Preliminary Validation of a General Factor Model of Chronic Overlapping Pain Conditions

Chronic overlapping pain conditions (COPCs) by definition, frequently co-occur, perhaps reflecting their shared etiologies. Their overlapping nature presents a methodological challenge, possibly masking associations between COPCs and health outcomes attributable to either general or specific processes. To address this challenge, we used population-based cohort data to evaluate the predictive validity of a bifactor model of 9 self-reported COPCs by assessing its association with incident pain-related clinical diagnoses; pain-relevant pharmacotherapy; and other health outcomes. We obtained data from a 2005 to 2006 study of Swedish adult twins linked with health data from nationwide registers through 2016 (N = 25,418). We then fit a bifactor model comprising a general COPC factor and 2 independent specific factors measuring pain-related somatic symptoms and neck and shoulder pain. Accounting for age, biological sex, and cancer, the general factor was associated with increased risk of all pain-related outcomes (eg, COPC diagnosis adjusted odds ratio [aOR], 1.71; 95% confidence interval [1.62, 1.81]), most mental health-related outcomes (eg, depression aOR, 1.72 [1.60, 1.85]), and overdose and mortality (eg, all-cause mortality aOR, 1.25 [1.09, 1.43]). The somatic symptoms specific factor was associated with pain-relevant pharmacotherapy (eg, prescribed opioids aOR, 1.25 [1.15, 1.36]), most mental health-related outcomes (eg, depression aOR, 1.95 [1.70, 2.23]), and overdose (eg, nonfatal overdose aOR, 1.66 [1.31, 2.10]). The neck and shoulder pain-specific factor was weakly and inconsistently associated with the outcomes. Findings provide initial support for the validity and utility of a general-factor model of COPCs as a tool to strengthen understanding of co-occurrence, etiology, and consequences of chronic pain.

Perspective

This article presents associations between a novel measurement model of COPCs and various health outcomes. Findings provide support for measuring pain across multiple domains rather than only measuring pain specific to one physical location in both research and clinical contexts.