Studies | Objective | Methodology | Population | Sample data |
---|---|---|---|---|
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. |