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Understanding medication adherence using stated-preference data
Gonzalez, J., Poulos, C., & Mollon, P. (2014). Understanding medication adherence using stated-preference data. Value in Health, 17(7), A492-A493. https://doi.org/10.1016/j.jval.2014.08.1460
OBJECTIVE: More than half of people who have experienced a myocardial infarction (MI) are not adherent to their medication regimen, which leads to poorer health outcomes. We used a stated-preference (SP) study to examine factors that could influence patient compliance to prophylactic cardiovascular treatments, and discuss practical issues in using SP methods to explain medication adherence.
METHODS: Preference data for treatments that lower the risk of cardiovascular events were collected from 464 respondents in the United States with self-reported history of MI using a discrete-choice experiment (DCE). All respondents answered 11 judgment questions that presented a pair of virtual patients who were prescribed different treatments defined by: reduction in the risks of nonfatal MI and fatal MI, treatment-related risk of serious infection, mode and frequency of administration, and monthly medication cost. Half of the choice questions asked respondents to select the treatment to which they would most likely be nonadherent. The other half asked respondents to state which of two virtual patients was better off after learning how adherent each was to each medication. Limited dependent-variable models were used to estimate weights indicating the impact of treatment and respondent characteristics on stated-adherence and quantifying the stated impact of nonadherence on respondents- well-being.
RESULTS: Results indicated that reductions in the risk of a nonfatal MI had the largest effect on stated adherence, followed by medication cost, the risk of serious infection, and lastly mode and frequency of administration. Results also show that reductions in compliance had a significant impact on the perceived overall benefits of prophylactic treatments.
CONCLUSIONS: We find that both clinical and nonclinical factors can impact treatment adherence, suggesting that the flexibility to include a variety of factors with SP models can be useful in understanding patient compliance.
