Good and poor adherence: optimal cut-point for adherence measures using administrative claims data
Good and poor adherence: optimal cut-point for adherence measures using administrative claims data
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DOI：
10.1185/03007990903126833
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发表时间：
2009-09-01
影响因子：
2.3
通讯作者：
Martin, Bradley C.
中科院分区：
文献类型：
--
作者：
Karve, Sudeep;Cleves, Mario A.;Martin, Bradley C.
目的：在诊断为精神分裂症、糖尿病、高血压、充血性心力衰竭（CHF）或高脂血症的符合Medicaid条件的患者中，确定使用行政衍生的依从性指标、药物拥有率（MPR）和使用住院事件作为主要结局的覆盖天数比例（PDC）对良好依从者与不良依从者进行最佳分层的依从性值截止点。研究设计和方法：这是对阿肯色州医疗补助行政索赔数据的回顾性分析。在2000年7月至2004年4月的招募期间，≥ 18岁的患者必须至少有一个研究疾病的ICD-9-CM代码，并且在首次处方目标疾病之前6个月和之后24个月连续合格。采用MPR和PDC评估1年内对疾病特异性药物治疗的依从率。主要结局指标和分析方案：主要结局指标为任何原因和疾病相关住院。单变量logistic回归模型用于预测住院。最佳的坚持值是基于坚持值，对应于ROC曲线的最左上角点对应的最大特异性和sensitivity.Results：MPR和PDC在预测任何原因住院治疗的最佳截止坚持值在0.63和0.89之间变化的五个队列。在预测5个队列的疾病特异性住院时，最佳的依从性截断值范围为0.58 ~ 0.85。结论：本研究为选择0.80作为合理的截断点提供了初步的经验基础，该截断点基于预测几种高度流行的慢性疾病的后续住院来对依从性和非依从性患者进行分层。这个截止点已被广泛用于以前的研究，我们的研究结果表明，它可能是有效的，在这些条件下，它是基于一个单一的结果测量，和额外的研究使用这些方法，以确定使用其他结果指标，如实验室或生理措施，这可能是更强的相关性，遵守阈值，是必要的。
Objective: To identify the adherence value cut-off point that optimally stratifies good versus poor compliers using administratively derived adherence measures, the medication possession ratio (MPR) and the proportion of days covered (PDC) using hospitalization episode as the primary outcome among Medicaid eligible persons diagnosed with schizophrenia, diabetes, hypertension, congestive heart failure (CHF), or hyperlipidemia.Research design and methods: This was a retrospective analysis of Arkansas Medicaid administrative claims data. Patients >= 18 years old had to have at least one ICD-9-CM code for the study diseases during the recruitment period July 2000 through April 2004 and be continuously eligible for 6 months prior and 24 months after their first prescription for the target condition. Adherence rates to disease-specific drug therapy were assessed during 1 year using MPR and PDC.Main outcome measure and analysis scheme: The primary outcome measure was any-cause and disease-related hospitalization. Univariate logistic regression models were used to predict hospitalizations. The optimum adherence value was based on the adherence value that corresponded to the upper most left point of the ROC curve corresponding to the maximum specificity and sensitivity.Results: The optimal cut-off adherence value for the MPR and PDC in predicting any-cause hospitalization varied between 0.63 and 0.89 across the five cohorts. In predicting disease-specific hospitalization across the five cohorts, the optimal cut-off adherence values ranged from 0.58 to 0.85.Conclusions: This study provided an initial empirical basis for selecting 0.80 as a reasonable cut-off point that stratifies adherent and non-adherent patients based on predicting subsequent hospitalization across several highly prevalent chronic diseases. This cut-off point has been widely used in previous research and our findings suggest that it may be valid in these conditions; it is based on a single outcome measure, and additional research using these methods to identify adherence thresholds using other outcome metrics such as laboratory or physiologic measures, which may be more strongly related to adherence, is warranted.