Who Is Getting the Help They Need? An AI-Driven Study of Intersectional Disparities in Mental Health Service Utilization Among Young Adults with Suicidal Ideation

Despite the critical role of mental health services in suicide prevention, disparities in service utilization persist across various individual and social determinants of health. This study identifies key factors and intersectional patterns associated with mental healthcare use among young adults with past-year suicidal ideation, by developing machine learning-based artificial intelligence to improve predictive accuracy while ensuring interpretability for clinicians and policymakers. Utilizing cross-sectional data from the 2015-2020 National Survey on Drug Use and Health, we analyzed 11,018 US young adults aged 18-34 who reported past-year suicidal ideation. Random Forest and Shapley Additive Explanations identified the strongest predictors of mental healthcare utilization among 23 individual and social determinants of health. The decision tree model visualized prediction of utilization rates across intersectional characteristics. Findings indicate that depression, race/ethnicity, sexual orientation, and private health insurance were the strongest predictors of service use. Individuals without depression, males, Black, Indigenous, and People of Color, heterosexual individuals, and individuals without private health insurance were significantly less likely to seek care. These findings highlight the current intersectional disparities in mental healthcare utilization among young adults at risk of suicide. Expanding culturally competent care and promoting equitable access to mental healthcare for young adults at risk are crucial steps in addressing these disparities.