Predictors of medication adherence postdischarge: the impact of patient age, insurance status, and prior adherence.
Predictors of medication adherence postdischarge: the impact of patient age, insurance status, and prior adherence.
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DOI：
10.1002/jhm.1940
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发表时间：
2012-07
影响因子：
2.6
通讯作者：
Schnipper, Jeffrey L.
中科院分区：
文献类型：
--
作者：
Cohen, Marya J.;Shaykevich, Shimon;Cawthon, Courtney;Kripalani, Sunil;Paasche-Orlow, Michael K.;Schnipper, Jeffrey L.
Optimizing post-discharge medication adherence is a target for avoiding adverse events. Nevertheless, few studies have focused on predictors of post-discharge medication adherence. The Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL-CVD) study used counseling and follow-up to improve post-discharge medication safety. In this secondary data analysis, we analyzed predictors of self-reported medication adherence after discharge. Based on an interview at 30 days post-discharge, an adherence score was calculated as the mean adherence in the previous week of all regularly scheduled medications. Multivariable linear regression was used to determine the independent predictors of post-discharge adherence. The mean age of the 646 included patients was 61.2 years, and they were prescribed an average of 8 daily medications. The mean post-discharge adherence score was 95% (SD = 10.2%). For every 10 year increase in age, there was a 1% absolute increase in post-discharge adherence (95% CI 0.4% −2.0%). Compared to patients with private insurance, patients with Medicaid were 4.5% less adherent (95% CI −7.6% to −1.4%). For every 1-point increase in baseline medication adherence score, as measured by the 4-item Morisky score, there was a 1.6% absolute increase in post-discharge medication adherence (95% CI 0.8% to 2.4%). Surprisingly, health literacy was not an independent predictor of post-discharge adherence. In patients hospitalized for cardiovascular disease, predictors of lower medication adherence post-discharge included younger age, Medicaid insurance, and baseline non-adherence. These factors can help predict patients who may benefit from further interventions.
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