Identifying Intersecting Factors Associated with Suicidal Thoughts and Behaviors among Transgender and Gender Diverse Adults: A Conditional Inference Tree Analysis (Preprint)

Abstract

Transgender and gender diverse (TGD) individuals are disproportionately impacted by suicidal thoughts and behaviors (STBs), and intersecting demographic and psychosocial factors may contribute to STB disparities.

In the U.S. Transgender Population Health Survey (N=274), we identified intersecting factors associated with increased risk for suicidal ideation, intent, plan, and attempts; and age of onset for each outcome using conditional inference trees.

This approach iteratively partitions samples into subgroups of greater homogeneity with respect to a specific outcome. In separate analyses, we (1) restricted variables to those typically available within electronic medical records (EMRs) and (2) expanded the variable set to include factors not typically within EMRs.

In restricted analyses, younger adults endorsed more frequent ideation, intent, and planning, with intersecting younger age and receiving public assistance associated with increased ideation; no variables were associated with previous suicide attempts. Ages of onset for ideation, plan, and attempts were associated with the intersections of age and gender identity, sexual minority identity, and receiving public assistance. In expanded analyses, psychiatric distress was associated with ideation, intent, and planning, but not attempts. High distress intersecting with high healthcare stereotype threat (HST) was associated with increased ideation, with younger age and lower income exacerbating risk. High discrimination was associated with past attempts, with lower discrimination increasing risk in the context of high HST. Ages of onset for ideation, plan, and attempts were associated with intersecting age, distress, and HST; distress alone, intersecting distress and HST; and intersecting HST and discrimination.

In this initial test of the conditional inference tree approach to identifying key subgroups with increased STB risk, risk was primarily influenced by intersecting age, distress, HST, and income. Identifying intersecting factors liked to these outcomes is vital for early detection STB risk among TGD individuals. This approach should be tested on a larger scale utilizing EMR data to facilitate service provision to TGD individuals who are at increased risk for STBs.