Drug characteristics associated with medication adherence across eight disease states
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Background/Aims: Adherence to prescribed medications is a widespread problem and determinants of medication non-adherence have been previously described as health system, social/economic, therapy-related, condition-related, and patient-related factors. Medication non-adherence clearly represents a multifaceted challenge among patients and health care providers. The impact of drug characteristics on medication adherence has not been studied across disease states. The purpose of this study was to describe medication adherence by drug characteristics across multiple disease states to identify drug-specific factors that may serve as targets for future intervention to improve medication adherence.
Methods: We conducted a retrospective analysis of patients with one diagnosis and oral medication using medical claims and pharmacy utilization data for the following conditions: asthma/COPD, cancer, depression, diabetes, hypercholesterolemia, hypertension, multiple sclerosis (MS) or osteoporosis between January 1, 2007 and March 31, 2009. Adherence was calculated using the Medication Possession Ratio for each patient from prescription fills occurring within a 12-month window.
Results: Analysis of members with one diagnosis and one medication (n=14,875) found the majority female (61%; n=9059) and Caucasian (83%; n=12,317). The most commonly occurring conditions were hypertension (n=5,505), depression (n=4,349) and hyperlipidemia (n=2,744). Generic utilization rates varied widely by diagnosis as did copayment. Binary adherence (>80%) varied significantly by diagnosis, drug class (within condition), generic use, and copayment. Regression analysis showed significant adherence variations within condition for brand and generic use and copayment.
Conclusions: We ascertained adherence varies by disease state, drug class within disease states, and drug characteristics. Next steps include an interventional study that will use this data to target specific populations by drug-related characteristics.