Analysis Group, Inc., Boston, MA 02199, USA.
Pain Med. 2012 Sep;13(9):1162-73. doi: 10.1111/j.1526-4637.2012.01450.x. Epub 2012 Jul 30.
The objective of this study was to use administrative claims data to identify and analyze patient characteristics and behavior associated with diagnosed opioid abuse.
Patients, aged 12-64 years, with at least one prescription opioid claim during 2007-2009 (n = 821,916) were selected from a de-identified administrative claims database of privately insured members (n = 8,316,665). Patients were divided into two mutually exclusive groups: those diagnosed with opioid abuse during 1999-2009 (n = 6,380) and those without a diagnosis for opioid abuse (n = 815,536). A logistic regression model was developed to estimate the association between an opioid abuse diagnosis and patient characteristics, including patient demographics, prescription drug use and filling behavior, comorbidities, medical resource use, and family member characteristics. Sensitivity analyses were conducted on the model's predictive power.
In addition to demographic factors associated with abuse (e.g., male gender), the following were identified as "key characteristics" (i.e., odds ratio [OR] > 2): prior opioid prescriptions (OR = 2.23 for 1-5 prior Rxs; OR = 6.85 for 6+ prior Rxs); at least one prior prescription of buprenorphine (OR = 51.75) or methadone (OR = 2.97); at least one diagnosis of non-opioid drug abuse (OR = 9.89), mental illness (OR = 2.45), or hepatitis (OR = 2.36); and having a family member diagnosed with opioid abuse (OR = 3.01).
Using medical as well as drug claims data, it is feasible to develop models that could assist payers in identifying patients who exhibit characteristics associated with increased risk for opioid abuse. These models incorporate medical information beyond that available to prescription drug monitoring programs that are reliant on drug claims data and can be an important tool to identify potentially inappropriate opioid use.
本研究旨在利用行政索赔数据识别和分析与诊断性阿片类药物滥用相关的患者特征和行为。
从一个经身份识别的私人保险成员行政索赔数据库（n = 831665）中选择 2007-2009 年至少有一次处方阿片类药物索赔的患者（n = 821916），年龄为 12-64 岁。患者分为两组：1999-2009 年诊断为阿片类药物滥用的患者（n = 6380）和未诊断为阿片类药物滥用的患者（n = 815536）。建立了一个逻辑回归模型来估计阿片类药物滥用诊断与患者特征之间的关联，包括患者人口统计学特征、处方药物使用和配药行为、合并症、医疗资源使用和家庭成员特征。对模型的预测能力进行了敏感性分析。
除了与滥用相关的人口统计学因素（例如，男性）外，以下因素被确定为“关键特征”（即比值比[OR]＞2）：以前的阿片类药物处方（OR = 1-5 个以前处方的 2.23；OR = 6 个以上处方的 6.85）；至少有一个丁丙诺啡（OR = 51.75）或美沙酮（OR = 2.97）的处方；至少有一个非阿片类药物滥用（OR = 9.89）、精神疾病（OR = 2.45）或肝炎（OR = 2.36）的诊断；以及有家庭成员被诊断为阿片类药物滥用（OR = 3.01）。
使用医疗和药物索赔数据，可以开发出有助于支付方识别具有阿片类药物滥用风险增加特征的患者的模型。这些模型纳入了超出依赖药物索赔数据的药物监测计划可获得的医疗信息，是识别潜在不当阿片类药物使用的重要工具。
