Mohebbi Fahimeh, Forati Amir Masoud, Torres Lucas, deRoon-Cassini Terri A, Harris Jennifer, Tomas Carissa W, Mantsch John R, Ghose Rina
College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.
Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.
JMIR Public Health Surveill. 2024 May 3;10:e52691. doi: 10.2196/52691.
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.
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.
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.
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.
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.
结构性种族主义导致心理健康差异。虽然已有研究探讨了贫困和教育等个体因素的影响，但这些因素作为结构性种族主义的表现形式，其共同作用却较少被研究。威斯康星州的密尔沃基县具有种族和社会经济多样性，为这一多因素调查提供了独特背景。
本研究旨在运用地理空间和深度学习技术，描绘密尔沃基县结构性种族主义与心理健康差异之间的关联。我们使用了二次数据集，所有数据在由联邦机构发布前均已汇总并匿名化。
我们汇编了217个跨领域的地理参考解释变量，最初有意排除基于种族的因素，专注于非种族决定因素。这种方法旨在揭示导致心理健康不佳的风险因素的潜在模式，随后重新纳入种族因素以定量评估种族主义的影响。变量选择结合了基于树的方法（随机森林）和传统技术，并通过方差膨胀因子和皮尔逊相关分析来缓解多重共线性。地理加权随机森林模型用于研究空间异质性和依赖性。自组织映射结合K均值聚类，用于分析密尔沃基社区的数据，重点是量化结构性种族主义对心理健康不佳患病率的影响。
虽然12个有影响的因素共同占各社区心理健康变异性的95.11%，但前6个因素——吸烟、贫困、睡眠不足、缺乏医疗保险、就业和年龄——的影响尤为显著。主要是，非裔美国人社区受到的影响尤为严重，遭遇心理健康不佳高风险集群的可能性是其他社区的2.23倍。
研究结果表明，结构性种族主义塑造了心理健康差异，黑人社区成员受到的影响尤为严重。多方面的方法强调了整合地理空间分析和深度学习以理解心理健康复杂社会决定因素的价值。这些见解凸显了有针对性干预的必要性，既要解决个体因素，也要解决系统性因素，以减轻源于结构性种族主义的心理健康差异。
