BMC Neurology (Jul 2008)

Medication Persistence Rates and Factors Associated with Persistence in Patients Following Stroke: A Cohort Study

  • Joffres Michel R,
  • Gubitz Gordon J,
  • Sketris Ingrid S,
  • Lummis Heather L,
  • Flowerdew Gordon J

DOI
https://doi.org/10.1186/1471-2377-8-25
Journal volume & issue
Vol. 8, no. 1
p. 25

Abstract

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Abstract Background Medication nonadherence can be as high as 50% and results in suboptimal patient outcomes. Stroke patients in particular can benefit from pharmacotherapy for thrombosis, hypertension, and dyslipidemia but are at high risk for medication nonpersistence. Methods Patients who were admitted to the Queen Elizabeth II Health Sciences Centre in Halifax, Nova Scotia, with stroke between January 1, 2001 and December 31, 2002 were analyzed. Data collected were pre-stroke function, stroke subtype, stroke severity, patient outcomes, and medication use at discharge, and six and 12 months post discharge. Medication persistence at six and 12 months and the factors associated with nonpersistence at six months were examined using multivariable stepwise logistic regression. Results At discharge, 420 patients (mean age 68.2 years, 55.7% male) were prescribed an average of 6.4 medications and mean prescription drug cost was $167 monthly. Antihypertensive (91%) and antithrombotic (96%) drug use at discharge were frequent, antilipidemic (73%) and antihyperglycemic (25%) drug use were less common. Self-reported persistence at six and 12 months after stroke was high (> 90%) for all categories. In the multivariable model of medication nonpersistence at six months, people aged 65 to 79 years were less likely to be nonpersistent with antihypertensive medications than people aged 80 years or more (Odds ratio (OR) 0.11, 95% Confidence Interval (CI) 0.03–0.39). Monthly drug costs of Conclusion Patients reported high medication persistence rates six and 12 months after stroke. Identification of factors associated with nonpersistence (such as older age and prior disability) will help predict which patients are at higher risk for discontinuing their medications.