Intersectional identity approach to chronic pain disparities using latent class analysis

Area of Science:

• Health Disparities
• Chronic Pain Research
• Sociomedical Sciences

Background:

• Research on intersectionality and chronic pain disparities remains limited.
• Intersectionality examines how multiple identity facets interact to shape health disparities.
• Understanding these intersections is crucial for addressing inequities in chronic pain care.

Purpose of the Study:

• To apply an intersectional identity approach using Latent Class Analysis (LCA) to examine chronic pain disparities.
• To investigate how socioeconomic status, age, and race interact in the experience of chronic pain.
• To identify distinct patient subgroups experiencing chronic pain disparities.

Main Methods:

• Utilized pretreatment data from the "Learning About My Pain" trial, a study on psychosocial interventions for chronic pain.
• Employed Latent Class Analysis (LCA), a novel statistical method, to identify patient subgroups based on intersecting identity factors.
• Analyzed cross-sectional data from patients in low-income rural and suburban Alabama clinics.

Main Results:

• A 5-class model was identified: older adults, younger adults, severe disparity, older Black or African American, and Working Women.
• The "severe disparity" group exhibited the lowest functioning, while the "Working Women" group showed the highest.
• The "younger adults" group, despite being younger and more literate, reported pain interference and depressive symptoms similar to the "severe disparity" group.

Conclusions:

• The study underscores the critical role of intersecting socioeconomic status, age, and race in chronic pain experiences.
• Latent Class Analysis with an intersectional lens provides a comprehensive view of chronic pain disparities in underserved populations.
• Findings emphasize the need for tailored interventions considering the complex interplay of identity factors in chronic pain management.