Prescription Opioid Misuse in a Large HMO

描述(由申请人提供)：在过去六年中，华盛顿州和全国合法和非法使用处方阿片类药物以及相关的发病率和死亡率显著增加。处方阿片类药物滥用是以不稳定或混乱的用药模式和异常的就医模式为特征的一系列行为。生物心理社会模型将被用来从卫生系统和临床医生的角度来审查阿片类药物慢性疼痛治疗中从阿片类药物使用到阿片类药物滥用的转变。本申请的具体目的是：1.确定处方阿片类药物滥用、滥用和依赖的发生率，以及确定阿片类药物滥用和依赖的常用诊断类别与阿片类药物滥用和依赖之间的相关性。2.对自动病历数据在区分阿片类药物滥用和非滥用方面的效用进行建模，并确定导出模型的筛选属性(例如，敏感性、特异性、阳性和阴性预测值、似然比)。3.评估在目标2中获得的自动化数据模型的实用性，并通过与疼痛严重程度、药物滥用和精神健康症状有关的简短访谈项目来加强，以确定潜在的阿片类药物滥用者。为了达到这些目标，我们建议对华盛顿州一家大型医疗保健组织中的中长期阿片类药物使用者进行一项回顾性队列研究。符合条件的受试者包括所有HMO参与者，包括那些从初级和专科护理医生那里接受阿片类药物治疗的人。这一大样本(n=1,000)和多样化的样本将使研究结果比迄今为止的大多数初步研究具有更大的概括性，这些初步研究通常在疼痛诊所或以男性客户为主的退伍军人事务部进行。有关药物使用、医疗状况和其他滥用风险因素的数据将从自动数据文件中获得。将使用结构化电话访谈来获得AIM 3的预测变量、与阿片类药物滥用有关的结果变量以及DSM IV对阿片类药物滥用和依赖的诊断。将用一半的样本开发确定阿片类药物滥用预测因素的模型，并用另一半样本进行验证。关于非受访者的未确认数据将被用于调整分析，以消除任何未回复的偏见。

DESCRIPTION (provided by applicant): Licit and illicit use of prescription opioids, as well as associated morbidity and mortality, have increased significantly in the past six years in Washington State and nationally. Prescription opioid misuse is a constellation of behaviors characterized by unstable or chaotic patterns of medication use and aberrant patterns of medical care seeking. The biopsychosocial model will be used to examine the transition from opioid use to opioid misuse in the context of chronic pain treatment with opioids from the perspectives of health systems and clinicians. The specific aims of this application are to: 1. Determine the prevalence of prescription opioid misuse, abuse and dependence among those prescribed chronic opioids for non-malignant pain, and to determine the correlation between opioid misuse and the commonly used diagnostic categories of opioid abuse and dependence. 2. Model the utility of automated medical record data in differentiating between opioid misusers and non- misusers and determine the screening properties of the derived model (e.g. sensitivity, specificity, positive and negative predictive values, likelihood ratio). 3. Assess the utility of the automated data model obtained in Aim #2, enhanced with brief interview items related to pain severity, substance abuse, and mental health symptoms to identify potential opioid misusers. To meet these aims, we propose a retrospective cohort study of moderate to long-term opioid users in a large HMO in Washington State. Eligible subjects include all HMO enrollees, including those receiving opioids from primary and specialty care physicians. This large (n=1,000) and diverse sample will allow for greater generalizability of the findings than most of the preliminary studies to date, which were often conducted in pain clinics or the V.A. with its primarily male clientele. Data on medication use, medical conditions, and other risk factors for misuse will be obtained from automated data files. A structured phone interview will be used to obtain the predictor variables for Aim 3, the outcome variables related to opioid misuse, and DSM IV diagnoses of opioid abuse and dependence. Models to identify predictors of opioid misuse will be developed with half of the sample and validated with the other half. De-identified data on non- respondents will be utilized to adjust analyses for any non-response bias.