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首页> 外文期刊>Journal of Hospital Medicine >Predictors of medication adherence postdischarge: The impact of patient age, insurance status, and prior adherence
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Predictors of medication adherence postdischarge: The impact of patient age, insurance status, and prior adherence

机译:出院后药物依从性的预测因素:患者年龄,保险状况和先前依从性的影响

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摘要

BACKGROUND: Optimizing postdischarge medication adherence is a target for avoiding adverse events. Nevertheless, few studies have focused on predictors of postdischarge medication adherence. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. Journal of Hospital Medicine 2012;
机译:背景:优化出院后药物依从性是避免不良事件的目标。然而,很少有研究集中在出院后药物依从性的预测因素上。方法:心血管疾病低素素药师干预研究(PILL-CVD)通过咨询和随访来提高出院后用药的安全性。在此二级数据分析中,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,将依从分数计算为所有常规计划药物在前一周的平均依从性。多变量线性回归用于确定放电后依从性的独立预测因子。结果:646名患者的平均年龄为61.2岁,平均每天服用8种药物。平均出院后依从性得分为95%(标准差[SD] = 10.2%)。年龄每增加10年,放电后依从性绝对增加1%(95%的置信区间[CI]为0.4%至2.0%)。与拥有私人保险的患者相比,医疗补助患者的依从性降低了4.5%(95%CI -7.6%至-1.4%)。基线用药依从性得分每增加1点(通过4项Morisky评分衡量),出院后用药依从性绝对增加1.6%(95%CI为0.8%至2.4%)。令人惊讶的是,健康素养不是出院后依从性的独立预测因子。结论:在因心血管疾病住院的患者中,出院后依从性较低的预测因素包括年龄较小,医疗补助保险和基线不依从性。这些因素可以帮助预测可能从进一步干预中受益的患者。医院医学杂志2012;

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