• Shichao Du, Jie Yao, Gordon C Shen, Betty Lin, Tomoko Udo, Julia Hastings, Fei Wang, Fusheng Wang, Zhe Zhang, Xinyue Ye, and Kai Zhang.
• Department of Sociology, University at Albany, State University of New York, Albany, New York.
• Am J Prev Med. 2023 Nov 1; 65 (5): 827834827-834.

IntroductionSocial 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 U.S.MethodsData for 38,379 census tracts in the U.S. 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 on the basis of the unit of census tracts using the Extreme Gradient Boosting machine learning approach in 2022. The leading social drivers were found in each domain in the main sample and in the subsamples divided on the basis of poverty and racial segregation.ResultsThe 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 impacts of social drivers on mental health problems; the major social drivers differed by census tract poverty and racial segregation.ConclusionsPopulation mental health is highly contextualized. Better interventions can be developed on the basis of census tract-level analyses of social drivers that characterize the upstream causes of mental health problems.Copyright © 2023 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

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