Associate Professor in the Department of Family Medicine in the Max Rady College of Medicine of the Rady Faculty of Health Sciences at the University of Manitoba in Winnipeg.
Research Facilitator in the Department of Family Medicine at the University of Manitoba.
Can Fam Physician. 2022 Jul;68(7):520-527. doi: 10.46747/cfp.6807520.
To analyze primary medication nonadherence across several prescription indications and test the predictors of drug nonadherence in an adult primary care population.
Retrospective observational study using primary care provider prescriptions linked to pharmacy-based dispensing data from 2012 to 2014.
Manitoba.
Patients in the Manitoba Primary Care Research Network.
Prevalence of primary medication nonadherence by drug class. Multivariable logistic regression models were used to test the associations of patient demographic and clinical or provider characteristics with primary medication nonadherence. The C statistic was used to assess the models' discriminative performance.
A total of 91,660 unique prescriptions were assessed from a cohort of more than 200,000 patients. Primary medication nonadherence ranged from 13.7% (antidepressants) to 30.3% (antihypertensives). In conditions that typically present symptomatically (eg, infections, anxiety) nonadherence ranged from 13.7% to 17.5%. The range was 21.2% to 30.0% for medications related to asymptomatic conditions or those typically detected by screening. The discriminative performance of the models based on patient demographic, clinical, or provider characteristics was weak.
Primary medication nonadherence is common, occurring more often in asymptomatic conditions. The poor predictability of the models suggests that caution is required when considering characteristic-based interventions or prediction tools to improve primary medication nonadherence.
分析几种处方适应证下的主要药物不依从性,并检验成年初级保健人群中药物不依从的预测因素。
使用 2012 年至 2014 年的初级保健提供者处方与基于药房的配药数据相链接的回顾性观察性研究。
曼尼托巴省。
曼尼托巴初级保健研究网络的患者。
按药物类别评估主要药物不依从的发生率。使用多变量逻辑回归模型检验患者人口统计学和临床或提供者特征与主要药物不依从的关联。使用 C 统计量评估模型的判别性能。
从超过 200,000 名患者的队列中评估了 91,660 个独特的处方。主要药物不依从率从 13.7%(抗抑郁药)到 30.3%(抗高血压药)不等。在通常表现出症状的情况下(例如感染、焦虑),不依从率从 13.7%到 17.5%不等。与无症状情况或通常通过筛查检测到的药物相关的药物不依从率为 21.2%至 30.0%。基于患者人口统计学、临床或提供者特征的模型的判别性能较弱。
主要药物不依从很常见,在无症状情况下更为常见。模型的可预测性差表明,在考虑基于特征的干预措施或预测工具来改善主要药物不依从时需要谨慎。