Validation of the Functional Comorbidity Index for Use with Workers' Compensation Data: Best Practices for Predicting Work-Related and Functional Outcomes

验证与工伤赔偿数据一起使用的功能性合并症指数：预测工作相关和功能性结果的最佳实践

• 批准号: 10709620
• 负责人: Jeanne M. Sears
• 金额: $ 7.76万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10709620
• 关键词: Validation; Functional; Comorbidity; Index; Use

Project Summary/Abstract oject Summary/Abstract Roughly half of U.S. workers report at least one chronic condition; roughly a quarter report multimorbidity. Workers with chronic conditions have higher health care utilization, and poorer health and employment outcomes. Chronic conditions are relevant to many occupational health research projects, either as the primary study focus, or to adjust for comorbidity burden; however, they are challenging to measure when relying on administrative workers’ compensation (WC) data. The Functional Comorbidity Index (FCI), an instrument particularly well-suited to working populations, has not yet been validated for use specifically with WC data. There are significant knowledge gaps regarding the importance of—and methods for—comorbidity adjustment when studying worker outcomes, due in part to uncertainty about the extent to which chronic conditions are captured in the WC databases that are often used to study worker outcomes at scale. Consequently, there is a pressing need for a chronic condition/comorbidity instrument validated for use with WC data. The overall objective of this proposal is to assess and maximize predictive validity of a WC-based FCI, using WC data linked to a large prospective longitudinal survey of injured workers in Washington State and Ohio. Aim 1: Assess the validity of using WC-based diagnosis codes for: (1A) identifying individual chronic conditions, and (1B) constructing a WC-based FCI. For each of the 18 individual FCI conditions, prevalence and concordance will be calculated, comparing identification via WC data to self-report. The WC-based FCI will be compared to the self-report FCI, assessing: (1) concurrent validity (concordance), (2) predictive validity for work-related and functional outcomes, and (3) utility for control of confounding. Aim 2: Assess methods of optimizing accurate chronic condition identification and FCI predictive validity when using WC data. Comparisons will include varying (1) WC data sources, (2) measurement timeframes, and (3) approaches to FCI construction. This research is innovative because it will provide heretofore unavailable data on (1) the feasibility and sensitivity of identifying chronic conditions via WC data, and (2) the performance, validity, and best practices for use of the FCI instrument with WC data. Expected outputs include contributing a validated WC-based FCI—potentially weighted or otherwise modified—to the public domain, along with a description of best methodological practices and limitations. This contribution will be significant because it will enable the expected outcomes of improved chronic condition surveillance and research, as well as increased focus on comorbidity adjustment in WC-based research, with appropriate interpretation and reporting of limitations. In turn, the expected increase in large-scale chronic condition research will support longer-term goals of quality improvement in WC-related health care, a healthier workforce, higher productivity, lower WC costs, and improved employment outcomes.

项目摘要/摘要 对象摘要/摘要 大约一半的美国工人报告至少有一种慢性病；大约四分之一的人报告说患有多发性疾病。 患有慢性病的工人的医疗保健利用率较高，健康和就业状况较差 结果。慢性疾病与许多职业健康研究项目有关，无论是作为主要的 研究重点，或针对共病负担进行调整；然而，当依赖于 管理人员薪酬(WC)数据。功能性共病指数(FCI)，一种工具 特别适合工作人口的数据，尚未被特别用于WC数据的验证。 关于共病调整的重要性和方法，存在着重大的知识差距 在研究工人的结果时，部分原因是对慢性病的程度不确定 在WC数据库中捕获，这些数据库通常用于研究大规模的工人结果。因此，有一个 迫切需要一种慢性病/共病仪器，经验证可与WC数据一起使用。整体而言 该建议的目的是使用WC数据来评估和最大化基于WC的FCI的预测有效性 与华盛顿州和俄亥俄州受伤工人的大型前瞻性纵向调查有关。目标1： 评估使用基于WC的诊断代码的有效性：(1A)识别个人慢性病，以及 (1B)构建基于WC的FCI。对于18种单独的FCI情况，患病率和一致性 将进行计算，将通过WC数据进行的识别与自我报告进行比较。基于WC的FCI将与 自我报告的FCI，评估：(1)同时效度(一致性)，(2)与工作相关的预测效度 功能结果，以及(3)控制混淆的效用。目标2：评估优化精确度的方法 使用WC数据时的慢性病识别和FCI预测有效性。比较内容包括 不同的(1)WC数据源，(2)测量时间范围，以及(3)构建FCI的方法。这 研究是创新的，因为它将提供迄今无法获得的以下数据：(1) 通过WC数据识别慢性疾病，以及(2)使用的性能、有效性和最佳实践 带有WC数据的FCI仪器。预期产出包括提供经过验证的基于WC的FCI-可能 加权或以其他方式修改--公有领域，以及最佳方法的描述 实践和限制。这一贡献将是重大的，因为它将使 改善慢性病监测和研究，以及在#年更加重视共病调整 以WC为基础的研究，对局限性进行适当的解释和报告。反过来，预期的增长 在大规模慢性病研究中，将支持与厕所相关的质量改进的长期目标 医疗保健、更健康的劳动力、更高的生产率、更低的厕所成本和更好的就业结果。