Medication adherence as a predictor of 30-day hospital readmissions
Authors Rosen OZ, Fridman R, Rosen BT, Shane R, Pevnick JM
Received 25 October 2016
Accepted for publication 1 February 2017
Published 20 April 2017 Volume 2017:11 Pages 801—810
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Colin Mak
Peer reviewer comments 4
Editor who approved publication: Dr Johnny Chen
Olga Z Rosen,1 Rachel Fridman,2 Bradley T Rosen,3,4 Rita Shane,1 Joshua M Pevnick4,5
1Department of Pharmacy Services, Cedars-Sinai Medical Center, 2Resources & Outcomes Management, Cedars-Sinai Health System, 3Department of Medicine, ISP Hospitalist Service, Cedars-Sinai Medical Center, 4University of California, Los Angeles School of Medicine, 5Division of General Internal Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Purpose: The aim of this study was to test whether patient medication adherence, a modifiable risk factor obtainable at hospital admission, predicts readmission within 30 days.
Patients and methods: We used a retrospective cohort study design to test whether patient medication adherence to all chronic medications, as determined by the 4-item Morisky Medication Adherence Scale (MMAS-4) administered by a pharmacist at the time of hospital admission, predicts 30-day readmissions. We compared readmission rates among 385 inpatients who had their adherence assessed from February 1, 2013, to January 31, 2014. Multiple logistic regression was used to examine the benefit of adding medication adherence to previously published variables that have been shown to predict 30-day readmissions.
Results: Patients with low and intermediate adherence (combined) had readmission rates of 20.0% compared to a readmission rate of 9.3% for patients with high adherence (P=0.005). By adding MMAS-4 data to previously published variables that have been shown to predict 30-day readmissions, we found that patients with low and intermediate medication adherence had an adjusted 2.54-fold higher odds of readmission compared to those in patients with high adherence (95% confidence interval [CI]: 1.32–4.90, P=0.005). The model’s predictive power, as measured by the c-statistic, improved from 0.65 to 0.70 after adding adherence.
Conclusion: Because medication adherence assessed at hospital admission was independently associated with 30-day readmission risk, it offers potential for targeting interventions to improve adherence.
Keywords: rehospitalization, predictive model, transition of care, care transitions, nonadherence, MMAS-4
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