Exploring the Association Between Structural Racism and MentalHealth:Geospatial and Machine Learning Analysis

Background: Structural racism produces mental health disparities. While studies have examined the impact of individual factorssuch as poverty and education, the collective contribution of these elements, as manifestations of structural racism, has been lessexplored. Milwaukee County, Wisconsin, with its racial and socioeconomic diversity, provides a unique context for thismulti factorial investigation. Objective: This research aimed to delineate the association between structural racism and mental health disparities in MilwaukeeCounty, using a combination of geospatial and deep learning techniques. We used secondary data sets where all data wereaggregated and anonymized before being released by federal agencies. Methods: We compiled 217 georeferenced explanatory variables across domains, initially deliberately excluding race-basedfactors to focus on nonracial determinants. This approach was designed to reveal the underlying patterns of risk factors contributingto poor mental health, subsequently reintegrating race to assess the effects of racism quantitatively. The variable selection combinedtree-based methods (random forest) and conventional techniques, supported by variance inflation factor and Pearson correlationanalysis for multicollinearity mitigation. The geographically weighted random forest model was used to investigate spatialheterogeneity and dependence. Self-organizing maps, combined with K-means clustering, were used to analyze data fromMilwaukee 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 particularlyimpactful. Predominantly, African American neighborhoods were disproportionately affected, which is 2.23 times more likelyto encounter high-risk clusters for poor mental health. Conclusions: The findings demonstrate that structural racism shapes mental health disparities, with Black community membersdisproportionately impacted. The multifaceted methodological approach underscores the value of integrating geospatial analysisand deep learning to understand complex social determinants of mental health. These insights highlight the need for targetedinterventions, addressing both individual and systemic factors to mitigate mental health disparities rooted in structural racism