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: 10517246
• 负责人: Jeanne M. Sears
• 金额: $ 7.74万
• 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/10517246
• 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.

项目总结/文摘