HMN 2026: How Directory of health datasets makes it easier to navigate publicly available youth mental health data

The percentage of teens reporting sadness and hopelessness has increased over the past decade (from 28% in 2011 to 40% in 2023). Accurate information on youth mental health outcomes is critical for health improvement programs. However, accessing mental health data has been a barrier to studying this important topic.

Many parents wish to protect their child’s records through the Health Insurance Portability and Accountability Act (HIPAA), and these protections are essential. As a result, researchers must rely on ethically collected, publicly available, and de-identified data to study youth mental health trends. However, unearthing public data sources is not so simple.

Data accessibility researcher Hua Min of George Mason University has tackled the challenge of navigating multiple datasets. Min recently developed a curated directory that centralizes publicly available data resources focused on youth mental health, addressing the fragmented state of current information sources.

The work is published in the journal JMIR Mental Health.

The directory compiles major national surveys conducted by organizations such as the National Institutes of Health, Centers for Disease Control and Prevention, Substance Abuse and Mental Health Services Administration, and the U.S. Census Bureau. Min’s research into the infrastructure of youth mental health data sources and subsequent creation of the curated repository found that:

• Compiling youth mental health resources streamlines access, enhances research impact, and informs interventions and policies.
• Youth mental health data is robust, but difficult to locate due to disorganization and privacy protections (e.g., HIPAA), barring researchers from viewing the most recent information.
• The curated directory centralizes public datasets, enabling easier discovery, reuse, data analysis, and supports future research into the application of artificial intelligence and machine learning in youth mental health research.

Min’s research offers a novel collection of health datasets and provides a starting point for creating centralized directories not only for youth mental health information, but all research fields.

The content is provided for information purposes only.