Patients preferences for generic and branded over-the-counter medicines: An adaptive conjoint analysis approach

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Abstract

Background: Despite increased use of generic medicines, little is known about either the attitudes of patients towards them or the decision-making process surrounding them. Young adults use over-the-counter (OTC) analgesics relatively often. Objective: To assess the preferences of patients for generic and branded OTC pain medicines, to identify clusters with different preference structures, and to estimate the price elasticity of a generic alternative among university students. Methods: Finnish university students (n = 256; students in courses at the Helsinki School of Economics) responded to an adaptive conjoint analysis (ACA) questionnaire on the choice between branded and generic OTC ibuprofen products. Product attributes of price, brand, onset time of effect, place of purchase and source of information were included in the questionnaire on the basis of the literature, a focus group and a previous pilot study. Several socioeconomic and health behavior descriptors were employed. Individuallevel utility functions were estimated, preference clusters were identified, and the price elasticity of the generic medicine was assessed. Results: Five clusters with characteristic individual-level preferences and price elasticity but few differences in socioeconomic background were detected. Approximately half of the respondents were strongly price sensitive while the others had other preferences such as brand or an opportunity to buy the medicine at a pharmacy or to have a physician or a pharmacist as an information source. Conclusion: The study provided new information on the concomitant effects of brand, price and other essential product attributes on the choice by patients between branded and generic medicines. © 2009 Adis Data Information BV. All rights reserved.

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Halme, M., Linden, K., & Kääriä, K. (2009). Patients preferences for generic and branded over-the-counter medicines: An adaptive conjoint analysis approach. Patient, 2(4), 243–255. https://doi.org/10.2165/11314130-000000000-00000

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