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Professor and Chair, Clinical Research and Development, Department of Pharmacy Practice, School of Pharmacy, Texas Tech University Health Sciences Center, Amarillo, TX
Professor, Department of Pharmacy Practice, School of Pharmacy, Texas Tech University Health Sciences Center
Research Project Associate, Department of Pharmacy Practice, School of Pharmacy, Texas Tech University Health Sciences Center
Professor and Chair Clinical Practice and Management, Department of Pharmacy Practice, School of Pharmacy, Texas Tech University Health Sciences Center
Assistant Professor, Department of Pharmacy Practice, School of Pharmacy, Texas Tech University Health Sciences Center
Reprints: Dr. Raehl, School of Pharmacy, Texas Tech University Health Sciences Center, 1300 Coulter, Amarillo, TX 79106-1712, fax 806/356-4018, Cynthia.Raehl{at}ttuhsc.edu
BACKGROUND: Medication nonadherence is increasingly recognized as a cause of preventable adverse events, hospitalizations, and poor healthcare outcomes. While comprehensive medication adherence assessment for the elderly is likely to identify and prevent drug-related problems, it is time consuming for patient and healthcare providers alike.
OBJECTIVE: To identify screening tools to predict elderly patients' intended medication adherence that are suitable for primary-care settings and community pharmacies.
METHODS: This study evaluated 57 English-speaking persons aged 65 years and older who were from diverse socioeconomic backgrounds. Intended adherence was quantified, and the relationships to demographic, medical history, socioeconomic, and literacy variables were determined.
RESULTS: In a multivariate analysis with the composite MedTake Test (a quantitative measure of each subject's intent to adhere to prescribed oral medications) as the dependent variable, independent predictors of intended adherence included: age, car ownership in the last 10 years, receipt of food assistance in the last 10 years, number of over-the-counter (OTC) medicines, and REALM (Rapid Estimate of Adult Literacy in Medicine). The strongest predictor was the REALM word-recognition pronunciation test (ß = 0.666; R2 = 0.271; p < 0.001).
CONCLUSIONS: We observed that the REALM word-recognition pronunciation test, along with age, number of OTC drugs, and 2 socioeconomic questions, predicted the intent of seniors to correctly take their own prescribed oral medications.
Key Words: adherence, health literacy, MedTake Test, REALM
Published Online, April 4, 2006. www.theannals.com, DOI 10.1345/aph.1G478