A Bayesian network analysis to examine the effects of HIV stigma processes on self-concept and depressive symptoms among persons living with HIV

J Pers. 2024 Mar 17. doi: 10.1111/jopy.12930. Online ahead of print.

Abstract

Objective: This study examines the relationships between HIV stigma dimensions, self-related mechanisms, and depressive symptoms among persons living with HIV.

Background: HIV stigma hinders the well-being of individuals living with HIV, which is linked to depressive symptoms and increased risk of poor clinical outcomes. However, the mechanisms underlying stigma's impact on depression are poorly understood. Psychosocial theories propose that experiencing HIV stigma leads to internalized stigma, impacting self-concept and mental health.

Method: Using Bayesian network analysis, we explored associations among HIV stigma processes (experienced, anticipated, internalized, perceived community stigma, and HIV status disclosure) and self-related mechanisms (self-esteem, fear of negative evaluation [FNE], self-blame coping, and social exclusion), and depressive symptoms.

Results: Our diverse sample of 204 individuals, primarily men, gay/bisexual, Black, and lower-middle SES, who experienced stigma showed increased anticipated, internalized, and perceived community stigma, FNE, and depressive symptoms. Internalized stigma contributed to self-blame coping and higher depressive symptoms. Anticipated and perceived community stigma and FNE correlated with increased social exclusion.

Discussion: This study investigates potential mechanisms through which HIV stigma may impact depression. Identifying these mechanisms establishes a foundation for future research to inform targeted interventions, enhancing mental health and HIV outcomes among individuals living with HIV, especially from minority backgrounds. Insights gained guide evidence-based interventions to mitigate HIV stigma's detrimental effects, ultimately improving overall well-being and health-related outcomes for people with HIV.

Keywords: HIV; mental health; network analysis; stigma.