A discrete choice experiment to assess patients' preferences for HIV treatment in the urban population in Colombia.

Aim: This study aimed to assess patients' preferences for HIV treatment in an urban Colombian population.Methods: A Discrete Choice Experiment (DCE) was conducted. Urban Colombian HIV patients were asked to repetitively choose between two hypothetical treatments that differ in regard to five attributes 'effect on life expectancy', 'effect on physical activity', 'risk of moderate side effects, 'accessibility to clinic' and 'economic cost to access controls'. Twelve choice sets were made using an efficient design. A Mixed Logit Panel Model was used for the analysis and subgroup analyses were performed according to age, gender, education level and sexual preference.Results: A total of 224 HIV patients were included. All attributes were significant, indicating that there were differences between at least two levels of each attribute. Patients preferred to be able to perform all physical activity without difficulty, to have large positive effects on life expectancy, to travel less than 2 h, to have lower risk of side-effects and to have subsidized travel costs. The attributes 'effect on physical activity' and 'effects on life expectancy' were deemed the most important. Sub-analyses showed that higher educated patients placed more importance on the large positive effects of HIV treatment, and a more negative preference for subsidized travel cost (5% level).Limitations: A potential limitation is selection bias as it is difficult to make a systematic urban/rural division of respondents. Additional, questionnaires were partly administered in the waiting rooms, which potentially led to some noise in the data.Conclusions: Findings suggests that short-term efficacy (i.e. effect on physical activity) and long-term efficacy (i.e. effect on life expectancy) are the most important treatment characteristics for HIV urban patients in Colombia. Preference data could provide relevant information for clinical and policy decision-making to optimize HIV care.