Medications for Alcohol Use Disorder:  Unfilled Prescriptions and Treatment Trajectories

项目摘要：许多患有酒精使用障碍 (AUD) 的人没有接受治疗，包括 基于证据的药物治疗。之前对 AUD 药物使用的研究主要依赖于索赔 数据，因此仅关注 AUD 药物治疗过程的一部分（即配药）。 仅使用索赔数据，无法区分未收到索赔的个人的特征 临床医生开具的处方，来自那些收到处方但未配药的人。也是无法理解的 药物处方和配药如何适应 AUD 治疗的轨迹。设计实践和 临床医生和政策制定者需要了解能够改善 AUD 药物使用的政策干预措施 谁在治疗过程中的每个点“脱落”。我们建议使用来自大型国家的数据集 拥有 2010-2023 年期间关联索赔和电子健康记录 (EHR) 数据的保险公司，以及 在一项混合方法研究中对澳元提供者进行的访谈。链接的数据将使我们能够观察书面的 每位患者的处方以及处方是否已配药。链接的数据也将 允许我们纳入 EHR 中可用的临床相关信息，但不包括索赔。利用这些数据，我们 将： 1) 对于诊断为 AUD 的个人，确定与以下情况相关的患者和提供者特征： 开出 AUD 药物以及从指数 AUD 到开处方的时间； 2) 对于个人 被诊断患有 AUD 且接受 AUD 药物治疗的患者，确定患者和处方者的特征 与配药和配药时间相关； 3) 确定 AUD 护理的轨迹 其中包含为诊断为 AUD 的个人开具的处方信息，并检查 不同治疗轨迹与随后复发和损伤指标之间的关联。的 研究的混合方法，包括对治疗 AUD 的提供者进行半结构化访谈 影响他们 AUD 药物处方决定的因素，将允许更细致地检查 AUD 药物处方超出了仅进行定量分析所允许的范围。研究结果将有助于查明 治疗过程 不同类型的人退出治疗以及治疗轨迹有何不同 与复发和损伤相关，为未来的干预和政策设计提供信息。

PROJECT SUMMARY: Many individuals with alcohol use disorder (AUD) receive no treatment, including evidence-based pharmacotherapies. Previous research on AUD medication use has primarily relied on claims data, and thus focuses on only a portion of the AUD medication treatment process (i.e., filled prescriptions). Using only claims data, it is not possible to distinguish the characteristics of individuals who do not receive a prescription from a clinician from those who receive one but do not fill it. It is also not possible to understand how medication prescriptions and fills fit into the trajectory of AUD treatment received. To design practice and policy interventions that will improve AUD medication use, clinicians and policy makers need to understand who “falls off” at each point in the treatment process. We propose to use a dataset from a large national insurer with linked claims and electronic health record (EHR) data for the period 2010-2023, as well as interviews of AUD providers, in a mixed methods study. The linked data will allow us to observe written prescriptions for each patient as well as whether or not the prescriptions were filled. The linked data will also allow us to incorporate clinically relevant information available in EHRs but not claims. Using these data, we will: 1) For individuals diagnosed with AUD, identify patient and provider characteristics associated with being prescribed an AUD medication and the time from index AUD encounter to prescription; 2) For individuals diagnosed with AUD who are prescribed an AUD medication, identify patient and prescriber characteristics associated with filling the prescription and with time to prescription fill; and 3) Identify trajectories of AUD care that incorporate information on prescriptions written for individuals diagnosed with AUD, and examine the association between different treatment trajectories and indicators of subsequent relapse and injury. The study's mixed methods approach, including semi-structured interviews of providers who treat AUD about the factors that influence their AUD medication prescribing decisions, will allow a more nuanced examination of AUD medication prescribing than quantitative analyses alone allow. The findings will help pinpoint where in the treatment process different types of people fall out of treatment and how different treatment trajectories are associated with relapse and injury, informing future intervention and policy design.