On March 8, 2020, there was a 650% increase in Twitter retweets using the term “Chinese virus” and related terms. On March 9, there was an 800% increase in the use of these terms in conservative news media articles. Using data from non-Asian respondents of the Project Implicit “Asian Implicit Association Test” from 2007–2020 ( n = 339,063), we sought to ascertain if this change in media tone increased bias against Asian Americans. Local polynomial regression and interrupted time-series analyses revealed that Implicit Americanness Bias—or the subconscious belief that European American individuals are more “American” than Asian American individuals—declined steadily from 2007 through early 2020 but reversed trend and began to increase on March 8, following the increase in stigmatizing language in conservative media outlets. The trend reversal in bias was more pronounced among conservative individuals. This research provides evidence that the use of stigmatizing language increased subconscious beliefs that Asian Americans are “perpetual foreigners.” Given research that perpetual foreigner bias can beget discriminatory behavior and that experiencing discrimination is associated with adverse mental and physical health outcomes, this research sounds an alarm about the effects of stigmatizing media on the health and welfare of Asian Americans.

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Objective: To examine the association between self-reported racial discrimination and allostatic load, and whether the association differs by socioeconomic position. Methods: We recruited a purposive cross-section of midlife (ages 30–50) African American women residing in four San Francisco Bay area counties (n=208). Racial discrimination Was measured using the Experience of Discrimination scale. Allostatic load was measured as a comPosite of 15 biomarkers assessing cardiometabolic, neuroendocrine, and inflammatory activity. We calculated four composite measures of allostatic load and three system-specific measures of biological dysregulation. Multivariable regression was used to examine associations, while adjusting for relevant confounders. Results: In the high education group, reporting low (b=−1.09, P=.02, 95% CI=−1.99,−0.18) and very high (b=−1.88, P=.003, 95% CI=−3.11,−0.65) discrimination was associated with lower allostatic load (reference=moderate). Among those with lower education, reporting low (b=2.05, P=.008, 95% CI=0.55,3.56) discrimination was associated with higher allostatic load. Similar but less consistent associations were found for poverty status. Associations were similar for cardiometabolic functioning, but not for neuroendocrine or inflammatory activity. Conclusions: Racial discrimination may be an important predictor of cumulative physiologic dysregulation. Factors associated with educational attainment may mitigate this association for African American women and other groups experiencing chronic social stress.

An abundance of research has documented health inequalities by race and socioeconomic position (SEP) in the United States. However, conceptual and methodological challenges complicate the interpretation of study findings, thereby limiting progress in understanding health inequalities and in achieving health equity. Fundamental to these challenges is a lack of clarity about what race is and the implications of that ambiguity for scientific inquiry. Additionally, there is wide variability in how SEP is conceptualized and measured, resulting in a lack of comparability across studies and significant misclassification of risk. The objectives of this review are to synthesize the literature regarding common approaches to examining race and SEP health inequalities and to discuss the conceptual and methodological challenges associated with how race and SEP have been employed in 169

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Purpose-Black women have the highest estimated allostatic load (AL). AL and self-perceived health are strong health predictors and have been linked to racial discrimination. Research suggests that everyday and institution-specific racial discrimination may predict different AL and self-reported health (SRH) outcomes. Furthermore, discrepancies between AL and self-perceived health could widen disparities. We estimated associations between everyday versus institutionspecific racial discrimination with AL and SRH. Methods-Data are from a San Francisco Bay Area community sample of 208 black women aged 30-50 years. Participation involved a questionnaire, self-interview, blood draw, and anthropometric measurements. Adjusted generalized linear regression models estimated associations of racial discrimination with AL and SRH. Results-After adjusting for age, socioeconomic position, and medication use, institutionspecific discrimination was negatively associated with AL (i.e., better health), whereas everyday experiences showed no association. Those reporting very-high (vs. moderate) institution-specific discrimination had lower AL (β = −1.31 [95% CI: −2.41, −0.20]; AL range: 0-15). No racial discrimination-SRH association was found. Conclusions-For black women, (1) institution-specific racial discrimination may be differentially embodied compared with everyday experiences and (2) institutional racism may contribute to physiologic stress-regulation regardless of self-perceived health status. Potential

BackgroundStudies suggest that racial discrimination impacts health via biological dysregulation due to continual adaptation to chronic psychosocial stress. Therefore, quantifying chronicity is critical for operationalising the relevant aetiological exposure and hence maximising internal validity. Using one of the most common discrimination scales in the epidemiological literature, we develop a novel approach for more accurately assessing chronicity and compare it with conventional approaches to determine whether coding influences differential exposure classification and associations with hypertension and depression among African American women.MethodsData are from a socioeconomically diverse cross section of 208 mid-life African American women in Northern California (data collection: 2012–2013). Racial discrimination was assessed using the Everyday Discrimination Scale (α=0.95), and was coded using two conventional approaches: (1) situation-based coding: number of different situations ever experienced; (2) frequency-based coding: sum of Likert scale responses ranging from 'never' to 'almost everyday'; and (3) a new chronicity-based coding approach: sum of responses, weighted to capture annual chronicity (eg, ‘a few times a month’=3×12=36×/year). Outcomes are hypertension and depressive symptomatology (10-item Center for Epidemiologic Studies-Depression Scale).FindingsExposure classification differed by coding approach, by up to 41%. There was a positive association between racial discrimination and hypertension prevalence for chronicity coding only (prevalence ratio=1.61, 95% CI 1.03 to 2.49). For depressive symptoms, a dose–response relationship of similar magnitude was observed for all three coding approaches.ConclusionScale coding is an important methodological consideration for valid exposure assessment in epidemiological research. Coding can impact exposure classification and associations with important indicators of African American women’s mental and physical health.

Mounting evidence reveals considerable racial inequities in coronavirus disease 2019 (COVID-19) outcomes in the United States (US). Area-level racial bias has been associated with multiple adverse health outcomes, but its association with COVID-19 is yet unexplored. Combining county-level data from Project Implicit on implicit and explicit anti-Black bias among non-Hispanic Whites, Johns Hopkins Coronavirus Resource Center, and The New York Times, we used adjusted linear regressions to estimate overall COVID-19 incidence and mortality rates through 01 July 2020, Black and White incidence rates through 28 May 2020, and Black–White incidence rate gaps on average area-level implicit and explicit racial bias. Across 2994 counties, the average COVID-19 mortality rate (standard deviation) was 1.7/10,000 people (3.3) and average cumulative COVID-19 incidence rate was 52.1/10,000 (77.2). Higher racial bias was associated with higher overall mortality rates (per 1 standard deviation higher implicit bias b = 0.65/10,000 (95% confidence interval: 0.39, 0.91); explicit bias b = 0.49/10,000 (0.27, 0.70)) and higher overall incidence (implicit bias b = 8.42/10,000 (4.64, 12.20); explicit bias b = 8.83/10,000 (5.32, 12.35)). In 957 counties with race-specific data, higher racial bias predicted higher White and Black incidence rates, and larger Black–White incidence rate gaps. Anti-Black bias among Whites predicts worse COVID-19 outcomes and greater inequities. Area-level interventions may ameliorate health inequities.