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Can one or two simple questions predict poor medication adherence?

Bruce C Stuart1, Veronica Timmons2, Feng-Hua E Loh1

  • 1Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy, Baltimore, Maryland, USA.

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|March 27, 2020
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Summary
This summary is machine-generated.

A brief patient activation status (PAS) measure can help identify Medicare beneficiaries at risk of poor medication adherence for type 2 diabetes. Certain subgroups, including younger, non-Hispanic black, and morbidly obese individuals, show particularly low adherence.

Keywords:
medication adherencepatient activation

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Area of Science:

  • Clinical Pharmacy
  • Health Services Research
  • Patient Adherence

Background:

  • Poor adherence to evidence-based medications is a significant challenge in clinical practice.
  • Identifying patients at high risk for non-adherence requires improved prognostic tools.

Purpose of the Study:

  • To evaluate if a two-item patient activation status (PAS) measure can identify Medicare beneficiaries at risk for poor adherence to type 2 diabetes medications.
  • To assess the relationship between PAS and medication adherence in a Medicare population.

Main Methods:

  • Utilized data from the 2009 Medicare Current Beneficiary Survey.
  • Assessed patient activation status (PAS) and medication adherence using proportion of days covered (PDC).
  • Classified PAS into active, high effort, complacent, or passive categories based on patient confidence and medication list use.

Main Results:

  • Overall, PAS had a small effect on medication adherence (3% lower PDC for complacent/passive vs. active/high effort).
  • Interactions revealed specific subgroups with significantly poorer adherence: beneficiaries under 65 (PDC -11%), non-Hispanic black beneficiaries (PDC -13%), and morbidly obese beneficiaries (PDC -9%).

Conclusions:

  • A simple question about bringing medication lists to doctor visits may help identify at-risk patient subgroups.
  • Larger sample sizes are needed to validate and expand these findings on patient adherence prediction.