Division of General Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA.
J Hosp Med. 2012 Jul-Aug;7(6):470-5. doi: 10.1002/jhm.1940. Epub 2012 Apr 2.
Optimizing postdischarge medication adherence is a target for avoiding adverse events. Nevertheless, few studies have focused on predictors of postdischarge medication adherence.
The Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL-CVD) study used counseling and follow-up to improve postdischarge medication safety. In this secondary data analysis, we analyzed predictors of self-reported medication adherence after discharge. Based on an interview at 30-days postdischarge, 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 postdischarge 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 postdischarge adherence score was 95% (standard deviation [SD] = 10.2%). For every 10-year increase in age, there was a 1% absolute increase in postdischarge adherence (95% confidence interval [CI] 0.4% to 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 postdischarge medication adherence (95% CI 0.8% to 2.4%). Surprisingly, health literacy was not an independent predictor of postdischarge adherence.
In patients hospitalized for cardiovascular disease, predictors of lower medication adherence postdischarge included younger age, Medicaid insurance, and baseline nonadherence. These factors can help predict patients who may benefit from further interventions.
优化出院后药物依从性是避免不良事件的目标。然而，很少有研究关注出院后药物依从性的预测因素。
心血管疾病低文化程度药师干预（PILL-CVD）研究使用咨询和随访来提高出院后药物安全性。在这项二次数据分析中，我们分析了出院后自我报告药物依从性的预测因素。根据出院后 30 天的访谈，计算了依从性评分，即所有定期服用药物前一周的平均依从性。采用多变量线性回归确定出院后依从性的独立预测因素。
纳入的 646 例患者的平均年龄为 61.2 岁，平均每天服用 8 种药物。出院后依从性评分的平均值为 95%（标准差[SD] = 10.2%）。年龄每增加 10 岁，出院后依从性就会增加 1%（95%置信区间[CI]为 0.4%至 2.0%）。与有私人保险的患者相比，有医疗补助的患者的依从性低 4.5%（95%CI为-7.6%至-1.4%）。基线药物依从性评分每增加 1 分（通过 4 项 Morisky 评分衡量），出院后药物依从性就会增加 1.6%（95%CI为 0.8%至 2.4%）。令人惊讶的是，健康素养并不是出院后依从性的独立预测因素。
在因心血管疾病住院的患者中，出院后药物依从性较低的预测因素包括年龄较小、拥有医疗补助保险和基线不依从。这些因素可以帮助预测可能受益于进一步干预的患者。
