Impact of initial buprenorphine prescribing strategies on retention in care for patients with opioid use disorder

丁丙诺啡初始处方策略对阿片类药物使用障碍患者保留护理的影响

• 批准号: 10644402
• 负责人: Alyssa Tilhou
• 金额: $ 18.92万
• 依托单位: BOSTON MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644402
• 关键词: Award; Buprenorphine; Caring; Cessation of life; Clinical; Clinical Trials; Complex; Data; Data Science; Databases; Disparity; Dose; Ensure; Environment; Epidemic; Ethnic Origin; Evidence based treatment; Fentanyl; Goals; Guidelines; Health Insurance; Incidence; Intervention; K-Series Research Career Programs; Learning; Literature; Machine Learning; Maintenance; Massachusetts; Medicaid; Mentors; Meta-Analysis; Methodology; Methods; Modeling; National Institute of Drug Abuse; Observational Study; Opioid; Outcome Measure; Overdose; Patient Care; Patients; Penetration; Pharmaceutical Preparations; Pharmacy facility; Physicians; Policies; Population; Positioning Attribute; Prevention strategy; Primary Care; Privatization; Quality-Adjusted Life Years; Race; Recommendation; Records; Regimen; Research; Research Personnel; Resources; Risk Reduction; Role; Sample Size; Speed; Substance Use Disorder; Testing; Therapeutic; Titrations; Training; United States Substance Abuse and Mental Health Services Administration; Variant; Withdrawal; Work; buprenorphine treatment; career; cost; cost-effectiveness ratio; craving; dosage; experience; illicit opioid; improved; incremental cost-effectiveness; innovation; insurance claims; models and simulation; mortality; opioid mortality; opioid overdose; opioid use; opioid use disorder; overdose prevention; prevent; protective effect; retention rate; simulation; skills; treatment and outcome; trend; trial comparing

PROJECT SUMMARY / ABSTRACT Opioid overdose continues to take lives at an astounding rate, reaching a record high of 69,710 deaths in 2020. Medications for OUD such as buprenorphine reduce risk of opioid overdose mortality. While policy efforts have expanded buprenorphine treatment capacity, buprenorphine remains underutilized due to poor retention in care. Observational data from real-world settings demonstrate 180-day retention rates as low as 25-40%. Thus, the federal Overdose Prevention Strategy identifies research to improve retention as a key priority. To this end, research examining buprenorphine dosage regimens could identify strategies to improve retention. Data from trials and observational studies suggest that higher induction and maintenance dosage could improve retention. Scarce literature describes current buprenorphine dosage practices, but the data we do have suggest variable dosage practices, with many patients never achieving recommended dosage targets. Furthermore, research has demonstrated downward trends in buprenorphine dosage levels, meaning that underdosage may be growing. In this Mentored Research Career Development Award application, I propose to examine the relationship between buprenorphine dosage regimens and OUD treatment retention. Specifically, the goals of this work are to 1) describe current buprenorphine dosage practices in terms of dosage trajectories, 2) estimate the impact of two distinct buprenorphine dosage strategies on retention, and 3) estimate the population-level clinical and cost impact of improved buprenorphine dosage practices on retention and overdose for the state of Massachusetts. In parallel, I have identified three training objectives: 1) develop skills using machine learning clustering methods, 2) learn causal inference using a target trial emulation, and 3) develop simulation modeling skills. Through these training objectives, I will develop comprehensive synergistic skills in the data sciences. The research in this proposal builds directly on my prior work using pharmacy claims data to examine trends in buprenorphine receipt. This proposal leverages this prior experience to investigate the role of specific buprenorphine dosage strategies on retention. I have identified a supportive research environment and an experienced mentor team with a) content and methodologic expertise and b) extensive track records in guiding trainees to independent research careers. The strength of these resources will ensure that I gain the expertise necessary to succeed in completing the proposed scope of work and achieve my long-term goal of becoming an independent physician investigator using large data to study innovations for substance use disorder treatment. At the end of this award, I will be well-positioned to apply for an R-level award using national Medicaid data to examine a) the impact of emerging buprenorphine dosage strategies on retention as fentanyl adulteration penetrates the opioid supply, and b) disparities in buprenorphine dosage by race, ethnicity and region.

项目概要/摘要 阿片类药物过量继续以惊人的速度夺去生命，2017 年死亡人数达到创纪录的 69,710 人。 2020.丁丙诺啡等 OUD 药物可降低阿片类药物过量死亡的风险。虽然政策 尽管努力扩大了丁丙诺啡的治疗能力，但由于贫困，丁丙诺啡仍未得到充分利用 保留护理。来自现实环境的观察数据表明，180 天保留率低至 25-40%。因此，联邦过量预防战略将提高保留率的研究确定为关键 优先事项。为此，检查丁丙诺啡剂量方案的研究可以确定改善策略 保留。试验和观察研究的数据表明，较高的诱导和维持剂量 可以提高保留率。很少有文献描述当前丁丙诺啡的剂量实践，但我们的数据 确实建议可变剂量实践，但许多患者从未达到推荐的剂量目标。 此外，研究表明丁丙诺啡剂量水平呈下降趋势，这意味着 剂量不足的情况可能正在增加。在本指导研究职业发展奖申请中，我建议 检查丁丙诺啡剂量方案与 OUD 治疗保留之间的关系。具体来说， 这项工作的目标是 1) 描述当前丁丙诺啡的剂量实践 轨迹，2) 估计两种不同的丁丙诺啡剂量策略对保留的影响，以及 3) 估计改进的丁丙诺啡剂量实践对保留率的人口水平临床和成本影响 以及马萨诸塞州的用药过量。与此同时，我确定了三个培训目标：1）发展 使用机器学习聚类方法的技能，2）使用目标试验模拟学习因果推理，以及 3）培养仿真建模技能。通过这些培训目标，我将全面发展 数据科学中的协同技能。本提案中的研究直​​接建立在我之前使用的工作的基础上 药房声称数据用于检查丁丙诺啡接收趋势。该提案利用了先前的 研究特定丁丙诺啡剂量策略对保留作用的经验。我已经确定了一个 支持性的研究环境和经验丰富的导师团队，具有 a) 内容和方法方面的专业知识 b) 在指导学员走向独立研究生涯方面拥有丰富的记录。这些的实力 资源将确保我获得成功完成拟议工作范围所需的专业知识 并实现我的长期目标，成为一名独立的医师研究者，利用大数据进行研究 物质使用障碍治疗的创新。在这个奖项结束时，我将有能力申请 R 级奖项使用国家医疗补助数据来检查 a) 新兴丁丙诺啡剂量的影响 当芬太尼掺假渗透到阿片类药物供应中时的保留策略，以及 b) 丁丙诺啡剂量按种族、民族和地区划分。

项目成果

Telehealth Expansion, Internet Speed, and Primary Care Access Before and During COVID-19.

COVID-19 之前和期间的远程医疗扩展、互联网速度和初级保健服务。

• 发表时间: 2024-01-02
• 影响因子: 13.8
• 作者: Tilhou, Alyssa Shell;Jain, Arjun;DeLeire, Thomas
• 通讯作者: DeLeire, Thomas