Intersectional identity approach to chronic pain disparities using latent class analysis

Abstract

Research on intersectionality and chronic pain disparities is very limited. Intersectionality explores the interconnections between multiple aspects of identity and provides a more accurate image of disparities. This study applied a relatively novel statistical approach (ie, Latent Class Analysis) to examine chronic pain disparities with an intersectional identity approach. Cross-sectional data were analyzed using pretreatment data from the Learning About My Pain trial, a randomized comparative effectiveness study of group-based psychosocial interventions (Patient-Centered Outcomes Research Institute Contract #941, Beverly Thorn, PI; clinicaltrials.gov identifier NCT01967342) for patients receiving care for chronic pain at low-income clinics in rural and suburban Alabama. Latent Class Analysis results suggested a 5-class model. To easily identify each class, the following labels were created: older adults, younger adults, severe disparity, older Black or African American, and Working Women. The latent disparity classes varied by pretreatment chronic pain functioning. Overall, the severe disparity group had the lowest levels of functioning, and the Working Women group had the highest levels of functioning. Although younger and with higher literacy levels, the younger adults group had similar levels of pain interference and depressive symptoms to the severe disparity group (P's < 0.05). The younger adults group also had higher pain catastrophizing than the older adults group (P < 0.005). Results highlighted the importance of the interactions between the multiple factors of socioeconomic status, age, and race in the experience of chronic pain. The intersectional identity theory approach through Latent Class Analysis provided an integrated image of chronic pain disparities in a highly understudied and underserved population.

In Brief

Various aspects of social identity interact to influence chronic pain. Latent class analysis proved to be a successful approach to exploring intersectional pain disparities.