Real-world complexities in opioid use disorder treatment: understanding family comorbidity, high-risk medication use, and costs related to treatment adherence and health outcomes

阿片类药物使用障碍治疗的现实复杂性：了解家庭合并症、高风险药物使用以及与治疗依从性和健康结果相关的成本

• 批准号: 10449784
• 负责人: Marissa J Seamans
• 金额: $ 18.29万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10449784
• 关键词: Address; Adherence; Adverse event; American; Anxiety; Benzodiazepines; Buprenorphine; Child; Clinical; Clinical assessments; Data; Data Analyses; Databases; Development; Drug Prescriptions; Educational workshop; Emergency department visit; Environmental Risk Factor; Family; Family health status; Family member; Foundations; Genetic; Goals; Health; Health Benefit; Health Care Costs; Health Insurance; Health Insurance Reimbursement; Health Services Research; Health Status; Healthcare; Hospitalization; Household; Improve Access; Incidence; Individual; Insurance Carriers; Intervention; Knowledge; Learning; Life; Medicine; Mental Depression; Mental Health; Mental disorders; Mentored Research Scientist Development Award; Mentors; Mentorship; Methods; Muscle relaxants; Opiate Addiction; Opioid; Outcome; Overdose; Pain; Parents; Patient-Focused Outcomes; Patients; Personal Satisfaction; Persons; Pharmaceutical Preparations; Pharmacoepidemiology; Pharmacotherapy; Play; Policies; Psychiatry; Public Health; Publishing; Recording of previous events; Research; Research Personnel; Risk; Risk Factors; Role; Savings; Serious Adverse Event; Siblings; Spouses; Stimulant; Substance Use Disorder; Time; Training; addiction; adverse outcome; alcohol use disorder; beneficiary; buprenorphine treatment; career; chronic depression; chronic pain; clinical decision support; clinical diagnosis; clinical practice; clinically relevant; comorbidity; contextual factors; cost; data warehouse; design; drug misuse; economic evaluation; health care service utilization; health plan; high risk; improved; indexing; innovation; insight; insurance claims; medication compliance; meetings; opioid overdose; opioid use; opioid use disorder; predictive modeling; psychosocial; responsible research conduct; sedative; skills; substance use treatment; support tools; treatment adherence

PROJECT SUMMARY Medications for opioid use disorder (MOUD) have the potential to improve the health and well-being of more than 2.1 million Americans with OUD, however, long-term adherence particularly to buprenorphine is alarmingly poor. Pain, mental health, and substance use disorders are increasingly recognized as risk factors for inadequate treatment adherence and often co-occur in families due to shared genetic and environmental factors. Understanding comorbidities in patients with OUD and their family members, and the impact of these comorbidities on poor opioid use outcomes, can help identify patients at risk for inadequate treatment adherence and serious adverse events. Further, information on the costs associated with buprenorphine non-adherence and family comorbidities can inform health insurance reimbursement policies. The overall career goal of the recipient is to become a leading pharmacoepidemiologist focused on improving treatment for substance use disorders, particularly opioid use disorders. The goal of this K01 is to train the recipient to investigate associations between family comorbidities and/or prescription medications with a high risk of misuse and buprenorphine treatment adherence, opioid use outcomes, and costs to the family unit and health insurer. Research aims of this project are to: (1) develop a clinically relevant prediction model to identify patients prescribed buprenorphine at risk of inadequate adherence; (2) determine whether other prescriptions in the family are associated with poor buprenorphine adherence and opioid-related adverse events; and (3) compare overall healthcare costs to the family and health insurer across varying levels of buprenorphine adherence. The training aims of this project are to: (1) gain understanding of the clinical assessment and diagnosis of opioid use disorders and comorbid mental health conditions; (2) learn and apply innovative methods for dyadic data analyses; (3) learn and apply methods for conducting economic evaluations of substance use treatment; (4) hone professional skills in research, publishing, and project administration; and (5) responsible conduct of research. Training aims will be pursued through tutorials with world-renowned experts forming the recipient's mentorship team, graduate-level coursework, workshops and seminars, participation in scientific meetings, and mentored research. Research aims will be accomplished using the OptumLabs Data Warehouse, a large integrated commercial healthcare insurance claims database that tracks beneficiaries, spouses, and dependents across health plans and over time. This project will fill an important gap in our understanding of how family comorbidities and medication use by family members influence MOUD treatment adherence, outcomes, and costs, and will provide evidence to support interventions by clinicians and health insurers to improve MOUD adherence outcomes.

项目总结