How Well Can We Measure Chronic Pain Impact in Existing Longitudinal Cohort Studies? Lessons Learned

Area of Science:

• Epidemiology
• Psychometrics
• Health Services Research

Background:

• Large longitudinal cohort studies offer valuable data for pain research, even when pain assessment was not an initial focus.
• Repurposing existing datasets requires careful evaluation of measurement properties to ensure data validity for new research questions.

Purpose of the Study:

• To illustrate methods for assessing measurement properties of existing cohort data for pain impact research.
• To guide researchers in evaluating the suitability of large datasets for studying chronic pain trajectories and predictors.
• To highlight the potential and limitations of using prospectively collected data for pain research.

Main Methods:

• Screening large cohort datasets (UK Biobank, English Longitudinal Study of Ageing) for variables related to pain impact.
• Applying psychometric evaluation techniques to assess the measurement properties of selected variables.
• Developing and testing measurement models to capture the impact of chronic pain on self-care, productivity, and social activities.

Main Results:

• In UK Biobank, insufficient internal consistency prevented a satisfactory measurement model for chronic pain impact.
• In the English Longitudinal Study of Ageing, a measurement model was identified but limited to self-care and productivity, lacking social participation aspects.
• Both examples underscore the challenges and lessons learned in repurposing large cohort data for pain research.

Conclusions:

• Repurposing large cohort data for pain impact research is feasible but requires rigorous psychometric evaluation.
• Researchers must be aware of the limitations of existing data, particularly in capturing the full spectrum of chronic pain's effects.
• A theory-driven approach to fitting and evaluating measurement models is essential for maximizing the utility of large cohort datasets in pain research.