Leveraging large-scale administrative claims data to evaluate prescription opioid use, risks, and outcomes in older adults living with HIV

利用大规模行政索赔数据来评估老年艾滋病毒感染者的处方阿片类药物使用、风险和结果

• 批准号: 10406363
• 负责人: Stephanie Shiau
• 金额: $ 19.56万
• 依托单位: RBHS-SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10406363
• 关键词: Accident and Emergency department; Address; Adverse event; Affect; Age; Age-Years; Aging; Anti-Retroviral Agents; Area; Area Under Curve; Benzodiazepines; Data; Data Set; Databases; Dementia; Development Plans; Disease; Dose; Drug Interactions; Drug Prescriptions; Drug Utilization; Drug usage; Elderly; Environment; Epidemic; Epidemiology; Foundations; Fracture; General Population; Goals; HIV; HIV Infections; HIV risk; Health; Hospitalization; Individual; Injecting drug user; Inpatients; Interdisciplinary Study; Intervention; Longevity; Machine Learning; Medicare; Medicare claim; Mental Health; Mentored Research Scientist Development Award; Mentors; Methods; Morbidity - disease rate; Older Population; Opioid; Outcome; Outpatients; Pattern; Persons; Pharmaceutical Preparations; Pharmacoepidemiology; Polypharmacy; Population; Positioning Attribute; Prevention; Prevention program; Protease Inhibitor; Quality of life; Reading; Reporting; Research; Research Infrastructure; Research Personnel; Research Priority; Research Support; Risk; Sampling; Techniques; Training; United States National Institutes of Health; Universities; Visit; adverse event risk; adverse outcome; antiretroviral therapy; base; beneficiary; career development; chronic pain; clinical decision support; clinical practice; co-infection; comorbidity; experience; falls; health care service utilization; high risk; improved; infection risk; insight; machine learning method; mortality; multidisciplinary; non-nucleoside reverse transcriptase inhibitors; novel; opioid use; prescription opioid; profiles in patients; programs; research and development; sedative; substance use; symposium; tool; treatment program

PROJECT SUMMARY The purpose of this Mentored Research Scientist Development Award (K01) is to provide support for me to become an independent investigator with a multidisciplinary research program in HIV at the intersection of prescription drug use, comorbidities, and aging. In this project, I propose to leverage existing administrative claims data from Medicare beneficiaries ≥65 years of age from 2007-2018 to determine how high-risk prescription opioid use, e.g. high dose use (≥120 mg/day) or prolonged use (≥90 days), affects health outcomes and health care utilization in older people living with HIV (PLWH). Building on my strong foundation in HIV epidemiology and HIV comorbidities across the lifespan, the training from this K01 will allow me to (1) obtain proficiency in the analysis of large-scale, longitudinal administrative claims data; (2) attain content expertise in prescription drug utilization research and pharmacoepidemiology; and (3) develop expertise in the application of machine learning methods. My career development plan includes specific coursework, seminars, conferences, directed readings, and tailored mentoring from a multidisciplinary team comprised of experts in administrative claims data, substance use epidemiology, pharmacoepidemiology, and machine learning methods. Rutgers University provides an exceptional environment for completion of this training, with research support and infrastructure for analyzing large, multiyear datasets (including Medicare claims) and conducting high-impact HIV research. The proposed research is significant given that a substantial gap exists in understanding how prescription opioid use affects a growing population of aging PLWH who commonly report chronic pain, and have multiple comorbidities, increasing polypharmacy, and increased risk for untoward drug-drug interactions. This research is critical to target appropriate prevention and treatment programs to optimize health outcomes for this population. To fill this gap, specific aims are to: (1) Assess the associations between HIV infection and a) adverse health outcomes (e.g. falls/fractures, dementia, mortality) and b) health care utilization (e.g. emergency department use, inpatient hospitalizations, and outpatient visits) among older adults, and estimate the interaction between HIV infection and high-risk prescription opioid use on adverse outcomes and utilization; (2) Among older PLWH, estimate drug-drug interactions between high-risk prescription opioid use and specific antiretroviral drug classes or sedatives (e.g. benzodiazepines) on risk of adverse health outcomes and health care utilization; and (3) Examine the feasibility of applying existing machine learning approaches to predict adverse health outcomes and high health care utilization among older PLWH based on patient profiles and opioid prescription patterns in a large administrative claims database. Findings from this project will generate valuable new information and tools to support clinical decisions and more precisely target prevention and treatment interventions to improve health for older PLWH who are prescribed opioids, and directly inform an R01 application to study drug-drug and drug- disease interactions between widely used prescription drugs and common comorbidities among older PLWH.

项目摘要 这个受过指导的研究科学家发展奖（K01）的目的是为我提供支持 成为艾滋病毒多学科研究计划的独立研究者 处方药的使用，合并症和衰老。在这个项目中，我建议利用现有的管理 从2007 - 2018年≥65岁的Medicare受益人的索赔数据来确定高风险处方 Opioid使用，例如高剂量使用（≥120mg/天）或长时间使用（≥90天），会影响健康结果和健康 艾滋病毒（PLWH）的老年人的护理利用。建立在我在艾滋病毒流行病学领域的坚强基础的基础上 和整个生命周期的HIV合并症，该K01的培训将使我能够（1）获得熟练程度 分析大规模的纵向行政索赔数据； （2）获得处方药的内容专家 利用研究和药物电子学； （3）在应用机器学习方面发展专业知识 方法。我的职业发展计划包括特定的课程，半小伙，会议，定向阅读， 并从跨学科团队量身定制的心理，完成了行政索赔数据专家 药物使用流行病学，药物ePidemiology和机器学习方法。罗格斯大学 为完成此培训提供了一个特殊的环境，并在研究支持和基础设施中为 分析大型多年数据集（包括医疗保险索赔）并进行高影响力的HIV研究。这 鉴于在理解处方如何使用方面存在巨大差距，因此提出的研究显着。 影响越来越多的衰老PLWH人群，他们通常报告慢性疼痛，并且有多种合并症， 增加了多药的增加，并增加了抗药药物相互作用的风险。这项研究对 针对适当的预防和治疗计划，以优化该人群的健康状况。填写这个 差距，具体目的是：（1）评估艾滋病毒感染与a）不良健康结果之间的关联 （例如跌倒/骨折，痴呆，死亡率）和b）医疗保健利用（例如急诊科使用，住院 老年人中的住院和门诊就诊，并估计艾滋病毒感染之间的相互作用 以及在广告结果和利用率上使用高风险的处方阿片类药物； （2）在较老的PLWH中，估算 高风险处方OID使用与特定抗逆转录病毒药物类别的药物相互作用或 镇静剂（例如苯二氮卓类药物）就健康结果和医疗保健利用的风险； （3）检查 采用现有的机器学习方法来预测不良健康结果和较高的可行性 较旧的PLWH中的医疗保健利用基于患者概况和阿片类药物处方模式 行政索赔数据库。该项目的发现将生成有价值的新信息和工具 支持临床决策，更精确地针对预防和治疗干预措施，以改善健康 处方阿片类药物的较旧的PLWH，直接通知R01申请，以研究药物和药物 - 较旧的PLWH中广泛使用的处方药与常见合并症之间的疾病相互作用。