Background/Objectives: Lesbian, gay, bisexual, and transgender (LGBT) cancer survivors face disproportionately high structural and psychosocial barriers to post-diagnosis care. However, heterogeneity within this population remains understudied. This study aimed to characterize healthcare utilization (HCU) barriers among LGBT cancer survivors, assess psychosocial vulnerabilities (discrimination, stress, and social support), and identify survivor subgroups at greatest risk for care disengagement. Methods: Data were drawn from the All of Us Research Program. A sample of 3502 LGBT cancer survivors was analyzed, including lesbian (n = 730), gay (n = 1285), bisexual (n = 1296), and transgender/gender expansive (TGE) (n = 209) individuals. HCU barriers were assessed using 21 binary indicators. Psychosocial measures included the Everyday Discrimination Scale, Perceived Stress Scale, and MOS Social Support Survey. Agglomerative hierarchical cluster analysis identified latent HCU barrier profiles. Differences across clusters and identity groups were assessed using ANOVA and chi-square tests, and multinomial logistic regression examined demographics, socioeconomic, and psychosocial predictors of cluster membership. Results: Three distinct HCU barrier clusters were identified: low (59.7%), moderate (27.8%), and high (12.5%). Bisexual and TGE survivors were disproportionately represented in the high-barrier cluster, which was characterized by widespread cost-related nonadherence, structural delays in care, and higher levels of perceived discrimination and stress. In adjusted models, bisexual identity, lower income, female sex assigned at birth, and higher discrimination and perceived stress were independently associated with increased odds of high-barrier cluster membership. Conclusions: Substantial heterogeneity exists in HCU barriers among LGBT cancer survivors. Bisexual and TGE survivors experience a concentrated burden of structural and psychosocial barriers to survivorship care, highlighting the relevance of targeted, data-driven approaches to reduce access inequities within this population.
Brown-Savita et al. (Tue,) studied this question.