the information that
they are receiving from the various
international organisations as well as
other governments into obligations and
pressures. In doing so, the countries
would be better able to more positively lay
out their policy in alignment with their
priorities and national interest.
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Diversifying the decisionmaking process
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National governments should diversify the
decision-making process on treaties and
other agreements linked to intellectual
property. This comes with a growing
recognition that this issue can no longer
be limited to a few specialists. For
example, professionals and decisionmakers from the health sector should be
involved in the discussion and negotiation
of both FTA and internal policies when
they are likely to have an impact on the
price and access to medicines and health 55
services.
Value in involving civil
society at multilateral
forums
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Members of the group recognised
that independent civil society has a
useful role to play at the multilateral
level and their involvement and
views should be better taken into
consideration in the decision-making
processes within the WTO and WIPO.
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
For additional information, please
contact Joan Rovira
[email protected]
Thanks for your attention
57