Potential Bias in Medication Adherence Studies of Prevalent Users

Studies have shown that increased medication cost sharing is associated with reductions in medication adherence among prevalent users (Goldman, Joyce, and Zheng 2007), yet the magnitude of adherence reductions has varied across studies. Prevalent users of a medication are patients with a history of filling a medication of interest within a defined lookback period (e.g., 12 months) prior to the copayment change (Ray 2003). Researchers interested in examining medication adherence of prevalent users typically utilize pharmacy administrative data both as a source from which to apply inclusion criteria as well as the substrate data from which adherence measurements are calculated. Prevalent use is typically specified within a defined duration of a lookback period, a number of intervals within the lookback period (e.g., two 6-month periods), and a minimum number of medication fills per interval. Such specifications may vary from inclusive (e.g., one medication fill in the prior 12 months) to restrictive (e.g., two fills within the prior 6 months). The impact of different durations of the lookback periods have been examined in risk adjustment studies (Zhang, Iwashyna, and Christakis 1999; Preen et al. 2006) and in studies identifying incident, or new, medication users (Gardarsdottir, Heerdink, and Egberts 2006). However, the impact of applying different inclusion criteria for identifying prevalent medication users has not been examined previously and is a potential source of between-study difference in estimated adherence changes. This has been demonstrated in two prior evaluations of a $5 VA copayment increase on statin adherence. Both studies defined prevalent users as patients who filled one or more medications in the quarter prior to the copayment change (Doshi et al. 2009; Maciejewski et al. 2010a). One study also required 1+ fills within the 24 months prior to the copayment increase (Doshi et al. 2009) for inclusion, whereas the second study also required 1+ fills in the 4–12 months prior to the copayment increase. The first study with the longer (24-month) medication fill criteria found a significant reduction in statin adherence (Doshi et al. 2009). Another study (Maciejewski et al. 2010a) found no significant reduction, suggesting that the chosen lookback period and criteria may unduly influence findings or inferences. Studies of medication adherence have applied different lookback and historic fill criteria to define prevalent users (Table 1), which may have contributed to the variability in the estimated adherence response across studies. Table 1 Summary of Prior Literature on Medication Inclusion Criteria for Identifying Prevalent User Cohorts in Copay Change Evaluations Alternative inclusion criteria are likely to retain differing proportions of poorly adherent, episodic, and highly adherent patients, as well as differing proportions of current and noncurrent medication users. In the absence of gold standard inclusion criteria for identifying prevalent medication users, we conducted two analyses to examine whether the choice of medication use inclusion criteria causes variation in estimated adherence changes associated with a copayment increase. In the first analysis, we applied different medication use inclusion criteria to a sampling frame for a fixed index date (February 2002) when the VA medication copayment increase was implemented. This first approach allowed us to examine whether different inclusion criteria generated similar or different adherence trends when the index date was held constant. This is analogous to four research teams independently evaluating the same policy change, each using a different set of inclusion criteria to identify prevalent users. In the second analysis, we applied the same medication use inclusion criteria to the same sampling frame but at four different time periods. The time periods included the date of the actual copayment change (February 2002) when we expected an adherence response, and three alternative time periods when we did not expect an adherence response because no cost-sharing changes occurred. These alternative time periods were established 6 months before the copayment increase (August 2001), 6 months after (August 2002), and 12 months after the copayment increase (February 2003). We retained similar proportions of poorly adherent, episodic, and highly adherent patients using a common inclusion criterion. This allowed us to examine whether adherence trends associated with the copayment change differed from adherence trends observed in time periods when no medication copayment changes occurred. This second evaluation is analogous to testing the stability in adherence trends in the absence of copayment changes. We would have greater confidence that the adherence changes around the actual copayment increase were valid if no adherence changes were observed at these alternative three time points. On the other hand, the validity of our results would be called into question if similar adherence trends were observed at all four time periods because the similar trends would suggest that we induced an artifact by our particular choice of inclusion criteria. These two analyses are informative for researchers and policy makers identifying prevalent medication user cohorts from secondary data to evaluate the effects of policy changes on medication use. To ensure that comparative effectiveness research (CER) for medication use based on observational studies generates unbiased results, it is important to understand the possible influence of inclusion criteria for identifying prevalent users on adherence results.