Monday, October 24, 2011: 1:45 PM
Grand Ballroom CD (Hyatt Regency Chicago)
(ESP) Applied Health Economics, Services, and Policy Research

Bijan J. Borah, Ph.D., College of Medicine, Mayo Clinic, Rochester, MN

Purpose: Medication adherence among chronic disease patients has been shown to improve outcomes, which in turn results in reduced overall healthcare costs. A comprehensive understanding of the predictors of adherence is essential to formulate targeted strategies for improving adherence. Existing methods have not considered evaluation of heterogeneous impacts of adherence predictors at different parts (quantiles) of the adherence distribution as defined by medication possession ratio. Using the novel econometric framework of unconditional quantile regression (UQR), this study assesses the heterogeneity of impacts of the adherence predictors for an Alzheimer’s disease (AD) population.

Method: This retrospective claims analysis identified AD patients from a large US health plan that initiated oral AD therapy (rivastigmine, donepezil, galantamine, or memantine) between 1/1/2006 and 12/31/2007. Baseline characteristics were assessed during the 6-month pre-index period; medication adherence was assessed during the 1-year post-index period. UQR was estimated at 10th, 20th, …, 90th quantiles. Predictors of adherence identified from the data included age, age squared, male gender, interaction of age and gender, indicator of mental health insurance coverage, region, commercial vs. Medicare insurance, log cost, comorbidity, and formulary tier for the AD medication.

Result:  Baseline medication count was positively associated with adherence (p<0.05) in the upper half of the adherence distribution. Having mental health coverage is negatively associated with adherence in all but the 10th and 20th quantiles but the impact was substantially higher in the first half of the adherence distribution. Baseline (log) cost was positively associated with adherence in the 40th and upper quantiles of the adherence distribution. For patients in the 80th and 90th quantiles, the number of baseline office visits predicted lower adherence. Compared to patients from the East, patients from the South were less likely to be adherent in the 60th and 70th quantiles.  

Conclusion:  The study results underscore that the predictors can have heterogeneous impacts on different parts of the adherence distributions, that is, predictors of a highly adherent subject differ from a medium- or low-adherent subject. The complete picture of the impacts of the predictors on the entire medication adherence distribution will help the decision-maker to formulate actionable policy to improve adherence.