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.

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.

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.

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.

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.

Reliability and Validity of the Perceived Racism Scale-Racism on the Job Subscale-in a Sample of Black Nurses.

Background and Purpose: We utilized the Perceived Racism Scale-Racism on the Job subscale-to assess how frequently Black nurses experienced racism on the job in the past year (ROTJ-Y) and throughout their lifetime (ROTJ-L). We aimed to assess the reliability and assess construct validity of each subscale in a sample of 53 nurses. Methods: Reliability was evaluated using coefficient alphas, item correlations, and interitem correlations. Construct validity was examined using exploratory factor analysis. Results: Results demonstrated that the subscales are reliable and valid. Coefficient alphas for the ROTJ-Y and ROTJ-L were .93 and .91, respectively. Exploratory factor analysis revealed a unidimensional factor for both subscales. Conclusion: This study demonstrated that the Racism on the Job subscales are psychometrically sound measures of workplace racism among Black nurses.

Education, Income, Wealth, and Discrimination in Black-White Allostatic Load Disparities.

INTRODUCTION: Allostatic load (AL) is a significant marker of aging, associated with disease and mortality. Research has elucidated the impact of education and income on AL. However, the roles of wealth and discrimination in contributing to AL and shaping AL disparities remain underexplored. This study aimed to investigate the association between wealth and AL, while also examining the independent contributions of education, income, wealth, and everyday discrimination in shaping AL disparities. METHODS: Using 2016 data from the nationally representative Health and Retirement Study (N=3,866), this study employed multilinear regression analysis to quantify the association between education and income, wealth (calculated as assets minus debts), and everyday discrimination with AL. Oaxaca-Blinder decomposition analysis was conducted to determine the proportion of AL disparities between Black and White participants attributed to education and income, wealth, and everyday discrimination. Analyses were performed in 2023. RESULTS: Having a college degree or more (b = -0.32; 95% CI: -0.46, -0.17), higher income (b = -0.06; 95% CI: -0.11, -0.01), and greater wealth (b = -0.11; 95% CI: -0.16, -0.07) were linked to reduced AL. Conversely, increased experiences of everyday discrimination were associated with heightened AL (b = 0.07; 95% CI: 0.01, 0.16). Collectively, differences in possessing a college degree or more, wealth, and exposure to discrimination accounted for about 18% of the observed Black-White AL disparities. CONCLUSIONS: Education, income, wealth, and experiences of discrimination may independently contribute to AL and partially explain Black-White disparities in AL. There is a need to elucidate the underlying mechanisms governing these relationships, particularly wealth, and extend the research to additional social determinants of racial health disparities.

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.

Structural gendered racism and preterm birth inequities in the United States.

Structural gendered racism - the "totality of interconnectedness between structural racism and sexism" - is conceptualized as a fundamental cause of the persistent preterm birth inequities experienced by Black and Indigenous people in the United States. Our objective was to develop a state-level latent class measure of structural gendered racism and examine its association with preterm birth among all singleton live births in the US in 2019. Using previously-validated inequity indicators between White men and Black women across 9 domains (education, employment, poverty, homeownership, health insurance, segregation, voting, political representation, incarceration), we conducted a latent profile analysis to identify a latent categorical variable with k number of classes that have similar values on the observed continuous input variables. Racialized group-stratified multilevel modified Poisson regression models with robust variance and random effects for state assessed the association between state-level classes and preterm birth. We found four distinct latent classes that were all characterized by higher levels of disadvantage for Black women and advantages for White men, but the magnitude of that difference varied by latent class. We found preterm birth risk among Black birthing people was higher across all state-level latent classes compared to White birthing people, and there was some variation of preterm birth risk across classes among Black but not White birthing people. These findings further emphasize the importance of understanding and interrogating the whole system and the need for multifaceted policy solutions.

Everyday Discrimination Is Associated With Higher Odds of Hospitalizations Among Older African Americans.

BACKGROUND: Everyday discrimination-experiences of being treated unfairly based on background characteristics like race-is linked to poor physical and mental health throughout the lifespan. Whether more experiences of discrimination are associated with higher odds of being hospitalized in older African Americans has not been explored. METHODS: Community-dwelling participants from 3 longitudinal cohort studies (N = 446, age 65+ years) with discrimination scores and ≥12 months of linked Medicare claims were included. Hospitalizations were identified using Medicare fee-for-service claims, available for an average of 6.2 (SD: 3.7) years of follow-up after baseline. RESULTS: In mixed-effects ordinal logistic regression models (outcomes of 0, 1, or 2+ hospitalizations per year) adjusted for age, sex, education, and income, higher discrimination was associated with higher odds of total annual hospitalizations (odds ratio [OR] per point higher = 1.09, 95% confidence intervals [95% CI]: 1.02-1.17). Results were similar when accounting for depressive symptoms. CONCLUSIONS: Higher exposure to everyday discrimination is associated with higher odds of hospitalization among older African Americans. Mechanisms underlying associations should be explored further to understand how hospitalizations may be reduced in older African Americans.

Historic Redlining and Impact of Structural Racism on Diabetes Prevalence in a Nationally Representative Sample of U.S. Adults.

OBJECTIVE: We investigated direct and indirect relationships between historic redlining and prevalence of diabetes in a U.S. national sample. RESEARCH DESIGN AND METHODS: Using a previously validated conceptual model, we hypothesized pathways between structural racism and prevalence of diabetes via discrimination, incarceration, poverty, substance use, housing, education, unemployment, and food access. We combined census tract-level data, including diabetes prevalence from the Centers for Disease Control and Prevention PLACES 2019 database, redlining using historic Home Owners' Loan Corporation (HOLC) maps from the Mapping Inequality project, and census data from the Opportunity Insights database. HOLC grade (a score between 1 [best] and 4 [redlined]) for each census tract was based on overlap with historically HOLC-graded areas. The final analytic sample consisted of 11,375 U.S. census tracts. Structural equation modeling was used to investigate direct and indirect relationships adjusting for the 2010 population. RESULTS: Redlining was directly associated with higher crude prevalence of diabetes within a census tract (r = 0.01; P = 0.008) after adjusting for the 2010 population (χ2(54) = 69,900.95; P < 0.001; root mean square error of approximation = 0; comparative fit index = 1). Redlining was indirectly associated with diabetes prevalence via incarceration (r = 0.06; P < 0.001), poverty (r = -0.10; P < 0.001), discrimination (r = 0.14; P < 0.001); substance use (measured by binge drinking: r = -0.65, P < 0.001; and smoking: r = 0.35, P < 0.001), housing (r = 0.06; P < 0.001), education (r = -0.17; P < 0.001), unemployment (r = -0.17; P < 0.001), and food access (r = 0.14; P < 0.001) after adjusting for the 2010 population. CONCLUSIONS: Redlining has significant direct and indirect relationships with diabetes prevalence. Incarceration, poverty, discrimination, substance use, housing, education, unemployment, and food access may be possible targets for interventions aiming to mitigate the impact of structural racism on diabetes.

Racial disparities in the selection of chief resident: A cross-sectional analysis of a national sample of senior residents in the United States.

INTRODUCTION: Part of the difficulty in recruiting and retaining a diverse physician workforce, as well as within medical leadership, is due to racial disparities in medical education. We investigated whether self-identified race-ethnicity is associated with the likelihood of selection as chief resident (CR). MATERIALS AND METHODS: We performed a cross sectional analysis using de-identified person-level data from the GME Track, a national resident database and tracking system, from 2015 through 2018. The exposure variable, self-identified race-ethnicity, was categorized as African American or Black, American Indian or Alaskan Native, Asian, Hispanic, Latino or of Spanish Origin, Native Hawaiian or Pacific Islander, White, and Multi-racial. The primary study outcome was CR selection among respondents in their final program year. Logistic regression was used to estimate the adjusted odds ratios (aOR) and 95% confidence intervals (CI) of CR selection for each racial group, as compared to the White referent group. RESULTS: Among the study population (N=121,247), Black, Asian and Hispanic race-ethnicity was associated with a significantly decreased odds of being selected as CR in unadjusted and adjusted analyses. Black, Asian and Hispanic residents had a 26% (aOR=0.74, 95% CI 0.66-0.83), 29% (aOR=0.71, 95% CI 0.66-0.76) and 28% (aOR=0.72, 95% CI 0.66-0.94) decreased likelihood of becoming CR, respectively. Multi-racial residents also had a decreased likelihood, but to a lesser degree (aOR=0.92, 95% CI 0.89-0.95). CONCLUSIONS: In as much as CR is an honor that sets one up for future opportunity, our findings suggest that residents of color are disproportionately disadvantaged compared to their White peers.

Racial and ethnic inequities in psychiatric inpatient building and unit assignment.

Racism is a social determinant of mental health which has a disproportionally negative impact on the experiences of psychiatric inpatients of color. Distinct differences in the physical space and clinical settings of two inpatient buildings at a hospital system in the tristate (New York, New Jersey, Connecticut) area of the United States led to the present investigation of racial inequities in the assignment of patients to specific buildings and units. Archival electronic medical record data were analyzed from over 18,000 unique patients over a period of six years. Hierarchical logistic regression analyses were conducted with assigned building (old vs. new building) as the binary outcome variable. Non-Hispanic White patients were set as the reference group. Black, Hispanic/Latinx, and Asian patients were significantly less likely to be assigned to better resourced units in the new building. When limiting the analysis to only general adult units, Black and Hispanic/Latinx patients were significantly less likely to be assigned to units in the new building. These results suggest ethnoracial inequities in patient assignment to buildings which differed in clinical and physical conditions. The findings serve as a call to action for hospital systems to examine the ways in which structural racism impact clinical care.

Everyday Discrimination and HIV Testing Among Partnered Latino/x Sexual Minority Men in the United States: A Stratified Analysis by Birth Location.

We examined the association between everyday discrimination and HIV testing patterns-current (≤ 6 months), recent (7-12 months), and delayed (> 12 months or never tested)-among partnered Latino/x sexual minority men (SMM). Multinomial regression analyses revealed that in the full sample (N = 484) experiencing discrimination based on sexual orientation and race/ethnicity attributions concurrently (vs. no discrimination) was associated with higher odds of delayed (vs. current) HIV testing (AOR = 2.6, 95% CI [1.0, 6.7]). Similarly, in the subset of Latino/x SMM born outside the mainland U.S. (n = 209), experiencing concurrent sexual orientation- and race/ethnicity-based discrimination (vs. no discrimination) was associated with higher odds of recent (AOR = 12.4, 95% CI [1.3, 115.7]) and delayed HIV testing (AOR = 7.3, 95% CI [1.6, 33.0]), compared with current testing. Findings suggest that addressing discrimination may improve HIV testing uptake among partnered Latino/x SMM, particularly those born outside the U.S.

Factors leading to disparity in lung cancer diagnosis among black/African American communities in the USA: a qualitative study.

OBJECTIVE: This study has two objectives: first, to explore the diagnostic experiences of black/African American (BAA) patients with lung cancer to pinpoint pitfalls, suboptimal experiences and instances of discrimination leading to disparities in outcomes compared with patients of other ethnic backgrounds, especially white patients. The second objective is to identify the underlying causes contributing to health disparities in the diagnosis of lung cancer among BAA patients. METHODS: We employed a phenomenological research approach, guiding in-depth interviews with patients self-identifying as BAA diagnosed with lung cancer, as well as caregivers, healthcare professionals and community advocates knowledgeable about BAA experiences with lung cancer. We performed thematic analysis to identify experiences at patient, primary care and specialist levels. Contributing factors were identified using the National Institute of Minority Health and Health Disparities (NIMHD) health disparity model. RESULTS: From March to November 2021, we conducted individual interviews with 19 participants, including 9 patients/caregivers and 10 providers/advocates. Participants reported recurring and increased pain before seeking treatment, treatment for non-cancer illnesses, delays in diagnostic tests and referrals, poor communication and bias when dealing with specialists and primary care providers. Factors contributing to suboptimal experiences included reluctance by insurers to cover costs, provider unwillingness to conduct comprehensive testing, provider bias in recommending treatment, high healthcare costs, and lack of healthcare facilities and qualified staff to provide necessary support. However, some participants reported positive experiences due to their insurance, availability of services and having an empowered support structure. CONCLUSIONS: BAA patients and caregivers encountered suboptimal experiences during their care. The NIMHD model is a useful framework to organise factors contributing to these experiences that may be leading to health disparities. Additional research is needed to fully capture the extent of these experiences and identify ways to improve BAA patient experiences in the lung cancer diagnosis pathway.

Experiences with Racism Among Asian American Medical Students.

Importance: Asian American physicians have experienced a dual pandemic of racism and COVID-19 since 2020; understanding how racism has affected the learning environment of Asian American medical students is necessary to inform strategies to promoting a more inclusive medical school environment and a diverse and inclusive workforce. While prior research has explored the influence of anti-Asian racism on the experiences of Asian American health care workers, to our knowledge there are no studies investigating how racism has impacted the training experiences of Asian American medical students. Objective: To characterize how Asian American medical students have experienced anti-Asian racism in a medical school learning environment. Design, Setting, and Participants: This qualitative study included online video interviews of Asian American medical students performed between July 29, 2021, and August 22, 2022. Eligible participants were recruited through the Asian Pacific American Medical Students Association and snowball sampling, and the sample represented a disaggregated population of Asian Americans and all 4 medical school years. Main Outcomes and Measures: The medical school experiences of Asian American medical students. Results: Among 25 participants, Asian ethnicities included 8 Chinese American (32%), 5 Korean American (20%), 5 Indian American (20%), 3 Vietnamese American (12%), 2 Filipino American (8%), and 1 (4%) each Nepalese, Pakistani, and Desi American; 16 (64%) were female. Participants described 5 major themes concerning their experience with discrimination: (1) invisibility as racial aggression (eg, "It took them the whole first year to be able to tell me apart from the other Asian guy"); (2) visibility and racial aggression ("It transitioned from these series of microaggressions that every Asian person felt to actual aggression"); (3) absence of the Asian American experience in medical school ("They're not going to mention Asian Americans at all"); (4) ignored while seeking support ("I don't know what it means to have this part of my identity supported"); and (5) envisioning the future. Conclusions and Relevance: In this qualitative study, Asian American medical students reported feeling invisible within medical school while a target of anti-Asian racism. Addressing these unique challenges related to anti-Asian racism is necessary to promote a more inclusive medical school learning environment.

Racism, early psychosis, and institutional contact: A qualitative study of Indigenous experiences.

BACKGROUND: There is evidence of Indigenous and ethnic minority inequities in the incidence and outcomes of early psychosis. Racism has been implicated as having an important role. AIM: To use Indigenous experiences to develop a more detailed understanding of how racism operates to impact early psychosis outcomes. METHODS: Critical Race Theory informed the methodology used. Twenty-three Indigenous participants participated in four family focus group interviews and thirteen individual interviews, comprising of 9 Maori youth with early psychosis, 10 family members and 4 Maori mental health professionals. An analysis of the data was undertaken using deductive structural coding to identify descriptions of racism, followed by inductive descriptive and pattern coding. RESULTS: Participant experiences revealed how racism operates as a socio-cultural phenomenon that interacts with institutional policy and culture across systems pertaining to social responsiveness, risk discourse, and mental health service structures. This is described across three major themes: 1) selective responses based on racial stereotypes, 2) race related risk assessment bias and 3) institutional racism in the mental health workforce. The impacts of racism were reported as inaction in the face of social need, increased use of coercive practices and an under resourced Indigenous mental health workforce. CONCLUSION: The study illustrated the inter-related nature of interpersonal, institutional and structural racism with examples of interpersonal racism in the form of negative stereotypes interacting with organizational, socio-cultural and political priorities. These findings indicate that organizational cultures may differentially impact Indigenous and minority people and that social responsiveness, risk discourse and the distribution of workforce expenditure are important targets for anti-racism efforts.

Racial Inequities in Drug Tests Ordered by Clinicians for Pregnant People Who Disclose Prenatal Substance Use.

OBJECTIVE: To measure racial inequities in drug testing among pregnant people during the first prenatal visit based on their drug use disclosure pattern. METHODS: We used data from a cohort study of patient-clinician communication patterns regarding substance use in first prenatal visits from February 2011 to August 2014. We assessed racial differences (Black-White) in the receipt of urine toxicology testing, stratifying on patients' drug use disclosure to the clinician. RESULTS: Among 341 study participants (205 Black [60.1%] and 136 White [39.9%] participants), 70 participants (33 Black [47.1%] and 37 White [52.9%] participants) disclosed drug use, and 271 participants (172 Black [63.5%] and 99 White [36.5%] participants) did not disclose drug use during their first obstetric visit. Of 70 participants who disclosed drug use, 50 (28 Black [56.0%] and 22 White [44.0%] White) had urine drug testing conducted. Black pregnant patients who disclosed drug use were more likely to be tested for drugs than their White counterparts in the adjusted regression analysis (adjusted odds ratio [aOR] 8.9, 95% CI 1.3-58.6). Among the 271 participants who did not disclose drug use, 38 (18 Black [47.4%] and 20 White [52.6%] participants) had urine drug testing conducted. For those who did not disclose drug use, the adjusted model showed no statistically significant differences in urine drug testing by patients' race (aOR 0.7, 95% CI 0.3-1.6). CONCLUSION: When pregnant people disclosed drug use, clinicians were more likely to order urine drug testing for Black pregnant people compared with their White counterparts, suggesting clinician racial bias. Current practice patterns and protocols such as urine drug testing in pregnancy care deserve review to identify and mitigate areas of potential clinician discrimination.