Beyond Diagnostic Categories: Examining the Interplay of Autism, Gender, and Social Context in Mental Health

• In Event: Cross-Cutting Research on Risk and Resilience Factors Among LGBTQIA+ Individuals

Abstract

Objective: One in 45 American adults has a documented autism diagnosis (Dietz et al., 2020). Yet, research addressing the mental health needs of autistic people beyond childhood is lacking. Moreover, research that considers the heterogeneity of autistic people’s identities is necessary to provide tailored support when needed. Using a national database and data-driven statistical approaches, the current study aimed to evaluate survey-based mental health and quality of life (QoL) data and subgroup differences within a large cohort of autistic adults.

Theoretical Framework: While minority stress theory has been utilized to understand unique stressors among LGBTQ, and more recently, autistic, individuals (Botha & Frost, 2020; Meyer, 2003), research originating from Black feminist and critical scholars further emphasize the additive and intersectional impacts of stress among multiply-minoritized individuals (Annamma et al., 2013; Crenshaw, 1989). These perspectives provide a framework for evaluating the extent to which mental health outcomes may differentially impact individuals within a marginalized group based on their simultaneous experiences of multiple forms of oppression (e.g., ableism, heterosexism, cisnormativity).

Methods: A cohort of autistic adults was identified from the National Institutes of Health (NIH) All of Us Research Program (N=2446). Machine learning hierarchical clustering techniques were used to identify potential demographically-derived subgroups within the cohort. Post hoc comparisons were conducted between clusters to assess potential differences in sociodemographics, mental health and QoL among the subgroups.

Data Sources: The All of Us Research Program is a nation-wide effort aimed at advancing precision medicine by increasing representation of historically underrepresented groups in biomedical research. The current study utilized data collected between 2018-2023 and came from three participant surveys: “Basics” (e.g., age, sex, gender, education); “Personal/Family Medical History” (e.g., disability, mental health history) and “Social Determinants of Health” (e.g., stress, discrimination, healthcare, social support).

Findings: Three subgroups were derived from the total autism sample (75% White, 62% between 18-39 years): Cluster 1 identified predominantly as cisgender, heterosexual men (n=792, 32%); Cluster 2 as largely cisgender, heterosexual women (n=1287, 53%); and Cluster 3 (n=367, 16%) identified predominantly with AFAB and sexual and gender minority (SGM) identities. At the time the data was collected, Cluster 3 participants tended to be younger and never married, and Clusters 1 and 2 had attained more education and higher income. Preliminary post hoc analyses revealed that the three clusters consistently differed in lifetime prevalence of anxiety, depression, ADHD, PTSD, eating disorder, and alcohol/other substance use disorders (Cluster 3>2>1). In terms of QoL, participants reported comparable levels of social support, but Cluster 3 also reported greater levels of loneliness (3>1=2) and everyday and healthcare-specific discrimination (both 3>2>1).

Significance: Preliminary findings reiterate that autistic individuals are a heterogeneous population with varied mental health needs, and that additional gender marginalization within this population may be associated with increased risk for certain mental health and QoL-related challenges. Disaggregated subgroup analyses may help challenge one-size-fits-all approaches to intervention and underscore the need for contextually responsive and identity-informed research and practice.

Authors

• Shalini Sivathasan, Boston College

• Chang Gu, Boston College

• Samantha Martin, Boston College

• Amy Hartman, University of Pittsburgh