Prescription Opioid Misuse in a Large HMO

描述（由申请人提供）：在过去六年中，华盛顿州和全国范围内，处方阿片类药物的合法和非法使用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一系列行为，其特征是药物使用的不稳定或混乱模式和寻求医疗保健的异常模式。生物心理社会模型将用于从卫生系统和临床医生的角度研究在阿片类药物治疗慢性疼痛的背景下从阿片类药物使用到阿片类药物滥用的过渡。本申请的具体目的是：1.确定用于治疗非恶性疼痛的处方慢性阿片类药物中处方阿片类药物误用、滥用和依赖的患病率，并确定阿片类药物误用与阿片类药物滥用和依赖的常用诊断类别之间的相关性。2.对自动化病历数据在区分阿片类药物滥用者和非滥用者方面的效用进行建模，并确定衍生模型的筛选特性（例如灵敏度、特异性、阳性和阴性预测值、似然比）。3.评估目标2中获得的自动化数据模型的效用，通过与疼痛严重程度、药物滥用和精神健康症状相关的简短访谈项目进行增强，以识别潜在的阿片类药物滥用者。为了实现这些目标，我们建议在华盛顿州的一家大型HMO中对中度至长期阿片类药物使用者进行回顾性队列研究。合格的受试者包括所有HMO登记者，包括从初级和专科护理医生那里接受阿片类药物的受试者。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大多数初步研究具有更大的普遍性，这些研究通常在疼痛诊所或VA进行。以男性顾客为主。将从自动化数据文件中获取有关药物使用、医疗状况和其他误用风险因素的数据。将使用结构化电话访谈来获得目标3的预测变量、与阿片类药物滥用相关的结局变量以及阿片类药物滥用和依赖的DSM IV诊断。确定阿片类药物滥用预测因素的模型将使用一半样本开发，并使用另一半样本进行验证。将使用非应答者的去识别数据来调整任何非应答偏倚的分析。

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.