Improving the treatment cascade of MAT initiation and retention for opioid use disorder

项目摘要：高质量的证据支持药物辅助治疗（MAT）作为第一线 阿片类药物使用障碍（OUD）的治疗，特别是用丁丙诺啡或 美沙酮作为长期维持至少一年。然而，患者脱落（通常在6个月时>50%）， 和提供者的抵制仍然是有效治疗的主要障碍。迄今为止，很少有研究 影响治疗开始、稳定和长期保持的关键步骤的患者特征， 丁丙诺啡大多数已发表的研究评估了高度活跃患者仅2-3个月积极治疗的结果 在临床试验中选择患者。我将借鉴有影响力的，连续的“级联护理”框架， 艾滋病毒/艾滋病领域作为一个概念模型，以确定丁丙诺啡连续性的阶段性障碍 通过适应OUD治疗级联进行治疗。更好地理解每个顺序 级联的阶段包括1）丁丙诺啡的开始，2）阿片类药物使用的稳定，和3）保留 长期治疗反应，可以为临床努力提供信息，以改善结局和死亡率。 在医疗改革和医疗补助重新设计下，越来越多的患者获得保险 药物滥用治疗的报销。我建议将电子健康记录中的临床数据 (EHR)到保险索赔数据，以评估患者因素，促进连续性的护理沿着级联。 该策略将收集关键信息（艾德就诊、诊断、治疗服务、死亡率）， 临床试验机构的记录之外。保险索赔数据的优势在于提供近乎完整的 随访和客观结果，不依赖于自我报告，并反映现实世界的临床复杂性。 为了完成这些目标，实现我的长远培养目标， 在定量方法，流行病学，管理和推理分析的专业知识， 通过观察数据集，我明确了五个连续的训练目标：1）流行病学和统计数据 分析方法2）服务研究、项目评估和模型设计3）保险索赔数据 分析4）观察数据的推理方法，和5）资助的写作和管理。履行我 研究的目的，我将纳入电子记录，从一个大容量，多站点MAT提供商，已开始 自2009年以来，有18，000名患者服用丁丙诺啡，并经常进行内部尿液药物检测。这项研究将 确定与成功开始丁丙诺啡治疗、稳定和保留相关的患者特征 并将在处理级联的每个阶段模拟屏障。根据这些发现，我还试图评估 通过对达到每个阶段的治疗和改善之间的关联进行建模， 临床结果。调查结果将基于一个大规模的、高度可推广的、以社区为基础的样本 的成人OUD治疗，将评估长期MAT的临床后果 维护并帮助指导质量改进计划。

Project Summary: High quality evidence supports medication assisted treatment (MAT) as the first line treatment for opioid use disorder (OUD), especially opioid substitution therapy with buprenorphine or methadone as long-term maintenance of at least one year. Yet patient dropout (typically >50% at six months), and provider resistance remain major impediments to effective treatment. To date, few studies have examined patient characteristics that influence key steps in treatment initiation, stabilization, and long-term retention with buprenorphine. Most published studies assess outcomes for only 2-3 months of active treatment among highly selected patients in clinical trials. I will draw from the influential, sequential “cascade of care” framework from the HIV/AIDS field as a conceptual model to identify stage-specific barriers to continuity of buprenorphine treatment through an adapted OUD treatment cascade. A greater understanding of barriers at each sequential stage of the cascade including 1) initiation of buprenorphine, 2) stabilization of opioid use, and 3) retention with long-term treatment response, may inform clinical efforts to improve outcomes and mortality. Under healthcare reform and Medicaid redesign, increasing numbers of patients receive insurance reimbursement for substance abuse treatment. I propose to link clinical data from electronic health records (EHR) to insurance claims data to assess patient factors that promote continuity of care along the cascade. This strategy will capture critical information (ED visits, diagnoses, treatment services, mortality) that are outside of a given clinical site's records. Insurance claims data have the advantage of providing near complete follow up and objective outcomes that do not rely on self-report and reflect real-world clinical complexities. In order to accomplish these goals and achieve my long-term training objectives of developing expertise in quantitative methods, epidemiology, and the management and inferential analysis of large observational data sets, I have articulated five sequential training goals: 1) epidemiologic and statistical data analysis methods 2) services research, program evaluation, and model design 3) insurance claims data analysis 4) inferential methods with observational data, and 5) grant writing and administration. To fulfill my research aims, I will incorporate electronic records from a high-volume, multi-site MAT provider that has started 18,000 patients on buprenorphine since 2009 with frequent in-house urine drug testing. The research will identify patient characteristics associated with successful buprenorphine initiation, stabilization, and retention and will model barriers at each stage of the treatment cascade. With these findings, I also seek to evaluate the clinical utility of each stage by modeling associations between attaining each stage of treatment and improved clinical outcomes. The results, which will be based on a large, highly generalizable, community-based sample of adults with OUD presenting for treatment, will evaluate the clinical consequences of long-term MAT maintenance and help guide quality improvement initiatives.