Objectives: To assess co-relation of GDP per capita (purchasing power parity) on pharmaceutical pricing. MethOds: Based on empirical research, 18 drugs were selected and grouped into seven therapeutic categories: (1) Blood Based Disorders; (2) Cardiovascular Disorders; (3) Inflammatory Disorders; (4) Oncology; (5) Respiratory Disorders (only fluticasone); (6) Diabetes; and (7) Viral Diseases Price per unit (mg, IU, and U; at ex-factory level) data was collected from IHS PharmOnline International (POLI) Database across 41 countries (States) from 2007 to 2012. Prices were converted into Euros on a yearly exchange rate basis and adjusted for inflation. Additionally, GDP data was collected from World Bank for the same period. We fit the regression equation for the log price per unit (dependent variable), log GDP per capita, generic status, strength, percentage of population aged 65 and above, an indicator for the US market, and year (independent variables) as follows: Y (Price per Unit) = α + ∑ β i * X i + ε . Results: eltrombopag (-0..00+.215, n= 413), Stocrin (-12.75+.803, n= 556) and Truvada (-5.08+.157, n= 168) had statistically significant GDP (PPP) coefficients at the 0.01 level, whereas Tasigna, bevacizumab, dabigatran, rivaroxaban, exenatide, liraglutide, saxagliptin, and interferon alpha were not significant at 0.01 level. cOnclusiOns: Our model finds varying degrees of co-relation between GDP per capita (PPP) and price per unit. Nonetheless, sitagliptin, cetuximab, filgrastim, Stocrin, Truvada, and adalimumab exhibited highest co-relation; they are thus most differentially priced.
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