MOX - Modeling and Scientific Computing Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy.
CHRP - National Center for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
Biom J. 2021 Feb;63(2):305-322. doi: 10.1002/bimj.201900365. Epub 2020 Aug 31.
Adherence to medication is the process by which patients take their drugs as prescribed, and represents an issue in pharmacoepidemiological studies. Poor adherence is often associated with adverse health conditions and outcomes, especially in case of chronic diseases such as heart failure (HF). This turns out in an increased request for health care services, and in a greater burden for the health care system. In recent years, there has been a substantial growth in pharmacotherapy research, aimed at studying effects and consequences of proper/improper adherence to medication both for the increasing awareness of the problem and for the pervasiveness of poor adherence among patients. However, the way adherence is computed and accounted for into predictive models is far from being informative as it may be. In fact, it is usually analyzed as a fixed baseline covariate, without considering its time-varying behavior. The purpose and novelty of this study is to define a new personalized monitoring tool exploiting time-varying definition of adherence to medication, within a joint modeling approach. In doing so, we are able to capture and quantify the association between the longitudinal process of dynamic adherence to medication with the long-term survival outcome. Another novelty of this approach consists of exploiting the potential of health care administrative databases in order to reconstruct the dynamics of drugs consumption through pharmaceutical administrative registries. In particular, we analyzed administrative data provided by Regione Lombardia - Healthcare Division related to patients hospitalized for HF between 2000 and 2012.
患者遵医嘱服药的过程即为用药依从性，这也是药物流行病学研究中的一个问题。通常情况下，用药依从性差与不良健康状况和结果有关，尤其是在心力衰竭（HF）等慢性疾病的情况下。这会导致对医疗服务的需求增加，并给医疗系统带来更大的负担。近年来，药物治疗学的研究有了实质性的增长，其目的是研究适当/不适当的用药依从性对药物的影响和后果，这既提高了人们对该问题的认识，也反映了患者用药依从性差的普遍性。然而，将用药依从性计算并纳入预测模型的方法远非信息丰富，因为它通常被分析为固定的基线协变量，而不考虑其随时间变化的行为。本研究的目的和新颖之处在于，通过联合建模方法，利用药物依从性的时变定义来定义一种新的个性化监测工具。通过这样做，我们能够捕捉和量化动态药物依从性的纵向过程与长期生存结果之间的关联。这种方法的另一个新颖之处在于利用医疗保健管理数据库的潜力，通过药物管理登记处来重建药物消费的动态。具体来说，我们分析了伦巴第大区（Regione Lombardia）医疗部门提供的与 2000 年至 2012 年期间因心力衰竭住院的患者相关的行政数据。
