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.

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.

Preliminary evidence indicates that the experience of the novel coronavirus is not shared equally across geographic areas. Findings in the United States suggest that the burden of COVID-19 morbidity and mortality may be hardest felt in disadvantaged and racially segregated places. Deprived neighbourhoods are disproportionately populated by people of colour, the same populations that are becoming sicker and dying more often from COVID-19. This commentary examines how structurally vulnerable neighbourhoods contribute to racial/ethnic inequities in SARS-COV-2 exposure and COVID-19 morbidity and mortality and considers opportunities to intervene through place-based initiatives and the implementation of a Health in All Policies strategy.

This study examined whether killings of George Floyd, Ahmaud Arbery, and Breonna Taylor by current or former law enforcement officers in 2020 were followed by shifts in public sentiment toward Black people. Methods : Google searches for the names “Ahmaud Arbery,” “Breonna Taylor,” and “George Floyd” were obtained from the Google Health Application Programming Interface (API). Using the Twitter API, we collected a 1% random sample of publicly available U.S. race-related tweets from November 2019–September 2020 (N = 3,380,616). Sentiment analysis was performed using Support Vector Machines, a supervised machine learning model. A qualitative content analysis was conducted on a random sample of 3,000 tweets to understand themes in discussions of race and racism and inform interpretation of the quantitative trends. Results: The highest rate of Google searches for any of the three names was for George Floyd during the week of May 31 to June 6, the week after his murder. The percent of tweets referencing Black people that were negative decreased by 32% (from 49.33% in November 4–9 to 33.66% in June 1–7) (p < 0.001), but this decline was temporary, lasting just a few weeks. Themes that emerged during the content analysis included discussion of race or racism in positive (14%) or negative (38%) tones, call for action related to racism (18%), and counter movement/arguments against racism-related changes (6%). Conclusion: Although there was a sharp decline in negative Black sentiment and increased public awareness of structural racism and desire for long-lasting social change, these shifts were transitory and returned to baseline after several weeks. Findings suggest that negative attitudes towards Black people remain deeply entrenched.