Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA 02120, USA.
J Gen Intern Med. 2010 Apr;25(4):284-90. doi: 10.1007/s11606-010-1253-9. Epub 2010 Feb 4.
Non-adherence to essential medications represents an important public health problem. Little is known about the frequency with which patients fail to fill prescriptions when new medications are started ("primary non-adherence") or predictors of failure to fill.
Evaluate primary non-adherence in community-based practices and identify predictors of non-adherence.
75,589 patients treated by 1,217 prescribers in the first year of a community-based e-prescribing initiative.
We compiled all e-prescriptions written over a 12-month period and used filled claims to identify filled prescriptions. We calculated primary adherence and non-adherence rates for all e-prescriptions and for new medication starts and compared the rates across patient and medication characteristics. Using multivariable regressions analyses, we examined which characteristics were associated with non-adherence.
Primary medication non-adherence.
Of 195,930 e-prescriptions, 151,837 (78%) were filled. Of 82,245 e-prescriptions for new medications, 58,984 (72%) were filled. Primary adherence rates were higher for prescriptions written by primary care specialists, especially pediatricians (84%). Patients aged 18 and younger filled prescriptions at the highest rate (87%). In multivariate analyses, medication class was the strongest predictor of adherence, and non-adherence was common for newly prescribed medications treating chronic conditions such as hypertension (28.4%), hyperlipidemia (28.2%), and diabetes (31.4%).
Many e-prescriptions were not filled. Previous studies of medication non-adherence failed to capture these prescriptions. Efforts to increase primary adherence could dramatically improve the effectiveness of medication therapy. Interventions that target specific medication classes may be most effective.
不遵医嘱服用基本药物是一个重要的公共卫生问题。人们对新药物开始使用时患者未能配药（“初始不遵医嘱”）的频率或导致其失败的预测因素知之甚少。
评估社区实践中初始不遵医嘱的情况，并确定不遵医嘱的预测因素。
在社区电子处方计划实施的第一年，1217 名开方医生为 75589 名患者提供治疗。
我们编译了在 12 个月期间开出的所有电子处方，并使用已配药的索赔来确定已配药的处方。我们计算了所有电子处方以及新药物开始治疗的初始遵医嘱和不遵医嘱的比例，并比较了患者和药物特征的比例。使用多变量回归分析，我们研究了哪些特征与不遵医嘱有关。
初始药物不遵医嘱。
在 195930 张电子处方中，有 151837 张（78%）得到了配药。在 82245 张新药物的电子处方中，有 58984 张（72%）得到了配药。由初级保健专家，尤其是儿科医生开具的处方初始遵医嘱率更高（84%）。年龄在 18 岁及以下的患者配药率最高（87%）。在多变量分析中，药物类别是遵医嘱的最强预测因素，新处方治疗高血压（28.4%）、高血脂（28.2%）和糖尿病（31.4%）等慢性疾病的药物不遵医嘱的情况很常见。
许多电子处方未得到配药。以前关于药物不遵医嘱的研究未能捕捉到这些处方。增加初始遵医嘱的努力可以显著提高药物治疗的效果。针对特定药物类别的干预措施可能最有效。
