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

> **NIH ALLCDC R03** · UNIVERSITY OF WASHINGTON · 2022 · $77,445

## Abstract

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 workfo...

## Key facts

- **NIH application ID:** 10517246
- **Project number:** 1R03OH012309-01A1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Jeanne M. Sears
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2022
- **Award amount:** $77,445
- **Award type:** 1
- **Project period:** 2022-09-30 → 2024-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10517246

## Citation

> US National Institutes of Health, RePORTER application 10517246, Validation of the Functional Comorbidity Index for Use with Workers' Compensation Data: Best Practices for Predicting Work-Related and Functional Outcomes (1R03OH012309-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10517246. Licensed CC0.

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