Determinants and effectiveness of non-pharmacologic pain interventions versus prescription opioids

项目摘要 我是一名有执照的职业治疗师，目前在康复科学部攻读博士学位。 德克萨斯大学医学分支，位于德克萨斯州的加尔维斯顿。这个F31提案的目的是发展我的 知识，方法熟练程度，分析技能和研究经验，成为一个 成功的临床科学家这个建议对实现我获得博士后的长期目标很重要 职位，起草K99，并在医疗康复研究资源网络工作，目标是 使用大量数据来改善非药物疼痛干预的公平获得。我的F31应用程序 侧重于比较非药物疼痛干预，如职业和物理治疗， 处方阿片类药物，用于那些经历手术或慢性疼痛的人。我作为职业治疗师的训练 让我对多模式疼痛干预、护理途径和 获得医疗服务。我作为一名研究人员的经验提供了使用大数据的基础知识， 研究行为干预措施，并认识到健康和公众的社会决定因素的重要性 政策在F31期间，我将在2个领域进行培训，这些领域建立在我之前的临床和研究经验基础上： (1)学术和研究培训，包括：新的数据管理和分析方法，补充 关于比较有效性研究、观察性研究设计、健康的社会决定因素的教育 及临床药理学原理;及（2）专业发展培训，包括： 在地方和国家会议上的经验，提高科学写作和口头交流， 为职业治疗专业的学生或博士生提供指导。这些方面的培训将使我成为 更好的研究人员，并将提供机会，使用严格的方法，有效地沟通的结果， 通过指导未来的医疗保健提供者将研究转化为实践。我的训练活动是综合性的 在一个研究项目中，我使用了来自全国代表性医疗支出小组的大量数据， 调查（2011 - 2018年）和医疗保险索赔（2015 - 2018年），以完成以下目标： 按手术和慢性疼痛分层的变化-与非处方阿片类药物相比， 药物干预，重点是与更好地获得这些药物有关的健康的社会决定因素， （B）比较处方阿片类药物与职业和身体服务之间的有效性 在接受家庭保健的患者中，对疼痛、活动能力和自我护理措施进行治疗。预期结果 这项研究可以确定获得疼痛干预措施的差异，这对我们的成长很重要。 患有慢性和致残性疼痛的成人人群。这些发现也有助于开发临床 低风险疼痛干预的实践指南。完成F31的培训和研究将为我提供一个 有机会发展成为一个临床科学家使用大数据，以促进公平和有效的护理。

PROJECT SUMMARY I am a licensed occupational therapist and current PhD student in the Division of Rehabilitation Sciences at the University of Texas Medical Branch in Galveston, Texas. The purpose of this F31 proposal is to develop my knowledge, methodological proficiency, analytical skill, and research experience necessary to become a successful clinician-scientist. This proposal is important to achieve my long-term goals of obtaining a postdoc position, drafting a K99, and working in a Medical Rehabilitation Research Resource Network with the goal of using large data to improve equitable access to non-pharmacologic pain interventions. My F31 application focuses on comparing non-pharmacologic pain interventions, such as occupational and physical therapy, to prescription opioids for those experiencing surgical or chronic pain. My training as an occupational therapist has given me a strong understanding of multimodal pain interventions, care pathways, and disparities in healthcare access. My experiences as a researcher have provided foundational knowledge in using large data, studying behavioral interventions, and recognizing the importance of social determinants of health and public policy. During the F31 period, I will train in 2 areas that build upon my prior clinical and research experience: (1) Academic and Research training including: new data management and analytic methods, supplementary education on comparative effectiveness research, observational study design, social determinants of health and principles of clinical pharmacology; and (2) Professional Development training including: networking experiences at local and national conferences, improving scientific writing and verbal communication, and offering mentorship to occupational therapy students or PhD students. Training in these areas will make me a better researcher and will provide opportunities to use rigorous methods, effectively communicate findings, and translate research into practice via mentoring future health care providers. My training activities are integrated into a research project in which I use large data from the nationally representative Medical Expenditure Panel Survey (2011-2018) and Medicare claims (2015-2018) to complete the following aims: (A) Examine temporal changes—stratified by surgical and chronic pain--in the prevalence of prescription opioids compared to non- drug interventions, with emphasis on social determinants of health associated with better access to these services; and (B) Compare the effectiveness between prescription opioids versus occupational and physical therapy on measures of pain, mobility, and self-care in patients receiving home health. The expected findings of this research can identify disparities in access to pain interventions, which is important for our growing population of adults with chronic and disabling pain. The findings can also contribute to developing clinical practice guidelines for low-risk pain interventions. Completing the F31 training and research will provide me an opportunity to develop into a clinician-scientist using large data to facilitate equitable and effective care.