The War on Drugs, Racialized Capitalism, and Health Care Utilization Among White People Who Use Drugs in 22 Rural Appalachian Counties.

Objectives. To analyze War on Drugs encounters and their relationships to health care utilization among White people who use drugs (PWUD) in 22 Appalachian rural counties in Kentucky, West Virginia, Ohio, and North Carolina. Methods. We recruited White PWUD using chain referral sampling in 2018 to 2020. Surveys asked about criminal-legal encounters, unmet health care needs, and other covariates. We used generalized estimating equations to regress unmet need on criminal-legal encounters in multivariable models. Results. In this sample (n = 957), rates of stop and search, arrest, incarceration, and community supervision were high (44.0%, 26.8%, 36.3%, and 31.1%, respectively), as was unmet need (68.5%). Criminal-legal encounters were unrelated to unmet need (stops: adjusted prevalence ratio [APR] = 1.13; 95% confidence interval [CI] = 0.97, 1.32; arrest: APR = 0.95; 95% CI = 0.78, 1.15; incarceration: APR = 1.01; 95% CI = 0.89, 1.14; community supervision: APR = 0.99; 95% CI = 0.90, 1.09). Conclusions. Contrasting with findings from predominantly Black urban areas, criminal-legal encounters and unmet need were unrelated among White Appalachian PWUD. Research should explore whether and under what conditions White supremacy's benefits might buffer adverse impacts of the War on Drugs in Appalachia. (Am J Public Health. 2024;114(10):1086-1096. https://doi.org/10.2105/AJPH.2024.307744).

Skin tone discrimination and birth control avoidance among women in Harris County, Texas: a cross-sectional study.

INTRODUCTION: Structural racism plays a major role in reproductive health inequities. Colorism, discrimination based on skin color, may profoundly impact reproductive health access and service delivery. However, quantitative research in this area is limited. METHODS: We administered an online survey of women (n = 1,299) aged 18-44 from Harris County, Texas to assess the relationship between skin color discrimination and reproductive health service avoidance. The survey included questions on demographics, self-reported skin tone, and dichotomous measures of previous discrimination experiences and avoidance of care because of perceived discrimination. Binary logistic regression was used to examine whether race/ethnicity, skin tone, and previous discrimination experiences were related to avoidance of contraceptive care because of perceived discrimination. RESULTS: Approximately one-third (31.5%) of the sample classified themselves as non-Hispanic Whites (31.5%), 22.4% as Black, 27.4% as Hispanic and born within the US, and 7.6% as Hispanic born outside of the US. Approximately one-third of women classified themselves in the lightest skin tones, whereas almost one in five women classified themselves in the darkest skin tone palates. Darker skin tones had increasingly greater odds of reporting that they avoided seeking birth control out of a concern for discrimination compared to the lightest skin tone. After adjusting for race/ethnicity and sociodemographic variables (model 3), darker skin tones remained significantly associated with avoiding birth control. DISCUSSION: This study demonstrates the role that skin color discrimination plays in negative reproductive health experiences. While this is not surprising given that those with racist ideologies developed the concept of these racial and ethnic categories, the apparent association with darker skin colors and avoidance of seeking birth control provides evidence that structural and individual racism continues to have far-reaching and insidious consequences. CONCLUSION: Contraception is recognized for reducing maternal mortality, improving child health, increasing female empowerment, and decreasing poverty. However, not all women equally enjoy the benefits of access to contraception. Addressing colorism within reproductive healthcare has become critically important as the nation becomes increasingly diverse. Focusing on skin tone-based discrimination and its roots in anti-blackness expands our understanding beyond a Black-White binary traditionally applied when addressing racism in healthcare delivery.

Associations between Structural Racism, Environmental Burden, and Cancer Rates: An Ecological Study of US Counties.

Objective: In this study, we examined associations between county-level measures of structural racism and county-level cancer incidence and mortality rates between race groups while accounting for factors associated with cancer rates and county-level measures of environmental burden. Methods: To explore this relationship, we conducted multiple linear regression analyses. Data for these analyses came from an index of county-level structural racism and publicly available data on 2015 to 2019 age-adjusted cancer rates from the US Cancer Statistics Data Visualization Tool, 2019 County Health Rankings and Roadmaps, the Environmental Protection Agency's 2006 to 2010 Environmental Quality Index, and 2015 to 2019 estimates from the US Census American Community Survey. Results: County-level structural racism was associated with higher county cancer incidence rates among Black (adjusted incidence rate: 17.4, 95% confidence interval [95% CI]: 9.3, 25.5) and Asian/Pacific Islander populations (adjusted incidence rate: 9.3, 95% CI: 1.8, 16.9) and higher mortality rates for American Indian/Alaskan Native (adjusted mortality rate [AMR]: 17.4, 95% CI: 4.2, 30.6), Black (AMR: 11.9, 95% CI: 8.9, 14.8), and Asian/Pacific Islander (AMR: 4.7, 95% CI: 1.3, 8.1) populations than White populations. Conclusion: Our findings highlight the detrimental impact of structural racism on cancer outcomes among minoritized populations. Strategies aiming to mitigate cancer disparities must embed processes to recognize and address systems, policies, laws, and norms that create and reproduce patterns of discrimination.

Spatial and spatio-temporal statistical implications for measuring structural racism: A review of three widely used residential segregation measures.

Social determinants of health are the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning and quality of life outcomes and risks - these social determinants of health often aid in explaining the racial and ethnic health inequities present in the United States (US). The root cause of these social determinants of health has been tied to structural racism, and residential segregation is one such domain of structural racism that allows for the operationalization of the geography of structural racism. This review focuses on three residential segregation measures that are often utilized to capture segregation as a function of race/ethnicity, income, and simultaneously race/ethnicity and income. Empirical findings related to the spatial and spatio-temporal heterogeneity of these residential segregation measures are presented. We also discuss some of the implications of utilizing these three residential segregation measures.

Fairness in Predicting Cancer Mortality Across Racial Subgroups.

Importance: Machine learning has potential to transform cancer care by helping clinicians prioritize patients for serious illness conversations. However, models need to be evaluated for unequal performance across racial groups (ie, racial bias) so that existing racial disparities are not exacerbated. Objective: To evaluate whether racial bias exists in a predictive machine learning model that identifies 180-day cancer mortality risk among patients with solid malignant tumors. Design, Setting, and Participants: In this cohort study, a machine learning model to predict cancer mortality for patients aged 21 years or older diagnosed with cancer between January 2016 and December 2021 was developed with a random forest algorithm using retrospective data from the Mount Sinai Health System cancer registry, Social Security Death Index, and electronic health records up to the date when databases were accessed for cohort extraction (February 2022). Exposure: Race category. Main Outcomes and Measures: The primary outcomes were model discriminatory performance (area under the receiver operating characteristic curve [AUROC], F1 score) among each race category (Asian, Black, Native American, White, and other or unknown) and fairness metrics (equal opportunity, equalized odds, and disparate impact) among each pairwise comparison of race categories. True-positive rate ratios represented equal opportunity; both true-positive and false-positive rate ratios, equalized odds; and the percentage of predictive positive rate ratios, disparate impact. All metrics were estimated as a proportion or ratio, with variability captured through 95% CIs. The prespecified criterion for the model's clinical use was a threshold of at least 80% for fairness metrics across different racial groups to ensure the model's prediction would not be biased against any specific race. Results: The test validation dataset included 43 274 patients with balanced demographics. Mean (SD) age was 64.09 (14.26) years, with 49.6% older than 65 years. A total of 53.3% were female; 9.5%, Asian; 18.9%, Black; 0.1%, Native American; 52.2%, White; and 19.2%, other or unknown race; 0.1% had missing race data. A total of 88.9% of patients were alive, and 11.1% were dead. The AUROCs, F1 scores, and fairness metrics maintained reasonable concordance among the racial subgroups: the AUROCs ranged from 0.75 (95% CI, 0.72-0.78) for Asian patients and 0.75 (95% CI, 0.73-0.77) for Black patients to 0.77 (95% CI, 0.75-0.79) for patients with other or unknown race; F1 scores, from 0.32 (95% CI, 0.32-0.33) for White patients to 0.40 (95% CI, 0.39-0.42) for Black patients; equal opportunity ratios, from 0.96 (95% CI, 0.95-0.98) for Black patients compared with White patients to 1.02 (95% CI, 1.00-1.04) for Black patients compared with patients with other or unknown race; equalized odds ratios, from 0.87 (95% CI, 0.85-0.92) for Black patients compared with White patients to 1.16 (1.10-1.21) for Black patients compared with patients with other or unknown race; and disparate impact ratios, from 0.86 (95% CI, 0.82-0.89) for Black patients compared with White patients to 1.17 (95% CI, 1.12-1.22) for Black patients compared with patients with other or unknown race. Conclusions and Relevance: In this cohort study, the lack of significant variation in performance or fairness metrics indicated an absence of racial bias, suggesting that the model fairly identified cancer mortality risk across racial groups. It remains essential to consistently review the model's application in clinical settings to ensure equitable patient care.

Quantification and visualization of US racial geography using the National Racial Geography Dataset 2020.

Racial geography studies the spatial distributions of multiracial populations. Technical challenges arise from the fact that US Census data, upon which all US-based studies rely, is only available in the form of spatial aggregates at a few levels of granularity. This negatively affects spatial analysis and, consequently, the quantification of racial segregation, especially on a smaller length scale. A recent methodology called the Racial Landscape (RL) stochastically disaggregates racial data at the level of census block aggregates into a grid of monoracial cells. RL-transformed racial data makes possible pattern-based, zoneless analysis, and visualization of racial geography. Here, we introduce the National Racial Geography Dataset 2020 (NRGD2020)-a collection of RL-based grids calculated from the 2020 census data and covering the entire conterminous US. It includes a virtual image layer for a bird's-eye-like view visualization of the spatial distribution of racial sub-populations, numerical grids for calculating racial diversity and segregation within user-defined regions, and precalculated maps of racial diversity and segregation on various length scales. NRGD2020 aims to facilitate and extend spatial analyses of racial geography and to make it more interpretable by tightly integrating quantitative analysis with visualization (mapping).

Epigenetic Aging and Racialized, Economic, and Environmental Injustice: NIMHD Social Epigenomics Program.

Importance: Epigenetic age acceleration is associated with exposure to social and economic adversity and may increase the risk of premature morbidity and mortality. However, no studies have included measures of structural racism, and few have compared estimates within or across the first and second generation of epigenetic clocks. Objective: To determine whether epigenetic age acceleration is positively associated with exposures to diverse measures of racialized, economic, and environmental injustice measured at different levels and time periods. Design, Setting, and Participants: This cross-sectional study used data from the My Body My Story (MBMS) study between August 8, 2008, and December 31, 2010, and examination 5 of the Multi-Ethnic Atherosclerosis Study (MESA) from April 1, 2010, to February 29, 2012. In the MBMS, DNA extraction was performed in 2021; linkage of structural measures to the MBMS and MESA, in 2022. US-born individuals were randomly selected from 4 community health centers in Boston, Massachusetts (MBMS), and 4 field sites in Baltimore, Maryland; Forsyth County, North Carolina; New York City, New York; and St Paul, Minnesota (MESA). Data were analyzed from November 13, 2021, to August 31, 2023. Main Outcomes and Measures: Ten epigenetic clocks (6 first-generation and 4 second-generation), computed using DNA methylation data (DNAm) from blood spots (MBMS) and purified monocytes (MESA). Results: The US-born study population included 293 MBMS participants (109 men [37.2%], 184 women [62.8%]; mean [SD] age, 49.0 [8.0] years) with 224 Black non-Hispanic and 69 White non-Hispanic participants and 975 MESA participants (492 men [50.5%], 483 women [49.5%]; mean [SD] age, 70.0 [9.3] years) with 229 Black non-Hispanic, 191 Hispanic, and 555 White non-Hispanic participants. Of these, 140 (11.0%) exhibited accelerated aging for all 5 clocks whose estimates are interpretable on the age (years) scale. Among Black non-Hispanic MBMS participants, epigenetic age acceleration was associated with being born in a Jim Crow state by 0.14 (95% CI, 0.003-0.27) SDs and with birth state conservatism by 0.06 (95% CI, 0.01-0.12) SDs, pooling across all clocks. Low parental educational level was associated with epigenetic age acceleration, pooling across all clocks, for both Black non-Hispanic (0.24 [95% CI, 0.08-0.39] SDs) and White non-Hispanic (0.27 [95% CI, 0.03-0.51] SDs) MBMS participants. Adult impoverishment was positively associated with the pooled second-generation clocks among the MESA participants (Black non-Hispanic, 0.06 [95% CI, 0.01-0.12] SDs; Hispanic, 0.07 [95% CI, 0.01-0.14] SDs; White non-Hispanic, 0.05 [95% CI, 0.01-0.08] SDs). Conclusions and Relevance: The findings of this cross-sectional study of MBMS and MESA participants suggest that epigenetic age acceleration was associated with racialized and economic injustice, potentially contributing to well-documented inequities in premature mortality. Future research should test the hypothesis that epigenetic accelerated aging may be one of the biological mechanisms underlying the well-documented elevated risk of premature morbidity and mortality among social groups subjected to racialized and economic injustice.

Assessment of unmeasured confounding in the association between perceived discrimination and mental health in a predominantly African American cohort using g-estimation.

BACKGROUND: Perceived discrimination in health care settings can have adverse consequences on mental health in minority groups. However, the association between perceived discrimination and mental health is prone to unmeasured confounding. The study aims to quantitatively evaluate the influence of unmeasured confounding in this association, using g-estimation. METHODS: In a predominantly African American cohort, we applied g-estimation to estimate the association between perceived discrimination and mental health, adjusted and unadjusted for measured confounders. Mental health was measured using clinical diagnoses of anxiety, depression and bipolar disorder. Perceived discrimination was measured as the number of patient-reported discrimination events in health care settings. Measured confounders included demographic, socioeconomic, residential and health characteristics. The influence of confounding was denoted as α1 from g-estimation. We compared α1 for measured and unmeasured confounding. RESULTS: Strong associations between perceived discrimination in health care settings and mental health outcomes were observed. For anxiety, the odds ratio (95% confidence interval) unadjusted and adjusted for measured confounders were 1.30 (1.21, 1.39) and 1.26 (1.17, 1.36), respectively. The α1 for measured confounding was -0.066. Unmeasured confounding with α1=0.200, which was over three times that of measured confounding, corresponds to an odds ratio of 1.12 (1.01, 1.24). Similar results were observed for other mental health outcomes. CONCLUSION: Compared with measured confounding, unmeasured that was three times measured confounding was not enough to explain away the association between perceived discrimination and mental health, suggesting that this association is robust to unmeasured confounding. This study provides a novel framework to quantitatively evaluate unmeasured confounding.

Maori Medical Student and Physician Exposure to Racism, Discrimination, Harassment, and Bullying.

Importance: Discrimination, bullying, and harassment in medicine have been reported internationally, but exposures for Indigenous medical students and physicians, and for racism specifically, remain less examined. Objective: To examine the prevalence of racism, discrimination, bullying, and harassment for Maori medical students and physicians in New Zealand and associations with demographic and clinical characteristics. Design, Setting, and Participants: This cross-sectional study used data from an anonymous national survey of Maori medical students and physicians in New Zealand in late 2021 and early 2022. Data were analyzed from March 2022 to April 2024. Exposures: Age, gender, marginalized status (ie, in addition to being Maori, belonging to other groups traditionally marginalized or underrepresented in medicine), year of medical school, year of graduation, and main work role. Main Outcomes and Measures: Direct and witnessed racism, discrimination, bullying, and harassment were measured as any experience in the last year and ever. Any exposure to negative comments about social groups and witnessing discriminatory treatment toward Maori patients or whanau (extended family). Considering leaving medicine, including because of mistreatment, was measured. Results: Overall, 205 Maori medical students (median [IQR] age, 23.1 [21.6-24.3] years; 137 [67.2%] women) and 200 physicians (median [IQR] age, 36.6 [30.1-45.3] years; 123 [62.8%] women) responded. Direct and witnessed exposure to racism (184 students [91.5%]; 176 physicians [90.7%]) and discrimination (176 students [85.9%]; 179 physicians [89.5%]) ever in medical education, training, or work environments was common. Ever exposure to witnessed and direct bullying (123 students [66.5%]; 150 physicians [89.3%]) and harassment (73 students [39.5%]; 112 physicians [66.7%]) was also common. Most respondents reported witnessing Maori patients or their whanau being treated badly in clinical settings, in direct interactions (67 students [57.8%]; 112 physicians [58.9%]) or behind their backs (87 students [75.0%]; 138 physicians [72.6%]). One-quarter of Maori medical students (45 students), and 37.0% of physicians (61 physicians) had considered leaving or taken a break from medicine because of these experiences. Additional marginalized statuses were significantly associated with any direct experience of mistreatment in the last year for students and physicians. Exposure to some forms of mistreatment were also significantly associated with higher likelihood of thinking about leaving or taking a break from medicine for physicians. Conclusions and Relevance: In this study, Maori medical students and physicians reported high exposure to multiple forms of racism, discrimination, bullying, and harassment in medical education, training, and work environments, requiring an urgent response from medical institutions.

Neighborhood Racial Composition and Unequal Exposure to Violent Crime in Everyday Contexts.

Exposure to violence is a critical aspect of contemporary racial inequality in the United States. While extensive research has examined variations in violent crime rates across neighborhoods, less attention has been given to understanding individuals' everyday exposure to violent crimes. This study investigates patterns of exposure to violent crimes among neighborhood residents using cell phone mobility data and violent crime reports from Chicago. The analysis reveals a positive association between the proportion of Black residents in a neighborhood and the level of exposure to violent crimes experienced by residents. Controlling for a neighborhood's level of residential disadvantage and other neighborhood characteristics did not substantially diminish the relationship between racial composition and exposure to violent crimes in everyday life. Even after controlling for violence within residents' neighborhoods, individuals residing in Black neighborhoods continue to experience significantly higher levels of violence in their day-to-day contexts compared to those living in White neighborhoods. This suggests that racial segregation in everyday exposures, rather than residential segregation, plays a central role in racial inequality in exposure to violence. Additionally, the analysis suggests that neighborhoods with more Hispanic and Asian residents are exposed to less and more violent crime, respectively, compared to neighborhoods with more White residents. However, this is only observed when not adjusting for the volume of visits points of interest receive; otherwise, the finding is reversed. This study offers valuable insights into potentially novel sources of racial disparities in exposure to violent crimes in everyday contexts, highlighting the need for further investigation.

Do digital technologies reduce racially biased reporting? Evidence from NYPD administrative data.

Recent work has emphasized the disproportionate bias faced by minorities when interacting with law enforcement. However, research on the topic has been hampered by biased sampling in administrative data, namely that records of police interactions with citizens only reflect information on the civilians that police elect to investigate, and not civilians that police observe but do not investigate. In this work, we address a related bias in administrative police data which has received less empirical attention, namely reporting biases around investigations that have taken place. Further, we investigate whether digital monitoring tools help mitigate this reporting bias. To do so, we examine changes in reports of interactions between law enforcement and citizens in the wake of the New York City Police Department's replacement of analog memo books with mobile smartphones. Results from a staggered difference in differences estimation indicate a significant increase in reports of citizen stops once the new smartphones are deployed. Importantly, we observe that the rise is driven by increased reports of "unproductive" stops, stops involving non-White citizens, and stops occurring in areas characterized by a greater concentration of crime and non-White residents. These results reinforce the recent observation that prior work has likely underestimated the extent of racial bias in policing. Further, they highlight that the implementation of digital monitoring tools can mitigate the issue to some extent.

White men can't jump, but do they still get picked first? Race and player selection in the NBA draft, 1980-2021.

Despite excelling at recruiting Black players, studies have repeatedly produced evidence of racial discrimination in the National Basketball Association (NBA). Through this study, we re-examine the topic of racial discrimination within the NBA through an analysis of the Association's annual entry draft. Using a novel dataset, we statistically model the relationship between player race and draft pick number using pooled data from 1980 to 2021. Overall, we find only limited evidence of racial discrimination. These findings are generally robust to sub-sample analyses, alternative specifications of our race variable, and alternate statistical modeling techniques. However, analyses performed on sub-samples of draft picks that participated in the NBA combine-and for whom we have measurements of player athleticism-produce some evidence of racial discrimination. Through such models we estimate that Black players are picked roughly three picks later in the draft. We consider the implications of these findings for contemporary theorizing about racial discrimination in the NBA and more mainstream labor markets.

Bien qu'elle excelle dans le recrutement de joueurs noirs, des études ont démontré à plusieurs reprises l'existence d'une discrimination raciale au sein de la National Basketball Association (NBA). Dans le cadre de cette étude, nous réexaminons le sujet de la discrimination raciale au sein de la NBA en analysant la sélection annuelle (draft) de l'association. À l'aide d'un nouvel ensemble de données, nous modélisons statistiquement la relation entre la race du joueur et le numéro de sélection à la draft en utilisant des données regroupées de 1980 à 2021. Dans l'ensemble, nous ne trouvons que des preuves limitées de discrimination raciale. Ces résultats sont généralement robustes aux analyses de sous­échantillons, aux spécifications alternatives de notre variable raciale et aux autres techniques de modélisation statistique. Toutefois, les analyses effectuées sur des sous­échantillons de sélectionnés ayant participé au NBA combine­et pour lesquels nous disposons de mesures de l'athlétisme des joueurs­produisent certains éléments de preuve de la discrimination raciale. Grâce à ces modèles, nous estimons que les joueurs noirs sont sélectionnés environ 3 fois plus tard dans la draft. Nous théorisons les implications de ces résultats pour les théories contemporaines sur la discrimination raciale dans la NBA et les marchés du travail ordinaires.

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.

Structural Racism as a Contributor to Lung Cancer Incidence and Mortality Rates Among Black Populations in the United States.

BACKGROUND: Although racial disparities in lung cancer incidence and mortality have diminished in recent years, lung cancer remains the second most diagnosed cancer among US Black populations. Many factors contributing to disparities in lung cancer are rooted in structural racism. To quantify this relationship, we examined associations between a multidimensional measure of county-level structural racism and county lung cancer incidence and mortality rates among Black populations, while accounting for county levels of environmental quality. METHODS: We merged 2016-2020 data from the United States Cancer Statistics Data Visualization Tool, a pre-existing county-level structural racism index, the Environmental Protection Agency's 2006-2010 Environmental Quality Index (EQI), 2023 County Health Rankings, and the 2021 United States Census American Community Survey. We conducted multivariable linear regressions to examine associations between county-level structural racism and county-level lung cancer incidence and mortality rates. RESULTS: Among Black males and females, each standard deviation increase in county-level structural racism score was associated with an increase in county-level lung cancer incidence of 6.4 (95% CI: 4.4, 8.5) cases per 100,000 and an increase of 3.3 (95% CI: 2.0, 4.6) lung cancer deaths per 100,000. When examining these associations stratified by sex, larger associations between structural racism and lung cancer rates were observed among Black male populations than among Black females. CONCLUSION: Structural racism contributes to both the number of new lung cancer cases and the number of deaths caused by lung cancer among Black populations. Those aiming to reduce lung cancer cases and deaths should consider addressing racism as a root-cause.

Food insecurity and childhood outcomes: a cross-sectional analysis of 2016-2020 National Survey of Children's Health data.

CONTEXT: Racial inequalities across social determinants of health (SDOHs) are often influenced by discriminatory policies that reinforce systems that further uphold these disparities. There is limited data describing the influence of food insecurity (FI) on childhood racial discrimination. OBJECTIVES: Our objective was to determine if the likelihood of experiencing racial discrimination was exacerbated by FI. METHODS: We conducted a cross-sectional analysis of the 2016-2020 National Survey of Children's Health (NSCH) to extract data on childhood racial discrimination and food security. We extracted sociodemographic variables to utilize as controls and constructed logistic regression models to determine associations, via odds ratios (ORs), between food security and whether the child experienced racial discrimination. RESULTS: We found statistically significant associations between experiencing FI and childhood racial discrimination. Individuals who experienced food shortages were significantly more likely to experience racial discrimination compared to those without food limitations when controlling for race, food voucher usage, age, and % federal poverty guidelines (FPG, adjusted odds ratio [AOR]: 3.34; 95 % CI: 2.69-4.14). CONCLUSIONS: Our study found that parents of minority children all reported high rates of racial discrimination, which was exacerbated by concurrent FI. Children of families that were the most food insecure reported the highest percentage of racial discrimination at 11.13 %, compared with children who always had enough nutritious meals to eat at 2.87 %. Acknowledging the intersection that exists between FI, race, gender, and socioeconomic status (SES), might be a way forward in addressing the adverse health effects experienced by food-insecure children and adults.

"Hate has chipped away at my sense of security": A survey study of the impacts of anti-Asian hate on the health of older Asians.

BACKGROUND: Since the beginning of the COVID-19 pandemic, older Asians have experienced a rise in racism and discrimination based on their race and ethnicity. This study examines how anti-Asian hate impacts older Asians' mental, social, and physical health. METHODS: From March 18, 2022 to January 24, 2023, we conducted a cross-sectional survey study of community-dwelling Asian/Asian American adults aged ≥50 years from the San Francisco Bay Area. Measures included perceptions of anti-Asian hate; direct encounters with hate incidents; indirect experiences with hate incidents (e.g. knowing a friend who was a victim); reports of anxiety, depression, loneliness, and changes in daily activities; ways to address these issues; and discussions with clinicians about hate incidents. RESULTS: Of the 293 older Asians, 158 (54%) were Vietnamese and 97 (33%) Chinese. Eighty-five (29%) participants were direct victims of anti-Asian hate, 112 (38%) reported anxiety, 105 (36%) reported depression, 161 (55%) reported loneliness, and 142 (48%) reported decreased daily activities. Compared with those who were "not-at-all" to "moderately" worried about hate incidents, participants who were "very" to "extremely" worried experienced heightened anxiety (42% versus 16%), loneliness (30% versus 14%), and changes in daily activities (66% versus 31%), p < 0.01 for all. Most participants (72%) felt comfortable discussing hate incidents with clinicians; however, only 31 (11%) reported that a clinician had talked with them about these incidents. CONCLUSION: Both directly and indirectly, anti-Asian hate negatively impacts older Asians' mental, social, and physical health. Clinicians have a role in addressing the health impacts of anti-Asian hate.

Examining the relationship between social determinants of health, measures of structural racism and county-level overdose deaths from 2017-2020.

INTRODUCTION: Despite being an important determinant of health outcomes, measures of structural racism are lacking in studies examining the relationship between the social determinants of health (SDOH) and overdose deaths. The aim of this study is to examine the association between per capita revenue generated from fines and forfeitures, a novel measure of structural racism, and other SDOH with county-level overdose deaths from 2017-2020. METHODS: This longitudinal analysis of 2,846 counties from 2017-2020 used bivariate and multivariate Generalized Estimating Equations models to estimate associations between county overdose mortality rates and SDOH characteristics, including the fines and forfeitures measure. RESULTS: In our multivariate model, higher per capita fine and forfeiture revenue (5.76; CI: 4.76, 6.78), households receiving food stamps (1.15; CI: 0.77, 1.53), residents that are veterans (1.07; CI: 0.52, 1.63), substance use treatment availability (4.69; CI: 3.03, 6.33) and lower population density (-0.002; CI: -0.004, -0.001) and percent of Black residents (-0.7`; CI: -1.01, -0.42) were significantly associated with higher overdose death rates. There was a significant additive interaction between the fines and forfeitures measure (0.10; CI: 0.03, 0.17) and the percent of Black residents. CONCLUSIONS: Our findings suggest that structural racism, along with other SDOH, is associated with overdose deaths. Future research should focus on connecting individual-level data on fines and forfeitures to overdose deaths and other health outcomes, include measures of justice-related fines, such as court fees, and assess whether interventions aimed at increasing economic vitality in disadvantaged communities impact overdose deaths in a meaningful way.

Spatiotemporal Changes in the Slavery-Inequality Relationship: The Diffusion of the Legacy of Slavery.

Despite the persistence of relationships between historical racist violence and contemporary Black-White inequality, research indicates, in broad strokes, that the slavery-inequality relationship in the United States has changed over time. Identifying the timing of such change across states can offer insights into the underlying processes that generate Black-White inequality. In this study, we use integrated nested Laplace approximation models to simultaneously account for spatial and temporal features of panel data for Southern counties during the period spanning 1900 to 2018, in combination with data on the concentration of enslaved people from the 1860 census. Results provide the first evidence on the timing of changes in the slavery-economic inequality relationship and how changes differ across states. We find a region-wide decline in the magnitude of the slavery-inequality relationship by 1930, with declines traversing the South in a northeasterly-to-southwesterly pattern over the study period. Different paces in declines in the relationship across states suggest the expansion of institutionalized racism first in places with the longest-standing overt systems of slavery. Results provide guidance for further identifying intervening mechanisms-most centrally, the maturity of racial hierarchies and the associated diffusion of racial oppression across institutions, and how they affect the legacy of slavery in the United States.

Nursing student perceptions of racism and health disparities in the United States: A critical race theory perspective.

BACKGROUND: Limited research has been done on nursing students' awareness of racial disparities and their readiness to address bias and racism in clinical practice. PURPOSE: This study investigated nursing students' perceptions of how racial disparities affect health outcomes, including maternal outcomes, in the United States. METHODS: Interpretive description was used and supported by the critical race theory as a framework to guide the data collection, analysis, and interpretation to understand participants' perceptions surrounding racism and health disparities. DISCUSSION: Nurse educators should guide students to look beyond individual behavioral and risk factors and consider systemic issues as a leading contributors to health disparities. CONCLUSION: The most critical finding was the lack of participants' understanding of systemic racism and its impact on health disparities. While they often attributed racial disparities to low socioeconomic status and lack of education, they did not understand the relationships between social determinants of health and systemic racism.

Individual and Contextual Correlates of Latent Bystander Profiles toward Racist Hate Speech: A Multilevel Person-centered Approach.

Prior research into bystander responses to hate speech has utilized variable-centered analyses - such approaches risk simplifying the complex nature of bystander behaviors. Hence, the present study used a person-centered analysis to investigate latent hate speech bystander profiles. In addition, individual and classroom-level correlates associated with the various profiles were studied. The sample included 3225 students in grades 7-9 (51.7% self-identified as female; 37.2% with immigrant background) from 215 classrooms in Germany and Switzerland. The latent profile analysis revealed that four distinct profiles could be distinguished: Passive Bystanders (34.2%), Defenders (47.3%), Revengers (9.8%), and Contributors (8.6%). Multilevel logistic regression models showed common and distinct correlates. For example, students who believed that certain social groups are superior were more likely to be Revengers and Contributors than Passive Bystanders, students who felt more connected with teachers were more likely to be Defenders, and students who were more open to diversity were less likely to be Contributors than Passive Bystanders. Students were less likely Defenders and more likely Revengers and Contributors than Passive Bystanders in classrooms with high rates of hate speech perpetration. Further, in classrooms with high hate speech intervention, students were more likely to be Defenders and less likely to be Contributors than Passive Bystanders. In classrooms with stronger cohesion, students were more likely to be Defenders and less likely to be Contributors than Passive Bystanders. In conclusion, the findings add to our understanding of bystander profiles concerning racist hate speech and the relevance of individual and classroom-level factors in explaining various profiles of bystander behavior.