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Exploring the Association Between Structural Racism and Mental Health: Geospatial and Machine Learning Analysis.

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Structural racism significantly impacts mental health disparities, disproportionately affecting African American communities. Key factors include smoking, poverty, and lack of health insurance, necessitating targeted interventions.

Keywords:
deep learninggeospatialhealth disparitiesmachine learningmental healthracial disparitiessocial determinant of healthstructural racism

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Area of Science:

  • Public Health
  • Geospatial Analysis
  • Deep Learning

Background:

  • Structural racism is a known driver of mental health disparities.
  • Previous research has focused on individual factors, with less exploration of their collective impact as manifestations of structural racism.
  • Milwaukee County's diversity provides a unique setting for investigating these multifactorial influences.

Purpose of the Study:

  • To delineate the association between structural racism and mental health disparities in Milwaukee County.
  • To employ geospatial and deep learning techniques for a comprehensive analysis.
  • To quantify the impact of structural racism on poor mental health prevalence.

Main Methods:

  • Compiled 217 georeferenced variables, initially excluding race to identify nonracial determinants.
  • Utilized tree-based methods (random forest) and conventional techniques for variable selection, addressing multicollinearity.
  • Applied geographically weighted random forest and self-organizing maps with K-means clustering to analyze spatial heterogeneity and quantify racism's impact.

Main Results:

  • Twelve influential factors explained 95.11% of mental health variability.
  • Top factors included smoking, poverty, insufficient sleep, lack of health insurance, employment, and age.
  • African American neighborhoods showed a 2.23 times higher likelihood of high-risk mental health clusters.

Conclusions:

  • Structural racism demonstrably shapes mental health disparities, with Black communities bearing a disproportionate burden.
  • The integrated geospatial and deep learning approach effectively elucidates complex social determinants of mental health.
  • Findings underscore the necessity for targeted interventions addressing both individual and systemic factors to reduce mental health inequities.