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

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.

The Association between State-Level Structural Racism and Alcohol and Tobacco Use Behaviors among a National Probability Sample of Black Americans.

BACKGROUND: Structural racism is how society maintains and promotes racial hierarchy and discrimination through established and interconnected systems. Structural racism is theorized to promote alcohol and tobacco use, which are risk factors for adverse health and cancer-health outcomes. The current study assesses the association between measures of state-level structural racism and alcohol and tobacco use among a national sample of 1,946 Black Americans. METHODS: An existing composite index of state-level structural racism including five dimensions (subscales; i.e., residential segregation and employment, economic, incarceration, and educational inequities) was merged with individual-level data from a national sample dataset. Hierarchical linear and logistic regression models, accounting for participant clustering at the state level, assessed associations between structural racism and frequency of alcohol use, frequency of binge drinking, smoking status, and smoking frequency. Two models were estimated for each behavioral outcome, one using the composite structural racism index and one modeling dimensions of structural racism in lieu of the composite measure, each controlling for individual-level covariates. RESULTS: Results indicated positive associations between the incarceration dimension of the structural racism index and binge drinking frequency, smoking status, and smoking frequency. An inverse association was detected between the education dimension and smoking status. CONCLUSIONS: Results suggest that state-level structural racism expressed in incarceration disparities, is positively associated with alcohol and tobacco use among Black Americans. IMPACT: Addressing structural racism, particularly in incarceration practices, through multilevel policy and intervention may help to reduce population-wide alcohol and tobacco use behaviors and improve the health outcomes of Black populations.

Disparities in Socioeconomic Factors Mediate the Impact of Racial Segregation Among Patients With Hepatopancreaticobiliary Cancer.

BACKGROUND: Structural racism within the U.S. health care system contributes to disparities in oncologic care. This study sought to examine the socioeconomic factors that underlie the impact of racial segregation on hepatopancreaticobiliary (HPB) cancer inequities. METHODS: Both Black and White patients who presented with HPB cancer were identified from the linked Surveillance, Epidemiology, and End Results (SEER)-Medicare database (2005-2015) and 2010 Census data. The Index of Dissimilarity (IoD), a validated measure of segregation, was examined relative to cancer stage at diagnosis, surgical resection, and overall mortality. Principal component analysis and structural equation modeling were used to determine the mediating effect of socioeconomic factors. RESULTS: Among 39,063 patients, 86.4 % (n = 33,749) were White and 13.6 % (n = 5314) were Black. Black patients were more likely to reside in segregated areas than White patients (IoD, 0.62 vs. 0.52; p < 0.05). Black patients in highly segregated areas were less likely to present with early-stage disease (relative risk [RR], 0.89; 95 % confidence interval [CI] 0.82-0.95) or undergo surgery for localized disease (RR, 0.81; 95% CI 0.70-0.91), and had greater mortality hazards (hazard ratio 1.12, 95% CI 1.06-1.17) than White patients in low segregation areas (all p < 0.05). Mediation analysis identified poverty, lack of insurance, education level, crowded living conditions, commute time, and supportive income as contributing to 25 % of the disparities in early-stage presentation. Average income, house price, and income mobility explained 17 % of the disparities in surgical resection. Notably, average income, house price, and income mobility mediated 59 % of the effect that racial segregation had on long-term survival. CONCLUSION: Racial segregation, mediated through underlying socioeconomic factors, accounted for marked disparities in access to surgical care and outcomes for patients with HPB cancer.

Health System Structure: An Opportunity to Address Structural Racism and Discrimination.

OBJECTIVE: To examine the role of hub-and-spoke systems as a factor in structural racism and discrimination. BACKGROUND: Health systems are often organized in a "hub-and-spoke" manner to centralize complex surgical care to 1 high-volume hospital. Although the surgical health care disparities are well described across health care systems, it is not known how they seem across a single system's hospitals. METHODS: Adult patients who underwent 1 of 10 general surgery operations in 12 geographically diverse states (2016-2018) were identified using the Healthcare Cost and Utilization Project's State Inpatient Databases. System status was assigned using the American Hospital Association dataset. Hub designation was assigned in 2 ways: (1) the hospital performing the most complex operations (general hub) or (2) the hospital performing the most of each specific operation (procedure-specific hub). Independent multivariable logistic regression was used to evaluate the risk-adjusted odds of treatment at hubs by race and ethnicity. RESULTS: We identified 122,236 patients across 133 hospitals in 43 systems. Most patients were White (73.4%), 14.2% were Black, and 12.4% Hispanic. A smaller proportion of Black and Hispanic patient underwent operations at general hubs compared with White patients (B: 59.6% H: 52.0% W: 62.0%, P <0.001). After adjustment, Black and Hispanic patients were less likely to receive care at hub hospitals relative to White patients for common and complex operations (general hub B: odds ratio: 0.88 CI, 0.85, 0.91 H: OR: 0.82 CI, 0.79, 0.85). CONCLUSIONS: When White, Black, and Hispanic patients seek care at hospital systems, Black and Hispanic patients are less likely to receive treatment at hub hospitals. Given the published advantages of high-volume care, this new finding may highlight an opportunity in the pursuit of health equity.