Measuring Chronic Pain Impact

测量慢性疼痛的影响

• 批准号: 10559712
• 负责人: RONALD Dale HAYS
• 金额: $ 54.93万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10559712
• 关键词: Address; Advisory Committees; Agreement; Area; Back Pain; Case Mixes; Chronic low back pain; Chronic neck pain; Classification; Complementary Health; Data; Data Collection; Data Set; Effectiveness; Equilibrium; Exhibits; Future; Goals; Guidelines; Individual; Information Systems; Integrative Medicine; Intervention; Investments; Link; Measurement; Measures; Mechanics; Methods; Morbidity - disease rate; Neck; Nonpharmacologic Therapy; Outcome; Outcome Measure; Outcome Study; Pain; Patient Outcomes Assessments; Patients; Pharmacologic Substance; Population Research; Probability; Productivity; Property; Questionnaires; Recommendation; Research; Research Personnel; Sampling; Sampling Studies; Scheme; Source; Stratification; Subgroup; Time; Translating; United States National Institutes of Health; Validation; Work; chronic back pain; chronic pain; chronic pain management; chronic pain patient; cost; crowdsourcing; disability; effectiveness study; efficacy evaluation; health care service utilization; health related quality of life; improved; indexing; pain patient; patient subsets; relative effectiveness; working group

Project Summary/Abstract Many complementary and integrative health (CIH) approaches have been shown to be effective for chronic pain and included in guidelines. This evidence of effectiveness is built on hundreds of studies representing millions of research dollars, and the ability to analyze and better compare results across these studies is essential to obtain the full value of this investment. However, useful across-study comparisons which would allow better understanding and targeting of these interventions are hampered by at least two challenges: the lack of common outcome measures and the inability to meaningfully stratify or classify patients. To address the first challenge, in Aim 1 this study will develop and evaluate crosswalks or links between components of the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS®) short form (PROMIS-29) and common legacy measures used for chronic pain. In particular, we will create crosswalks/links for the two most commonly used measures for CLBP, including the Roland-Morris Disability Questionnaire (RMDQ) and the Oswestry Disability Index (ODI). In addition, depending on data availability and input from our Advisory Council we will create at least two other crosswalks/links between the PROMIS-29 and other legacy measures for CLBP (e.g., the Back Pain Functional Scale) or legacy measures for other types of chronic pain (e.g., the Neck Disability Index for chronic neck pain). To address the second challenge, in Aim 2 we will evaluate and refine the chronic pain impact stratification scheme proposed by the NIH Research Task Force on chronic low back pain. The proposed scheme uses the Impact Stratification Score (ISS) which is calculated using 9 items from the PROMIS-29. We will first evaluate the ISS and its properties to assess whether they are stable across different samples and determine whether they can be improved. After we have finalized the components and calculation of the ISS, we will examine its effect on the impacts of chronic pain (e.g., health-related quality of life, healthcare utilization, worker productivity) to identify meaningful cut-points to use to stratify chronic pain patients into subgroups who exhibit different levels of chronic pain impact. Three types of data will be used in the analyses to address Aims 1 and 2: data from three large in-house existing datasets, data collected from a national convenience sample using Amazon’s Mechanical Turk (MTurk) crowdsourcing platform, and data from the probability-based nationally representative KnowledgePanel. Aim 3 will evaluate crowdsourcing as a reliable, efficient method to collect data on individuals with chronic pain through the comparison of its results to what was found using KnowledgePanel.

项目概要/摘要 许多补充和综合健康 (CIH) 方法已被证明对慢性病有效 疼痛并包含在指南中。这种有效性的证据建立在数百项研究的基础上 数百万的研究资金，以及分析和更好地比较这些研究结果的能力 对于获得此项投资的全部价值至关重要。然而，有用的跨研究比较 至少有两个挑战阻碍了这些干预措施的更好理解和针对性： 缺乏共同的结果衡量标准，并且无法对患者进行有意义的分层或分类。 为了解决第一个挑战，在目标 1 中，本研究将开发和评估人行横道或人行横道之间的联系 29 项患者报告结果测量信息系统 (PROMIS®) 的组成部分 表格 (PROMIS-29) 和用于慢性疼痛的常见遗留措施。特别是，我们将创建 两种最常用的 CLBP 措施的人行横道/连接路，包括 Roland-Morris 残疾人 问卷 (RMDQ) 和 Oswestry 残疾指数 (ODI)。此外，根据数据可用性和 根据我们咨询委员会的意见，我们将在 PROMIS-29 和 CLBP 的其他遗留测量（例如，背痛功能量表）或其他类型的遗留测量 慢性疼痛（例如，慢性颈部疼痛的颈部残疾指数）。 为了应对第二个挑战，在目标 2 中，我们将评估和完善慢性疼痛影响分层 美国国立卫生研究院研究工作组针对慢性腰痛提出的计划。所提出的方案使用 影响分层分数 (ISS) 使用 PROMIS-29 中的 9 个项目计算得出。我们首先会评估 ISS 及其特性来评估它们在不同样本中是否稳定并确定是否 它们可以得到改进。在我们最终确定国际空间站的组成和计算之后，我们将检查它 对慢性疼痛的影响（例如，与健康相关的生活质量、医疗保健利用、工人 生产力）来确定有意义的切点，用于将慢性疼痛患者分为表现出表现的亚组 不同程度的慢性疼痛影响。 分析中将使用三种类型的数据来实现目标 1 和 2：来自三个大型内部机构的数据 现有数据集，使用亚马逊 Mechanical Turk 从国家便利样本中收集的数据 （MTurk）众包平台，数据来自基于概率的全国代表性 知识面板。目标 3 将评估众包作为一种可靠、高效的数据收集方法 通过将其结果与使用 KnowledgePanel 发现的结果进行比较，了解患有慢性疼痛的个体。