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首页> 外文期刊>Journal of pharmacy practice >Predictors of Medication Nonadherence From Outpatient Pharmacy Data Within a Large, Academic Health System
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Predictors of Medication Nonadherence From Outpatient Pharmacy Data Within a Large, Academic Health System

机译：从大型学术卫生系统内的门诊药房数据中的药物不正常的预测因素

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Background: Medication nonadherence is a worldwide issue that can lead to poor clinical outcomes and increased health-care costs. Objective: To determine the predictors of medication nonadherence. Methods: A retrospective chart review was conducted for patients who received prescription medications from Cleveland Clinic outpatient pharmacies. Prediction variables consisted of demographics, socioeconomic status, number of medications, and number of daily administrations. These variables were analyzed using a logistic regression to determine independent predictors of medication adherence. Results: Between January and September 2015, over 300 000 eligible prescriptions were filled, corresponding with over 70 000 unique patients. Of these, 29 134 patients were included. After multivariable regression, increasing age (odds ratio [OR]: 1.01), household income (OR: 1.03), and medication count (OR: 1.05) were found to be associated with adherence. Male gender (OR: 0.88), African American (OR: 0.45), Hispanic (OR: 0.62), or other race (OR: 0.87), being single (OR: 0.92), and increasing frequency of administrations per day (OR: 0.76) were associated with nonadherence. Conclusion: Medication nonadherence was associated with nonwhite race, single status, male gender, low socioeconomic status, and increasing frequency of medication administration. Based on these results, a risk prediction tool could be created to determine which patients are at the highest risk of medication nonadherence.
机译：背景：疗程不正常是一个全球问题，可以导致临床结果不佳，恢复卫生费用增加。目的：确定药物不正常的预测因子。方法：对从克利夫兰诊所门诊药房接受处方药的患者进行了回顾性图表审查。预测变量由人口统计数据，社会经济状态，药物数量和日常行政人员数量组成。使用逻辑回归分析这些变量以确定药物遵守的独立预测因子。结果：2015年1月至9月，填补了300 000多个符合条件的处方，对应于超过70 000名独特的患者。其中，包括29名134名患者。多变量回归后，增加年龄（差距[或]：1.01），家庭收入（或：1.03）和药物计数（或：1.05）与遵守相关。男性性别（或：0.88），非洲裔美国人（或：0.45），西班牙裔（或：0.62），或其他种族（或：0.87），单身（或：0.92），每天增加频率（或： 0.76）与非正常相关。结论：药物不正常与非白族，单一地位，男性性别，低社会经济状况以及增加的药物管理频率有关。基于这些结果，可以创建风险预测工具以确定哪些患者处于患有的最高风险。

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