Improving the Detection of Medication Nonadherence

描述（由申请人提供）：药物不依从性是高血压管理中的一个重要临床问题，发生率范围为30-80%，平均为50%。没有衡量不遵守的“金标准”。自我报告是有利的，因为它们可以在任何环境中进行管理，花费很少的时间来完成，可以在护理点提供即时反馈，管理和分析成本很低，并且可以检测不依从的具体原因，从而识别干预区域。尽管如此，自我报告被批评为产生低的不遵守率，比其他方法获得的比率低10-20%。这些限制部分是由于与潜变量模型有关的测量问题，这些问题在很大程度上被忽视了。第一个衡量问题是因果和效果指标模型的合并。在效果指标模型中，对度量项目的响应受潜在变量（在本例中为依从性）的反映（影响）。在因果指标模型中，对测量项目的反应会产生潜在变量。这两种类型的指标对于检测不依从性都很重要;效果指标反映了患者不依从的程度（例如，错过剂量的频率），而因果指标则评估不依从的具体原因（例如，有副作用）。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一个两步的方法来评估药物不依从性。第一份简短的问卷将评估不依从的存在和程度。第二份较长的问卷将评估不遵守的原因。在研究1中，将对200名诊断为高血压（HTN）并服用至少一种血压（BP）药物至少3个月的患者进行两次测量，间隔3至5天。组内相关性将提供证据的稳定性（可靠性）的措施。新制定的措施与BP和其他措施（例如，社会期望）将提供结构效度的证据。第二个被忽略的测量问题是，不遵守是横截面分析的，这假设它是特质样的（即，随时间稳定）。虽然有些人可能会持续服用药物，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，以确定在何种程度上坚持是特质样与状态样。在研究2中，研究1中开发的两种措施将通过电话以2周的间隔对250名服用至少一种BP药物至少3个月的HTN患者进行4次给药。将进行混合分布潜在状态-特质分析，以确定用药者的潜在类别（亚组）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，将在基线处评估潜在类别（即潜在性），以确定是否可以预测每个潜在类别中的成员资格。 公共卫生相关性：治疗决定是基于对药物不依从性的估计。如果这些估计值包括漏服剂量的频率、漏服剂量的原因以及漏服剂量是偶然的还是持续的问题，则这些估计值将提供最多的信息。拟议的研究的目标是开发新的问卷来评估这些方面的不遵守。新的调查问卷将帮助研究人员和临床医生更准确、更有意义地评估药物不依从性，从而改进定制干预措施，减少药物不依从性。

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.