Socioecological Study of Autism, Suicide Risk, and Mental Health Care: Multilevel Analyses and Community Consultation for Suicide Prevention - Autistic people are at increased risk of suicide behaviors (SB), including suicidal ideation and suicide attempts, and have higher rates of suicide death compared to non-autistic people (use of ‘autistic’ reflects community language preferences). Yet there is limited understanding of the mutable factors related to risk of SB for the autistic population. Beyond biological and behavioral traits, research with the general public reveals multiple social determinants of health (e.g., community-level resources, state-level systems) that contribute to risk of SB. However, the impact of individual, community, and administrative factors on risk of SB in the autistic population is unknown. Meaningful improvements in suicide prevention and mental health service delivery will require an understanding of (1) how risk of SB clusters for autistic individuals across interdependent individual, community, and administrative factors and (2) the current state of mental health service (MHS) provision among autistic people. This understanding will enable the generation of evidence to inform interventions. To contribute to the long-term goal of reducing risk of SB for the autistic population, this project uses a sequential, mixed-methods design, in collaboration with autistic stakeholders, healthcare leaders, and practice experts, to analyze multilevel, socioecological factors associated with risk of SB and receipt of MHS. Aim 1 will identify clusters of socioecological factors (at individual, community, and administrative levels) associated with risk of SB among two national samples of autistic youth and adults. Autistic people (aged ≤64) will be identified in two national healthcare claims databases (MarketScan private, Medicaid) and integrated with public-use and proprietary databases to create multilevel, longitudinal datasets containing individual, community, and administrative factors. Data reduction, hierarchical clustering, and multilevel analytic techniques will partition individuals into homogeneous groups based on shared characteristics to identify underlying factors associated with risk of SB. Using these databases, Aim 2 will evaluate socioecological factors associated with MHS receipt (psychotherapy, pharmacology, both, neither), dose (visits/year), and delivery modality (face-to-face, telemental health, both) for autistic people with documented SB and/or co-occurring mental health conditions. Aim 3 will translate results into actionable practice recommendations through stakeholder engagement. Aim 4a will quantify the effects of modifiable state-level factors on SB and receipt of MHS using counterfactual simulation, generating predicted outcomes under alternative scenarios. Aim 4b will estimate the effects of exogenous changes in state-level environments using quasi-experimental methods that exploit temporal and geographic variation. These approaches will assess the potential impacts of the projected intervention. Stakeholder engagement will contextualize findings and prioritize strategies. This study will generate relevant evidence on the effects of modifiable state-level factors and healthcare system characteristics on SB and receipt of MHS. Results will inform clinical practice, support prevention strategies, and advance health equity for autistic populations.
