Electronic Theses and Dissertations

Date of Award


Document Type


Degree Name

M.S. in Pharmaceutical Science

First Advisor

Benjamin F. Banahan

Second Advisor

Donna West-Strum

Third Advisor

John P. Bentley

Relational Format



Medication adherence has been shown to be influenced by demographics, health status and socio-economic status of the patient. Thus, adherence-based measures of pharmacy quality may be influenced by patient-related risk factors outside of the healthcare provider's control. This study examines the performance of a classical logistic regression model containing only patient characteristics and a random-effect model including patient characteristics and a pharmacy-specific effect in predicting medication adherence. These models were used to compute three different risk-adjusted scores on adherence-based pharmacy quality indicators: based on the classical logistic regression model (Method 1), the random effects model (Method 2) and the shrinkage estimators of the random-effects model (Method 3). Finally, we compared the classification as low, medium or high quality pharmacies based on unadjusted and adjusted scores. This retrospective cohort study used the 2007 Mississippi Medicare administrative claims dataset. Patient medication adherence was measured using the proportion of days covered (PDC) measure for seven therapeutic classes of medications. Pharmacy Quality scores on adherence-based measures were computed for all pharmacies serving Medicare beneficiaries in the state. The logistic regression model and the random-effect model displayed good predictive ability (c-statistic>0.7) for all therapeutic classes. The residual intra class correlation coefficient ranged from 0.008 to 0.012 indicating that although pharmacy level factors may have a significant impact, they may not be as important as patient level factors in determining adherence. Higher levels of agreement was observed between pharmacy classification based on unadjusted scores and risk-adjusted scores obtained from Methods 1 and 2 (0.5



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