Exploring the Association Between Structural Racism and Mental Health: Geospatial and Machine Learning Analysis. JMIR Public Health Surveill 2024 May 03;10:e52691
Date
05/03/2024Pubmed ID
38701436Pubmed Central ID
PMC11102033DOI
10.2196/52691Scopus ID
2-s2.0-85192123640 (requires institutional sign-in at Scopus site) 1 CitationAbstract
BACKGROUND: Structural racism produces mental health disparities. While studies have examined the impact of individual factors such as poverty and education, the collective contribution of these elements, as manifestations of structural racism, has been less explored. Milwaukee County, Wisconsin, with its racial and socioeconomic diversity, provides a unique context for this multifactorial investigation.
OBJECTIVE: This research aimed to delineate the association between structural racism and mental health disparities in Milwaukee County, using a combination of geospatial and deep learning techniques. We used secondary data sets where all data were aggregated and anonymized before being released by federal agencies.
METHODS: We compiled 217 georeferenced explanatory variables across domains, initially deliberately excluding race-based factors to focus on nonracial determinants. This approach was designed to reveal the underlying patterns of risk factors contributing to poor mental health, subsequently reintegrating race to assess the effects of racism quantitatively. The variable selection combined tree-based methods (random forest) and conventional techniques, supported by variance inflation factor and Pearson correlation analysis for multicollinearity mitigation. The geographically weighted random forest model was used to investigate spatial heterogeneity and dependence. Self-organizing maps, combined with K-means clustering, were used to analyze data from Milwaukee communities, focusing on quantifying the impact of structural racism on the prevalence of poor mental health.
RESULTS: While 12 influential factors collectively accounted for 95.11% of the variability in mental health across communities, the top 6 factors-smoking, poverty, insufficient sleep, lack of health insurance, employment, and age-were particularly impactful. Predominantly, African American neighborhoods were disproportionately affected, which is 2.23 times more likely to encounter high-risk clusters for poor mental health.
CONCLUSIONS: The findings demonstrate that structural racism shapes mental health disparities, with Black community members disproportionately impacted. The multifaceted methodological approach underscores the value of integrating geospatial analysis and deep learning to understand complex social determinants of mental health. These insights highlight the need for targeted interventions, addressing both individual and systemic factors to mitigate mental health disparities rooted in structural racism.
Author List
Mohebbi F, Forati AM, Torres L, deRoon-Cassini TA, Harris J, Tomas CW, Mantsch JR, Ghose RAuthors
John Mantsch PhD Chair, Professor in the Pharmacology and Toxicology department at Medical College of WisconsinCarissa W. Tomas PhD Assistant Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Terri A. deRoon Cassini PhD Center Director, Professor in the Surgery department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
AdultFemale
Health Status Disparities
Humans
Machine Learning
Male
Mental Health
Middle Aged
Racism
Spatial Analysis
Wisconsin
