Social Drivers of Mental Health: A U.S. Study Using Machine Learning.
Academic Article
Overview
abstract
INTRODUCTION: Social drivers of mental health can be compared on an aggregated level. This study employed a machine learning approach to identify and rank social drivers of mental health across census tracts in the United States. METHODS: Data for 38,379 census tracts in the United States were collected from multiple sources in 2021. Two measures of mental health problems-self-reported depression and self-assessed poor mental health-among adults and three domains of social drivers (behavioral, environmental, and social) were analyzed based on the unit of census tracts using the extreme gradient boost (XGBoost) machine learning approach in 2022. The leading social drivers were found in each domain in the main sample, as well as in subsamples divided based on poverty and racial segregation. RESULTS: The three domains combined explained more than 90% of the variance of both mental illness indicators. Self-reported depression and self-assessed poor mental health differed in major social drivers. The two outcome indicators had one overlapping correlate from the behavioral domain: smoking. Other than smoking, climate zone and racial composition were the leading correlates from the environmental and social domains, respectively. Census tract characteristics moderated the effects of social drivers on mental health problems; the major social drivers differed by census tract poverty and racial segregation. CONCLUSIONS: Population mental health is highly contextualized. Better interventions can be developed based on census tract-level analyses of social drivers that characterize the upstream causes of mental health problems.
