Improving the Detection of Medication Nonadherence

描述(申请人提供)：药物不依从性是高血压管理中的一个重大临床问题，发生率从30-80%到平均50%不等。没有衡量不遵守情况的“黄金标准”。自我报告是有利的，因为它们可以在任何环境下管理，只需很短的时间即可完成，可以在护理时提供即时反馈，管理和分析成本很低，并且可以检测不遵守的特定原因，从而确定干预领域。尽管如此，自我报告的不依从率很低，比其他方法获得的不依从率低10%-20%，因此受到批评。这些限制在一定程度上是由于与潜变量模型相关的测量问题，而这些模型在很大程度上被忽视了。第一个衡量问题是因果指标模型的合并。在效果指标模型中，对测量项目的响应受潜在变量(在本例中为遵守)的反映(影响)。在因果指标模型中，对测量项目的反应产生了潜在变量。这两类指标对于检测不遵守情况都很重要；效果指标反映了患者不遵守的程度(例如，错过剂量的频率)，而因果指标则评估不遵守的具体原因(例如，经历副作用)。因此，我们的第一个具体目标是使用这两种类型的指标来开发和验证评估药物不依从性的两步法。第一个简短的问卷将评估不遵守的存在和程度。第二份较长的问卷将评估不遵守的原因。在研究1中，这些措施将对200名被诊断为高血压(HTN)的患者实施两次，相隔3至5天，至少服用一种降压药至少3个月。组内相关性将为这些措施的稳定性(可靠性)提供证据。新开发的测量与BP和其他测量(例如，药房配药、社会合意性)之间的关联将提供结构效度的证据。第二个被忽视的测量问题是，不坚持被横截面分析，这假设它是类似特质的(即，随着时间的推移是稳定的)。虽然有些人可能会始终如一地服药，但其他人可能不会。因此，第二个具体目标是使用纵向数据分析方法来确定依从性在多大程度上类似于特质，而不是类似于国家。在研究2中，研究1中开发的两种措施将通过电话给250名HTN患者进行四次电话治疗，每隔两周至少服用一种BP药物，持续至少3个月。将进行混合分布潜在状态-特征分析，以确定服药者的潜在类别(子组)的数量、每个类别的大小以及每个人属于每个类别的概率。个性变量(例如，尽职尽责)将在基线上进行评估，以确定是否可以预测每个潜在类别的成员。 公共卫生相关性：治疗决定是基于对药物不依从性的估计。如果这些估计包括错过剂量的频率、错过剂量的原因以及错过剂量是偶然的还是持续的问题，那么这些估计将是最有信息量的。拟议研究的目标是开发新的问卷来评估这些方面的不遵守。新的问卷将帮助研究人员和临床医生更准确、更有意义地评估用药不依从，从而改进量身定做的干预措施，以减少用药不依从。

DESCRIPTION (provided by applicant): Medication non-adherence is a significant clinical problem in the management of hypertension, with rates ranging from 30-80% and an average of 50%. There is no 'gold standard' for measuring non-adherence. Self- reports are advantageous because they can be administered in any setting, take little time to complete, can provide immediate feedback at the point of care, cost little to administer and analyze, and can detect the specific reasons for non-adherence, thereby identifying areas for intervention. Nonetheless, self-reports are criticized for yielding low non-adherence rates that are 10-20% lower than rates obtained by other methods. These limitations are due, in part, to measurement issues related to latent variable models that have been largely ignored. The first measurement issue is the conflation of causal and effect indicator models. In an effect indicator model, responses to items on a measure are reflected (influenced) by the underlying latent variable (in this case, adherence). In a causal indicator model, responses to items on a measure give rise to the latent variable. Both types of indicators are important for detecting non-adherence; effect indicators reflect the extent to which patients are nonadherent (e.g., how often doses are missed), whereas causal indicators assess specific reasons for non-adherence (e.g., experiencing side effects). Accordingly, our first specific aim is to use both types of indicators to develop and validate a two-step method for assessing medication non-adherence. The first, brief questionnaire will assess the presence and extent of non-adherence. The second, longer questionnaire will assess the reasons for non-adherence. In Study 1, the measures will be administered twice, 3 to 5 days apart, to 200 patients with a diagnosis of hypertension (HTN) taking at least one blood pressure (BP) medication for at least 3 months. Intraclass correlations will provide evidence of the stability (reliability) of the measures. The association between the newly developed measures and BP and other measures (e.g., pharmacy refills, social desirability) will provide evidence of construct validity. The second measurement issue that has been ignored is that non-adherence is analyzed cross-sectionally, which assumes that it is trait-like (i.e., stable over time). Although some people may take their medications consistently, others may not. Accordingly, the second specific aim is to use longitudinal data analytic methods to determine the extent to which adherence is trait-like versus state-like. In Study 2, the two measures developed in Study 1 will be administered by telephone four times at 2-week intervals to 250 patients with HTN taking at least one BP medication for at least 3 months. Mixture distribution latent state-trait analyses will be conducted to determine the number of latent classes (subgroups) of medication takers, the size of each class, and the probability that each person belongs to each class. Personality variables (e.g., conscientiousness) will be assessed at baseline to determine whether membership in each latent class can be predicted. PUBLIC HEALTH RELEVANCE: Treatment decisions are based on estimates of medication non-adherence. These estimates would be most informative if they included how often doses are missed, reasons for missed doses, and whether missing doses is an occasional or ongoing problem. The goals of the proposed research are to develop new questionnaires to assess these aspects of non-adherence. The new questionnaires will help researchers and clinicians assess medication non-adherence more accurately and meaningfully, leading to improved tailored interventions to decrease medication non-adherence.