Associations between clusters of perceived social support level, depression, and suicidal ideation among transgender women: A latent class analysis

Abstract

Background: Suicide is recognized as a pivotal public health issue and has become a significant cause of death worldwide. Transgender persons are at greater risk of suicide than the general population. This study aims to identify suicidal ideation in transgender women according to clusters of depressive symptoms, and levels of perceived social support using latent class analysis (LCA) and identify associations between the identified classes and suicidal ideation. Design and methods: This cross-sectional study was conduct-ed between March 2019 and May 2019 using the snowball sampling method in a sample of 280 transgender women in Bangkok, Thailand. Data were collected using a self-administered question-naire, and LCA was performed according to the level of perceived social support and depression. The questionnaire included the fol-lowing: demographic information, measures of social support (MSPSS), depression (CES-D), and suicidal ideation (C-SSRS). Multivariable logistic regression was used to examine the associations between the identified classes and suicidal ideation. Results: The multivariable logistic regression analysis showed that suicidal ideation was significantly associated with perceived moderate social support with depression [class 1; odds ratio (OR) 5.57, 95% confidence interval (CI) 2.64-11.71; P<0.001] and perceived low social support with depression (class 4; OR 4.55, 95% CI 1.67-12.42; P=0.003) after adjusting for income sufficiency, chronic disease, and alcohol drinking. Conclusions: The findings indicate that less perceived social support and depression significantly increased suicidal ideation among transgender women. To tackle this issue efficiently, it is necessary for public health service providers, parties, and individuals concerned to collaborate and prioritize key agendas that support the social and psychological aspects of transgender women.