Reducing racial disparities in the treatment of opioid use disorder using machine learning-based causal analysis

使用基于机器学习的因果分析减少阿片类药物使用障碍治疗中的种族差异

基本信息

批准号：
10190881
负责人：
Mathew Vinhhoa Kiang
金额：
$ 17.96万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2022-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10190881
关键词：
Address Affect Affordable Care Act African American American Area Big Data Biometry Buprenorphine Caring Characteristics Client satisfaction Clinical Treatment Communities Community Surveys Complement Computerized Medical Record Critical Care Data Databases Development Plans Effectiveness Epidemiology Ethnic Origin Ethnic group Face Foundations Health Health Personnel Health Resources Health Services Accessibility Healthcare Systems Individual Inequality Intervention Machine Learning Measures Medical Mental Health Mentorship Methods Minority Groups Modeling Natural experiment Opioid Patients Perception Pharmaceutical Preparations Phase Population Provider Public Health Race Research Research Training Specialist Structure Substance Use Disorder Therapeutic Training Treatment outcome Veterans Veterans Health Administration Visit Work barrier to care base black patient career career development caucasian American data warehouse design electronic data experience forest health disparity improved improved outcome innovation machine learning method mortality multidisciplinary multiple data sources novel strategies opioid overdose opioid use disorder prescription opioid racial and ethnic racial bias racial disparity skills social standard of care substance use treatment success treatment disparity

项目摘要

PROJECT SUMMARY The opioid overdose crisis emerged in predominantly White communities, but the opioid-related mortality rate is increasing most rapidly in the Black population. A key driver of the crisis is opioid use disorder, which affects over 2 million Americans. Despite their effectiveness, medications for opioid use disorder remain underused, especially among Black Americans. Compared to White Americans, Black Americans have lower access to medications for opioid use disorder, are one-third as likely to initiate treatment, and have lower retention in care. Black Americans face unique structural obstacles to care, such as mistrust of the health care system, lack of representation among medical providers, and racially-biased providers’ perceptions. There is a critical gap in our understanding of the structural factors associated with treatment initiation and retention in care for Black patients with OUD. The scientific objective of this research plan is to identify modifiable structural factors at the community, provider, and facility levels that affect treatment initiation and retention in care for opioid use disorder in the Black population. This innovative project proposes to leverage machine learning-based causal inference methods with a combination of large national electronic medical records, corporate data warehouses, and publicly available data. By combining multiple data sources, this project will empirically evaluate modifiable factors such as provider characteristics (e.g., years of experience, patient satisfaction scores), facility characteristics (e.g., mental health staffing to patient ratios, number of buprenorphine-eligible prescribers), and patient-provider characteristics (e.g., number of previous visits or interactions). While focused on promoting equitable access to treatment for opioid use disorder in Black Americans, the public health implications of this proposal are expected to apply broadly to ameliorate the overall health burden of substance use disorders and reduce health disparities. This research plan is complemented by a career development plan that builds on the applicant’s background in epidemiology and biostatistics. Specifically, this career development plan outlines new training in three areas: (1) the clinical treatment of opioid use disorder, (2) analysis of the massive data of electronic medical records, and (3) machine learning-based causal inference methods. The combined research and training plan will prepare the applicant for a successful independent research career identifying, evaluating, and implementing multilevel interventions to reduce racial/ethnic inequalities in treatment for substance use disorders.

项目概要阿片类药物过量危机出现在以白人为主的社区，但与阿片类药物相关的黑人死亡率增长最快，危机的一个关键驱动因素是阿片类药物使用障碍，尽管治疗阿片类药物使用障碍的药物有效，但影响了超过 200 万美国人。未得到充分利用，特别是在美国黑人中，与美国白人相比，美国黑人的利用率较低。获得阿片类药物使用障碍药物的机会，开始治疗的可能性是三分之一，并且有较低的美国黑人在护理方面面临独特的结构性障碍，例如对医疗保健的不信任。系统、医疗提供者缺乏代表性以及提供者存在种族偏见的看法。我们对与治疗开始和保留相关的结构因素的理解存在重大差距该研究计划的科学目标是确定可修改的方法。影响治疗开始和保留的社区、提供者和设施层面的结构性因素这个创新项目建议利用机器来治疗黑人群体中的阿片类药物使用障碍。基于学习的因果推理方法结合全国大型电子病历，通过结合多个数据源，该项目将整合企业数据仓库和公开数据。根据经验评估可修改的因素，例如提供者特征（例如，多年的经验、患者满意度得分）、设施特征（例如，心理健康人员与患者的比例、丁丙诺啡合格的处方者），以及患者提供者的特征（例如，之前就诊的次数或相互作用），同时致力于促进黑人公平获得阿片类药物使用障碍的治疗。美国人认为，该提案对公共卫生的影响预计将广泛适用，以改善该研究计划旨在减轻物质使用障碍的总体健康负担并减少健康差异。辅之以基于申请人的流行病学背景的职业发展计划具体来说，该职业发展计划概述了三个领域的新培训：（1）临床。阿片类药物使用障碍的治疗，（2）电子病历海量数据分析，以及（3）基于机器学习的因果推理方法将结合研究和培训计划来准备。申请人成功的独立研究生涯识别、评估和实施多层次减少药物滥用障碍治疗中种族/民族不平等的干预措施。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Robustness of estimated access to opioid use disorder treatment providers in rural vs. urban areas of the United States.

DOI：
10.1016/j.drugalcdep.2021.109081
发表时间：
2021-11-01
期刊：
Drug and alcohol dependence
影响因子：
4.2
作者：
Kiang MV;Barnett ML;Wakeman SE;Humphreys K;Tsai AC
通讯作者：
Tsai AC

Excess Mortality With Alzheimer Disease and Related Dementias as an Underlying or Contributing Cause During the COVID-19 Pandemic in the US.

DOI：
10.1001/jamaneurol.2023.2226
发表时间：
2023-09-01
期刊：
JAMA NEUROLOGY
影响因子：
29
作者：
Chen, Ruijia;Charpignon, Marie-Laure;Raquib, Rafeya V.;Wang, Jingxuan;Meza, Erika;Aschmann, Helene E.;DeVost, Michelle A.;Mooney, Alyssa;Bibbins-Domingo, Kirsten;Riley, Alicia R.;Kiang, Mathew V.;Chen, Yea-Hung;Stokes, Andrew C.;Glymour, M. Maria
通讯作者：
Glymour, M. Maria

Routine asymptomatic testing strategies for airline travel during the COVID-19 pandemic: a simulation analysis.