Background
Methods
Eligibility criteria
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Criteria 1 - Study design: We included only quantitative ex-ante studies that used structural modeling and simulations to derive quantitative predictions and ex-post studies that utilized empirical data and econometric techniques to quantify the effects of IP provisions in FTA on the importing country’s access to pharmaceuticals.
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Criteria 2 - Countries: We included studies that estimated the effects for low and middle-income countries. We used the World Bank classification to identify the low and middle-income countries [15].
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Criteria 3 - Time: We only considered post 2000 studies for inclusion. We note that the dateline to implement IP provisions under WTO’s TRIPS agreement is no later than 2000 for all countries except certain low and middle income countries. Most of the TRIPS-plus provisions in different bilateral FTAs are also a post-2000 phenomenon, for example the US-Jordan FTA (2000) and the US-Chile FTA (2004).
Information sources
Category (AND) | MeSH terms/Key words (OR) |
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Population | Developing Countries (MeSH), Low income countries, Least Developed Country (LDC), Middle income country |
Intervention | Free Trade Agreement, Trade treaty, TRIPS/TRIPS-plus IP/Intellectual Property Right (IPR) (MeSH), Patent Data exclusivity/protection |
Outcome | Medicine/Medicine Costs, Health Services Accessibility, Essential/supply & distribution, Access to medicines, Average/market price, Pharmaceutical Preparations/supply & distribution. |
Search results and selection process
Data items
Studies (1) | Objective (2) | Methodology (3) | Country and medicine(s) studied (4) | Sample size (5) |
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Chaudhuri, Goldberg, & Gia [16] | To investigate the impacts of pharmaceutical patents for quinolones on prices and welfare in India | Two stage budgeting framework. Outcomes: medicine prices, consumer and social welfare. Comparison groups: sub-segments of systemic anti-bacterials medicine prices before and after implementing stronger patent laws. | Country: India, Medicines: quinolone sub-segment of anti-infectives. | Sample: 300 largest firms, representing roughly 90% of domestic retail sales. Range: January 1999 to December 2000. |
Dutta [2] | To simulate the changes in consumer surplus, profits, market prices, and market quantities that would result from patent enforcement. | Structural model of demand, supply and entry. Outcomes: medicine prices, consumer and social welfare. Comparison groups: all pharmaceutical product prices before and after implementing stronger patent laws. | Country: India, Medicines: All pharmaceutical products sold in India. | Sample: The sample covers approximately 90% of all pharmaceutical sales in India; Range: 2001 to 2003. |
Akaleephan et al [17]. | To quantify the impact of TRIPs-plus provisions, especially the extension of market exclusivity of innovative medicines, in the proposed Thailand-USA FTA on medicine expense and medicine accessibility. | Simulation framework. Outcome variable: cost savings Comparison groups: generic medicines and innovative medicines under 10 years data exclusivity. | Country: Thailand; Medicines: 1136 International Non-proprietary Name (INN) of imported medicines. | Sample: 74 items out of 1136 INN; Range: 2000–2003 |
Azam [18] | To analyze the effects of TRIPS compliance on the prices, affordability and accessibility of pharmaceuticals in Bangladesh. | Use of different methods to analyze the different research questions posed in the paper: (a) doctrinal research for regulatory effects of TRIPS on pharmaceutical industry, (b) surveys, (c) case studies and interviews to analyze the expectation and perception regarding price, availability, affordability, etc., by the different stakeholders. | Country: Bangladesh; Medicines: All pharmaceutical products in Bangladesh. | Sample: 22 CEO interviews, top 20 medicines sales and top 10 medicines prices for time trend analysis. Range: Interview is from 2008, sales from 2008 to 09, price data for 1981 and 1991–92, and retail price survey for 2008–09. |
Chaves et al. [19] | To assess the impact of TRIPS-plus measures as outlined in Mercosur-EU FTA on the public health in Brazil, especially on the public procurement of medicines. | Intellectual Property Rights Impact Aggregate (IPRIA) Model Outcome variables: public expenditures, domestic sales of medicines in Brazil, The current Brazilian market is used as a base for the calculations. | Country: Brazil. Medicines: HIV/AIDS and Hepatitis C. | Range: ARV medicines if from 2008 to 2015, Hepatitis C medicines is from 2006 to 2016. |
Kessomboon et al. [20] | To measure the effects of US-Thai FTA on the access to medicines. | Model of Impact of Changes in Intellectual Property Rights (MICIPR) developed by Joan Rovira and jointly produced by the World Health Organization and the Pan- American Health Organization (WHO/ PAHO Region) Outcome variables: Different scenarios of patent extension and data exclusivity periods under the TRIPS-plus agreement. | Country: Thailand. Medicines: all active ingredients. | Range of projection is from 1992 to 2042, |
Studies | Objective | Methodology | Population | Sample data |
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Abbott et al. [21] | To assess the impact of stronger intellectual property protection in Jordan on the access to medicines | Mean and frequency comparison. Outcome: lag years in launching new medicines. Comparison groups: difference in years of lag in launching new innovative medicines in Jordan before and after the US-Jordan FTA. | Country: Jordan; Medicines: 46 essential medicines. | Sample: a sample of 29 of 46 essential medicines; Range: 1999 and 2004, pooled cross-section |
Alawi & Alabbadi [22] | To analyze the effect of data exclusivity on the pharmaceutical sector in Jordan before and after the implementation of data exclusivity. | Trend analysis Outcome variables: prices, sale values, sale volume and sales Comparison groups: generic medicines, only originator medicines, originator to generic medicines, and generic to originator. | Country: Jordan; Medicines: all pharmaceutical products in Jordan. | Sample: a sample of 140 products representing 36.8% of total sales value in 2010. Range: 2004–2010. |
Borrell [23] | To estimate the impact of patents on pricing of HIV/AIDS medicines in low and middle income countries in the late 1990’s. | Quasi-experimental study is used to study how the outcome variable differs for treatment groups and comparison groups that are not randomly assigned. Treatment group: all the country medicine pairs for which any ARV medicine is under a patent regime Comparison group: all the country-medicine pairs for which the medicine is not under a patent regime. Outcome variable: price | Country: Developing and least developed countries. Medicines: HIV/AIDS’ ARV medicines. | Sample: 21 developing and least developed countries with two groups of developing and low income countries, and 15 ARVs. Range: January 1995 to June 2000. |
Duggan, Garthwaite & Goyal [24] | To estimate the effects of the 2005 implementation of a product patent system in India on pharmaceutical prices, quantities sold, and market structure. | OLS regressions Outcome variables: prices, sales volume difference specification and event study framework, where OLS regressions with patent dummy that takes value 1 in post patent regime and 0 in pre-patent regime are estimated, to investigate whether there is any statistically difference in log prices | Country: India. Medicines: All single molecule medicines | Sample: approximately 5100 Indian stockists. Range: 2003q1 to 2012q2. |
Jung & Kwon [25] | To estimate the effect of stronger IPR on medicine access in low and middle income countries | Pooled cross-country multilevel techniques with subgroup analyses to identify factors both at country level and individual level that affect access to medicine and financial burden of purchasing medicines. | Country: all developing and least developed countries. Medicines: all medicines. | Sample: 35 countries, 660 to 38424households and 585 to 38,618 individuals. Range: 2002–2003. |
Kyle & Qian [26] | To examines how TRIPS affects new medicine launches, prices and sales using data from 59 countries of varying levels of development. | Difference-in-difference estimation framework Outcome variables: speed of launch or new medicines, price, sales volume | Country: 59 countries of varying degrees of development. | Sample: 716 medicine-country pairs linked with patents; Range: 2000–2013 for prices and units sold and 1990–2013 for launch of new medicines. |
Berndt & Cockburn [27] | To study the trade-off between stronger patent laws and early access to new medicines. | Survival analysis Outcome variable: sales volume, lag time of new medicine launch in India as compared to Germany and the U.S. due to Indian patent policies. | Country: India, Germany and USA; Medicines: new innovative medicines. | Sample: 184 new molecular entities approved by the US FDA. Range: 2000 to 2009. |
Shaffer & Brenner [28] | To estimate the effect of IPR provisions in the Central American Free Trade Agreement on access to low price generic medicines in Guatemala. | Price comparison Outcome variables: Price Intervention group: Medicines purchased by both private and public sector in Guatemala of those that received data protection due to IPR provisions in the CAFTA Comparison group: Brand or generic equivalents that have no data protection. | Country: Guatemala. Medicines: all medicines available through various public-sector health programs. | Sample: 730 medicines on the Open Contract list. Range: 2005–2007. |
Results
Ex-ante studies
Ex-post studies
Measuring “access to medicines”
Studies | Main outcomes /dependent variables | Results | Examples of Limitations | |
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Data | Method | |||
Chaudhuri, Goldberg, & Gia [16] | Impact of patent enforcement on consumer surplus, profits and social welfare in the quinolone product market in India. | The total annual welfare losses to the Indian economy due to stringent patent laws in the quinolone sub-segment would be on the order of Rs. 20.16 billion or US$450 million. | Data range used to estimate the demand and supply parameters are from 1999 and 2000 and needs to be updated to obtain more accurate measures. The estimated demand elasticities are some proxies of real demand elasticities. | Elasticities are estimated using a reduced form demand system rather than estimating structurally. Further, assumes households are homogeneous in terms of income. Certainly, this assumption is not realistic |
Dutta [2] | Impact of patent enforcement on prices, consumer surplus, profits and social welfare in 43 medicines market in India. | Profits for the patent holding firms increases from 0 to 3412% due to patent enforcement. | Data range used to estimate the demand and supply parameters are from 2001 and 2003 so data needs to be closer to 2005 to have better measure of elasticities and welfare. | Demand estimation does not include some important variable such as income of consumers. This implies that the elasticities are the same across different income levels, which is not a realistic assumption. |
Akaleephan et al. [17] | Impact of data exclusivity on the accessibility, prices and total cost savings in 74 out of 1136 International Non-proprietary Name (INN) imported medicines in Thailand. | Consumption volume would be lower by 34.9% without generics | This paper uses only public sector data and since public sector might have higher bargaining power and costs of medicine for public sector are expected to very different from costs of medicine in private sector, | A simple linear regression is used to estimate market share following the generic entry. This simple linear regression would be biased due to omitting many important factors, which will also give biased estimates of cost savings. |
Azam [18] | Impact of patent enforcement on sales, demand and prices in pharmaceutical sector in Bangladesh. | 77% of surveyed executives of pharmaceutical firms either agreed (54%) or strongly agreed (23%) that the prices of medicines have increased and will go up further as a result of TRIPS compliance. | Data points used in trend analysis have at least 10 years’ interval, so by comparing price changes will not lend any sensible conclusion. | Trend analysis with large interval is not a rigorous way to find the effect of TRIPS provisions on medicine prices, many confounding factors can influence medicine prices. |
Chaves et al. [19] | Impact of patent extension and data exclusivity on expenditure of HIV/AIDS and Hepatitis C medicines in Brazil. | Under the scenario that patent extension and 8 years’ data exclusivity are both adopted as proposed by EU, the ARV expenditure will increase by 69% and the expenditure on Hepatitis C will increase by more than 3000% in 35 years. | Simulations of alternative scenarios only considers the present average growth rate of expenditure on these two types of medicines, however, other factors such as change in demographics and disease prevalence rate would significantly affect the expenditure on these medicines. | |
Kessomboon et al. [20] | Impact of patent and data exclusivity extension on quantity and expenditure of all active ingredients in Thailand | Combining 10 years of patent extension with 5 years of delayed generic entry due to data linkage and 10 years of delayed generic entry due to data exclusivity will increase price index by 67%, expenditure to 23.6 billion USD over a 20 year period. | Outcome variable ‘expenditure’ is calculated by assuming a constant annual consumption growth of 12%, using actual expenditure for all the available years to calculate consumption growth would increase the precision of expenditure projection. | Projection does not consider other factors such income or population growth or change in demographics that might significantly change demand for medicines and hence the prices. |
Studies | Main outcomes /dependent variables | Results | Examples of Limitations | |
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Data | Method | |||
Abbott et al. [21] | Impact of market exclusivity and trade data protection on the number of generics, aggregate sales, and average price per daily dose for 29 essential medicines in Jordan. | Total cost of medicines increases from 81 million USD in 1999 to 125 million USD in 2004, a 53% increase in the total cost of medicines. Adjusting for increased sales volume and inflation, this represents an increase of 17% in the total cost of medicines. | Data used in this paper is for the year 1999 and 2004 and Jordan ratified new patent law in December 1999, registration of generic medicines may be artificially high in 1999 before the new patent law had become effective. | Comparing mean difference is a poor way to ascertain the rise in prices or costs to IPR provisions as equilibrium prices and quantity are influenced by many factors such as domestic and local economic factors, demographics, etc. |
Alawi & Alabbadi [22] | Impact of data exclusivity on sales and cost saving in all pharmaceutical products in Jordan. | Following the expiration of data exclusivity, the medicine prices fall by about 56%. | Data is lacking both before and after the policy change. There is not even enough data for the trend analysis. | Trend analysis is not appropriate for causal analysis of data exclusivity. Outcome variables in this case are generally upward trending due to growth in population and disease prevalence and not due to policy change. |
Borrell [23] | Impact of patent enforcement on pricing of HIV/AIDS’ ARV medicines in developing and least developed countries. | Medicine bundles containing at least one original medicine in a patent regime are on average priced 70% higher than medicine bundles containing only local copies marketed in no patent regimes. | Countries where there were patent laws in the pre 2000 era may not have same economic conditions and so treatment and control groups may also significantly differ in other dimensions in addition to patent policy. | Calculating price as sales divided by quantity is a very poor measure of actual prices or costs borne by the patients as HIV/AIDS medicines are publicly provided in developing countries. |
Duggan, Garthwaite & Goyal [24] | Impact of patent laws on the average price, number of daily doses, and the number firms in India. | A small, negative, and statistically insignificant decrease (5.4%) in the quantity sold following a patent approval of a medicine | Regression analysis does not control for any economic or demographic variables that might significantly affect the outcome variables, prices, sales, quantity sold. | |
Jung & Kwon [25] | Impact of patent enforcement on the access to prescribed medicines and catastrophic medicine expenditure in developing countries. | A higher level of IPR protection is associated with higher probability that patients could not get access to their prescribed medicines. | Sample used in the analysis is from 2002 to 2003, when TRIPS implementation was not binding for the developing countries. Lack of access to medicine before 2005 cannot be attributed to IPR protection for most of the countries in the sample. | GP index is a poor measure of IPR protection as it does not consider the actual level of implementation of IPR laws. |
Kyle & Qian [26] | Impact of patent and data exclusivity on launch speed, price level and quantity sold of medicines in 59 countries of varying degrees of development. | Products with expired patents sell in lower quantities and at lower prices than those that are on patent, but at higher prices and quantities relative to those that were never protected. | Difference-in-difference framework may not be a good framework to measure the effects of IPR on access to medicines as control and treatment groups of countries are very different. | |
Berndt & Cockburn [27] | Impact of patent laws on the difference in launch dates of new medicines in India, Germany and USA. | Almost one-quarter of the sample medicines were not available in India within the 10 years of their worldwide launch. | Launch date of medicines in a country is estimated by sales rather than using the official data of medicine approval. | No analysis is conducted to show that the difference in launch lag is due to IPR. The launch lags seem to be same before and after 2005 and so launch lag might be driven by other factors, which are not controlled in this study. |
Shaffer & Brenner [28] | Impact of data protection on prices of all medicines provided through public sector health programs in Guatemala. | Medicine prices under data protection increase by 342 to 846% compared to equivalent generic medicines. | Only uses data of prices of public sector medicines, but prices could be higher or lower in private sector. | This paper uses trend analysis, but it does not test any structural break due to data protection and hence simply calculating changes in prices over time cannot be entirely attributed to the change in policy regime. |