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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.

While much public health research has shown that racism is a fundamental determinant of health outcomes and disparities, racist policy and practice have also been integral to the historical formation of the medical academy in the USA. While no single concept can capture the complexity or full dynamics of racism, the brief historical examples we discuss here show that structural violence is helpful for understanding how the histories of violence, neglect, and oppression that crisscross law enforcement, politics, medical care, and public health are inextricably linked and manifested in the present. Like the history of US policing, the history of medicine and health care in the USA is marked by racial injustice and myriad forms of violence: unequal access to health care, the segregation of medical facilities, and the exclusion of African Americans from medical education are some of the most obvious examples. The health community needs to confront these painful histories of structural violence to develop more effective anti-racist and benevolent public health responses to entrenched health inequalities, the COVID-19 pandemic, and future pandemics. Since 1619 when the first enslaved people were brought to the British Colony of Virginia until June 19, 1865, when the last enslaved Black person was emancipated in the USA, Black people, and especially Black women, endured violent medical treatment and experimentation against their will.

There are substantial gaps in educational outcomes between black and white students in the United States. Recently, increased attention has focused on differences in the rates at which black and white students are disciplined, finding that black students are more likely to be seen as problematic and more likely to be punished than white students are for the same offense. Although these disparities suggest that racial biases are a contributor, no previous research has shown associations with psychological measurements of bias and disciplinary outcomes. We show that county-level estimates of racial bias, as measured using data from approximately 1.6 million visitors to the Project Implicit website, are associated with racial disciplinary disparities across approximately 96,000 schools in the United States, covering around 32 million white and black students. These associations do not extend to sexuality biases, showing the specificity of the effect. These findings suggest that acknowledging that racial biases and racial disparities in education go hand-in-hand may be an important step in resolving both of these social ills.

Implicit racial bias remains widespread, even among individuals who explicitly reject prejudice. One reason for the persistence of implicit bias may be that it is maintained through structural and historical inequalities that change slowly. We investigated the historical persistence of implicit bias by comparing modern implicit bias with the proportion of the population enslaved in those counties in 1860. Counties and states more dependent on slavery before the Civil War displayed higher levels of pro-White implicit bias today among White residents and less pro-White bias among Black residents. These associations remained significant after controlling for explicit bias. The association between slave populations and implicit bias was partially explained by measures of structural inequalities. Our results support an interpretation of implicit bias as the cognitive residue of past and present structural inequalities.

Disparities in adverse birth outcomes for Black women continue. Research suggests that societal factors such as structural racism explain more variation in adverse birth outcomes than individual-level factors and societal poverty alone. The Index of Concentration at the Extremes (ICE) measures spatial social polarization by quantifying extremes of deprived and privileged social groups using a single metric and has been shown to partially explain racial disparities in black carbon exposures, mortality, fatal and non-fatal assaults, and adverse birth outcomes such as preterm birth and infant mortality. The objective of this analysis was to assess if local measures of racial and economic segregation as proxies for structural racism are associated and preterm birth and infant mortality experienced by Black women residing in California. California birth cohort files were merged with the American Community Survey by zip code (2011–2012). The ICE was used to quantify privileged and deprived groups (i.e., Black vs. White; high income vs. low income; Black low income vs. White high income) by zip code. ICE scores range from − 1 (deprived) to 1 (privileged). ICE scores were categorized into five quintiles based on sample distributions of these measures: quintile 1 (least privileged)–quintile 5 (most privileged). Generalized linear mixed models were used to test the likelihood that ICE measures were associated with preterm birth or with infant mortality experienced by Black women residing in California. Black women were most likely to reside in zip codes with greater extreme income concentrations, and moderate extreme race and race + income concentrations. Bivariate analysis revealed that greater extreme income, race, and race + income concentrations increased the odds of preterm birth and infant mortality. For example, women residing in least privileged zip codes (quintile 1) were significantly more likely to experience preterm birth (race + income ICE OR = 1.31, 95% CI = 1.72–1.46) and infant mortality (race + income ICE OR = 1.70, 95% CI = 1.17–2.47) compared to women living in the most privileged zip codes (quintile 5). Adjusting for maternal characteristics, income, race, and race + income concentrations remained negatively associated with preterm birth. However, only race and race + income concentrations remained associated with infant mortality. Findings support that ICE is a promising measure of structural racism that can be used to address racial disparities in preterm birth and infant mortality experienced by Black women in California.

Biomedical science and federal funding for scientific research are not immune to the systemic racism that pervades American society. A groundbreaking analysis of NIH grant success revealed in 2011 that grant applications submitted to the National Institutes of Health in the US by African-American or Black Principal Investigators (PIs) are less likely to be funded than applications submitted by white PIs, and efforts to narrow this funding gap have not been successful. A follow-up study in 2019 showed that this has not changed. Here, we review those original reports, as well as the response of the NIH to these issues, which we argue has been inadequate. We also make recommendations on how the NIH can address racial disparities in grant funding and call on scientists to advocate for equity in federal grant funding.

Some interventions could help to reduce racism and rein in the use of unnecessary force in police work, but the evidence base is still evolving. Some interventions could help to reduce racism and rein in the use of unnecessary force in police work, but the evidence base is still evolving.

We assessed racial disparities in policing in the United States by compiling and analysing a dataset detailing nearly 100 million traffic stops conducted across the country. We found that black drivers were less likely to be stopped after sunset, when a ‘veil of darkness’ masks one’s race, suggesting bias in stop decisions. Furthermore, by examining the rate at which stopped drivers were searched and the likelihood that searches turned up contraband, we found evidence that the bar for searching black and Hispanic drivers was lower than that for searching white drivers. Finally, we found that legalization of recreational marijuana reduced the number of searches of white, black and Hispanic drivers—but the bar for searching black and Hispanic drivers was still lower than that for white drivers post-legalization. Our results indicate that police stops and search decisions suffer from persistent racial bias and point to the value of policy interventions to mitigate these disparities. Pierson et al. compiled and analysed a dataset detailing nearly 100 million traffic stops conducted across the United States, finding evidence of persistent racial bias against black and Hispanic drivers in police stops and search decisions.

Objectives Experiences of vicarious racism—hearing about racism directed toward one’s racial group or racist acts committed against other racial group members—and vigilance about racial discrimination have been salient during the COVID-19 pandemic. This study examined vicarious racism and vigilance in relation to symptoms of depression and anxiety among Asian and Black Americans. Methods We used data from a cross-sectional study of 604 Asian American and 844 Black American adults aged ≥18 in the United States recruited from 5 US cities from May 21 through July 15, 2020. Multivariable linear regression models examined levels of depression and anxiety by self-reported vicarious racism and vigilance. Results Controlling for sociodemographic characteristics, among both Asian and Black Americans, greater self-reported vicarious racism was associated with more symptoms of depression (Asian: β = 1.92 [95% CI, 0.97-2.87]; Black: β = 1.72 [95% CI, 0.95-2.49]) and anxiety (Asian: β = 2.40 [95% CI, 1.48-3.32]; Black: β = 1.98 [95% CI, 1.17-2.78]). Vigilance was also positively related to symptoms of depression (Asian: β = 1.54 [95% CI, 0.58-2.50]; Black: β = 0.90 [95% CI, 0.12-1.67]) and anxiety (Asian: β = 1.98 [95% CI, 1.05-2.91]; Black: β = 1.64 [95% CI, 0.82-2.45]). Conclusions Mental health problems are a pressing concern during the COVID-19 pandemic. Results from our study suggest that heightened racist sentiment, harassment, and violence against Asian and Black Americans contribute to increased risk of depression and anxiety via vicarious racism and vigilance. Public health efforts during this period should address endemic racism as well as COVID-19.

The present research documents the widespread misperception of race-based economic equality in the United States. Across four studies (n = 1,377) sampling White and Black Americans from the top and bottom of the national income distribution, participants overestimated progress toward Black–White economic equality, largely driven by estimates of greater current equality than actually exists according to national statistics. Overestimates of current levels of racial economic equality, on average, outstripped reality by roughly 25% and were predicted by greater belief in a just world and social network racial diversity (among Black participants). Whereas high-income White respondents tended to overestimate racial economic equality in the past, Black respondents, on average, underestimated the degree of past racial economic equality. Two follow-up experiments further revealed that making societal racial discrimination salient increased the accuracy of Whites’ estimates of Black–White economic equality, whereas encouraging Whites to anchor their estimates on their own circumstances increased their tendency to overestimate current racial economic equality. Overall, these findings suggest a profound misperception of and unfounded optimism regarding societal race-based economic equality—a misperception that is likely to have any number of important policy implications.